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Maruyama S, Watanabe H, Shimosegawa M. An image quality assessment index based on image features and keypoints for X-ray CT images. PLoS One 2024; 19:e0304860. [PMID: 38990930 PMCID: PMC11238976 DOI: 10.1371/journal.pone.0304860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 05/21/2024] [Indexed: 07/13/2024] Open
Abstract
Optimization tasks in diagnostic radiological imaging require objective quantitative metrics that correlate with the subjective perception of observers. However, although one such metric, the structural similarity index (SSIM), is popular, it has limitations across various aspects in its application to medical images. In this study, we introduce a novel image quality evaluation approach based on keypoints and their associated unique image feature values, focusing on developing a framework to address the need for robustness and interpretability that are lacking in conventional methodologies. The proposed index quantifies and visualizes the distance between feature vectors associated with keypoints, which varies depending on changes in the image quality. This metric was validated on images with varying noise levels and resolution characteristics, and its applicability and effectiveness were examined by evaluating images subjected to various affine transformations. In the verification of X-ray computed tomography imaging using a head phantom, the distances between feature descriptors for each keypoint increased as the image quality degraded, exhibiting a strong correlation with the changes in the SSIM. Notably, the proposed index outperformed conventional full-reference metrics in terms of robustness to various transformations which are without changes in the image quality. Overall, the results suggested that image analysis performed using the proposed framework could effectively visualize the corresponding feature points, potentially harnessing lost feature information owing to changes in the image quality. These findings demonstrate the feasibility of applying the novel index to analyze changes in the image quality. This method may overcome limitations inherent in conventional evaluation methodologies and contribute to medical image analysis in the broader domain.
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Affiliation(s)
- Sho Maruyama
- Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, Japan
| | - Haruyuki Watanabe
- Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, Japan
| | - Masayuki Shimosegawa
- Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, Japan
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2
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Vorbau R, Hulthén M, Omar A. Task-based image quality assessment of an intraoperative CBCT for spine surgery compared with conventional CT. Phys Med 2024; 124:103426. [PMID: 38986263 DOI: 10.1016/j.ejmp.2024.103426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 05/24/2024] [Accepted: 06/29/2024] [Indexed: 07/12/2024] Open
Abstract
PURPOSE To analyze the image quality of a novel, state-of-the art platform for CBCT image-guided spine surgery, focusing particularly on the dose-effectiveness compared with conventional CT (the gold standard for postoperative assessment). METHODS The ClarifEye platform (Philips Healthcare) with integrated augmented-reality surgical navigation, has been compared with a GE Revolution CT (GE Healthcare). The 3D spatial resolution (TTF) and noise (NPS) were evaluated considering relevant feature contrasts (200-900 HU) and background noise for differently sized patients (200-300 mm water-equivalent diameter). These measures were used to determine the noise equivalent quanta (NEQ) and observer model detectability. RESULTS The CBCT system exhibited a linear response with 50% TTF at 5.7 cycles/cm (10% TTF at 9.2 cycles/cm), and the axial noise power peaking at about 3.6 cycles/cm (average frequency of 4.1 cycles/cm). The noise magnitude and texture differed markedly compared to iteratively reconstructed CT images (GE ASiR-V). The CBCT system had 26% lower detectability for a high-frequency task (related to edge detection) compared with CT images reconstructed using the Bone kernel combined with ASiR-V 50%. Likewise, it had 18% lower detectability for low- and mid-frequency tasks compared with CT images reconstructed using the Standard kernel. This difference translates to 50%-80% higher CBCT imaging doses required to match the CT image quality. CONCLUSIONS The ClarifEye platform demonstrates intraoperative CBCT-imaging capabilities that under certain circumstances are comparable with conventional CT. However, due to limited dose-effectiveness, a trade-off between timeliness and radiation exposure must be considered if end-of-procedure CBCT is to replace postoperative CT.
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Affiliation(s)
- Robert Vorbau
- Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Markus Hulthén
- Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Artur Omar
- Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden; Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Sweden.
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3
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Rajagopal JR, Farhadi F, Saboury B, Sahbaee P, Negussie AH, Pritchard WF, Jones EC, Samei E. Multivariate signal-to-noise ratio as a metric for characterizing spectral computed tomography. Phys Med Biol 2024; 69:145005. [PMID: 38942009 DOI: 10.1088/1361-6560/ad5d4a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 06/28/2024] [Indexed: 06/30/2024]
Abstract
Objective.With the introduction of spectral CT techniques into the clinic, the imaging capacities of CT were expanded to multiple energy levels. Due to a variety of factors, the acquired signal in spectral CT datasets is shared between these images. Conventional image quality metrics assume independence between images which is not preserved within spectral CT datasets, limiting their utility for characterizing energy selective images. The purpose of this work was to develop a metrology to characterize energy selective images by incorporating the shared information between images within a spectral CT dataset.Approach.The signal-to-noise ratio (SNR) was extended into a multivariate space where each image within a spectral CT dataset was treated as a separate information channel. The general definition was applied to the specific case of contrast to define a multivariate contrast-to-noise ratio (CNR). The matrix contained two types of terms: a conventional CNR term which characterized image quality within each image in the spectral CT dataset and covariance weighted CNR (Covar-CNR) which characterized the contrast in each image relative to the covariance between images. Experimental data from an investigational photon-counting CT scanner was used to demonstrate the insight of this metrology. A cylindrical water phantom containing vials of iodine and gadolinium (2, 4, and 8 mg ml-1) was imaged under conditions of variable tube current, tube voltage, and energy threshold. Two image series (threshold and bin images) containing two images each were defined based upon the contribution of photons to reconstructed images. Analysis of variance (ANOVA) was calculated between CNR terms and image acquisition variables. A multivariate regression was then fitted to experimental data.Main Results.Image type had a major difference on how Covar-CNR values were distributed. Bin images had a slightly higher mean and wider standard deviation (Covar-CNRlo: 3.38 ±17.25, Covar-CNRhi: 5.77 ± 30.64) compared to threshold images (Covar-CNRlo: 2.08 ±1.89, Covar-CNRhi: 3.45 ± 2.49) across all conditions. ANOVA found that each acquisition variable had a significant relationship with both Covar-CNR terms. The multivariate regression model suggested that material concentration had the largest impact on all CNR terms.Signficance.In this work, we described a theoretical framework to extend the SNR to a multivariate form that is able to characterize images independently and also provide insight regarding the relationship between images. Experimental data was used to demonstrate the insight that this metrology provides about image formation factors in spectral CT.
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Affiliation(s)
- Jayasai R Rajagopal
- Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Department of Radiology, Duke University Medical Center, Durham, NC 27705, United States of America
- Medical Physics Graduate Program, Duke University Medical Center, Durham, NC 27705, United States of America
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, United States of America
| | - Faraz Farhadi
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, United States of America
- Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, United States of America
| | - Babak Saboury
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, United States of America
| | - Pooyan Sahbaee
- Siemens Medical Solutions, Malvern, PA 19335, United States of America
| | - Ayele H Negussie
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, United States of America
| | - William F Pritchard
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, United States of America
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, United States of America
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Department of Radiology, Duke University Medical Center, Durham, NC 27705, United States of America
- Medical Physics Graduate Program, Duke University Medical Center, Durham, NC 27705, United States of America
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4
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Emoto T, Nagayama Y, Takada S, Sakabe D, Shigematsu S, Goto M, Nakato K, Yoshida R, Harai R, Kidoh M, Oda S, Nakaura T, Hirai T. Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality. Phys Eng Sci Med 2024:10.1007/s13246-024-01423-y. [PMID: 38884668 DOI: 10.1007/s13246-024-01423-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 04/04/2024] [Indexed: 06/18/2024]
Abstract
This study aimed to evaluate the impact of radiation dose and focal spot size on the image quality of super-resolution deep-learning reconstruction (SR-DLR) in comparison with iterative reconstruction (IR) and normal-resolution DLR (NR-DLR) algorithms for cardiac CT. Catphan-700 phantom was scanned on a 320-row scanner at six radiation doses (small and large focal spots at 1.4-4.3 and 5.8-8.8 mGy, respectively). Images were reconstructed using hybrid-IR, model-based-IR, NR-DLR, and SR-DLR algorithms. Noise properties were evaluated through plotting noise power spectrum (NPS). Spatial resolution was quantified with task-based transfer function (TTF); Polystyrene, Delrin, and Bone-50% inserts were used for low-, intermediate, and high-contrast spatial resolution. The detectability index (d') was calculated. Image noise, noise texture, edge sharpness of low- and intermediate-contrast objects, delineation of fine high-contrast objects, and overall quality of four reconstructions were visually ranked. Results indicated that among four reconstructions, SR-DLR yielded the lowest noise magnitude and NPS peak, as well as the highest average NPS frequency, TTF50%, d' values, and visual rank at each radiation dose. For all reconstructions, the intermediate- to high-contrast spatial resolution was maximized at 4.3 mGy, while the lowest noise magnitude and highest d' were attained at 8.8 mGy. SR-DLR at 4.3 mGy exhibited superior noise performance, intermediate- to high-contrast spatial resolution, d' values, and visual rank compared to the other reconstructions at 8.8 mGy. Therefore, SR-DLR may yield superior diagnostic image quality and facilitate radiation dose reduction compared to the other reconstructions, particularly when combined with small focal spot scanning.
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Affiliation(s)
- Takafumi Emoto
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan.
| | - Sentaro Takada
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Daisuke Sakabe
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Shinsuke Shigematsu
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Makoto Goto
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Kengo Nakato
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Ryuya Yoshida
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Ryota Harai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Seitaro Oda
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
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5
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Higaki T, Tatsugami F, Ohana M, Nakamura Y, Kawashita I, Awai K. Super resolution deep learning reconstruction for coronary CT angiography: A structured phantom study. Eur J Radiol Open 2024; 12:100570. [PMID: 38828096 PMCID: PMC11140562 DOI: 10.1016/j.ejro.2024.100570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 06/05/2024] Open
Abstract
Purpose Super-resolution deep-learning-based reconstruction: SR-DLR is a newly developed and clinically available deep-learning-based image reconstruction method that can improve the spatial resolution of CT images. The image quality of the output from non-linear image reconstructions, such as DLR, is known to vary depending on the structure of the object being scanned, and a simple phantom cannot explicitly evaluate the clinical performance of SR-DLR. This study aims to accurately investigate the quality of the images reconstructed by SR-DLR by utilizing a structured phantom that simulates the human anatomy in coronary CT angiography. Methods The structural phantom had ribs and vertebrae made of plaster, a left ventricle filled with dilute contrast medium, a coronary artery with simulated stenosis, and an implanted stent graft. By scanning the structured phantom, we evaluated noise and spatial resolution on the images reconstructed with SR-DLR and conventional reconstructions. Results The spatial resolution of SR-DLR was higher than conventional reconstructions; the 10 % modulation transfer function of hybrid IR (HIR), DLR, and SR-DLR were 0.792-, 0.976-, and 1.379 cycle/mm, respectively. At the same time, image noise was lowest (HIR: 21.1-, DLR: 19.0-, and SR-DLR: 13.1 HU). SR-DLR could accurately assess coronary artery stenosis and the lumen of the implanted stent graft. Conclusions SR-DLR can obtain CT images with high spatial resolution and lower noise without special CT equipments, and will help diagnose coronary artery disease in CCTA and other CT examinations that require high spatial resolution.
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Affiliation(s)
- Toru Higaki
- Graduate School of Advanced Science and Engineering, Hiroshima University, Japan
| | - Fuminari Tatsugami
- Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan
| | | | - Yuko Nakamura
- Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan
| | - Ikuo Kawashita
- Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan
| | - Kazuo Awai
- Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan
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6
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Gunasekaran S, Szava-Kovats A, Battey T, Gross J, Picano E, Raman SV, Lee E, Bissell MM, Alasnag M, Campbell-Washburn AE, Hanneman K. Cardiovascular Imaging, Climate Change, and Environmental Sustainability. Radiol Cardiothorac Imaging 2024; 6:e240135. [PMID: 38900024 PMCID: PMC11211952 DOI: 10.1148/ryct.240135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/03/2024] [Accepted: 05/31/2024] [Indexed: 06/21/2024]
Abstract
Environmental exposures including poor air quality and extreme temperatures are exacerbated by climate change and are associated with adverse cardiovascular outcomes. Concomitantly, the delivery of health care generates substantial atmospheric greenhouse gas (GHG) emissions contributing to the climate crisis. Therefore, cardiac imaging teams must be aware not only of the adverse cardiovascular health effects of climate change, but also the downstream environmental ramifications of cardiovascular imaging. The purpose of this review is to highlight the impact of climate change on cardiovascular health, discuss the environmental impact of cardiovascular imaging, and describe opportunities to improve environmental sustainability of cardiac MRI, cardiac CT, echocardiography, cardiac nuclear imaging, and invasive cardiovascular imaging. Overarching strategies to improve environmental sustainability in cardiovascular imaging include prioritizing imaging tests with lower GHG emissions when more than one test is appropriate, reducing low-value imaging, and turning equipment off when not in use. Modality-specific opportunities include focused MRI protocols and low-field-strength applications, iodine contrast media recycling programs in cardiac CT, judicious use of US-enhancing agents in echocardiography, improved radiopharmaceutical procurement and waste management in nuclear cardiology, and use of reusable supplies in interventional suites. Finally, future directions and research are highlighted, including life cycle assessments over the lifespan of cardiac imaging equipment and the impact of artificial intelligence tools. Keywords: Heart, Safety, Sustainability, Cardiovascular Imaging Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Suvai Gunasekaran
- From the Biomedical Imaging Research Institute, Cedars-Sinai Medical
Center, Los Angeles, Calif (S.G.); Department of Radiology, Feinberg School of
Medicine, Northwestern University, Chicago, Ill (S.G.); Department of Nuclear
Medicine, Peter Lougheed Hospital, Alberta Health Services, Calgary, Canada
(A.S.K.); Department of Radiology, University of Calgary, Calgary, Canada
(A.S.K.); Department of Radiology & Medical Imaging, University of
Virginia, Charlottesville, Va (T.B.); Department of Radiology, Texas
Children’s Hospital, Baylor School of Medicine, Houston, Tex (J.G.);
Division of Cardiology, University Clinical Center of Serbia, University of
Belgrade, Belgrade, Serbia (E.P.); OhioHealth, Columbus, Ohio (S.V.R.); Langley
Memorial Hospital, British Columbia, Canada (E.L.); Department of Biomedical
Imaging Science, University of Leeds, Leeds, United Kingdom (M.M.B.); Cardiac
Center, King Fahad Armed Forces Hospital, Jeddah, Saudi Arabia (M.A.);
Cardiovascular Branch, Division of Intramural Research, National Heart, Lung,
and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.C.W.);
Joint Department of Medical Imaging, Peter Munk Cardiac Centre and Toronto
General Hospital Research Institute, University Medical Imaging Toronto,
University Health Network (UHN), 585 University Avenue, 1 PMB-298, Toronto, ON,
Canada M5G 2N2 (K.H.); and Department of Medical Imaging, University of Toronto,
Toronto, Canada (K.H.)
| | - Andrew Szava-Kovats
- From the Biomedical Imaging Research Institute, Cedars-Sinai Medical
Center, Los Angeles, Calif (S.G.); Department of Radiology, Feinberg School of
Medicine, Northwestern University, Chicago, Ill (S.G.); Department of Nuclear
Medicine, Peter Lougheed Hospital, Alberta Health Services, Calgary, Canada
(A.S.K.); Department of Radiology, University of Calgary, Calgary, Canada
(A.S.K.); Department of Radiology & Medical Imaging, University of
Virginia, Charlottesville, Va (T.B.); Department of Radiology, Texas
Children’s Hospital, Baylor School of Medicine, Houston, Tex (J.G.);
Division of Cardiology, University Clinical Center of Serbia, University of
Belgrade, Belgrade, Serbia (E.P.); OhioHealth, Columbus, Ohio (S.V.R.); Langley
Memorial Hospital, British Columbia, Canada (E.L.); Department of Biomedical
Imaging Science, University of Leeds, Leeds, United Kingdom (M.M.B.); Cardiac
Center, King Fahad Armed Forces Hospital, Jeddah, Saudi Arabia (M.A.);
Cardiovascular Branch, Division of Intramural Research, National Heart, Lung,
and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.C.W.);
Joint Department of Medical Imaging, Peter Munk Cardiac Centre and Toronto
General Hospital Research Institute, University Medical Imaging Toronto,
University Health Network (UHN), 585 University Avenue, 1 PMB-298, Toronto, ON,
Canada M5G 2N2 (K.H.); and Department of Medical Imaging, University of Toronto,
Toronto, Canada (K.H.)
| | - Thomas Battey
- From the Biomedical Imaging Research Institute, Cedars-Sinai Medical
Center, Los Angeles, Calif (S.G.); Department of Radiology, Feinberg School of
Medicine, Northwestern University, Chicago, Ill (S.G.); Department of Nuclear
Medicine, Peter Lougheed Hospital, Alberta Health Services, Calgary, Canada
(A.S.K.); Department of Radiology, University of Calgary, Calgary, Canada
(A.S.K.); Department of Radiology & Medical Imaging, University of
Virginia, Charlottesville, Va (T.B.); Department of Radiology, Texas
Children’s Hospital, Baylor School of Medicine, Houston, Tex (J.G.);
Division of Cardiology, University Clinical Center of Serbia, University of
Belgrade, Belgrade, Serbia (E.P.); OhioHealth, Columbus, Ohio (S.V.R.); Langley
Memorial Hospital, British Columbia, Canada (E.L.); Department of Biomedical
Imaging Science, University of Leeds, Leeds, United Kingdom (M.M.B.); Cardiac
Center, King Fahad Armed Forces Hospital, Jeddah, Saudi Arabia (M.A.);
Cardiovascular Branch, Division of Intramural Research, National Heart, Lung,
and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.C.W.);
Joint Department of Medical Imaging, Peter Munk Cardiac Centre and Toronto
General Hospital Research Institute, University Medical Imaging Toronto,
University Health Network (UHN), 585 University Avenue, 1 PMB-298, Toronto, ON,
Canada M5G 2N2 (K.H.); and Department of Medical Imaging, University of Toronto,
Toronto, Canada (K.H.)
| | - Jonathan Gross
- From the Biomedical Imaging Research Institute, Cedars-Sinai Medical
Center, Los Angeles, Calif (S.G.); Department of Radiology, Feinberg School of
Medicine, Northwestern University, Chicago, Ill (S.G.); Department of Nuclear
Medicine, Peter Lougheed Hospital, Alberta Health Services, Calgary, Canada
(A.S.K.); Department of Radiology, University of Calgary, Calgary, Canada
(A.S.K.); Department of Radiology & Medical Imaging, University of
Virginia, Charlottesville, Va (T.B.); Department of Radiology, Texas
Children’s Hospital, Baylor School of Medicine, Houston, Tex (J.G.);
Division of Cardiology, University Clinical Center of Serbia, University of
Belgrade, Belgrade, Serbia (E.P.); OhioHealth, Columbus, Ohio (S.V.R.); Langley
Memorial Hospital, British Columbia, Canada (E.L.); Department of Biomedical
Imaging Science, University of Leeds, Leeds, United Kingdom (M.M.B.); Cardiac
Center, King Fahad Armed Forces Hospital, Jeddah, Saudi Arabia (M.A.);
Cardiovascular Branch, Division of Intramural Research, National Heart, Lung,
and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.C.W.);
Joint Department of Medical Imaging, Peter Munk Cardiac Centre and Toronto
General Hospital Research Institute, University Medical Imaging Toronto,
University Health Network (UHN), 585 University Avenue, 1 PMB-298, Toronto, ON,
Canada M5G 2N2 (K.H.); and Department of Medical Imaging, University of Toronto,
Toronto, Canada (K.H.)
| | - Eugenio Picano
- From the Biomedical Imaging Research Institute, Cedars-Sinai Medical
Center, Los Angeles, Calif (S.G.); Department of Radiology, Feinberg School of
Medicine, Northwestern University, Chicago, Ill (S.G.); Department of Nuclear
Medicine, Peter Lougheed Hospital, Alberta Health Services, Calgary, Canada
(A.S.K.); Department of Radiology, University of Calgary, Calgary, Canada
(A.S.K.); Department of Radiology & Medical Imaging, University of
Virginia, Charlottesville, Va (T.B.); Department of Radiology, Texas
Children’s Hospital, Baylor School of Medicine, Houston, Tex (J.G.);
Division of Cardiology, University Clinical Center of Serbia, University of
Belgrade, Belgrade, Serbia (E.P.); OhioHealth, Columbus, Ohio (S.V.R.); Langley
Memorial Hospital, British Columbia, Canada (E.L.); Department of Biomedical
Imaging Science, University of Leeds, Leeds, United Kingdom (M.M.B.); Cardiac
Center, King Fahad Armed Forces Hospital, Jeddah, Saudi Arabia (M.A.);
Cardiovascular Branch, Division of Intramural Research, National Heart, Lung,
and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.C.W.);
Joint Department of Medical Imaging, Peter Munk Cardiac Centre and Toronto
General Hospital Research Institute, University Medical Imaging Toronto,
University Health Network (UHN), 585 University Avenue, 1 PMB-298, Toronto, ON,
Canada M5G 2N2 (K.H.); and Department of Medical Imaging, University of Toronto,
Toronto, Canada (K.H.)
| | - Subha V. Raman
- From the Biomedical Imaging Research Institute, Cedars-Sinai Medical
Center, Los Angeles, Calif (S.G.); Department of Radiology, Feinberg School of
Medicine, Northwestern University, Chicago, Ill (S.G.); Department of Nuclear
Medicine, Peter Lougheed Hospital, Alberta Health Services, Calgary, Canada
(A.S.K.); Department of Radiology, University of Calgary, Calgary, Canada
(A.S.K.); Department of Radiology & Medical Imaging, University of
Virginia, Charlottesville, Va (T.B.); Department of Radiology, Texas
Children’s Hospital, Baylor School of Medicine, Houston, Tex (J.G.);
Division of Cardiology, University Clinical Center of Serbia, University of
Belgrade, Belgrade, Serbia (E.P.); OhioHealth, Columbus, Ohio (S.V.R.); Langley
Memorial Hospital, British Columbia, Canada (E.L.); Department of Biomedical
Imaging Science, University of Leeds, Leeds, United Kingdom (M.M.B.); Cardiac
Center, King Fahad Armed Forces Hospital, Jeddah, Saudi Arabia (M.A.);
Cardiovascular Branch, Division of Intramural Research, National Heart, Lung,
and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.C.W.);
Joint Department of Medical Imaging, Peter Munk Cardiac Centre and Toronto
General Hospital Research Institute, University Medical Imaging Toronto,
University Health Network (UHN), 585 University Avenue, 1 PMB-298, Toronto, ON,
Canada M5G 2N2 (K.H.); and Department of Medical Imaging, University of Toronto,
Toronto, Canada (K.H.)
| | - Emil Lee
- From the Biomedical Imaging Research Institute, Cedars-Sinai Medical
Center, Los Angeles, Calif (S.G.); Department of Radiology, Feinberg School of
Medicine, Northwestern University, Chicago, Ill (S.G.); Department of Nuclear
Medicine, Peter Lougheed Hospital, Alberta Health Services, Calgary, Canada
(A.S.K.); Department of Radiology, University of Calgary, Calgary, Canada
(A.S.K.); Department of Radiology & Medical Imaging, University of
Virginia, Charlottesville, Va (T.B.); Department of Radiology, Texas
Children’s Hospital, Baylor School of Medicine, Houston, Tex (J.G.);
Division of Cardiology, University Clinical Center of Serbia, University of
Belgrade, Belgrade, Serbia (E.P.); OhioHealth, Columbus, Ohio (S.V.R.); Langley
Memorial Hospital, British Columbia, Canada (E.L.); Department of Biomedical
Imaging Science, University of Leeds, Leeds, United Kingdom (M.M.B.); Cardiac
Center, King Fahad Armed Forces Hospital, Jeddah, Saudi Arabia (M.A.);
Cardiovascular Branch, Division of Intramural Research, National Heart, Lung,
and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.C.W.);
Joint Department of Medical Imaging, Peter Munk Cardiac Centre and Toronto
General Hospital Research Institute, University Medical Imaging Toronto,
University Health Network (UHN), 585 University Avenue, 1 PMB-298, Toronto, ON,
Canada M5G 2N2 (K.H.); and Department of Medical Imaging, University of Toronto,
Toronto, Canada (K.H.)
| | - Malenka M. Bissell
- From the Biomedical Imaging Research Institute, Cedars-Sinai Medical
Center, Los Angeles, Calif (S.G.); Department of Radiology, Feinberg School of
Medicine, Northwestern University, Chicago, Ill (S.G.); Department of Nuclear
Medicine, Peter Lougheed Hospital, Alberta Health Services, Calgary, Canada
(A.S.K.); Department of Radiology, University of Calgary, Calgary, Canada
(A.S.K.); Department of Radiology & Medical Imaging, University of
Virginia, Charlottesville, Va (T.B.); Department of Radiology, Texas
Children’s Hospital, Baylor School of Medicine, Houston, Tex (J.G.);
Division of Cardiology, University Clinical Center of Serbia, University of
Belgrade, Belgrade, Serbia (E.P.); OhioHealth, Columbus, Ohio (S.V.R.); Langley
Memorial Hospital, British Columbia, Canada (E.L.); Department of Biomedical
Imaging Science, University of Leeds, Leeds, United Kingdom (M.M.B.); Cardiac
Center, King Fahad Armed Forces Hospital, Jeddah, Saudi Arabia (M.A.);
Cardiovascular Branch, Division of Intramural Research, National Heart, Lung,
and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.C.W.);
Joint Department of Medical Imaging, Peter Munk Cardiac Centre and Toronto
General Hospital Research Institute, University Medical Imaging Toronto,
University Health Network (UHN), 585 University Avenue, 1 PMB-298, Toronto, ON,
Canada M5G 2N2 (K.H.); and Department of Medical Imaging, University of Toronto,
Toronto, Canada (K.H.)
| | - Mirvat Alasnag
- From the Biomedical Imaging Research Institute, Cedars-Sinai Medical
Center, Los Angeles, Calif (S.G.); Department of Radiology, Feinberg School of
Medicine, Northwestern University, Chicago, Ill (S.G.); Department of Nuclear
Medicine, Peter Lougheed Hospital, Alberta Health Services, Calgary, Canada
(A.S.K.); Department of Radiology, University of Calgary, Calgary, Canada
(A.S.K.); Department of Radiology & Medical Imaging, University of
Virginia, Charlottesville, Va (T.B.); Department of Radiology, Texas
Children’s Hospital, Baylor School of Medicine, Houston, Tex (J.G.);
Division of Cardiology, University Clinical Center of Serbia, University of
Belgrade, Belgrade, Serbia (E.P.); OhioHealth, Columbus, Ohio (S.V.R.); Langley
Memorial Hospital, British Columbia, Canada (E.L.); Department of Biomedical
Imaging Science, University of Leeds, Leeds, United Kingdom (M.M.B.); Cardiac
Center, King Fahad Armed Forces Hospital, Jeddah, Saudi Arabia (M.A.);
Cardiovascular Branch, Division of Intramural Research, National Heart, Lung,
and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.C.W.);
Joint Department of Medical Imaging, Peter Munk Cardiac Centre and Toronto
General Hospital Research Institute, University Medical Imaging Toronto,
University Health Network (UHN), 585 University Avenue, 1 PMB-298, Toronto, ON,
Canada M5G 2N2 (K.H.); and Department of Medical Imaging, University of Toronto,
Toronto, Canada (K.H.)
| | - Adrienne E. Campbell-Washburn
- From the Biomedical Imaging Research Institute, Cedars-Sinai Medical
Center, Los Angeles, Calif (S.G.); Department of Radiology, Feinberg School of
Medicine, Northwestern University, Chicago, Ill (S.G.); Department of Nuclear
Medicine, Peter Lougheed Hospital, Alberta Health Services, Calgary, Canada
(A.S.K.); Department of Radiology, University of Calgary, Calgary, Canada
(A.S.K.); Department of Radiology & Medical Imaging, University of
Virginia, Charlottesville, Va (T.B.); Department of Radiology, Texas
Children’s Hospital, Baylor School of Medicine, Houston, Tex (J.G.);
Division of Cardiology, University Clinical Center of Serbia, University of
Belgrade, Belgrade, Serbia (E.P.); OhioHealth, Columbus, Ohio (S.V.R.); Langley
Memorial Hospital, British Columbia, Canada (E.L.); Department of Biomedical
Imaging Science, University of Leeds, Leeds, United Kingdom (M.M.B.); Cardiac
Center, King Fahad Armed Forces Hospital, Jeddah, Saudi Arabia (M.A.);
Cardiovascular Branch, Division of Intramural Research, National Heart, Lung,
and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.C.W.);
Joint Department of Medical Imaging, Peter Munk Cardiac Centre and Toronto
General Hospital Research Institute, University Medical Imaging Toronto,
University Health Network (UHN), 585 University Avenue, 1 PMB-298, Toronto, ON,
Canada M5G 2N2 (K.H.); and Department of Medical Imaging, University of Toronto,
Toronto, Canada (K.H.)
| | - Kate Hanneman
- From the Biomedical Imaging Research Institute, Cedars-Sinai Medical
Center, Los Angeles, Calif (S.G.); Department of Radiology, Feinberg School of
Medicine, Northwestern University, Chicago, Ill (S.G.); Department of Nuclear
Medicine, Peter Lougheed Hospital, Alberta Health Services, Calgary, Canada
(A.S.K.); Department of Radiology, University of Calgary, Calgary, Canada
(A.S.K.); Department of Radiology & Medical Imaging, University of
Virginia, Charlottesville, Va (T.B.); Department of Radiology, Texas
Children’s Hospital, Baylor School of Medicine, Houston, Tex (J.G.);
Division of Cardiology, University Clinical Center of Serbia, University of
Belgrade, Belgrade, Serbia (E.P.); OhioHealth, Columbus, Ohio (S.V.R.); Langley
Memorial Hospital, British Columbia, Canada (E.L.); Department of Biomedical
Imaging Science, University of Leeds, Leeds, United Kingdom (M.M.B.); Cardiac
Center, King Fahad Armed Forces Hospital, Jeddah, Saudi Arabia (M.A.);
Cardiovascular Branch, Division of Intramural Research, National Heart, Lung,
and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.C.W.);
Joint Department of Medical Imaging, Peter Munk Cardiac Centre and Toronto
General Hospital Research Institute, University Medical Imaging Toronto,
University Health Network (UHN), 585 University Avenue, 1 PMB-298, Toronto, ON,
Canada M5G 2N2 (K.H.); and Department of Medical Imaging, University of Toronto,
Toronto, Canada (K.H.)
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7
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Prod'homme S, Bouzerar R, Forzini T, Delabie A, Renard C. Detection of urinary tract stones on submillisievert abdominopelvic CT imaging with deep-learning image reconstruction algorithm (DLIR). Abdom Radiol (NY) 2024; 49:1987-1995. [PMID: 38470506 DOI: 10.1007/s00261-024-04223-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 03/14/2024]
Abstract
PURPOSE Urolithiasis is a chronic condition that leads to repeated CT scans throughout the patient's life. The goal was to assess the diagnostic performance and image quality of submillisievert abdominopelvic computed tomography (CT) using deep learning-based image reconstruction (DLIR) in urolithiasis. METHODS 57 patients with suspected urolithiasis underwent both non-contrast low-dose (LD) and ULD abdominopelvic CT. Raw image data of ULD CT were reconstructed using hybrid iterative reconstruction (ASIR-V 70%) and high-strength-level DLIR (DLIR-H). The performance of ULD CT for the detection of urinary stones was assessed by two readers and compared with LD CT with ASIR-V 70% as a reference standard. Image quality was assessed subjectively and objectively. RESULTS 266 stones were detected in 38 patients. Mean effective dose was 0.59 mSv for ULD CT and 1.96 mSv for LD CT. For diagnostic performance, sensitivity and specificity were 89% and 94%, respectively, for ULDCT with DLIR-H. There was an almost perfect intra-observer concordance on ULD CT with DLIR-H versus LDCT with ASIR-V 70% (ICC = 0.90 and 0.90 for the two readers). Image noise was significantly lower and signal-to-noise ratio significantly higher with DLIR-H compared to ASIR-V 70%. Subjective image quality was also significantly better with ULDCT with DLIR-H. CONCLUSION ULD CT with Deep Learning Image Reconstruction maintains a good diagnostic performance in urolithiasis, with better image quality than hybrid iterative reconstruction and a significant radiation dose reduction.
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Affiliation(s)
- Sarah Prod'homme
- Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, 80054, Amiens Cedex 01, France
| | - Roger Bouzerar
- Biophysics and Image Processing Unit, Amiens University Hospital, Amiens, France
| | - Thomas Forzini
- Department of Urology and Transplantation, Amiens University Hospital, Amiens, France
| | - Aurélien Delabie
- Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, 80054, Amiens Cedex 01, France
| | - Cédric Renard
- Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, 80054, Amiens Cedex 01, France.
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8
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Im JY, Halliburton SS, Mei K, Perkins AE, Wong E, Roshkovan L, Sandvold OF, Liu LP, Gang GJ, Noël PB. Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm. Phys Med Biol 2024; 69:115009. [PMID: 38604190 PMCID: PMC11097966 DOI: 10.1088/1361-6560/ad3dba] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/22/2024] [Accepted: 04/11/2024] [Indexed: 04/13/2024]
Abstract
Objective. Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.Method. The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error, structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.Results.DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25%-83% in the small phantom and by 50%-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR.Conclusion. DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.
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Affiliation(s)
- Jessica Y Im
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | | | - Kai Mei
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Amy E Perkins
- Philips Healthcare, Cleveland, OH, United States of America
| | - Eddy Wong
- Philips Healthcare, Cleveland, OH, United States of America
| | - Leonid Roshkovan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Olivia F Sandvold
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Leening P Liu
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Grace J Gang
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Peter B Noël
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
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9
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Yunaga H, Miyoshi H, Ochiai R, Gonda T, Sakoh T, Noma H, Fujii S. Image Quality and Lesion Detection of Multiplanar Reconstruction Images Using Deep Learning: Comparison with Hybrid Iterative Reconstruction. Yonago Acta Med 2024; 67:100-107. [PMID: 38803592 PMCID: PMC11128077 DOI: 10.33160/yam.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/16/2024] [Indexed: 05/29/2024]
Abstract
Background We assessed and compared the image quality of normal and pathologic structures as well as the image noise in chest computed tomography images using "adaptive statistical iterative reconstruction-V" (ASiR-V) or deep learning reconstruction "TrueFidelity". Methods Forty consecutive patients with suspected lung disease were evaluated. The 1.25-mm axial images and 2.0-mm coronal multiplanar images were reconstructed under the following three conditions: (i) ASiR-V, lung kernel with 60% of ASiR-V; (ii) TF-M, standard kernel, image filter (Lung) with TrueFidelity at medium strength; and (iii) TF-H, standard kernel, image filter (Lung) with TrueFidelity at high strength. Two radiologists (readers) independently evaluated the image quality of anatomic structures using a scale ranging from 1 (best) to 5 (worst). In addition, readers ranked their image preference. Objective image noise was measured using a circular region of interest in the lung parenchyma. Subjective image quality scores, total scores for normal and abnormal structures, and lesion detection were compared using Wilcoxon's signed-rank test. Objective image quality was compared using Student's paired t-test and Wilcoxon's signed-rank test. The Bonferroni correction was applied to the P value, and significance was assumed only for values of P < 0.016. Results Both readers rated TF-M and TF-H images significantly better than ASiR-V images in terms of visualization of the centrilobular region in axial images. The preference score of TF-M and TF-H images for reader 1 were better than that of ASiR-V images, and the preference score of TF-H images for reader 2 were significantly better than that of ASiR-V and TF-M images. TF-M images showed significantly lower objective image noise than ASiR-V or TF-H images. Conclusion TrueFidelity showed better image quality, especially in the centrilobular region, than ASiR-V in subjective and objective evaluations. In addition, the image texture preference for TrueFidelity was better than that for ASiR-V.
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Affiliation(s)
- Hiroto Yunaga
- Division of Radiology, Department of Multidisciplinary Internal Medicine, School of Medicine, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan
| | - Hidenao Miyoshi
- Division of Radiology, Department of Multidisciplinary Internal Medicine, School of Medicine, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan
| | - Ryoya Ochiai
- Division of Radiology, Department of Multidisciplinary Internal Medicine, School of Medicine, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan
| | - Takuro Gonda
- Division of Radiology, Department of Multidisciplinary Internal Medicine, School of Medicine, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan
| | - Toshio Sakoh
- Division of Clinical Radiology, School of Medicine, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan
| | - Hisashi Noma
- Department of Data Science, The Institute of Statistical Mathematics, Tachikawa 190-8562, Japan
| | - Shinya Fujii
- Division of Radiology, Department of Multidisciplinary Internal Medicine, School of Medicine, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan
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10
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Zhou Z, Gong H, Hsieh S, McCollough CH, Yu L. Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution. Med Phys 2024. [PMID: 38555876 DOI: 10.1002/mp.17029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/19/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images. PURPOSE In this work, we aim to develop a patient-data-based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method. METHODS The patient-data-based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR-processed images from projection or image data; (4) creation of ensembles of DLIR-processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (-500, -100, -50, -20, -10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered-backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN-weak, DCNN-medium, and DCNN-strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion-present images from the lesion absent images. Modulation transfer functions (MTFs) both in-plane and along the z-axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t-test was used to compare the z-axis and in-plane spatial resolution of five algorithms for five different contrasts and three dose levels. RESULTS The in-plane and z-axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in-plane and z-axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (-10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in-plane and z-axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of -100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in-plane and cross-plane spatial resolution: FBP > DCNN-Weak > IR > DCNN-Medium > DCNN-Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels. CONCLUSIONS A patient-data-based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non-linear image reconstruction and post-processing techniques, the evaluated DCNN method degraded the in-plane and z-axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.
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Affiliation(s)
- Zhongxing Zhou
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Hao Gong
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Scott Hsieh
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Lifeng Yu
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
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11
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Pouget E, Dedieu V. Applying Self-Supervised Learning to Image Quality Assessment in Chest CT Imaging. Bioengineering (Basel) 2024; 11:335. [PMID: 38671757 PMCID: PMC11048026 DOI: 10.3390/bioengineering11040335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
Many new reconstruction techniques have been deployed to allow low-dose CT examinations. Such reconstruction techniques exhibit nonlinear properties, which strengthen the need for a task-based measure of image quality. The Hotelling observer (HO) is the optimal linear observer and provides a lower bound of the Bayesian ideal observer detection performance. However, its computational complexity impedes its widespread practical usage. To address this issue, we proposed a self-supervised learning (SSL)-based model observer to provide accurate estimates of HO performance in very low-dose chest CT images. Our approach involved a two-stage model combining a convolutional denoising auto-encoder (CDAE) for feature extraction and dimensionality reduction and a support vector machine for classification. To evaluate this approach, we conducted signal detection tasks employing chest CT images with different noise structures generated by computer-based simulations. We compared this approach with two supervised learning-based methods: a single-layer neural network (SLNN) and a convolutional neural network (CNN). The results showed that the CDAE-based model was able to achieve similar detection performance to the HO. In addition, it outperformed both SLNN and CNN when a reduced number of training images was considered. The proposed approach holds promise for optimizing low-dose CT protocols across scanner platforms.
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Affiliation(s)
- Eléonore Pouget
- Department of Medical Physics, Jean Perrin Comprehensive Cancer Center, F-63000 Clermont-Ferrand, France;
- UMR 1240 INSERM IMoST, University of Clermont-Ferrand, F-63000 Clermont-Ferrand, France
| | - Véronique Dedieu
- Department of Medical Physics, Jean Perrin Comprehensive Cancer Center, F-63000 Clermont-Ferrand, France;
- UMR 1240 INSERM IMoST, University of Clermont-Ferrand, F-63000 Clermont-Ferrand, France
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12
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Takemitsu M, Kudomi S, Takegami K, Uehara T. The effect of a pre-reconstruction process in a filtered back projection reconstruction on an image quality of a low tube voltage computed tomography. Radiol Phys Technol 2024; 17:306-314. [PMID: 38100019 DOI: 10.1007/s12194-023-00764-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/07/2023] [Accepted: 11/22/2023] [Indexed: 03/01/2024]
Abstract
This study aims to evaluate the effect of pre-reconstruction process for low tube voltage computed tomography (CT) on image quality of filtered back projection (FBP) reconstruction. Small and large quality assurance water phantoms (19 and 33 cm diameter) were scanned on a third-generation dual-source CT with 70 kVp and 120 kVp at various dose levels. Image quality was assessed in terms of the noise power spectrum (NPS) and task-based transfer function (TTF). NPSs and TTFs in the small phantom were comparable between 70 and 120 kVp protocols. In the large phantom, the curves of the NPS changed and the TTF decreased even at the high-dose levels for 70 kVp protocol compared to 120 kVp protocol. Our results indicated that the pre-reconstruction process is performed in low tube voltage CT for large objects even for the FBP reconstruction and has an effect on the image quality.
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Affiliation(s)
- Masaki Takemitsu
- Department of Radiological Technology, Yamaguchi University Hospital, Yamaguchi, 755-8505, Japan.
| | - Shohei Kudomi
- Department of Radiological Technology, Yamaguchi University Hospital, Yamaguchi, 755-8505, Japan
| | - Kazuki Takegami
- Department of Radiological Technology, Yamaguchi University Hospital, Yamaguchi, 755-8505, Japan
| | - Takuya Uehara
- Department of Radiological Technology, Yamaguchi University Hospital, Yamaguchi, 755-8505, Japan
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13
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Ozaki M, Ichikawa S, Fukunaga M, Yamamoto H. Improving the depiction of small intracranial vessels in head computed tomography angiography: a comparative analysis of deep learning reconstruction and hybrid iterative reconstruction. Radiol Phys Technol 2024; 17:329-336. [PMID: 37897685 DOI: 10.1007/s12194-023-00749-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 10/30/2023]
Abstract
This study aimed to evaluate the ability of deep learning reconstruction (DLR) compared to that of hybrid iterative reconstruction (IR) to depict small vessels on computed tomography (CT). DLR and two types of hybrid IRs were used for image reconstruction. The target vessels were the basilar artery (BA), superior cerebellar artery (SCA), anterior inferior cerebellar artery (AICA), and posterior inferior cerebellar artery (PICA). The peak value, ΔCT values defined as the difference between the peak value and background, and full width at half maximum (FWHM), were obtained from the profile curves. In all target vessels, the peak and ΔCT values of DLR were significantly higher than those of the two types of hybrid IR (p < 0.001). Compared to that associated with hybrid IR, the FWHM of DLR was significantly lower in the SCA (p < 0.001), AICA (p < 0.001), and PICA (p < 0.001). In conclusion, DLR has the potential to improve visualization of small vessels.
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Affiliation(s)
- Makoto Ozaki
- Department of Radiological Technology, Kurashiki Central Hospital, 1-1-1, Miwa, Kurashiki, Okayama, 710-8602, Japan.
| | - Shota Ichikawa
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Niigata University, 2-746 Asahimachi-Dori, Chuo-Ku, Niigata, Niigata, 951-8518, Japan
- Institute for Research Administration, Niigata University, 8050 Ikarashi 2-No-Cho, Nishi-Ku, Niigata, Niigata, 950-2181, Japan
| | - Masaaki Fukunaga
- Department of Radiological Technology, Kurashiki Central Hospital, 1-1-1, Miwa, Kurashiki, Okayama, 710-8602, Japan
| | - Hiroyuki Yamamoto
- Department of Radiological Technology, Kurashiki Central Hospital, 1-1-1, Miwa, Kurashiki, Okayama, 710-8602, Japan
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14
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Tomasi S, Szilagyi KE, Barca P, Bisello F, Spagnoli L, Domenichelli S, Strigari L. A CT deep learning reconstruction algorithm: Image quality evaluation for brain protocol at decreasing dose indexes in comparison with FBP and statistical iterative reconstruction algorithms. Phys Med 2024; 119:103319. [PMID: 38422902 DOI: 10.1016/j.ejmp.2024.103319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 01/17/2024] [Accepted: 02/09/2024] [Indexed: 03/02/2024] Open
Abstract
PURPOSE To characterise the impact of Precise Image (PI) deep learning reconstruction algorithm on image quality, compared to filtered back-projection (FBP) and iDose4 iterative reconstruction for brain computed tomography (CT) phantom images. METHODS Catphan-600 phantom was acquired with an Incisive CT scanner using a dedicated brain protocol, at six different dose levels (volume computed tomography dose index (CTDIvol): 7/14/29/49/56/67 mGy). Images were reconstructed using FBP, levels 2/5 of iDose4, and PI algorithm (Sharper/Sharp/Standard/Smooth/Smoother). Image quality was assessed by evaluating CT numbers, image histograms, noise, image non-uniformity (NU), noise power spectrum, target transfer function, and detectability index. RESULTS The five PI levels did not significantly affect the mean CT number. For a given CTDIvol using Sharper-to-Smoother levels, the spatial resolution for all the investigated materials and the detectability index increased while the noise magnitude decreased, slightly affecting noise texture. For a fixed PI level increasing the CTDIvol the detectability index increased, the noise magnitude decreased. From 29 mGy, NU values converged within 1 Hounsfield Unit from each other without a substantial improvement at higher CTDIvol values. CONCLUSIONS The improved performances of intermediate PI levels in brain protocols compared to conventional algorithms seem to suggest a potential reduction of CTDIvol.
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Affiliation(s)
- Silvia Tomasi
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Klarisa Elena Szilagyi
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Patrizio Barca
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy; Unit of Medical Physics, Azienda Ospedaliero-Universitaria Pisana, 56126 Pisa, Italy
| | - Francesca Bisello
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Lorenzo Spagnoli
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Sara Domenichelli
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Lidia Strigari
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
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Schoen JH, Burdette JH, West TG, Geer CP, Lipford ME, Sachs JR. Savings in CT Net Scan Energy Consumption: Assessment Using Dose Report Metrics and Comparison With Savings in Idle State Energy Consumption. AJR Am J Roentgenol 2024; 222:e2330189. [PMID: 37937836 DOI: 10.2214/ajr.23.30189] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
BACKGROUND. CT scanners' net scan state (i.e., image acquisition period) represents a potential target for energy savings through protocol adjustments. However, gauging CT energy savings is difficult without installing costly energy monitors. OBJECTIVE. The purpose of this article was to assess correlations between CT dose report metrics and energy consumption during the system net scan state and to compare theoretic energy savings from matching percentage reductions in energy consumption during net scan and idle system states. METHODS. Current sensors were installed on a single CT scanner. A phantom was scanned at varying kilovoltage settings and effective tube current-rotation time settings. A retrospective assessment was performed in 32 patients (mean age, 61.2 ± 17.9 [SD] years; 17 men, 15 women) who underwent 32 single-energy noncontrast abdominopelvic CT examinations from September 22, 2021, to September 27, 2021, on the same scanner. Correlations between dose report metrics and net scan energy consumption were assessed in the phantom and clinical scans, and equations were generated to derive net scan energy consumption from DLP. An additional retrospective assessment was performed in 1355 patients (mean age, 59.3 ± 16.9 years; 663 men, 692 women) who underwent 1728 single-energy noncontrast abdominopelvic CT examinations from January 1, 2021, through December 31, 2021, on the same scanner to estimate net scan energy consumption per examination. This information was integrated with literature-derived values to compare estimated annual national energy savings resulting from 20% reductions in net scan and idle state energy consumption. RESULTS. Net scan energy consumption in the phantom scans showed high linear correlation with DLP (R2 = 0.87), and, in the clinical scans, high linear correlation with CTDIvol (R2 = 0.89) and very high linear correlation with DLP (R2 = 0.92). When combining mean DLP in examinations performed in the 1-year interval, an equation relating DLP and net scan energy consumption and literature values estimated that annual national energy savings was 14.9 times greater (40,437,870 kWh/2,704,000 kWh) by targeting the idle state rather than net scan state. CONCLUSION. CT net scan energy savings can be inferred from reductions in dose report metrics. However, targeting net scan energy consumption has modest impact relative to targeting idle state energy consumption. CLINICAL IMPACT. Environmental sustainability efforts should target the idle state energy consumption of CT.
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Affiliation(s)
- Julia H Schoen
- Department of Radiology, Wake Forest School of Medicine, One Medical Center Blvd, Winston-Salem, NC 27157
| | - Jonathan H Burdette
- Department of Radiology, Wake Forest School of Medicine, One Medical Center Blvd, Winston-Salem, NC 27157
| | - Thomas G West
- Department of Radiology, Wake Forest School of Medicine, One Medical Center Blvd, Winston-Salem, NC 27157
| | - Carol P Geer
- Department of Radiology, Wake Forest School of Medicine, One Medical Center Blvd, Winston-Salem, NC 27157
| | - Megan E Lipford
- Department of Radiology, Wake Forest School of Medicine, One Medical Center Blvd, Winston-Salem, NC 27157
| | - Jeffrey R Sachs
- Department of Radiology, Wake Forest School of Medicine, One Medical Center Blvd, Winston-Salem, NC 27157
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Lyu P, Li Z, Chen Y, Wang H, Liu N, Liu J, Zhan P, Liu X, Shang B, Wang L, Gao J. Deep learning reconstruction CT for liver metastases: low-dose dual-energy vs standard-dose single-energy. Eur Radiol 2024; 34:28-38. [PMID: 37532899 DOI: 10.1007/s00330-023-10033-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 05/28/2023] [Accepted: 06/05/2023] [Indexed: 08/04/2023]
Abstract
OBJECTIVES To assess image quality and liver metastasis detection of reduced-dose dual-energy CT (DECT) with deep learning image reconstruction (DLIR) compared to standard-dose single-energy CT (SECT) with DLIR or iterative reconstruction (IR). METHODS In this prospective study, two groups of 40 participants each underwent abdominal contrast-enhanced scans with full-dose SECT (120-kVp images, DLIR and IR algorithms) or reduced-dose DECT (40- to 60-keV virtual monochromatic images [VMIs], DLIR algorithm), with 122 and 106 metastases, respectively. Groups were matched by age, sex ratio, body mass index, and cross-sectional area. Noise power spectrum of liver images and task-based transfer function of metastases were calculated to assess the noise texture and low-contrast resolution. The image noise, signal-to-noise ratios (SNR) of liver and portal vein, liver-to-lesion contrast-to-noise ratio (LLR), lesion conspicuity, lesion detection rate, and the subjective image quality metrics were compared between groups on 1.25-mm reconstructed images. RESULTS Compared to 120-kVp images with IR, 40- and 50-keV VMIs with DLIR showed similar noise texture and LLR, similar or higher image noise and low-contrast resolution, improved SNR and lesion conspicuity, and similar or better perceptual image quality. When compared to 120-kVp images with DLIR, 50-keV VMIs with DLIR had similar low-contrast resolution, SNR, LLR, lesion conspicuity, and perceptual image quality but lower frequency noise texture and higher image noise. For the detection of hepatic metastases, reduced-dose DECT by 34% maintained observer lesion detection rates. CONCLUSION DECT assisted with DLIR enables a 34% dose reduction for detecting hepatic metastases while maintaining comparable perceptual image quality to full-dose SECT. CLINICAL RELEVANCE STATEMENT Reduced-dose dual-energy CT with deep learning image reconstruction is as accurate as standard-dose single-energy CT for the detection of liver metastases and saves more than 30% of the radiation dose. KEY POINTS • The 40- and 50-keV virtual monochromatic images (VMIs) with deep learning image reconstruction (DLIR) improved lesion conspicuity compared with 120-kVp images with iterative reconstruction while providing similar or better perceptual image quality. • The 50-keV VMIs with DLIR provided comparable perceptual image quality and lesion conspicuity to 120-kVp images with DLIR. • The reduction of radiation by 34% by DLIR in low-keV VMIs is clinically sufficient for detecting low-contrast hepatic metastases.
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Affiliation(s)
- Peijie Lyu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Zhen Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Yan Chen
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Huixia Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Nana Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Jie Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Pengchao Zhan
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Xing Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Bo Shang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Luotong Wang
- CT Imaging Research Center, GE Healthcare China, Beijing, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China.
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Kang HJ, Lee JM, Park SJ, Lee SM, Joo I, Yoon JH. Image Quality Improvement of Low-dose Abdominal CT using Deep Learning Image Reconstruction Compared with the Second Generation Iterative Reconstruction. Curr Med Imaging 2024; 20:e250523217310. [PMID: 37231764 DOI: 10.2174/1573405620666230525104809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 03/23/2023] [Accepted: 04/06/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND Whether deep learning-based CT reconstruction could improve lesion conspicuity on abdominal CT when the radiation dose is reduced is controversial. OBJECTIVES To determine whether DLIR can provide better image quality and reduce radiation dose in contrast-enhanced abdominal CT compared with the second generation of adaptive statistical iterative reconstruction (ASiR-V). AIMS This study aims to determine whether deep-learning image reconstruction (DLIR) can improve image quality. METHOD In this retrospective study, a total of 102 patients were included, who underwent abdominal CT using a DLIR-equipped 256-row scanner and routine CT of the same protocol on the same vendor's 64-row scanner within four months. The CT data from the 256-row scanner were reconstructed into ASiR-V with three blending levels (AV30, AV60, and AV100), and DLIR images with three strength levels (DLIR-L, DLIR-M, and DLIR-H). The routine CT data were reconstructed into AV30, AV60, and AV100. The contrast-to-noise ratio (CNR) of the liver, overall image quality, subjective noise, lesion conspicuity, and plasticity in the portal venous phase (PVP) of ASiR-V from both scanners and DLIR were compared. RESULTS The mean effective radiation dose of PVP of the 256-row scanner was significantly lower than that of the routine CT (6.3±2.0 mSv vs. 2.4±0.6 mSv; p< 0.001). The mean CNR, image quality, subjective noise, and lesion conspicuity of ASiR-V images of the 256-row scanner were significantly lower than those of ASiR-V images at the same blending factor of routine CT, but significantly improved with DLIR algorithms. DLIR-H showed higher CNR, better image quality, and subjective noise than AV30 from routine CT, whereas plasticity was significantly better for AV30. CONCLUSION DLIR can be used for improving image quality and reducing radiation dose in abdominal CT, compared with ASIR-V.
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Affiliation(s)
- Hyo-Jin Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Sae Jin Park
- Department of Radiology, G&E alphadom medical center, Seongnam, Korea
| | - Sang Min Lee
- Department of Radiology, Cha Gangnam Medical Center, Seoul, Korea
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
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Narita A, Ohsugi Y, Ohkubo M, Fukaya T, Sakai K, Noto Y. Method for measuring noise-power spectrum independent of the effect of extracting the region of interest from a noise image. Radiol Phys Technol 2023; 16:471-477. [PMID: 37515623 DOI: 10.1007/s12194-023-00733-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/11/2023] [Accepted: 07/11/2023] [Indexed: 07/31/2023]
Abstract
This study aimed to evaluate the impact of region of interest (ROI) size on noise-power spectrum (NPS) measurement in computed tomography (CT) images and to propose a novel method for measuring NPS independent of ROI size. The NPS was measured using the conventional method with an ROI of size P × P pixels in a uniform region in the CT image; the NPS is referred to as NPSR=P. NPSsR=256, 128, 64, 32, 16, and 8 were obtained and compared to assess their dependency on ROI size. In the proposed method, the true NPS was numerically modeled as an NPSmodel, with adjustable parameters, and a noise image with the property of the NPSmodel was generated. From the generated noise image, the NPS was measured using the conventional method with a P × P pixel ROI size; the obtained NPS was referred to as NPS'R=P. The adjustable parameters of the NPSmodel were optimized such that NPS'R=P was most similar to NPSR=P. When NPS'R=P was almost equivalent to NPSR=P, the NPSmodel was considered the true NPS. NPSsR=256, 128, 64, 32, 16, and 8 obtained using the conventional method were dependent on the ROI size. Conversely, the NPSs (optimized NPSsmodel) measured using the proposed method were not dependent on the ROI size, even when a much smaller ROI (P = 16 or 8) was used. The proposed method for NPS measurement was confirmed to be precise, independent of the ROI size, and useful for measuring local NPSs using a small ROI.
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Affiliation(s)
- Akihiro Narita
- Graduate School of Health Sciences, Niigata University, 2-746 Asahimachi-Dori, Chuo-Ku, Niigata, 951-8518, Japan.
| | - Yuki Ohsugi
- Department of Clinical Support, Niigata University Medical and Dental Hospital, 1-754 Asahimachi-Dori, Chuo-Ku, Niigata, 951-8520, Japan
| | - Masaki Ohkubo
- Graduate School of Health Sciences, Niigata University, 2-746 Asahimachi-Dori, Chuo-Ku, Niigata, 951-8518, Japan
| | - Takahiro Fukaya
- Department of Clinical Support, Niigata University Medical and Dental Hospital, 1-754 Asahimachi-Dori, Chuo-Ku, Niigata, 951-8520, Japan
| | - Kenichi Sakai
- Department of Clinical Support, Niigata University Medical and Dental Hospital, 1-754 Asahimachi-Dori, Chuo-Ku, Niigata, 951-8520, Japan
| | - Yoshiyuki Noto
- Department of Clinical Support, Niigata University Medical and Dental Hospital, 1-754 Asahimachi-Dori, Chuo-Ku, Niigata, 951-8520, Japan
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Nagayama Y, Emoto T, Kato Y, Kidoh M, Oda S, Sakabe D, Funama Y, Nakaura T, Hayashi H, Takada S, Uchimura R, Hatemura M, Tsujita K, Hirai T. Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography. Eur Radiol 2023; 33:8488-8500. [PMID: 37432405 DOI: 10.1007/s00330-023-09888-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 03/22/2023] [Accepted: 04/23/2023] [Indexed: 07/12/2023]
Abstract
OBJECTIVES To evaluate the effect of super-resolution deep-learning-based reconstruction (SR-DLR) on the image quality of coronary CT angiography (CCTA). METHODS Forty-one patients who underwent CCTA using a 320-row scanner were retrospectively included. Images were reconstructed with hybrid (HIR), model-based iterative reconstruction (MBIR), normal-resolution deep-learning-based reconstruction (NR-DLR), and SR-DLR algorithms. For each image series, image noise, and contrast-to-noise ratio (CNR) at the left main trunk, right coronary artery, left anterior descending artery, and left circumflex artery were quantified. Blooming artifacts from calcified plaques were measured. Image sharpness, noise magnitude, noise texture, edge smoothness, overall quality, and delineation of the coronary wall, calcified and noncalcified plaques, cardiac muscle, and valves were subjectively ranked on a 4-point scale (1, worst; 4, best). The quantitative parameters and subjective scores were compared among the four reconstructions. Task-based image quality was assessed with a physical evaluation phantom. The detectability index for the objects simulating the coronary lumen, calcified plaques, and noncalcified plaques was calculated from the noise power spectrum (NPS) and task-based transfer function (TTF). RESULTS SR-DLR yielded significantly lower image noise and blooming artifacts with higher CNR than HIR, MBIR, and NR-DLR (all p < 0.001). The best subjective scores for all the evaluation criteria were attained with SR-DLR, with significant differences from all other reconstructions (p < 0.001). In the phantom study, SR-DLR provided the highest NPS average frequency, TTF50%, and detectability for all task objects. CONCLUSION SR-DLR considerably improved the subjective and objective image qualities and object detectability of CCTA relative to HIR, MBIR, and NR-DLR algorithms. CLINICAL RELEVANCE STATEMENT The novel SR-DLR algorithm has the potential to facilitate accurate assessment of coronary artery disease on CCTA by providing excellent image quality in terms of spatial resolution, noise characteristics, and object detectability. KEY POINTS • SR-DLR designed for CCTA improved image sharpness, noise property, and delineation of cardiac structures with reduced blooming artifacts from calcified plaques relative to HIR, MBIR, and NR-DLR. • In the task-based image-quality assessments, SR-DLR yielded better spatial resolution, noise property, and detectability for objects simulating the coronary lumen, coronary calcifications, and noncalcified plaques than other reconstruction techniques. • The image reconstruction times of SR-DLR were shorter than those of MBIR, potentially serving as a novel standard-of-care reconstruction technique for CCTA performed on a 320-row CT scanner.
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Affiliation(s)
- Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan.
| | - Takafumi Emoto
- Department of Central Radiology, Kumamoto University Hospital, Kumamoto, Japan
| | - Yuki Kato
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Seitaro Oda
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Daisuke Sakabe
- Department of Central Radiology, Kumamoto University Hospital, Kumamoto, Japan
| | - Yoshinori Funama
- Department of Medical Radiation Sciences, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Hidetaka Hayashi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Sentaro Takada
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Ryutaro Uchimura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Masahiro Hatemura
- Department of Central Radiology, Kumamoto University Hospital, Kumamoto, Japan
| | - Kenichi Tsujita
- Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
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Li S, Yuan L, Lu T, Yang X, Ren W, Wang L, Zhao J, Deng J, Liu X, Xue C, Sun Q, Zhang W, Zhou J. Deep learning imaging reconstruction of reduced-dose 40 keV virtual monoenergetic imaging for early detection of colorectal cancer liver metastases. Eur J Radiol 2023; 168:111128. [PMID: 37816301 DOI: 10.1016/j.ejrad.2023.111128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/07/2023] [Accepted: 09/28/2023] [Indexed: 10/12/2023]
Abstract
OBJECTIVE To explore whether reduced-dose (RD) gemstone spectral imaging (GSI) and deep learning image reconstruction (DLIR) of 40 keV virtual monoenergetic image (VMI) enhanced the early detection and diagnosis of colorectal cancer liver metastases (CRLM). METHODS Thirty-five participants with pathologically confirmed colorectal cancer were prospectively enrolled from March to August 2022 after routine care abdominal computed tomography (CT). GSI mode was used for contrast-enhanced CT, and two portal venous phase CT images were obtained [standard-dose (SD) CT dose index (CTDIvol) = 15.51 mGy, RD CTDIvol = 7.95 mGy]. The 40 keV-VMI were reconstructed via filtered back projection (FBP) and iterative reconstruction (ASIR-V 60 %, AV60) of both SD and RD images. RD medium-strength deep learning image reconstruction (DLIR-M) and RD high-strength deep learning image reconstruction (DLIR-H) were used to reconstruct the 40 keV-VMI. The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the liver and the lesions were objectively evaluated. The overall image quality, lesion conspicuity, and diagnostic confidence were subjectively evaluated, to compare the differences in evaluation results among the different images. RESULTS All 35 participants (mean age: 59.51 ± 11.01 years; 14 females) underwent SD and RD GSI portal venous-phase CT scans. The dose-length product of the RD GSI scan was reduced by 49-53 % lower than that of the SD GSI scan (420.22 ± 31.95) vs (817.58 ± 60.56). A total of 219 lesions were identified, including 55 benign lesions and 164 metastases, with an average size of 7.37 ± 4.14 mm. SD-FBP detected 207 lesions, SD-AV60 detected 201 lesions, and DLIR-M and DLIR-H detected 199 and 190 lesions, respectively. For lesions ≤ 5 mm, there was no statistical difference between SD-FBP vs DLIR-M (χ2McNemar = 1.00, P = 0.32) and SD-AV60 vs DLIR-M (χ2McNemar = 0.33, P = 0.56) in the detection rate. The CNR, SNR, and noise of DLIR-M and DLIR-H 40 keV-VMI images were better than those of SD-FBP images (P < 0.01) but did not differ significantly from those of SD-AV60 images (P > 0.05). When the lesions ≤ 5 mm, there were statistical differences in the overall diagnostic sensitivity of lesions compared with SD-FBP, SD-AV60, DLIR-M and DLIR-H (P<0.01). There were no statistical differences in the sensitivity of lesions diagnosis between SD-FBP, SD-AV60 and DLIR-M (both P>0.05). However, the DLIR-M subjective image quality and lesion diagnostic confidence were higher for SD-FBP (both P < 0.01). CONCLUSION Reduced dose DLIR-M of 40 keV-VMI can be used for routine follow-up care of colorectal cancer patients, to optimize evaluations and ensure CT image quality. Meanwhile, the detection rate and diagnostic sensitivity and specificity of small lesions, early liver metastases is not obviously reduced.
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Affiliation(s)
- Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Long Yuan
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Ting Lu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Xinmei Yang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Wei Ren
- CT Imaging Research Center, GE Healthcare China, Beijing, 100176, China.
| | - Luotong Wang
- CT Imaging Research Center, GE Healthcare China, Beijing, 100176, China.
| | - Jun Zhao
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Qiu Sun
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China.
| | - Wenjuan Zhang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
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Jensen CT, Wong VK, Wagner-Bartak NA, Liu X, Padmanabhan Nair Sobha R, Sun J, Likhari GS, Gupta S. Accuracy of liver metastasis detection and characterization: Dual-energy CT versus single-energy CT with deep learning reconstruction. Eur J Radiol 2023; 168:111121. [PMID: 37806195 DOI: 10.1016/j.ejrad.2023.111121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 09/08/2023] [Accepted: 09/27/2023] [Indexed: 10/10/2023]
Abstract
PURPOSE To assess whether image quality differences between SECT (single-energy CT) and DECT (dual-energy CT 70 keV) with equivalent radiation doses result in altered detection and characterization accuracy of liver metastases when using deep learning image reconstruction (DLIR), and whether DECT spectral curve usage improves accuracy of indeterminate lesion characterization. METHODS In this prospective Health Insurance Portability and Accountability Act-compliant study (March through August 2022), adult men and non-pregnant adult women with biopsy-proven colorectal cancer and liver metastases underwent SECT (120 kVp) and a DECT (70 keV) portovenous abdominal CT scan using DLIR in the same breath-hold (Revolution CT ES; GE Healthcare). Participants were excluded if consent could not be obtained, if there were nonequivalent radiation doses between the two scans, or if the examination was cancelled/rescheduled. Three radiologists independently performed lesion detection and characterization during two separate sessions (SECT DLIRmedium and DECT DLIRhigh) as well as reported lesion confidence and overall image quality. Hounsfield units were measured. Spectral HU curves were provided for any lesions rated as indeterminate. McNemar's test was used to test the marginal homogeneity in terms of diagnostic sensitivity, accuracy and lesion detection. A generalized estimating equation method was used for categorical outcomes. RESULTS 30 participants (mean age, 58 years ± 11, 21 men) were evaluated. Mean CTDIvol was 34 mGy for both scans. 141 lesions (124 metastases, 17 benign) with a mean size of 0.8 cm ± 0.3 cm were identified. High scores for image quality (scores of 4 or 5) were not significantly different between DECT (N = 71 out of 90 total scores from the three readers) and SECT (N = 62) (OR, 2.01; 95% CI:0.89, 4.57; P = 0.093). Equivalent image noise to SECT DLIRmed (HU SD 10 ± 2) was obtained with DECT DLIRhigh (HU SD 10 ± 3) (P = 1). There was no significant difference in lesion detection between DECT and SECT (140/141 lesions) (99.3%; 95% CI:96.1%, 100%).The mean lesion confidence scores by each reader were 4.2 ± 1.3, 3.9 ± 1.0, and 4.8 ± 0.8 for SECT and 4.1 ± 1.4, 4.0 ± 1.0, and 4.7 ± 0.8 for DECT (odds ratio [OR], 0.83; 95% CI: 0.62, 1.11; P = 0.21). Small lesion (≤5mm) characterization accuracy on SECT and DECT was 89.1% (95% CI:76.4%, 96.4%; 41/46) and 84.8% (71.1%, 93.7%; 39/46), respectively (P = 0.41). Use of spectral HU lesion curves resulted in 34 correct changes in characterizations and no mischaracterizations. CONCLUSION DECT required a higher strength of DLIR to obtain equivalent noise compared to SECT DLIR. At equivalent radiation doses and image noise, there was no significant difference in subjective image quality or observer lesion performance between DECT (70 keV) and SECT. However, DECT spectral HU curves of indeterminate lesions improved characterization.
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Affiliation(s)
- Corey T Jensen
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, USA.
| | - Vincenzo K Wong
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, USA
| | - Nicolaus A Wagner-Bartak
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, USA
| | - Xinming Liu
- Department of Physics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, USA
| | - Renjith Padmanabhan Nair Sobha
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, USA
| | - Jia Sun
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, USA
| | - Gauruv S Likhari
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, USA
| | - Shiva Gupta
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, USA
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Lecomte A, Serrand A, Marteau L, Carlier B, Manigold T, Letocart V, Warin Fresse K, Nguyen JM, Serfaty JM. Coronary artery assessment on pre transcatheter aortic valve implantation computed tomography may avoid the need for additional coronary angiography. Diagn Interv Imaging 2023; 104:547-551. [PMID: 37331824 DOI: 10.1016/j.diii.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/07/2023] [Accepted: 06/09/2023] [Indexed: 06/20/2023]
Abstract
PURPOSE The purpose of this study was to evaluate the percentage of coronary angiography that can be securely avoided by the interpretation of coronary arteries on pre transcatheter aortic valve implantation CT (TAVI-CT), using CT images obtained with deep-learning reconstruction and motion correction algorithms. MATERIAL AND METHOD All consecutive patients who underwent TAVI-CT and coronary angiography, from December 2021 to July 2022 were screened for inclusion in the study. Patients who had previous coronary artery revascularization or who did not undergo TAVI were excluded. All TAVI-CT examinations were obtained using deep-learning reconstruction and motion correction algorithms. On TAVI-CT examinations, quality and stenosis of coronary artery were analyzed retrospectively. When insufficient image quality and/or when diagnosis or doubt of one significant coronary artery stenosis, patients were considered as having possible coronary artery stenosis. The results of coronary angiography were used as the standard of reference for significant CAS. RESULTS A total of 206 patients (92 men; mean age, 80.6 years) were included; of these 27/206 (13%) had significant coronary artery stenosis on coronary angiography and were referred for potential revascularization. Sensitivity, specificity, negative predictive value, positive predictive value, and accuracy of TAVI-CT to identify patients requiring coronary artery revascularization was 100% (95% confidence interval [CI]: 87.2-100%), 100% (95% CI: 96.3-100%), 54% (95% CI: 46.6-61.6), 25% (95% CI: 17.0-34.0%) and 60% (95% CI: 53.1-66.9%) respectively. Intra- and inter observer variability was substantial agreement for quality and decision to recommend coronary angiography. Mean reading time was 2 ± 1.2 (standard deviation) min (range: 1-5 min). Overall, TAVI-CT could potentially rule out indication for revascularization for 97 patients (47%). CONCLUSION Analysis of coronary artery on TAVI-CT using deep-learning reconstruction and motion correction algorithms can potentially safely avoid coronary angiography in 47% of patients.
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Affiliation(s)
- Adrien Lecomte
- Department of Cardiovascular Radiology, Nantes Université, CHU Nantes, 44000 Nantes, France.
| | - Aude Serrand
- Department of Cardiovascular Radiology, Nantes Université, CHU Nantes, 44000 Nantes, France
| | - Lara Marteau
- Department of Cardiovascular Radiology, Nantes Université, CHU Nantes, 44000 Nantes, France; Department of Cardiology, Nantes Université, CHU Nantes, Institut du thorax, 44000 Nantes, France; Department of Biostatistics and Epidemiology, CRCINA, INSERM U1232 Team2, CHU Nantes, 44000 Nantes, France; Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, F-44000 Nantes, France
| | - Baptiste Carlier
- Department of Cardiology, Nantes Université, CHU Nantes, Institut du thorax, 44000 Nantes, France
| | - Thibaut Manigold
- Department of Cardiology, Nantes Université, CHU Nantes, Institut du thorax, 44000 Nantes, France
| | - Vincent Letocart
- Department of Cardiology, Nantes Université, CHU Nantes, Institut du thorax, 44000 Nantes, France
| | - Karine Warin Fresse
- Department of Cardiovascular Radiology, Nantes Université, CHU Nantes, 44000 Nantes, France
| | - Jean-Michel Nguyen
- Department of Biostatistics and Epidemiology, CRCINA, INSERM U1232 Team2, CHU Nantes, 44000 Nantes, France
| | - Jean-Michel Serfaty
- Department of Cardiovascular Radiology, Nantes Université, CHU Nantes, 44000 Nantes, France; Department of Cardiology, Nantes Université, CHU Nantes, Institut du thorax, 44000 Nantes, France; Department of Biostatistics and Epidemiology, CRCINA, INSERM U1232 Team2, CHU Nantes, 44000 Nantes, France; Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, F-44000 Nantes, France
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Brady SL. Implementation of AI image reconstruction in CT-how is it validated and what dose reductions can be achieved. Br J Radiol 2023; 96:20220915. [PMID: 37102695 PMCID: PMC10546449 DOI: 10.1259/bjr.20220915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 03/10/2023] [Accepted: 03/15/2023] [Indexed: 04/28/2023] Open
Abstract
CT reconstruction has undergone a substantial change over the last decade with the introduction of iterative reconstruction (IR) and now with deep learning reconstruction (DLR). In this review, DLR will be compared to IR and filtered back-projection (FBP) reconstructions. Comparisons will be made using image quality metrics such as noise power spectrum, contrast-dependent task-based transfer function, and non-prewhitening filter detectability index (dNPW'). Discussion on how DLR has impacted CT image quality, low-contrast detectability, and diagnostic confidence will be provided. DLR has shown the ability to improve in areas that IR is lacking, namely: noise magnitude reduction does not alter noise texture to the degree that IR did, and the noise texture found in DLR is more aligned with noise texture of an FBP reconstruction. Additionally, the dose reduction potential for DLR is shown to be greater than IR. For IR, the consensus was dose reduction should be limited to no more than 15-30% to preserve low-contrast detectability. For DLR, initial phantom and patient observer studies have shown acceptable dose reduction between 44 and 83% for both low- and high-contrast object detectability tasks. Ultimately, DLR is able to be used for CT reconstruction in place of IR, making it an easy "turnkey" upgrade for CT reconstruction. DLR for CT is actively being improved as more vendor options are being developed and current DLR options are being enhanced with second generation algorithms being released. DLR is still in its developmental early stages, but is shown to be a promising future for CT reconstruction.
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Abstract
In 1971, the first patient CT examination by Ambrose and Hounsfield paved the way for not only volumetric imaging of the brain but of the entire body. From the initial 5-minute scan for a 180° rotation to today's 0.24-second scan for a 360° rotation, CT technology continues to reinvent itself. This article describes key historical milestones in CT technology from the earliest days of CT to the present, with a look toward the future of this essential imaging modality. After a review of the beginnings of CT and its early adoption, the technical steps taken to decrease scan times-both per image and per examination-are reviewed. Novel geometries such as electron-beam CT and dual-source CT have also been developed in the quest for ever-faster scans and better in-plane temporal resolution. The focus of the past 2 decades on radiation dose optimization and management led to changes in how exposure parameters such as tube current and tube potential are prescribed such that today, examinations are more customized to the specific patient and diagnostic task than ever before. In the mid-2000s, CT expanded its reach from gray-scale to color with the clinical introduction of dual-energy CT. Today's most recent technical innovation-photon-counting CT-offers greater capabilities in multienergy CT as well as spatial resolution as good as 125 μm. Finally, artificial intelligence is poised to impact both the creation and processing of CT images, as well as automating many tasks to provide greater accuracy and reproducibility in quantitative applications.
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Affiliation(s)
- Cynthia H. McCollough
- Department of Radiology, Mayo Clinic, 200 First St SW Rochester, MN, United States 55905
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Greffier J, Van Ngoc Ty C, Fitton I, Frandon J, Beregi JP, Dabli D. Impact of Phantom Size on Low-Energy Virtual Monoenergetic Images of Three Dual-Energy CT Platforms. Diagnostics (Basel) 2023; 13:3039. [PMID: 37835782 PMCID: PMC10572153 DOI: 10.3390/diagnostics13193039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/18/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023] Open
Abstract
The purpose of this study was to compare the quality of low-energy virtual monoenergetic images (VMIs) obtained with three Dual-Energy CT (DECT) platforms according to the phantom diameter. Three sections of the Mercury Phantom 4.0 were scanned on two generations of split-filter CTs (SFCT-1st and SFCT-2nd) and on one Dual-source CT (DSCT). The noise power spectrum (NPS), task-based transfer function (TTF), and detectability index (d') were assessed on VMIs from 40 to 70 keV. The highest noise magnitude values were found with SFCT-1st and noise magnitude was higher with DSCT than with SFCT-2nd for 26 cm (10.2% ± 1.3%) and 31 cm (7.0% ± 2.5%), and the opposite for 36 cm (-4.2% ± 2.5%). The highest average NPS spatial frequencies and TTF values at 50% (f50) values were found with DSCT. For all energy levels, the f50 values were higher with SFCT-2nd than SFCT-1st for 26 cm (3.2% ± 0.4%) and the opposite for 31 cm (-6.9% ± 0.5%) and 36 cm (-5.6% ± 0.7%). The lowest d' values were found with SFCT-1st. For all energy levels, the d' values were lower with DSCT than with SFCT-2nd for 26 cm (-6.2% ± 0.7%), similar for 31 cm (-0.3% ± 1.9%) and higher for 36 cm (5.4% ± 2.7%). In conclusion, compared to SFCT-1st, SFCT-2nd exhibited a lower noise magnitude and higher detectability values. Compared with DSCT, SFCT-2nd had a lower noise magnitude and higher detectability for the 26 cm, but the opposite was true for the 36 cm.
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Affiliation(s)
- Joël Greffier
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France; (J.F.); (J.-P.B.); (D.D.)
| | - Claire Van Ngoc Ty
- Department of Radiology, Assistance Publique Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Université de Paris, 75015 Paris, France; (C.V.N.T.); (I.F.)
| | - Isabelle Fitton
- Department of Radiology, Assistance Publique Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Université de Paris, 75015 Paris, France; (C.V.N.T.); (I.F.)
| | - Julien Frandon
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France; (J.F.); (J.-P.B.); (D.D.)
| | - Jean-Paul Beregi
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France; (J.F.); (J.-P.B.); (D.D.)
| | - Djamel Dabli
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France; (J.F.); (J.-P.B.); (D.D.)
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Yasui K, Saito Y, Ito A, Douwaki M, Ogawa S, Kasugai Y, Ooe H, Nagake Y, Hayashi N. Validation of deep learning-based CT image reconstruction for treatment planning. Sci Rep 2023; 13:15413. [PMID: 37723226 PMCID: PMC10507027 DOI: 10.1038/s41598-023-42775-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 09/14/2023] [Indexed: 09/20/2023] Open
Abstract
Deep learning-based CT image reconstruction (DLR) is a state-of-the-art method for obtaining CT images. This study aimed to evaluate the usefulness of DLR in radiotherapy. Data were acquired using a large-bore CT system and an electron density phantom for radiotherapy. We compared the CT values, image noise, and CT value-to-electron density conversion table of DLR and hybrid iterative reconstruction (H-IR) for various doses. Further, we evaluated three DLR reconstruction strength patterns (Mild, Standard, and Strong). The variations of CT values of DLR and H-IR were large at low doses, and the difference in average CT values was insignificant with less than 10 HU at doses of 100 mAs and above. DLR showed less change in CT values and smaller image noise relative to H-IR. The noise-reduction effect was particularly large in the low-dose region. The difference in image noise between DLR Mild and Standard/Strong was large, suggesting the usefulness of reconstruction intensities higher than Mild. DLR showed stable CT values and low image noise for various materials, even at low doses; particularly for Standard or Strong, the reduction in image noise was significant. These findings indicate the usefulness of DLR in treatment planning using large-bore CT systems.
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Affiliation(s)
- Keisuke Yasui
- Division of Medical Physics, School of Medical Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-Cho, Toyoake, Aichi, 470-1192, Japan.
| | - Yasunori Saito
- Department of Radiology, Fujita Health University Hospital, Toyoake, Aichi, Japan
| | - Azumi Ito
- Faculty of Radiological Technology, School of Medical Sciences, Fujita Health University, Toyoake, Aichi, Japan
| | - Momoka Douwaki
- Department of Radiology, Fujita Health University Hospital, Toyoake, Aichi, Japan
| | - Shuta Ogawa
- Department of Radiology, Fujita Health University Hospital, Toyoake, Aichi, Japan
| | - Yuri Kasugai
- Faculty of Radiological Technology, School of Medical Sciences, Fujita Health University, Toyoake, Aichi, Japan
| | - Hiromu Ooe
- Faculty of Radiological Technology, School of Medical Sciences, Fujita Health University, Toyoake, Aichi, Japan
| | - Yuya Nagake
- Faculty of Radiological Technology, School of Medical Sciences, Fujita Health University, Toyoake, Aichi, Japan
| | - Naoki Hayashi
- Division of Medical Physics, School of Medical Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-Cho, Toyoake, Aichi, 470-1192, Japan
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Koh S, Lee NK, Kim S, Hong SB, Kim DU, Han SY. The efficacy of low-dose CT with deep learning image reconstruction in the surveillance of incidentally detected pancreatic cystic lesions. Abdom Radiol (NY) 2023; 48:2585-2595. [PMID: 37204510 DOI: 10.1007/s00261-023-03958-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 05/20/2023]
Abstract
PURPOSE To evaluate the efficacy of low-dose CT (LDCT) with deep learning image reconstruction (DLIR) for the surveillance of pancreatic cystic lesions (PCLs) compared with standard-dose CT (SDCT) with adaptive statistical iterative reconstruction (ASIR-V). METHODS The study enrolled 103 patients who underwent pancreatic CT for follow-up of incidentally detected PCLs. The CT protocol included LDCT in the pancreatic phase with 40% ASIR-V, DLIR at medium (DLIR-M) and high levels (DLIR-H), and SDCT in the portal-venous phase with 40% ASIR-V. The overall image quality and conspicuity of PCLs were qualitatively assessed using five-point scales by two radiologists. The size of PCLs, presence of thickened/enhancing walls, enhancing mural nodules, and main pancreatic duct dilatation were reviewed. CT noise and cyst-to-pancreas contrast-to-noise ratio (CNR) were measured. Qualitative and quantitative parameters were analyzed using the chi-squared test, one-way ANOVA, and t-test. Additionally, interobserver agreement was analyzed using the kappa and weighted-kappa statistics. RESULTS The volume CT dose-indexes in LDCT and SDCT were 3.0 ± 0.6 mGy and 8.4 ± 2.9 mGy, respectively. LDCT with DLIR-H showed the highest overall image quality, the lowest noise, and the highest CNR. The PCL conspicuity in LDCT with either DLIR-M or DLIR-H was not significantly different from that in SDCT with ASIR-V. Other findings depicting PCLs also revealed no significant differences between LDCT with DLIR and SDCT with ASIR-V. Moreover, the results revealed good or excellent interobserver agreement. CONCLUSION LDCT with DLIR has a comparable performance with SDCT for the follow-up of incidentally detected PCLs.
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Affiliation(s)
- Sungho Koh
- Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, #179, Gudeok-Ro, Seo-Gu, Busan, 49241, Republic of Korea
| | - Nam Kyung Lee
- Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, #179, Gudeok-Ro, Seo-Gu, Busan, 49241, Republic of Korea.
| | - Suk Kim
- Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, #179, Gudeok-Ro, Seo-Gu, Busan, 49241, Republic of Korea
| | - Seung Baek Hong
- Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, #179, Gudeok-Ro, Seo-Gu, Busan, 49241, Republic of Korea
| | - Dong Uk Kim
- Department of Internal Medicine, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, Busan, Republic of Korea
| | - Sung Yong Han
- Department of Internal Medicine, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, Busan, Republic of Korea
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Shehata MA, Saad AM, Kamel S, Stanietzky N, Roman-Colon AM, Morani AC, Elsayes KM, Jensen CT. Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis. Abdom Radiol (NY) 2023; 48:2724-2756. [PMID: 37280374 DOI: 10.1007/s00261-023-03966-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/13/2023] [Accepted: 05/16/2023] [Indexed: 06/08/2023]
Abstract
OBJECTIVE To perform a systematic literature review and meta-analysis of the two most common commercially available deep-learning algorithms for CT. METHODS We used PubMed, Scopus, Embase, and Web of Science to conduct systematic searches for studies assessing the most common commercially available deep-learning CT reconstruction algorithms: True Fidelity (TF) and Advanced intelligent Clear-IQ Engine (AiCE) in the abdomen of human participants since only these two algorithms currently have adequate published data for robust systematic analysis. RESULTS Forty-four articles fulfilled inclusion criteria. 32 studies evaluated TF and 12 studies assessed AiCE. DLR algorithms produced images with significantly less noise (22-57.3% less than IR) but preserved a desirable noise texture with increased contrast-to-noise ratios and improved lesion detectability on conventional CT. These improvements with DLR were similarly noted in dual-energy CT which was only assessed for a single vendor. Reported radiation reduction potential was 35.1-78.5%. Nine studies assessed observer performance with the two dedicated liver lesion studies being performed on the same vendor reconstruction (TF). These two studies indicate preserved low contrast liver lesion detection (> 5 mm) at CTDIvol 6.8 mGy (BMI 23.5 kg/m2) to 12.2 mGy (BMI 29 kg/m2). If smaller lesion detection and improved lesion characterization is needed, a CTDIvol of 13.6-34.9 mGy is needed in a normal weight to obese population. Mild signal loss and blurring have been reported at high DLR reconstruction strengths. CONCLUSION Deep learning reconstructions significantly improve image quality in CT of the abdomen. Assessment of other dose levels and clinical indications is needed. Careful choice of radiation dose levels is necessary, particularly for small liver lesion assessment.
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Affiliation(s)
- Mostafa A Shehata
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA
| | | | - Serageldin Kamel
- Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA
| | - Nir Stanietzky
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA
| | | | - Ajaykumar C Morani
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA
| | - Khaled M Elsayes
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA
| | - Corey T Jensen
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA.
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Lell M, Kachelrieß M. Computed Tomography 2.0: New Detector Technology, AI, and Other Developments. Invest Radiol 2023; 58:587-601. [PMID: 37378467 PMCID: PMC10332658 DOI: 10.1097/rli.0000000000000995] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/04/2023] [Indexed: 06/29/2023]
Abstract
ABSTRACT Computed tomography (CT) dramatically improved the capabilities of diagnostic and interventional radiology. Starting in the early 1970s, this imaging modality is still evolving, although tremendous improvements in scan speed, volume coverage, spatial and soft tissue resolution, as well as dose reduction have been achieved. Tube current modulation, automated exposure control, anatomy-based tube voltage (kV) selection, advanced x-ray beam filtration, and iterative image reconstruction techniques improved image quality and decreased radiation exposure. Cardiac imaging triggered the demand for high temporal resolution, volume acquisition, and high pitch modes with electrocardiogram synchronization. Plaque imaging in cardiac CT as well as lung and bone imaging demand for high spatial resolution. Today, we see a transition of photon-counting detectors from experimental and research prototype setups into commercially available systems integrated in patient care. Moreover, with respect to CT technology and CT image formation, artificial intelligence is increasingly used in patient positioning, protocol adjustment, and image reconstruction, but also in image preprocessing or postprocessing. The aim of this article is to give an overview of the technical specifications of up-to-date available whole-body and dedicated CT systems, as well as hardware and software innovations for CT systems in the near future.
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Koo SA, Jung Y, Um KA, Kim TH, Kim JY, Park CH. Clinical Feasibility of Deep Learning-Based Image Reconstruction on Coronary Computed Tomography Angiography. J Clin Med 2023; 12:jcm12103501. [PMID: 37240607 DOI: 10.3390/jcm12103501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 04/24/2023] [Accepted: 05/14/2023] [Indexed: 05/28/2023] Open
Abstract
This study evaluated the feasibility of deep-learning-based image reconstruction (DLIR) on coronary computed tomography angiography (CCTA). By using a 20 cm water phantom, the noise reduction ratio and noise power spectrum were evaluated according to the different reconstruction methods. Then 46 patients who underwent CCTA were retrospectively enrolled. CCTA was performed using the 16 cm coverage axial volume scan technique. All CT images were reconstructed using filtered back projection (FBP); three model-based iterative reconstructions (MBIR) of 40%, 60%, and 80%; and three DLIR algorithms: low (L), medium (M), and high (H). Quantitative and qualitative image qualities of CCTA were compared according to the reconstruction methods. In the phantom study, the noise reduction ratios of MBIR-40%, MBIR-60%, MBIR-80%, DLIR-L, DLIR-M, and DLIR-H were 26.7 ± 0.2%, 39.5 ± 0.5%, 51.7 ± 0.4%, 33.1 ± 0.8%, 43.2 ± 0.8%, and 53.5 ± 0.1%, respectively. The pattern of the noise power spectrum of the DLIR images was more similar to FBP images than MBIR images. In a CCTA study, CCTA yielded a significantly lower noise index with DLIR-H reconstruction than with the other reconstruction methods. DLIR-H showed a higher SNR and CNR than MBIR (p < 0.05). The qualitative image quality of CCTA with DLIR-H was significantly higher than that of MBIR-80% or FBP. The DLIR algorithm was feasible and yielded a better image quality than the FBP or MBIR algorithms on CCTA.
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Affiliation(s)
- Seul Ah Koo
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
| | - Yunsub Jung
- Research Team, GE Healthcare Korea, Seoul 04637, Republic of Korea
| | - Kyoung A Um
- Research Team, GE Healthcare Korea, Seoul 04637, Republic of Korea
| | - Tae Hoon Kim
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
| | - Ji Young Kim
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
| | - Chul Hwan Park
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
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Li Y, Sun X, Wang S, Li X, Qin Y, Pan J, Chen P. MDST: multi-domain sparse-view CT reconstruction based on convolution and swin transformer. Phys Med Biol 2023; 68:095019. [PMID: 36889004 DOI: 10.1088/1361-6560/acc2ab] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 03/08/2023] [Indexed: 03/10/2023]
Abstract
Objective.Sparse-view computed tomography (SVCT), which can reduce the radiation doses administered to patients and hasten data acquisition, has become an area of particular interest to researchers. Most existing deep learning-based image reconstruction methods are based on convolutional neural networks (CNNs). Due to the locality of convolution and continuous sampling operations, existing approaches cannot fully model global context feature dependencies, which makes the CNN-based approaches less efficient in modeling the computed tomography (CT) images with various structural information.Approach.To overcome the above challenges, this paper develops a novel multi-domain optimization network based on convolution and swin transformer (MDST). MDST uses swin transformer block as the main building block in both projection (residual) domain and image (residual) domain sub-networks, which models global and local features of the projections and reconstructed images. MDST consists of two modules for initial reconstruction and residual-assisted reconstruction, respectively. The sparse sinogram is first expanded in the initial reconstruction module with a projection domain sub-network. Then, the sparse-view artifacts are effectively suppressed by an image domain sub-network. Finally, the residual assisted reconstruction module to correct the inconsistency of the initial reconstruction, further preserving image details.Main results. Extensive experiments on CT lymph node datasets and real walnut datasets show that MDST can effectively alleviate the loss of fine details caused by information attenuation and improve the reconstruction quality of medical images.Significance.MDST network is robust and can effectively reconstruct images with different noise level projections. Different from the current prevalent CNN-based networks, MDST uses transformer as the main backbone, which proves the potential of transformer in SVCT reconstruction.
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Affiliation(s)
- Yu Li
- Department of Information and Communication Engineering, North University of China, Taiyuan, People's Republic of China
- The State Key Lab for Electronic Testing Technology, North University of China, People's Republic of China
| | - XueQin Sun
- Department of Information and Communication Engineering, North University of China, Taiyuan, People's Republic of China
- The State Key Lab for Electronic Testing Technology, North University of China, People's Republic of China
| | - SuKai Wang
- Department of Information and Communication Engineering, North University of China, Taiyuan, People's Republic of China
- The State Key Lab for Electronic Testing Technology, North University of China, People's Republic of China
| | - XuRu Li
- Department of Information and Communication Engineering, North University of China, Taiyuan, People's Republic of China
- The State Key Lab for Electronic Testing Technology, North University of China, People's Republic of China
| | - YingWei Qin
- Department of Information and Communication Engineering, North University of China, Taiyuan, People's Republic of China
- The State Key Lab for Electronic Testing Technology, North University of China, People's Republic of China
| | - JinXiao Pan
- Department of Information and Communication Engineering, North University of China, Taiyuan, People's Republic of China
- The State Key Lab for Electronic Testing Technology, North University of China, People's Republic of China
| | - Ping Chen
- Department of Information and Communication Engineering, North University of China, Taiyuan, People's Republic of China
- The State Key Lab for Electronic Testing Technology, North University of China, People's Republic of China
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Xue G, Liu H, Cai X, Zhang Z, Zhang S, Liu L, Hu B, Wang G. Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors. Front Oncol 2023; 13:1167745. [PMID: 37091167 PMCID: PMC10113560 DOI: 10.3389/fonc.2023.1167745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 03/21/2023] [Indexed: 04/08/2023] Open
Abstract
ObjectiveTo evaluate the impact of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) on abdominal CT radiomic features acquired in portal venous phase in liver tumor patients.MethodsSixty patients with liver tumors who underwent contrast-enhanced abdominal CT were retrospectively enrolled. Six groups including filtered back projection (FBP), ASIR-V (30%, 70%) and DLIR at low (DLIR-L), medium (DLIR-M and high (DLIR-H), were reconstructed using portal venous phase data. CT-based radiomic features (first-order, texture and wavelet features) were extracted from 2D and 3D liver tumors, peritumor and liver parenchyma. All features were analyzed for comparison. P < 0.05 indicated statistically different. The consistency of 3D lesion feature extraction was assessed by calculating intraclass correlation coefficient (ICC).ResultsDifferent reconstruction algorithms influenced most radiomic features. The percentages of first-order, texture and wavelet features without statistical difference among 2D and 3D lesions, peritumor and liver parenchyma for all six groups were 27.78% (5/18), 5.33% (4/75) and 5.56% (1/18), respectively (all p > 0.05), and they decreased while the level of reconstruction strengthened for both ASIR-V and DLIR. Compared with FBP, the features of ASIR-V30% and 70% without statistical difference decreased from 71.31% to 23.95%, and DLIR-L, DLIR-M, and DLIR-H decreased from 31.65% to 27.11% and 23.73%. Among texture features, unaffected features of peritumor were larger than those of lesions and liver parenchyma, and unaffected 3D lesions features were larger than those of 2D lesions. The consistency of 3D lesion first-order features was excellent, with intra- and inter-observer ICCs ranging from 0.891 to 0.999 and 0.880 to 0.998.ConclusionsBoth ASIR-V and DLIR algorithms with different strengths influenced the radiomic features of abdominal CT images in portal venous phase, and the influences aggravated as reconstruction strength increased.
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Affiliation(s)
- Gongbo Xue
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
- Graduate School, Dalian Medical University, Dalian, China
| | - Hongyan Liu
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
| | - Xiaoyi Cai
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
- Graduate School, Dalian Medical University, Dalian, China
| | - Zhen Zhang
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
| | - Shuai Zhang
- CT Imaging Research Center, GE Healthcare China, Shanghai, China
| | - Ling Liu
- CT Imaging Research Center, GE Healthcare China, Shanghai, China
| | - Bin Hu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: Guohua Wang, ; Bin Hu,
| | - Guohua Wang
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
- *Correspondence: Guohua Wang, ; Bin Hu,
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Watanabe R, Zensho A, Ohishi Y, Funama Y. Image-quality characteristics in the longitudinal direction from different image-reconstruction algorithms during single-rotation volume acquisition on head computed tomography: A phantom study. Acta Radiol Open 2023; 12:20584601231168986. [PMID: 37089818 PMCID: PMC10116848 DOI: 10.1177/20584601231168986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 03/23/2023] [Indexed: 04/08/2023] Open
Abstract
Background A multi detector computed tomography (CT) scanner with wide-area coverage enables whole-brain volumetric scanning in a single rotation. Purpose To investigate variations in image-quality characteristics in the longitudinal direction for different image-reconstruction algorithms and strengths with phantoms. Material and methods Single-rotation volume scans were performed on a 320-row multidetector CT volume scanner using three types of phantoms. Tube current was set to 200 mA (standard dose) and 50 mA (low dose). All images were reconstructed with filtered back projection (FBP), mild and strong levels with hybrid iterative reconstruction (HIR), and model-based IR (MBIR). Computed tomography numbers, image noise, noise power spectrum (NPS), task-based transfer function (TTF), and visual spatial resolution were used to evaluate uniformity of image quality in the longitudinal direction ( Z-axis). Results The MBIR images showed smaller variation in CT numbers in the Z-axis. The difference in the highest and lowest CT numbers was smaller (5.0 Hounsfield units [HU]) for MBIR than for FBP (6.6 HU) and HIR (6.8 HU). The variations in image noise were the smallest for strong MBIR and the largest for FBP. The low-frequency component at NPS0.2 was lower for strong MBIR than for other algorithms. The high-frequency component at NPS0.8 was low in all reconstructions. For MBIR, the image resolution and TTFs were higher in the outer portion than in the center. Conclusion Model-based IR is the optimal image-reconstruction algorithm for single-volume scan of spherical subjects owing to its high in-plane resolution and uniformity of CT numbers, image noise, and NPS in the Z-axis.
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Affiliation(s)
- Ryo Watanabe
- Department of Radiology, Hospital of the University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Ayako Zensho
- Department of Radiology, Hospital of the University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Yoshitaka Ohishi
- Department of Radiology, Hospital of the University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Yoshinori Funama
- Department of Medical Radiation Sciences, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
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Yang K, Cao J, Pisuchpen N, Kambadakone A, Gupta R, Marschall T, Li X, Liu B. CT image quality evaluation in the age of deep learning: trade-off between functionality and fidelity. Eur Radiol 2023; 33:2439-2449. [PMID: 36350391 DOI: 10.1007/s00330-022-09233-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/16/2022] [Accepted: 10/09/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE To quantitatively compare DLIR and ASiR-V with realistic anatomical images. METHODS CT scans of an anthropomorphic phantom were acquired using three routine protocols (brain, chest, and abdomen) at four dose levels, with images reconstructed at five levels of ASiR-V and three levels of DLIR. Noise power spectrum (NPS) was estimated using a difference image by subtracting two matching images from repeated scans. Using the max-dose FBP reconstruction as the ground truth, the structure similarity index (SSIM) and gradient magnitude (GM) of difference images were evaluated. Image noise magnitude (σ), frequency location of the NPS peak (fpeak), mean SSIM (MSSIM), and mean GM (MGM) were used as quantitative metrics to compare image quality, for each anatomical region, protocol, algorithm, dose level, and slice thickness. RESULTS Image noise had a strong (R2 > 0.99) power law relationship with dose for all algorithms. For the abdomen and chest, fpeak shifted from 0.3 (FBP) down to 0.15 mm-1 (ASiR-V 100%) with increasing ASiR-V strength but remained 0.3 mm-1 for all DLIR levels. fpeak shifted down for the brain protocol with increasing DLIR levels. Three levels of DLIR produced similar image noise levels as ASiR-V 40%, 80%, and 100%, respectively. DLIR had lower MSSIM but higher MGM than ASiR-V while matching imaging noise. CONCLUSION Compared to ASiR-V, DLIR presents trade-offs between functionality and fidelity: it has a noise texture closer to FBP and more edge enhancement, but reduced structure similarity. These trade-offs and unique protocol-dependent behaviors of DLIR should be considered during clinical implementation and deployment. KEY POINTS • DLIR reconstructed images demonstrate closer noise texture and lower structure similarity to FBP while producing equivalent noise levels comparable to ASiR-V. • DLIR has additional edge enhancement as compared to ASiR-V. • DLIR has unique protocol-dependent behaviors that should be considered for clinical implementation.
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Affiliation(s)
- Kai Yang
- Division of Diagnostic Imaging Physics, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
| | - Jinjin Cao
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA
| | - Nisanard Pisuchpen
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA
| | - Avinash Kambadakone
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA
| | - Rajiv Gupta
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA
| | - Theodore Marschall
- Division of Diagnostic Imaging Physics, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Xinhua Li
- Division of Diagnostic Imaging Physics, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Bob Liu
- Division of Diagnostic Imaging Physics, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
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Greffier J, Durand Q, Serrand C, Sales R, de Oliveira F, Beregi JP, Dabli D, Frandon J. First Results of a New Deep Learning Reconstruction Algorithm on Image Quality and Liver Metastasis Conspicuity for Abdominal Low-Dose CT. Diagnostics (Basel) 2023; 13:diagnostics13061182. [PMID: 36980490 PMCID: PMC10047497 DOI: 10.3390/diagnostics13061182] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/07/2023] [Accepted: 03/17/2023] [Indexed: 03/30/2023] Open
Abstract
The study's aim was to assess the impact of a deep learning image reconstruction algorithm (Precise Image; DLR) on image quality and liver metastasis conspicuity compared with an iterative reconstruction algorithm (IR). This retrospective study included all consecutive patients with at least one liver metastasis having been diagnosed between December 2021 and February 2022. Images were reconstructed using level 4 of the IR algorithm (i4) and the Standard/Smooth/Smoother levels of the DLR algorithm. Mean attenuation and standard deviation were measured by placing the ROIs in the fat, muscle, healthy liver, and liver tumor. Two radiologists assessed the image noise and image smoothing, overall image quality, and lesion conspicuity using Likert scales. The study included 30 patients (mean age 70.4 ± 9.8 years, 17 men). The mean CTDIvol was 6.3 ± 2.1 mGy, and the mean dose-length product 314.7 ± 105.7 mGy.cm. Compared with i4, the HU values were similar in the DLR algorithm at all levels for all tissues studied. For each tissue, the image noise significantly decreased with DLR compared with i4 (p < 0.01) and significantly decreased from Standard to Smooth (-26 ± 10%; p < 0.01) and from Smooth to Smoother (-37 ± 8%; p < 0.01). The subjective image assessment confirmed that the image noise significantly decreased between i4 and DLR (p < 0.01) and from the Standard to Smoother levels (p < 0.01), but the opposite occurred for the image smoothing. The highest scores for overall image quality and conspicuity were found for the Smooth and Smoother levels.
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Affiliation(s)
- Joël Greffier
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France
| | - Quentin Durand
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France
| | - Chris Serrand
- Department of Biostatistics, Clinical Epidemiology, Public Health, and Innovation in Methodology (BESPIM), CHU Nimes, 30029 Nimes, France
| | - Renaud Sales
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France
| | - Fabien de Oliveira
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France
| | - Jean-Paul Beregi
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France
| | - Djamel Dabli
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France
| | - Julien Frandon
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France
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Low dose of contrast agent and low radiation liver computed tomography with deep-learning-based contrast boosting model in participants at high-risk for hepatocellular carcinoma: prospective, randomized, double-blind study. Eur Radiol 2023; 33:3660-3670. [PMID: 36934202 DOI: 10.1007/s00330-023-09520-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 12/18/2022] [Accepted: 02/03/2023] [Indexed: 03/20/2023]
Abstract
OBJECTIVE To investigate the image quality and lesion conspicuity of a deep-learning-based contrast-boosting (DL-CB) algorithm on double-low-dose (DLD) CT of simultaneous reduction of radiation and contrast doses in participants at high-risk for hepatocellular carcinoma (HCC). METHODS Participants were recruited and underwent four-phase dynamic CT (NCT04722120). They were randomly assigned to either standard-dose (SD) or DLD protocol. All CT images were initially reconstructed using iterative reconstruction, and the images of the DLD protocol were further processed using the DL-CB algorithm (DLD-DL). The primary endpoint was the contrast-to-noise ratio (CNR), the secondary endpoint was qualitative image quality (noise, hepatic lesion, and vessel conspicuity), and the tertiary endpoint was lesion detection rate. The t-test or repeated measures analysis of variance was used for analysis. RESULTS Sixty-eight participants with 57 focal liver lesions were enrolled (20 with HCC and 37 with benign findings). The DLD protocol had a 19.8% lower radiation dose (DLP, 855.1 ± 254.8 mGy·cm vs. 713.3 ± 94.6 mGy·cm, p = .003) and 27% lower contrast dose (106.9 ± 15.0 mL vs. 77.9 ± 9.4 mL, p < .001) than the SD protocol. The comparative analysis demonstrated that CNR (p < .001) and portal vein conspicuity (p = .002) were significantly higher in the DLD-DL than in the SD protocol. There was no significant difference in lesion detection rate for all lesions (82.7% vs. 73.3%, p = .140) and HCCs (75.7% vs. 70.4%, p = .644) between the SD protocol and DLD-DL. CONCLUSIONS DL-CB on double-low-dose CT provided improved CNR of the aorta and portal vein without significant impairment of the detection rate of HCC compared to the standard-dose acquisition, even in participants at high risk for HCC. KEY POINTS • Deep-learning-based contrast-boosting algorithm on double-low-dose CT provided an improved contrast-to-noise ratio compared to standard-dose CT. • The detection rate of focal liver lesions was not significantly differed between standard-dose CT and a deep-learning-based contrast-boosting algorithm on double-low-dose CT. • Double-low-dose CT without a deep-learning algorithm presented lower CNR and worse image quality.
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Greffier J, Viry A, Durand Q, Hajdu SD, Frandon J, Beregi JP, Dabli D, Racine D. Brain image quality according to beam collimation width and image reconstruction algorithm: A phantom study. Phys Med 2023; 108:102558. [PMID: 36905775 DOI: 10.1016/j.ejmp.2023.102558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 02/17/2023] [Accepted: 02/26/2023] [Indexed: 03/11/2023] Open
Abstract
PURPOSE To compare quantitatively and qualitatively brain image quality acquired in helical and axial modes on two wide collimation CT systems according to the dose level and algorithm used. METHODS Acquisitions were performed on an image quality and an anthropomorphic phantoms at three dose levels (CTDIvol: 45/35/25 mGy) on two wide collimation CT systems (GE Healthcare and Canon Medical Systems) in axial and helical modes. Raw data were reconstructed using iterative reconstruction (IR) and deep-learning image reconstruction (DLR) algorithms. The noise power spectrum (NPS) was computed on both phantoms and the task-based transfer function (TTF) on the image quality phantom. The subjective quality of images from an anthropomorphic brain phantom was evaluated by two radiologists including overall image quality. RESULTS For the GE system, noise magnitude and noise texture (average NPS spatial frequency) were lower with DLR than with IR. For the Canon system, noise magnitude values were lower with DLR than with IR for similar noise texture but the opposite was true for spatial resolution. For both CT systems, noise magnitude was lower with the axial mode than with the helical mode for similar noise texture and spatial resolution. Radiologists rated the overall quality of all brain images as "satisfactory for clinical use", whatever the dose level, algorithm or acquisition mode. CONCLUSIONS Using 16-cm axial acquisition reduces image noise without changing the spatial resolution and image texture compared to helical acquisitions. Axial acquisition can be used in clinical routine for brain CT examinations with an explored length of less than 16 cm.
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Affiliation(s)
- Joël Greffier
- Department of Medical Imaging, CHU Nîmes, Univ Montpellier, Nîmes Medical Imaging Group, EA 2992, France.
| | - Anaïs Viry
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Rue du Grand-Pré 1, 1007 Lausanne, Switzerland
| | - Quentin Durand
- Department of Medical Imaging, CHU Nîmes, Univ Montpellier, Nîmes Medical Imaging Group, EA 2992, France
| | - Steven David Hajdu
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Julien Frandon
- Department of Medical Imaging, CHU Nîmes, Univ Montpellier, Nîmes Medical Imaging Group, EA 2992, France
| | - Jean Paul Beregi
- Department of Medical Imaging, CHU Nîmes, Univ Montpellier, Nîmes Medical Imaging Group, EA 2992, France
| | - Djamel Dabli
- Department of Medical Imaging, CHU Nîmes, Univ Montpellier, Nîmes Medical Imaging Group, EA 2992, France
| | - Damien Racine
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Rue du Grand-Pré 1, 1007 Lausanne, Switzerland
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Matsumoto Y, Fujioka C, Yokomachi K, Kitera N, Nishimaru E, Kiguchi M, Higaki T, Kawashita I, Tatsugami F, Nakamura Y, Awai K. Evaluation of the second-generation whole-heart motion correction algorithm (SSF2) used to demonstrate the aortic annulus on cardiac CT. Sci Rep 2023; 13:3636. [PMID: 36869155 PMCID: PMC9984533 DOI: 10.1038/s41598-023-30786-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 03/01/2023] [Indexed: 03/05/2023] Open
Abstract
The main purpose of pre-transcatheter aortic valve implantation (TAVI) cardiac computed tomography (CT) for patients with severe aortic stenosis is aortic annulus measurements. However, motion artifacts present a technical challenge because they can reduce the measurement accuracy of the aortic annulus. Therefore, we applied the recently developed second-generation whole-heart motion correction algorithm (SnapShot Freeze 2.0, SSF2) to pre-TAVI cardiac CT and investigated its clinical utility by stratified analysis of the patient's heart rate during scanning. We found that SSF2 reconstruction significantly reduced aortic annulus motion artifacts and improved the image quality and measurement accuracy compared to standard reconstruction, especially in patients with high heart rate or a 40% R-R interval (systolic phase). SSF2 may contribute to improving the measurement accuracy of the aortic annulus.
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Affiliation(s)
- Yoriaki Matsumoto
- Department of Radiology, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan.
| | - Chikako Fujioka
- Department of Radiology, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Kazushi Yokomachi
- Department of Radiology, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Nobuo Kitera
- Department of Radiology, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Eiji Nishimaru
- Department of Radiology, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Masao Kiguchi
- Department of Radiology, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Toru Higaki
- Department of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Ikuo Kawashita
- Department of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Yuko Nakamura
- Department of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Kazuo Awai
- Department of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
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Svalkvist A, Fagman E, Vikgren J, Ku S, Diniz MO, Norrlund RR, Johnsson ÅA. Evaluation of deep-learning image reconstruction for chest CT examinations at two different dose levels. J Appl Clin Med Phys 2023; 24:e13871. [PMID: 36583696 PMCID: PMC10018655 DOI: 10.1002/acm2.13871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 11/17/2022] [Accepted: 11/29/2022] [Indexed: 12/31/2022] Open
Abstract
AIMS The aims of the present study were to, for both a full-dose protocol and an ultra-low dose (ULD) protocol, compare the image quality of chest CT examinations reconstructed using TrueFidelity (Standard kernel) with corresponding examinations reconstructed using ASIR-V (Lung kernel) and to evaluate if post-processing using an edge-enhancement filter affects the noise level, spatial resolution and subjective image quality of clinical images reconstructed using TrueFidelity. METHODS A total of 25 patients were examined with both a full-dose protocol and an ULD protocol using a GE Revolution APEX CT system (GE Healthcare, Milwaukee, USA). Three different reconstructions were included in the study: ASIR-V 40%, DLIR-H, and DLIR-H with additional post-processing using an edge-enhancement filter (DLIR-H + E2). Five observers assessed image quality in two separate visual grading characteristics (VGC) studies. The results from the studies were statistically analyzed using VGC Analyzer. Quantitative evaluations were based on determination of two-dimensional power spectrum (PS), contrast-to-noise ratio (CNR), and spatial resolution in the reconstructed patient images. RESULTS For both protocols, examinations reconstructed using TrueFidelity were statistically rated equal to or significantly higher than examinations reconstructed using ASIR-V 40%, but the ULD protocol benefitted more from TrueFidelity. In general, no differences in observer ratings were found between DLIR-H and DLIR-H + E2. For the three investigated image reconstruction methods, ASIR-V 40% showed highest noise and spatial resolution and DLIR-H the lowest, while the CNR was highest in DLIR-H and lowest in ASIR-V 40%. CONCLUSION The use of TrueFidelity for image reconstruction resulted in higher ratings on subjective image quality than ASIR-V 40%. The benefit of using TrueFidelity was larger for the ULD protocol than for the full-dose protocol. Post-processing of the TrueFidelity images using an edge-enhancement filter resulted in higher image noise and spatial resolution but did not affect the subjective image quality.
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Affiliation(s)
- Angelica Svalkvist
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Medical Radiation Sciences, Institute of Clinical Sciences, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Erika Fagman
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Jenny Vikgren
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Sara Ku
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Micael Oliveira Diniz
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Rauni Rossi Norrlund
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Åse A Johnsson
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Koetzier LR, Mastrodicasa D, Szczykutowicz TP, van der Werf NR, Wang AS, Sandfort V, van der Molen AJ, Fleischmann D, Willemink MJ. Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects. Radiology 2023; 306:e221257. [PMID: 36719287 PMCID: PMC9968777 DOI: 10.1148/radiol.221257] [Citation(s) in RCA: 49] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/26/2022] [Accepted: 10/13/2022] [Indexed: 02/01/2023]
Abstract
Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications. However, with faster and more advanced CT scanners, FBP has become increasingly obsolete. Higher image noise and more artifacts are especially noticeable in lower-dose CT imaging using FBP. This performance gap was partly addressed by model-based iterative reconstruction (MBIR). Yet, its "plastic" image appearance and long reconstruction times have limited widespread application. Hybrid iterative reconstruction partially addressed these limitations by blending FBP with MBIR and is currently the state-of-the-art reconstruction technique. In the past 5 years, deep learning reconstruction (DLR) techniques have become increasingly popular. DLR uses artificial intelligence to reconstruct high-quality images from lower-dose CT faster than MBIR. However, the performance of DLR algorithms relies on the quality of data used for model training. Higher-quality training data will become available with photon-counting CT scanners. At the same time, spectral data would greatly benefit from the computational abilities of DLR. This review presents an overview of the principles, technical approaches, and clinical applications of DLR, including metal artifact reduction algorithms. In addition, emerging applications and prospects are discussed.
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Affiliation(s)
| | | | - Timothy P. Szczykutowicz
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Niels R. van der Werf
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Adam S. Wang
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Veit Sandfort
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Aart J. van der Molen
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Dominik Fleischmann
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Martin J. Willemink
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
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Guido G, Polici M, Nacci I, Bozzi F, De Santis D, Ubaldi N, Polidori T, Zerunian M, Bracci B, Laghi A, Caruso D. Iterative Reconstruction: State-of-the-Art and Future Perspectives. J Comput Assist Tomogr 2023; 47:244-254. [PMID: 36728734 DOI: 10.1097/rct.0000000000001401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
ABSTRACT Image reconstruction processing in computed tomography (CT) has evolved tremendously since its creation, succeeding at optimizing radiation dose while maintaining adequate image quality. Computed tomography vendors have developed and implemented various technical advances, such as automatic noise reduction filters, automatic exposure control, and refined imaging reconstruction algorithms.Focusing on imaging reconstruction, filtered back-projection has represented the standard reconstruction algorithm for over 3 decades, obtaining adequate image quality at standard radiation dose exposures. To overcome filtered back-projection reconstruction flaws in low-dose CT data sets, advanced iterative reconstruction algorithms consisting of either backward projection or both backward and forward projections have been developed, with the goal to enable low-dose CT acquisitions with high image quality. Iterative reconstruction techniques play a key role in routine workflow implementation (eg, screening protocols, vascular and pediatric applications), in quantitative CT imaging applications, and in dose exposure limitation in oncologic patients.Therefore, this review aims to provide an overview of the technical principles and the main clinical application of iterative reconstruction algorithms, focusing on the strengths and weaknesses, in addition to integrating future perspectives in the new era of artificial intelligence.
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Affiliation(s)
- Gisella Guido
- From the Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit, Sant'Andrea University Hospital, Rome, Italy
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Lyu P, Liu N, Harrawood B, Solomon J, Wang H, Chen Y, Rigiroli F, Ding Y, Schwartz FR, Jiang H, Lowry C, Wang L, Samei E, Gao J, Marin D. Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely? Eur Radiol 2023; 33:1629-1640. [PMID: 36323984 DOI: 10.1007/s00330-022-09206-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/28/2022] [Accepted: 09/26/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVES To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR). METHODS A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results. Data sets with full dosage (13.6 mGy) and low dosages (9.5, 6.8, or 4.1 mGy) were acquired from two consecutive portal venous acquisitions, respectively. All images were reconstructed with FBP (reference), IR (control), and DLIR (test). Eleven readers evaluated phantom data sets for object detectability using a two-alternative forced-choice approach. Non-inferiority analyses were performed to interpret the differences in image quality and metastasis detection of low-dose DLIR relative to full-dose FBP/IR. RESULTS The phantom experiment showed the dose reduction potential from DLIR was up to 57% based on the reference FBP dose index. Radiation decreases of 30% and 50% resulted in non-inferior image quality and hepatic metastasis detection with DLIR compared to full-dose FBP/IR. Radiation reduction of 70% by DLIR performed inferiorly in detecting small metastases (< 1 cm) compared to full-dose FBP (difference: -0.112; 95% confidence interval [CI]: -0.178 to 0.047) and full-dose IR (difference: -0.123; 95% CI: -0.182 to 0.053) (p < 0.001). CONCLUSION DLIR enables a 50% dose reduction for detecting low-contrast hepatic metastases while maintaining comparable image quality to full-dose FBP and IR. KEY POINTS • Non-inferiority study showed that deep learning image reconstruction (DLIR) can reduce the dose to oncological patients with low-contrast lesions without compromising the diagnostic information. • Radiation dose levels for DLIR can be reduced to 50% of full-dose FBP and IR for detecting low-contrast hepatic metastases, while maintaining comparable image quality. • The reduction of radiation by 70% by DLIR is clinically acceptable but insufficient for detecting small low-contrast hepatic metastases (< 1 cm).
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Affiliation(s)
- Peijie Lyu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China.,Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA
| | - Nana Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Brian Harrawood
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA
| | - Justin Solomon
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA
| | - Huixia Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Yan Chen
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Francesca Rigiroli
- Beth Israel Deaconess Medical Center Department of Radiology, Harvard Medical School, 1 Deaconess Rd, 330 Brookline Ave, Boston, MA, 02215, USA
| | - Yuqin Ding
- Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA.,Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 20032, China
| | - Fides Regina Schwartz
- Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA
| | - Hanyu Jiang
- Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA.,Department of Radiology, West China Hospital of Sichuan University, 37 Guoxue Alley, Chengdu, 610041, Sichuan Province, China
| | - Carolyn Lowry
- Clinical Imaging Physics Group, Duke University Health System, 2424 Erwin Rd, Ste. 302, Durham, NC, 27705, USA
| | - Luotong Wang
- CT Imaging Research Center, GE Healthcare China, No.1 Tongji South Road, Beijing, 100176, China
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China.
| | - Daniele Marin
- Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA
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Goto M, Nagayama Y, Sakabe D, Emoto T, Kidoh M, Oda S, Nakaura T, Taguchi N, Funama Y, Takada S, Uchimura R, Hayashi H, Hatemura M, Kawanaka K, Hirai T. Lung-Optimized Deep-Learning-Based Reconstruction for Ultralow-Dose CT. Acad Radiol 2023; 30:431-440. [PMID: 35738988 DOI: 10.1016/j.acra.2022.04.025] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/18/2022] [Accepted: 04/30/2022] [Indexed: 01/25/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate the image properties of lung-specialized deep-learning-based reconstruction (DLR) and its applicability in ultralow-dose CT (ULDCT) relative to hybrid- (HIR) and model-based iterative-reconstructions (MBIR). MATERIALS AND METHODS An anthropomorphic chest phantom was scanned on a 320-row scanner at 50-mA (low-dose-CT 1 [LDCT-1]), 25-mA (LDCT-2), and 10-mA (ULDCT). LDCT were reconstructed with HIR; ULDCT images were reconstructed with HIR (ULDCT-HIR), MBIR (ULDCT-MBIR), and DLR (ULDCT-DLR). Image noise and contrast-to-noise ratio (CNR) were quantified. With the LDCT images as reference standards, ULDCT image qualities were subjectively scored on a 5-point scale (1 = substantially inferior to LDCT-2, 3 = comparable to LDCT-2, 5 = comparable to LDCT-1). For task-based image quality analyses, a physical evaluation phantom was scanned at seven doses to achieve the noise levels equivalent to chest phantom; noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated. Clinical ULDCT (10-mA) images obtained in 14 nonobese patients were reconstructed with HIR, MBIR, and DLR; the subjective acceptability was ranked. RESULTS Image noise was lower and CNR was higher in ULDCT-DLR and ULDCT-MBIR than in LDCT-1, LDCT-2, and ULDCT-HIR (p < 0.01). The overall quality of ULDCT-DLR was higher than of ULDCT-HIR and ULDCT-MBIR (p < 0.01), and almost comparable with that of LDCT-2 (mean score: 3.4 ± 0.5). DLR yielded the highest NPS peak frequency and TTF50% for high-contrast object. In clinical ULDCT images, the subjective acceptability of DLR was higher than of HIR and MBIR (p < 0.01). CONCLUSION DLR optimized for lung CT improves image quality and provides possible greater dose optimization opportunity than HIR and MBIR.
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Affiliation(s)
- Makoto Goto
- Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto 860-8556, Japan
| | - Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
| | - Daisuke Sakabe
- Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto 860-8556, Japan
| | - Takafumi Emoto
- Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto 860-8556, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
| | - Seitaro Oda
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
| | - Narumi Taguchi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
| | - Yoshinori Funama
- Department of Medical Radiation Sciences, Faculty of Life Sciences, Kumamoto University, Chuo-ku, Kumamoto 862-0976, Japan
| | - Sentaro Takada
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
| | - Ryutaro Uchimura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
| | - Hidetaka Hayashi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
| | - Masahiro Hatemura
- Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto 860-8556, Japan
| | - Koichi Kawanaka
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
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Thor D, Titternes R, Poludniowski G. Spatial resolution, noise properties, and detectability index of a deep learning reconstruction algorithm for dual-energy CT of the abdomen. Med Phys 2023; 50:2775-2786. [PMID: 36774193 DOI: 10.1002/mp.16300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 11/18/2022] [Accepted: 01/17/2023] [Indexed: 02/13/2023] Open
Abstract
BACKGROUND Iterative reconstruction (IR) has increasingly replaced traditional reconstruction methods in computed tomography (CT). The next paradigm shift in image reconstruction is likely to come from artificial intelligence, with deep learning reconstruction (DLR) solutions already entering the clinic. An enduring disadvantage to IR has been a change in noise texture, which can affect diagnostic confidence. DLR has demonstrated the potential to overcome this issue and has recently become available for dual-energy CT. PURPOSE To evaluate the spatial resolution, noise properties, and detectability index of a commercially available DLR algorithm for dual-energy CT of the abdomen and compare it to single-energy (SE) CT. METHODS An oval 25 cm x 35 cm custom-made phantom was scanned on a GE Revolution CT scanner (GE Healthcare, Waukesha, WI) at two dose levels (13 and 5 mGy) and two iodine concentrations (8 and 2 mg/mL), using three typical abdominal scan protocols: dual-energy (DE), SE 80 kV (SE-80 kV) and SE 120 kV (SE-120 kV). Reconstructions were performed with three strengths of IR (ASiR-V: AR0%, AR50%, AR100%) and three strengths of DLR (TrueFidelity: low, medium, high). The DE acquisitions were reconstructed as mono-energetic images between 40 and 80 keV. The noise power spectrum (NPS), task transfer function (TTF), and detectability index (d') were determined for the reconstructions following the recommendations of AAPM Task Group 233. RESULTS Noise magnitude reductions (relative to AR0%) for the SE protocols were on average (-29%, -21%) for (AR50%, TF-M), while for DE-70 keV were (-28%, -43%). There was less reduction in mean frequency (fav ) for DLR than for IR, with similar results for SE and DE imaging. There was, however, a substantial change in the NPS shape when using DE with DLR, quantifiable by a marked reduction in the peak frequency (fpeak ) that was absent in SE mode. All protocols and reconstructions (including AR0%) exhibited slight to moderate shifts towards lower spatial frequencies at the lower dose (<12% in fav ). Spatial resolution was consistently superior for DLR compared to IR for SE but not for DE. All protocols and reconstructions (including AR0%) showed decreased resolution with reduced dose and iodine concentration, with less decrease for DLR compared to IR. DLR displayed a higher d' than IR. The effect of energy was large: d' increased with lower keV, and SE-80 kV had higher d' than SE-120 kV. Using DE with DLR could provide higher d' than SE-80 kV at the higher dose but not at lower dose. CONCLUSIONS DE imaging with DLR maintained spatial resolution and reduced noise magnitude while displaying less change in noise texture than IR. The d' was also higher with DLR than IR, suggesting superiority in detectability of iodinated contrast. Despite these trends being consistent with those previously established for SE imaging, there were some noteworthy differences. For DE imaging there was no improvement in resolution compared to IR and a change in noise texture. DE imaging with low keV and DLR had superior detectability to SE DLR at the high dose but was not better than SE-80 kV at low dose.
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Affiliation(s)
- Daniel Thor
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden.,Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Rebecca Titternes
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden.,Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
| | - Gavin Poludniowski
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden.,Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
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Zhong J, Xia Y, Chen Y, Li J, Lu W, Shi X, Feng J, Yan F, Yao W, Zhang H. Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study. Eur Radiol 2023; 33:812-824. [PMID: 36197579 DOI: 10.1007/s00330-022-09119-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/26/2022] [Accepted: 08/17/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES To compare image quality between a deep learning image reconstruction (DLIR) algorithm and conventional iterative reconstruction (IR) algorithms in dual-energy CT (DECT) and to assess the impact of these algorithms on radiomics robustness. METHODS A phantom with clinical-relevant densities was imaged on seven DECT scanners with the same voxel size using typical abdominal-pelvis examination protocols. On one DECT scanner, raw data were reconstructed using both conventional IR (adaptive statistical iterative reconstruction-V, ASIR-V) and DLIR. Nine sets of corresponding images were generated on other six DECT scanners using scanner-equipped conventional IR. Regions of interest were delineated through rigid registrations. Image quality was compared. Pyradiomics platform was used for radiomics feature extraction. Test-retest repeatability was assessed by Bland-Altman analysis for repeated scans. Inter-reconstruction algorithm reproducibility between conventional IR and DLIR was tested by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-scanner reproducibility was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Robust features were identified. RESULTS DLIR significantly improved image quality. Ninety-four radiomics features were extracted and nine features were considered as robust. 93.87% features were repeatable between repeated scans. ASIR-V images showed higher reproducibility to other conventional IR than DLIR (ICC mean, 0.603 vs 0.558, p = 0.001; CCC mean, 0.554 vs 0.510, p = 0.004). 7.45% and 26.83% features were reproducible among scanners evaluated by CV and QCD, respectively. CONCLUSIONS DLIR improves quality of DECT images but may alter radiomics features compared to conventional IR. Nine robust DECT radiomics features were identified. KEY POINTS • DLIR improves DECT image quality in terms of signal-to-noise ratio and contrast-to-noise ratio compared with ASIR-V and showed the highest noise reduction rate and lowest peak frequency shift. • Most of radiomics features are repeatable between repeated DECT scans, while inter-reconstruction algorithm reproducibility between conventional IR and DLIR, and inter-scanner reproducibility, are low. • Although DLIR may alter radiomics features compared to IR algorithms, nine radiomics features survived repeatability and reproducibility analysis among DECT scanners and reconstruction algorithms, which allows further validation and clinical-relevant analysis.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yihan Xia
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jianying Li
- Computed Tomography Research Center, GE Healthcare, Beijing, 100176, China
| | - Wei Lu
- Computed Tomography Research Center, GE Healthcare, Shanghai, 201203, China
| | - Xiaomeng Shi
- Department of Materials, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
| | - Jianxing Feng
- Haohua Technology Co., Ltd., Shanghai, 201100, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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Watson RE, Yu L. Safety Considerations in MRI and CT. Continuum (Minneap Minn) 2023; 29:27-53. [PMID: 36795872 DOI: 10.1212/con.0000000000001213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
OBJECTIVE MRI and CT are indispensable imaging modalities for the evaluation of patients with neurologic disease, and each is particularly well suited to address specific clinical questions. Although both of these imaging modalities have excellent safety profiles in clinical use as a result of concerted and dedicated efforts, each has potential physical and procedural risks that the practitioner should be aware of, which are described in this article. LATEST DEVELOPMENTS Recent advancements have been made in understanding and reducing safety risks with MR and CT. The magnetic fields in MRI create risks for dangerous projectile accidents, radiofrequency burns, and deleterious interactions with implanted devices, and serious patient injuries and deaths have occurred. Ionizing radiation in CT may be associated with shorter-term deterministic effects on biological tissues at extremely high doses and longer-term stochastic effects related to mutagenesis and carcinogenesis at low doses. The cancer risk of radiation exposure in diagnostic CT is considered extremely low, and the benefit of an appropriately indicated CT examination far outweighs the potential risk. Continuing major efforts are centered on improving image quality and the diagnostic power of CT while concurrently keeping radiation doses as low as reasonably achievable. ESSENTIAL POINTS An understanding of these MRI and CT safety issues that are central to contemporary radiology practice is essential for the safe and effective treatment of patients with neurologic disease.
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"Image quality evaluation of the Precise image CT deep learning reconstruction algorithm compared to Filtered Back-projection and iDose 4: a phantom study at different dose levels". Phys Med 2023; 106:102517. [PMID: 36669326 DOI: 10.1016/j.ejmp.2022.102517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 12/08/2022] [Accepted: 12/27/2022] [Indexed: 01/19/2023] Open
Abstract
PURPOSE To characterize the performance of the Precise Image (PI) deep learning reconstruction (DLR) algorithm for abdominal Computed Tomography (CT) imaging. METHODS CT images of the Catphan-600 phantom (equipped with an external annulus) were acquired using an abdominal protocol at four dose levels and reconstructed using FBP, iDose4 (levels 2,5) and PI ('Soft Tissue' definition, levels 'Sharper','Sharp','Standard','Smooth','Smoother'). Image noise, image non-uniformity, noise power spectrum (NPS), target transfer function (TTF), detectability index (d'), CT numbers accuracy and image histograms were analyzed. RESULTS The behavior of the PI algorithm depended strongly on the selected level of reconstruction. The phantom analysis suggested that the PI image noise decreased linearly by varying the level of reconstruction from Sharper to Smoother, expressing a noise reduction up to 80% with respect to FBP. Additionally, the non-uniformity decreased, the histograms became narrower, and d' values increased as PI reconstruction levels changed from Sharper to Smoother. PI had no significant impact on the average CT number of different contrast objects. The conventional FBP NPS was deeply altered only by Smooth and Smoother levels of reconstruction. Furthermore, spatial resolution was found to be dose- and contrast-dependent, but in each analyzed condition it was greater than or comparable to FBP and iDose4 TTFs. CONCLUSIONS The PI algorithm can reduce image noise with respect to FBP and iDose4; spatial resolution, CT numbers and image uniformity are generally preserved by the algorithm but changes in NPS for the Smooth and Smoother levels need to be considered in protocols implementation.
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Greffier J, Frandon J, Durand Q, Kammoun T, Loisy M, Beregi JP, Dabli D. Contribution of an artificial intelligence deep-learning reconstruction algorithm for dose optimization in lumbar spine CT examination: A phantom study. Diagn Interv Imaging 2023; 104:76-83. [PMID: 36100524 DOI: 10.1016/j.diii.2022.08.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/12/2022] [Accepted: 08/29/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE The purpose of this study was to assess the impact of the new artificial intelligence deep-learning reconstruction (AI-DLR) algorithm on image quality and radiation dose compared with iterative reconstruction algorithm in lumbar spine computed tomography (CT) examination. MATERIALS AND METHODS Acquisitions on phantoms were performed using a tube current modulation system for four DoseRight Indexes (DRI) (i.e., 26/23/20/15). Raw data were reconstructed using the Level 4 of iDose4 (i4) and three levels of AI-DLR (Smoother/Smooth/Standard) with a bone reconstruction kernel. The Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed (d' modeled detection of a lytic and a sclerotic bone lesions). Image quality was subjectively assessed on an anthropomorphic phantom by two radiologists. RESULTS The Noise magnitude was lower with AI-DLR than i4 and decreased from Standard to Smooth (-31 ± 0.1 [SD]%) and Smooth to Smoother (-48 ± 0.1 [SD]%). The average NPS spatial frequency was similar with i4 (0.43 ± 0.01 [SD] mm-1) and Standard (0.42 ± 0.01 [SD] mm-1) but decreased from Standard to Smoother (0.36 ± 0.01 [SD] mm-1). TTF values at 50% decreased as the dose decreased but were similar with i4 and all AI-DLR levels. For both simulated lesions, d' values increased from Standard to Smoother levels. Higher detectabilities were found with a DRI at 15 and Smooth and Smoother levels than with a DRI at 26 and i4. The images obtained with these dose and AI-DLR levels were rated satisfactory for clinical use by the radiologists. CONCLUSION Using Smooth and Smoother levels with CT allows a significant dose reduction (up to 72%) with a high detectability of lytic and sclerotic bone lesions and a clinical overall image quality.
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Affiliation(s)
- Joël Greffier
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France; Department of Medical Physics, Nîmes University Hospital, 30029 Nîmes Cedex 9, France.
| | - Julien Frandon
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France
| | - Quentin Durand
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France
| | - Tarek Kammoun
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France
| | - Maeliss Loisy
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France
| | - Jean-Paul Beregi
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France
| | - Djamel Dabli
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France; Department of Medical Physics, Nîmes University Hospital, 30029 Nîmes Cedex 9, France
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Dabli D, Loisy M, Frandon J, de Oliveira F, Meerun AM, Guiu B, Beregi JP, Greffier J. Comparison of image quality of two versions of deep-learning image reconstruction algorithm on a rapid kV-switching CT: a phantom study. Eur Radiol Exp 2023; 7:1. [PMID: 36617620 PMCID: PMC9826773 DOI: 10.1186/s41747-022-00314-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 11/05/2022] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND To assess the impact of the new version of a deep learning (DL) spectral reconstruction on image quality of virtual monoenergetic images (VMIs) for contrast-enhanced abdominal computed tomography in the rapid kV-switching platform. METHODS Two phantoms were scanned with a rapid kV-switching CT using abdomen-pelvic CT examination parameters at dose of 12.6 mGy. Images were reconstructed using two versions of DL spectral reconstruction algorithms (DLSR V1 and V2) for three reconstruction levels. The noise power spectrum (NSP) and task-based transfer function at 50% (TTF50) were computed at 40/50/60/70 keV. A detectability index (d') was calculated for enhanced lesions at low iodine concentrations: 2, 1, and 0.5 mg/mL. RESULTS The noise magnitude was significantly lower with DLSR V2 compared to DLSR V1 for energy levels between 40 and 60 keV by -36.5% ± 1.4% (mean ± standard deviation) for the standard level. The average NPS frequencies increased significantly with DLSR V2 by 23.7% ± 4.2% for the standard level. The highest difference in TTF50 was observed at the mild level with a significant increase of 61.7% ± 11.8% over 40-60 keV energy with DLSR V2. The d' values were significantly higher for DLSR V2 versus DLSR V1. CONCLUSIONS The DLSR V2 improves image quality and detectability of low iodine concentrations in VMIs compared to DLSR V1. This suggests a great potential of DLSR V2 to reduce iodined contrast doses.
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Affiliation(s)
- Djamel Dabli
- Department of Medical Imaging, IMAGINE UR UM 103, Montpellier University, Nîmes University Hospital, Bd Prof Robert Debré, 30029, Nîmes Cedex 9, France.
| | - Maeliss Loisy
- Department of Medical Imaging, IMAGINE UR UM 103, Montpellier University, Nîmes University Hospital, Bd Prof Robert Debré, 30029 Nîmes Cedex 9, France
| | - Julien Frandon
- Department of Medical Imaging, IMAGINE UR UM 103, Montpellier University, Nîmes University Hospital, Bd Prof Robert Debré, 30029 Nîmes Cedex 9, France
| | - Fabien de Oliveira
- Department of Medical Imaging, IMAGINE UR UM 103, Montpellier University, Nîmes University Hospital, Bd Prof Robert Debré, 30029 Nîmes Cedex 9, France
| | - Azhar Mohamad Meerun
- grid.157868.50000 0000 9961 060XSaint-Eloi University Hospital, Montpellier, France
| | - Boris Guiu
- grid.157868.50000 0000 9961 060XSaint-Eloi University Hospital, Montpellier, France
| | - Jean-Paul Beregi
- Department of Medical Imaging, IMAGINE UR UM 103, Montpellier University, Nîmes University Hospital, Bd Prof Robert Debré, 30029 Nîmes Cedex 9, France
| | - Joël Greffier
- Department of Medical Imaging, IMAGINE UR UM 103, Montpellier University, Nîmes University Hospital, Bd Prof Robert Debré, 30029 Nîmes Cedex 9, France
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Mørup SD, Stowe J, Precht H, Kusk MW, Lambrechtsen J, Foley SJ. COMBINING HI-RESOLUTION SCAN MODE WITH DEEP LEARNING RECONSTRUCTION ALGORITHMS IN CARDIAC CT. RADIATION PROTECTION DOSIMETRY 2023; 199:79-86. [PMID: 36420841 DOI: 10.1093/rpd/ncac243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 10/22/2022] [Accepted: 10/31/2022] [Indexed: 06/16/2023]
Abstract
To investigate the impact of combining the high-resolution (Hi-res) scan mode with deep learning image reconstruction (DLIR) algorithm in CT. Two phantoms (Catphan600® and Lungman, small, medium, large size) were CT scanned using combinations of Hi-res/standard mode and high-definition (HD)/standard kernels. Images were reconstructed with ASiR-V and three levels of DLIR. Spatial resolution, noise and contrast-to-noise ratio (CNR) were assessed. The radiation dose was recorded. The spatial resolution increased using Hi-res & HD. Image noise in the Catphan600® (69%) and the Lungman (10-70%) significantly increased when Hi-res & HD was applied. DLIR reduced the mean noise (54%). The CNR was reduced (64%) for Hi-res & HD. The radiation dose increased for both small (+70%) and medium (+43%) Lungman phantoms but decreased slightly for the large ones (-3%) when Hi-res was applied. In conclusion, the Hi-res scan mode improved the spatial resolution. The HD kernel significantly increased the image noise. DLIR improved the image noise and CNR and did not affect the spatial resolution.
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Affiliation(s)
- Svea Deppe Mørup
- Health Sciences Research Centre, UCL University College, Niels Bohrs Alle 1, 5230 Odense M Denmark
- Cardiology Research Department, Odense University Hospital, Baagøes Alle 15, 5700 Svendborg, Denmark
- Radiography & Diagnostic Imaging, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - John Stowe
- Radiography & Diagnostic Imaging, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Helle Precht
- Health Sciences Research Centre, UCL University College, Niels Bohrs Alle 1, 5230 Odense M Denmark
- Department of Regional Health Research, University of Southern Denmark, J.B. Winsløws Vej 19, 3, 5000 Odense C, Denmark
- Department of Radiology, Hospital Little Belt Kolding, Sygehusvej 24, 6000 Kolding, Denmark
| | - Martin Weber Kusk
- Radiography & Diagnostic Imaging, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
- Department of Radiology and Nuclear Medicine, University Hospital of Southwest Jutland, Esbjerg, Denmark
| | - Jess Lambrechtsen
- Cardiology Research Department, Odense University Hospital, Baagøes Alle 15, 5700 Svendborg, Denmark
| | - Shane J Foley
- Radiography & Diagnostic Imaging, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
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