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Klemenz AC, Manzke M, Meinel FG. [Artificial intelligence in cardiovascular radiology : Image acquisition, image reconstruction and workflow optimization]. RADIOLOGIE (HEIDELBERG, GERMANY) 2024; 64:766-772. [PMID: 38913176 DOI: 10.1007/s00117-024-01335-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/05/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has the potential to fundamentally change radiology workflow. OBJECTIVES This review article provides an overview of AI applications in cardiovascular radiology with a focus on image acquisition, image reconstruction, and workflow optimization. MATERIALS AND METHODS First, established applications of AI are presented for cardiovascular computed tomography (CT) and magnetic resonance imaging (MRI). Building on this, we describe the range of applications that are currently being developed and evaluated. The practical benefits, opportunities, and potential risks of artificial intelligence in cardiovascular imaging are critically discussed. The presentation is based on the relevant specialist literature and our own clinical and scientific experience. RESULTS AI-based techniques for image reconstruction are already commercially available and enable dose reduction in cardiovascular CT and accelerated image acquisition in cardiac MRI. Postprocessing of cardiovascular CT and MRI examinations can already be considerably simplified using established AI-based segmentation algorithms. In contrast, the practical benefits of many AI applications aimed at the diagnosis of cardiovascular diseases are less evident. Potential risks such as automation bias and considerations regarding cost efficiency should also be taken into account. CONCLUSIONS In a market characterized by great expectations and rapid technical development, it is important to realistically assess the practical benefits of AI applications for your own hospital or practice.
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Affiliation(s)
- Ann-Christin Klemenz
- Universitätsmedizin Rostock, Institut für Diagnostische und Interventionelle Radiologie, Kinder- und Neuroradiologie, Schillingallee 36, 18057, Rostock, Deutschland
| | - Mathias Manzke
- Universitätsmedizin Rostock, Institut für Diagnostische und Interventionelle Radiologie, Kinder- und Neuroradiologie, Schillingallee 36, 18057, Rostock, Deutschland
| | - Felix G Meinel
- Universitätsmedizin Rostock, Institut für Diagnostische und Interventionelle Radiologie, Kinder- und Neuroradiologie, Schillingallee 36, 18057, Rostock, Deutschland.
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Otgonbaatar C, Kim H, Jeon PH, Jeon SH, Cha SJ, Ryu JK, Jung WB, Shim H, Ko SM. Super-resolution deep learning image reconstruction: image quality and myocardial homogeneity in coronary computed tomography angiography. J Cardiovasc Imaging 2024; 32:30. [PMID: 39304957 DOI: 10.1186/s44348-024-00031-4] [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: 03/27/2024] [Accepted: 08/06/2024] [Indexed: 09/22/2024] Open
Abstract
BACKGROUND The recently introduced super-resolution (SR) deep learning image reconstruction (DLR) is potentially effective in reducing noise level and enhancing the spatial resolution. We aimed to investigate whether SR-DLR has advantages in the overall image quality and intensity homogeneity on coronary computed tomography (CT) angiography with four different approaches: filtered-back projection (FBP), hybrid iterative reconstruction (IR), DLR, and SR-DLR. METHODS Sixty-three patients (mean age, 61 ± 11 years; range, 18-81 years; 40 men) who had undergone coronary CT angiography between June and October 2022 were retrospectively included. Image noise, signal to noise ratio, and contrast to noise ratio were quantified in both proximal and distal segments of the major coronary arteries. The left ventricle myocardium contrast homogeneity was analyzed. Two independent reviewers scored overall image quality, image noise, image sharpness, and myocardial homogeneity. RESULTS Image noise in Hounsfield units (HU) was significantly lower (P < 0.001) for the SR-DLR (11.2 ± 2.0 HU) compared to those associated with other image reconstruction methods including FBP (30.5 ± 10.5 HU), hybrid IR (20.0 ± 5.4 HU), and DLR (14.2 ± 2.5 HU) in both proximal and distal segments. SR-DLR significantly improved signal to noise ratio and contrast to noise ratio in both the proximal and distal segments of the major coronary arteries. No significant difference was observed in the myocardial CT attenuation with SR-DLR among different segments of the left ventricle myocardium (P = 0.345). Conversely, FBP and hybrid IR resulted in inhomogeneous myocardial CT attenuation (P < 0.001). Two reviewers graded subjective image quality with SR-DLR higher than other image reconstruction techniques (P < 0.001). CONCLUSIONS SR-DLR improved image quality, demonstrated clearer delineation of distal segments of coronary arteries, and was seemingly accurate for quantifying CT attenuation in the myocardium.
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Affiliation(s)
- Chuluunbaatar Otgonbaatar
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Medical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, Republic of Korea
| | - Hyunjung Kim
- Department of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Pil-Hyun Jeon
- Department of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Sang-Hyun Jeon
- Department of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Sung-Jin Cha
- Department of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Jae-Kyun Ryu
- Medical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, Republic of Korea
| | - Won Beom Jung
- Korea Brain Research Institute (KBRI), Daegu, Republic of Korea
| | - Hackjoon Shim
- Medical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, Republic of Korea
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sung Min Ko
- Department of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea.
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Caruso D, De Santis D, Tremamunno G, Santangeli C, Polidori T, Bona GG, Zerunian M, Del Gaudio A, Pugliese L, Laghi A. Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography. Eur Radiol 2024:10.1007/s00330-024-11059-x. [PMID: 39299952 DOI: 10.1007/s00330-024-11059-x] [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: 05/21/2024] [Revised: 06/28/2024] [Accepted: 08/16/2024] [Indexed: 09/22/2024]
Abstract
OBJECTIVE To evaluate radiation dose and image quality of a double-low CCTA protocol reconstructed utilizing high-strength deep learning image reconstructions (DLIR-H) compared to standard adaptive statistical iterative reconstruction (ASiR-V) protocol in non-obese patients. MATERIALS AND METHODS From June to October 2022, consecutive patients, undergoing clinically indicated CCTA, with BMI < 30 kg/m2 were prospectively included and randomly assigned into three groups: group A (100 kVp, ASiR-V 50%, iodine delivery rate [IDR] = 1.8 g/s), group B (80 kVp, DLIR-H, IDR = 1.4 g/s), and group C (80 kVp, DLIR-H, IDR = 1.2 g/s). High-concentration contrast medium was administered. Image quality analysis was evaluated by two radiologists. Radiation and contrast dose, and objective and subjective image quality were compared across the three groups. RESULTS The final population consisted of 255 patients (64 ± 10 years, 161 men), 85 per group. Group B yielded 42% radiation dose reduction (2.36 ± 0.9 mSv) compared to group A (4.07 ± 1.2 mSv; p < 0.001) and achieved a higher signal-to-noise ratio (30.5 ± 11.5), contrast-to-noise-ratio (27.8 ± 11), and subjective image quality (Likert scale score: 4, interquartile range: 3-4) compared to group A and group C (all p ≤ 0.001). Contrast medium dose in group C (44.8 ± 4.4 mL) was lower than group A (57.7 ± 6.2 mL) and B (50.4 ± 4.3 mL), all the comparisons were statistically different (all p < 0.001). CONCLUSION DLIR-H combined with 80-kVp CCTA with an IDR 1.4 significantly reduces radiation and contrast medium exposure while improving image quality compared to conventional 100-kVp with 1.8 IDR protocol in non-obese patients. CLINICAL RELEVANCE STATEMENT Low radiation and low contrast medium dose coronary CT angiography protocol is feasible with high-strength deep learning reconstruction and high-concentration contrast medium without compromising image quality. KEY POINTS Minimizing the radiation and contrast medium dose while maintaining CT image quality is highly desirable. High-strength deep learning iterative reconstruction protocol yielded 42% radiation dose reduction compared to conventional protocol. "Double-low" coronary CTA is feasible with high-strength deep learning reconstruction without compromising image quality in non-obese patients.
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Affiliation(s)
- Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Domenico De Santis
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Tremamunno
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Curzio Santangeli
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Tiziano Polidori
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Giovanna G Bona
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Marta Zerunian
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Antonella Del Gaudio
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Luca Pugliese
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Andrea Laghi
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.
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4
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Gnasso C, Vecsey-Nagy M, Schoepf UJ, Stock J, Zsarnoczay E, Pinos D, Tremamunno G, Giovagnoli V, Seidensticker P, Emrich T, Varga-Szemes A. Evaluation of a Tube Voltage-Based Contrast Media Adaptation in Coronary Computed Tomography Angiography Using Personalized Triphasic Injection Protocols: A Matched Case-Control Study. Acad Radiol 2024; 31:3570-3578. [PMID: 38734579 DOI: 10.1016/j.acra.2024.04.039] [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: 02/14/2024] [Revised: 04/16/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024]
Abstract
RATIONALE AND OBJECTIVES Coronary CT angiography (CCTA) has recently been established as a first-line test in patients with suspected coronary artery disease (CAD). Due to the increased use of CCTA, strategies to reduce radiation and contrast medium (CM) exposure are of high importance. The aim of this study was to evaluate the performance of automated tube voltage selection (ATVS)-adapted CM injection protocol for CCTA compared to a clinically established triphasic injection protocol in terms of image quality, radiation exposure, and CM administration MATERIAL AND METHODS: Patients undergoing clinically indicated CCTA were prospectively enrolled from July 2021 to July 2023. Patients underwent CCTA using a modified triphasic CM injection protocol tailored to the tube voltage by the ATVS algorithm, in a range of 70 to 130 kV with a 10 kV interval. The injection protocol consisted of two phases of mixed CM and saline boluses with different proportions to assure a voltage-specific iodine delivery rate, followed by a third phase of saline flush. This cohort was compared to a control group identified retrospectively and scanned on the same CT system but with a standard triphasic CM protocol. Radiation and contrast dose, subjective and objective image quality (contrast-to-noise-ratio [CNR] and signal-to-noise-ratio [SNR]) were compared between the two groups. RESULTS The final population consisted of 120 prospective patients matched with 120 retrospective controls, with 20 patients in each kV group. The 120 kV group was excluded from the statistical analysis due to insufficient sample size. A significant CM reduction was achieved in the prospective group overall (46.0 [IQR 37.0-52.0] vs. 51.3 [IQR 40.1-73.0] mL, p < 0.001) and at all kV levels too (all pairwise p < 0.001). There were no significant differences in radiation dose (6.13 ± 4.88 vs. 5.97 ± 5.51 mSv, p = 0.81), subjective image quality (median score of 4 [3-5] vs. 4 [3-5], p = 0.40), CNR, and SNR in the aorta and the left anterior descending coronary artery (all p > 0.05). CONCLUSION ATVS-adapted CM injection protocol allows for diagnostic quality CCTA with reduced CM volume while maintaining similar radiation exposure, subjective and objective image quality.
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Affiliation(s)
- Chiara Gnasso
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA; Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
| | - Milan Vecsey-Nagy
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA; Heart and Vascular Centre, Semmelweis University, Varosmajor utca 68, Budapest 1122, Hungary
| | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA.
| | - Jonathan Stock
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA; Paracelsus Medical University, Nuremberg, Prof.-Ernst-Nathan-Strasse 1, 90419 Nuremberg, Germany
| | - Emese Zsarnoczay
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA; MTA-SE Cardiovascular Imaging Research Group, Department of Radiology, Medical Imaging Centre, Semmelweis University, H-1082 Budapest, Üllői út 78, Budapest, Hungary
| | - Daniel Pinos
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA
| | - Giuseppe Tremamunno
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA; Radiology Unit, Department of Surgical and Medical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Vincent Giovagnoli
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA
| | - Peter Seidensticker
- Global Medical Affairs Radiology, Bayer US LLC, Pittsburgh, Pennsylvania, USA
| | - Tilman Emrich
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA; Department of Diagnostic and Interventional Radiology, University Medical Center of Johannes Gutenberg University, Langenbeckstr. 1, Mainz 55131, Germany; German Centre for Cardiovascular Research, Mainz 55131, Germany
| | - Akos Varga-Szemes
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA
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5
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Cho HH, Lee SM, You SK. Assessment of deep learning image reconstruction (DLIR) on image quality in pediatric cardiac CT datasets type of manuscript: Original research. PLoS One 2024; 19:e0300090. [PMID: 39186484 PMCID: PMC11346658 DOI: 10.1371/journal.pone.0300090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 05/28/2024] [Indexed: 08/28/2024] Open
Abstract
BAKGROUND To evaluate the quantitative and qualitative image quality using deep learning image reconstruction (DLIR) of pediatric cardiac computed tomography (CT) compared with conventional image reconstruction methods. METHODS Between January 2020 and December 2022, 109 pediatric cardiac CT scans were included in this study. The CT scans were reconstructed using an adaptive statistical iterative reconstruction-V (ASiR-V) with a blending factor of 80% and three levels of DLIR with TrueFidelity (low-, medium-, and high-strength settings). Quantitative image quality was measured using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The edge rise distance (ERD) and angle between 25% and 75% of the line density profile were drawn to evaluate sharpness. Qualitative image quality was assessed using visual grading analysis scores. RESULTS A gradual improvement in the SNR and CNR was noted among the strength levels of the DLIR in sequence from low to high. Compared to ASiR-V, high-level DLIR showed significantly improved SNR and CNR (P<0.05). ERD decreased with increasing angle as the level of DLIR increased. CONCLUSION High-level DLIR showed improved SNR and CNR compared to ASiR-V, with better sharpness on pediatric cardiac CT scans.
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Affiliation(s)
- Hyun-Hae Cho
- Department of Radiology and Medical Research Institute, College of Medicine, Ewha Womans University Seoul Hospital, Seoul, Republic of Korea
| | - So Mi Lee
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, South Korea
| | - Sun Kyoung You
- Department of Radiology, Chungnam National University Hospital, Daejeon, Republic of Korea
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Zheng Z, Ai Z, Liang Y, Li Y, Wu Z, Wu M, Han Q, Ma K, Xiang Z. Clinical value of deep learning image reconstruction on the diagnosis of pulmonary nodule for ultra-low-dose chest CT imaging. Clin Radiol 2024; 79:628-636. [PMID: 38749827 DOI: 10.1016/j.crad.2024.04.008] [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: 01/03/2024] [Revised: 03/20/2024] [Accepted: 04/15/2024] [Indexed: 07/10/2024]
Abstract
PURPOSE To compare the image quality and pulmonary nodule detectability between deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) in ultra-low-dose CT (ULD-CT). METHODS 142 participants required lung examination who underwent simultaneously ULD-CT (UL-A, 0.57 ± 0.04 mSv or UL-B, 0.33 ± 0.03 mSv), and standard CT (SDCT, 4.32 ± 0.33 mSv) plain scans were included in this prospective study. SDCT was the reference standard using ASIR-V at 50% strength (50%ASIR-V). ULD-CT was reconstructed with 50%ASIR-V, DLIR at medium and high strength (DLIR-M, DLIR-H). The noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective scores were measured. The presence and accuracy of nodules were analyzed using a combination of a deep learning-based nodule evaluation system and a radiologist. RESULTS A total of 710 nodules were detected by SDCT, including 358 nodules in UL-A and 352 nodules in UL-B. DLIR-H exhibited superior noise, SNR, and CNR performance, and achieved comparable or even higher subjective scores compared to 50%ASIR-V in ULD-CT. Nodules sensitivity detection of 50%ASIR-V, DLIR-M, and DLIR-H in ULD-CT were identical (96.90%). In multivariate analysis, body mass index (BMI), nodule diameter, and type were independent predictors for the sensitivity of nodule detection (p<.001). DLIR-H provided a lower absolute percent error (APE) in volume (3.10% ± 95.11% vs 8.29% ± 99.14%) compared to 50%ASIR-V of ULD-CT (P<.001). CONCLUSIONS ULD-CT scanning has a high sensitivity for detecting pulmonary nodules. Compared with ASIR-V, DLIR can significantly reduce image noise, and improve image quality, and accuracy of the nodule measurement in ULD-CT.
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Affiliation(s)
- Z Zheng
- Postgraduate Cultivation Base of Guangzhou University of Chinese Medicine, Panyu Central Hospital, Guangzhou, China; Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - Z Ai
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - Y Liang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - Y Li
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - Z Wu
- Postgraduate Cultivation Base of Guangzhou University of Chinese Medicine, Panyu Central Hospital, Guangzhou, China; Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - M Wu
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - Q Han
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - K Ma
- CT Imaging Research Center, GE HealthCare China, Guangzhou, China.
| | - Z Xiang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
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Kawai N, Noda Y, Nakamura F, Kaga T, Suzuki R, Miyoshi T, Mori F, Hyodo F, Kato H, Matsuo M. Low-tube-voltage whole-body CT angiography with extremely low iodine dose: a comparison between hybrid-iterative reconstruction and deep-learning image-reconstruction algorithms. Clin Radiol 2024; 79:e791-e798. [PMID: 38403540 DOI: 10.1016/j.crad.2024.02.002] [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: 09/08/2023] [Revised: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 02/27/2024]
Abstract
AIM To evaluate arterial enhancement, its depiction, and image quality in low-tube potential whole-body computed tomography (CT) angiography (CTA) with extremely low iodine dose and compare the results with those obtained by hybrid-iterative reconstruction (IR) and deep-learning image-reconstruction (DLIR) methods. MATERIALS AND METHODS This prospective study included 34 consecutive participants (27 men; mean age, 74.2 years) who underwent whole-body CTA at 80 kVp for evaluating aortic diseases between January and July 2020. Contrast material (240 mg iodine/ml) with simultaneous administration of its quarter volume of saline, which corresponded to 192 mg iodine/ml, was administered. CT raw data were reconstructed using adaptive statistical IR-Veo of 40% (hybrid-IR), DLIR with medium- (DLIR-M), and high-strength level (DLIR-H). A radiologist measured CT attenuation of the arteries and background noise, and the signal-to-noise ratio (SNR) was then calculated. Two reviewers qualitatively evaluated the arterial depictions and diagnostic acceptability on axial, multiplanar-reformatted (MPR), and volume-rendered (VR) images. RESULTS Mean contrast material volume and iodine weight administered were 64.1 ml and 15.4 g, respectively. The SNRs of the arteries were significantly higher in the following order of the DLIR-H, DLIR-M, and hybrid-IR (p<0.001). Depictions of six arteries on axial, three arteries on MPR, and four arteries on VR images were significantly superior in the DLIR-M or hybrid-IR than in the DLIR-H (p≤0.009 for each). Diagnostic acceptability was significantly better in the DLIR-M and DLIR-H than in the hybrid-IR (p<0.001-0.005). CONCLUSION DLIR-M showed well-balanced arterial depictions and image quality compared with the hybrid-IR and DLIR-H.
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Affiliation(s)
- N Kawai
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - Y Noda
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - F Nakamura
- Department of Radiology, Gifu Municipal Hospital, 7-1 Kashima, Gifu 500-8513, Japan
| | - T Kaga
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - R Suzuki
- Department of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu 501-1194, Japan
| | - T Miyoshi
- Department of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu 501-1194, Japan
| | - F Mori
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - F Hyodo
- Department of Pharmacology, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan; Center for One Medicine Innovative Translational Research (COMIT), Institute for Advanced Study, Gifu University, Japan
| | - H Kato
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - M Matsuo
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
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8
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Yuan D, Wang L, Lyu P, Zhang Y, Gao J, Liu J. Evaluation of image quality on low contrast media with deep learning image reconstruction algorithm in prospective ECG-triggering coronary CT angiography. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2024; 40:1377-1388. [PMID: 38722507 DOI: 10.1007/s10554-024-03113-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 04/08/2024] [Indexed: 06/29/2024]
Abstract
To assess the impact of low-dose contrast media (CM) injection protocol with deep learning image reconstruction (DLIR) algorithm on image quality in coronary CT angiography (CCTA). In this prospective study, patients underwent CCTA were prospectively and randomly assigned to three groups with different contrast volume protocols (at 320mgI/mL concentration and constant flow rate of 5ml/s). After pairing basic information, 210 patients were enrolled in this study: Group A, 0.7mL/kg (n = 70); Group B, 0.6mL/kg (n = 70); Group C, 0.5mL/kg (n = 70). All patients were examined via a prospective ECG-triggered scan protocol within one heartbeat. A high level DLIR (DLIR-H) algorithm was used for image reconstruction with a thickness and interval of 0.625mm. The CT values of ascending aorta (AA), descending aorta (DA), three main coronary arteries, pulmonary artery (PA), and superior vena cava (SVC) were measured and analyzed for objective assessment. Two radiologists assessed the image quality and diagnostic confidence using a 5-point Likert scale. The CM doses were 46.81 ± 6.41mL, 41.96 ± 7.51mL and 34.65 ± 5.38mL for Group A, B and C, respectively. The objective assessments on AA, DA and the three main coronary arteries and the overall subjective scoring showed no significant difference among the three groups (all p > 0.05). The subjective assessment proved that excellent CCTA images can be obtained from the three different contrast media protocols. There were no significant differences in intracoronary attenuation values between the higher HR subgroup and the lower HR subgroup among three groups. CCTA reconstructed with DLIR could be realized with adequate enhancement in coronary arteries, excellent image quality and diagnostic confidence at low contrast dose of a 0.5mL/kg. The use of lower tube voltages may further reduce the contrast dose requirement.
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Affiliation(s)
- Dian Yuan
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, Henan Province, 450052, China
| | - Luotong Wang
- CT Imaging Research Center, GE Healthcare China, Beijing, 100176, China
| | - Peijie Lyu
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, Henan Province, 450052, China
| | - Yonggao Zhang
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, Henan Province, 450052, China
| | - Jianbo Gao
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, Henan Province, 450052, China
| | - Jie Liu
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, Henan Province, 450052, China.
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Kitera N, Fujioka C, Higaki T, Nishimaru E, Yokomachi K, Matsumoto Y, Kiguchi M, Ohashi K, Kasai H, Awai K. [Validation of Optimal Imaging Conditions for Coronary Computed Tomography Angiography Using High-definition Mode and Deep Learning Image Reconstruction Algorithm]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2024; 80:499-509. [PMID: 38508756 DOI: 10.6009/jjrt.2024-1353] [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] [Indexed: 03/22/2024]
Abstract
PURPOSE To verify the optimal imaging conditions for coronary computed tomography angiography (CCTA) examinations when using high-definition (HD) mode and deep learning image reconstruction (DLIR) in combination. METHOD A chest phantom and an in-house phantom using 3D printer were scanned with a 256-row detector CT scanner. The scan parameters were as follows - acquisition mode: ON (HD mode) and OFF (normal resolution [NR] mode), rotation time: 0.28 s/rotation, beam coverage width: 160 mm, and the radiation dose was adjusted based on CT-AEC. Image reconstruction was performed using ASiR-V (Hybrid-IR), TrueFidelity Image (DLIR), and HD-Standard (HD mode) and Standard (NR mode) reconstruction kernels. The task-based transfer function (TTF) and noise power spectrum (NPS) were measured for image evaluation, and the detectability index (d') was calculated. Visual evaluation was also performed on an in-house coronary phantom. RESULT The in-plane TTF was better for the HD mode than for the NR mode, while the z-axis TTF was lower for DLIR than for Hybrid-IR. The NPS values in the high-frequency region were higher for the HD mode compared to those for the NR mode, and the NPS was lower for DLIR than for Hybrid-IR. The combination of HD mode and DLIR showed the best value for in-plane d', whereas the combination of NR mode and DLIR showed the best value for z-axis d'. In the visual evaluation, the combination of NR mode and DLIR showed the best values from a noise index of 45 HU. CONCLUSION The optimal combination of HD mode and DLIR depends on the image noise level, and the combination of NR mode and DLIR was the best imaging condition under noisy conditions.
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Affiliation(s)
- Nobuo Kitera
- Department of Radiology, Hiroshima University Hospital
| | | | - Toru Higaki
- Graduate School of Advanced Science and Engineering, Hiroshima University
| | | | | | | | - Masao Kiguchi
- Department of Radiology, Hiroshima University Hospital
| | - Kazuya Ohashi
- Department of Radiology, Nagoya City University Hospital
| | - Harumasa Kasai
- Department of Radiology, Nagoya City University Hospital
| | - Kazuo Awai
- Graduate School of Biomedical and Health Sciences, Hiroshima University
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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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11
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Viry A, Vitzthum V, Monnin P, Bize J, Rotzinger D, Racine D. Optimization of CT pulmonary angiography for pulmonary embolism using task-based image quality assessment and diagnostic reference levels: A multicentric study. Phys Med 2024; 121:103365. [PMID: 38663347 DOI: 10.1016/j.ejmp.2024.103365] [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: 11/20/2023] [Revised: 02/12/2024] [Accepted: 04/19/2024] [Indexed: 05/12/2024] Open
Abstract
PURPOSE To establish size-specific diagnostic reference levels (DRLs) for pulmonary embolism (PE) based on patient CT examinations performed on 74 CT devices. To assess task-based image quality (IQ) for each device and to investigate the variability of dose and IQ across different CTs. To propose a dose/IQ optimization. METHODS 1051 CT pulmonary angiography dose data were collected. DRLs were calculated as the 75th percentile of CT dose index (CTDI) for two patient categories based on the thoracic perimeters. IQ was assessed with two thoracic phantom sizes using local acquisition parameters and three other dose levels. The area under the ROC curve (AUC) of a 2 mm low perfused vessel was assessed with a non-prewhitening with eye-filter model observer. The optimal IQ-dose point was mathematically assessed from the relationship between IQ and dose. RESULTS The DRLs of CTDIvol were 6.4 mGy and 10 mGy for the two patient categories. 75th percentiles of phantom CTDIvol were 6.3 mGy and 10 mGy for the two phantom sizes with inter-quartile AUC values of 0.047 and 0.066, respectively. After the optimization, 75th percentiles of phantom CTDIvol decreased to 5.9 mGy and 7.55 mGy and the interquartile AUC values were reduced to 0.025 and 0.057 for the two phantom sizes. CONCLUSION DRLs for PE were proposed as a function of patient thoracic perimeters. This study highlights the variability in terms of dose and IQ. An optimization process can be started individually and lead to a harmonization of practice throughout multiple CT sites.
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Affiliation(s)
- Anaïs Viry
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Rue du Grand-Pré 1, 1007 Lausanne, Switzerland.
| | - Veronika Vitzthum
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Rue du Grand-Pré 1, 1007 Lausanne, Switzerland
| | - Pascal Monnin
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Rue du Grand-Pré 1, 1007 Lausanne, Switzerland
| | - Julie Bize
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Rue du Grand-Pré 1, 1007 Lausanne, Switzerland
| | - David Rotzinger
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - 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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12
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Gulizia M, Alamo L, Alemán-Gómez Y, Cherpillod T, Mandralis K, Chevallier C, Tenisch E, Viry A. Gated cardiac CT in infants: What can we expect from deep learning image reconstruction algorithm? J Cardiovasc Comput Tomogr 2024; 18:304-306. [PMID: 38480035 DOI: 10.1016/j.jcct.2024.03.001] [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: 11/25/2023] [Revised: 02/27/2024] [Accepted: 03/01/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND ECG-gated cardiac CT is now widely used in infants with congenital heart disease (CHD). Deep Learning Image Reconstruction (DLIR) could improve image quality while minimizing the radiation dose. OBJECTIVES To define the potential dose reduction using DLIR with an anthropomorphic phantom. METHOD An anthropomorphic pediatric phantom was scanned with an ECG-gated cardiac CT at four dose levels. Images were reconstructed with an iterative and a deep-learning reconstruction algorithm (ASIR-V and DLIR). Detectability of high-contrast vessels were computed using a mathematical observer. Discrimination between two vessels was assessed by measuring the CT spatial resolution. The potential dose reduction while keeping a similar level of image quality was assessed. RESULTS DLIR-H enhances detectability by 2.4% and discrimination performances by 20.9% in comparison with ASIR-V 50. To maintain a similar level of detection, the dose could be reduced by 64% using high-strength DLIR in comparison with ASIR-V50. CONCLUSION DLIR offers the potential for a substantial dose reduction while preserving image quality compared to ASIR-V.
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Affiliation(s)
- Marianna Gulizia
- Department of Radiology and Interventional Radiology, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland.
| | - Leonor Alamo
- Department of Radiology and Interventional Radiology, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland.
| | - Yasser Alemán-Gómez
- Department of Radiology and Interventional Radiology, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland.
| | - Tyna Cherpillod
- Department of Radiology and Interventional Radiology, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland.
| | - Katerina Mandralis
- Department of Radiology and Interventional Radiology, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland.
| | - Christine Chevallier
- Department of Radiology and Interventional Radiology, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland.
| | - Estelle Tenisch
- Department of Radiology and Interventional Radiology, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland.
| | - Anaïs Viry
- Institute of Radiation Physics, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue du Grand Pré 1, 1007 Lausanne, Switzerland.
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Greffier J, Pastor M, Si-Mohamed S, Goutain-Majorel C, Peudon-Balas A, Bensalah MZ, Frandon J, Beregi JP, Dabli D. Comparison of two deep-learning image reconstruction algorithms on cardiac CT images: A phantom study. Diagn Interv Imaging 2024; 105:110-117. [PMID: 37949769 DOI: 10.1016/j.diii.2023.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 11/12/2023]
Abstract
PURPOSE The purpose of this study was to compare the performance of Precise IQ Engine (PIQE) and Advanced intelligent Clear-IQ Engine (AiCE) algorithms on image-quality according to the dose level in a cardiac computed tomography (CT) protocol. MATERIALS AND METHODS Acquisitions were performed using the CT ACR 464 phantom at three dose levels (volume CT dose indexes: 7.1/5.2/3.1 mGy) using a prospective cardiac CT protocol. Raw data were reconstructed using the three levels of AiCE and PIQE (Mild, Standard and Strong). The noise power spectrum (NPS) and task-based transfer function (TTF) for bone and acrylic inserts were computed. The detectability index (d') was computed to model the detectability of the coronary lumen (350 Hounsfield units and 4-mm diameter) and non-calcified plaque (40 Hounsfield units and 2-mm diameter). RESULTS Noise magnitude values were lower with PIQE than with AiCE (-13.4 ± 6.0 [standard deviation (SD)] % for Mild, -20.4 ± 4.0 [SD] % for Standard and -32.6 ± 2.6 [SD] % for Strong levels). The average NPS spatial frequencies shifted towards higher frequencies with PIQE than with AiCE (21.9 ± 3.5 [SD] % for Mild, 20.1 ± 3.0 [SD] % for Standard and 12.5 ± 3.5 [SD] % for Strong levels). The TTF values at fifty percent (f50) values shifted towards higher frequencies with PIQE than with AiCE for acrylic inserts but, for bone inserts, f50 values were found to be close. Whatever the dose and DLR level, d' values of both simulated cardiac lesions were higher with PIQE than with AiCE. For the simulated coronary lumen, d' values were better by 35.1 ± 9.3 (SD) % on average for all dose levels for Mild, 43.2 ± 5.0 (SD) % for Standard, and 62.6 ± 1.2 (SD) % for Strong levels. CONCLUSION Compared to AiCE, PIQE reduced noise, improved spatial resolution, noise texture and detectability of simulated cardiac lesions. PIQE seems to have a greater potential for dose reduction in cardiac CT acquisition.
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Affiliation(s)
- Joël Greffier
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France.
| | - Maxime Pastor
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France
| | - Salim Si-Mohamed
- University Lyon, INSA-Lyon, University Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, 69100 Villeurbanne, France; Department of Radiology, Louis Pradel Hospital, Hospices Civils de Lyon, 69500 Bron, France
| | | | - Aude Peudon-Balas
- Department of Medical Imaging, Centre Hospitalier de Perpignan, 66000 Perpignan, France
| | | | - Julien Frandon
- 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
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Catapano F, Lisi C, Savini G, Olivieri M, Figliozzi S, Caracciolo A, Monti L, Francone M. Deep Learning Image Reconstruction Algorithm for CCTA: Image Quality Assessment and Clinical Application. J Comput Assist Tomogr 2024; 48:217-221. [PMID: 37621087 DOI: 10.1097/rct.0000000000001537] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
OBJECTIVE The increasing number of coronary computed tomography angiography (CCTA) requests raised concerns about dose exposure. New dose reduction strategies based on artificial intelligence have been proposed to overcome limitations of iterative reconstruction (IR) algorithms. Our prospective study sought to explore the added value of deep-learning image reconstruction (DLIR) in comparison with a hybrid IR algorithm (adaptive statistical iterative reconstruction-veo [ASiR-V]) in CCTA, even in clinical challenging scenarios, as obesity, heavily calcified vessels and coronary stents. METHODS We prospectively included 103 consecutive patients who underwent CCTA. Data sets were reconstructed with ASiR-V and DLIR. For each reconstruction signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) was calculated, and qualitative assessment was made with a four-point Likert scale by two independent and blinded radiologists with different expertise. RESULTS Both SNR and CNR were significantly higher in DLIR (SNR-DLIR median value [interquartile range] of 13.89 [11.06-16.35] and SNR-ASiR-V 25.42 [22.46-32.22], P < 0.001; CNR-DLIR 16.84 [9.83-27.08] vs CNR-ASiR-V 10.09 [5.69-13.5], P < 0.001).Median qualitative score was 4 for DLIR images versus 3 for ASiR-V ( P < 0.001), with a good interreader reliability [intraclass correlation coefficient(2,1)e intraclass correlation coefficient(3,1) 0.60 for DLIR and 0.62 and 0.73 for ASiR-V].In the obese and in the "calcifications and stents" groups, DLIR showed significantly higher values of SNR (24.23 vs 11.11, P < 0.001 and 24.55 vs 14.09, P < 0.001, respectively) and CNR (16.08 vs 8.04, P = 0.008 and 17.31 vs 10.14, P = 0.003) and image quality. CONCLUSIONS Deep-learning image reconstruction in CCTA allows better SNR, CNR, and qualitative assessment than ASiR-V, with an added value in the most challenging clinical scenarios.
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Affiliation(s)
| | - Costanza Lisi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Giovanni Savini
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Marzia Olivieri
- Department of neuroscience, Imaging and Clinical Sciences, "G. D'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Stefano Figliozzi
- From the Department of Radiology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Alessandra Caracciolo
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
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Kazimierczak W, Kazimierczak N, Wilamowska J, Wojtowicz O, Nowak E, Serafin Z. Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructions. Sci Rep 2024; 14:3845. [PMID: 38360941 PMCID: PMC10869818 DOI: 10.1038/s41598-024-54502-1] [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: 11/30/2023] [Accepted: 02/13/2024] [Indexed: 02/17/2024] Open
Abstract
To assess the image quality parameters of dual-energy computed tomography angiography (DECTA) 40-, and 60 keV virtual monoenergetic images (VMIs) combined with deep learning-based image reconstruction model (DLM) and iterative reconstructions (IR). CT scans of 28 post EVAR patients were enrolled. The 60 s delayed phase of DECTA was evaluated. Objective [noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR)] and subjective (overall image quality and endoleak conspicuity - 3 blinded readers assessment) image quality analyses were performed. The following reconstructions were evaluated: VMI 40, 60 keV VMI; IR VMI 40, 60 keV; DLM VMI 40, 60 keV. The noise level of the DLM VMI images was approximately 50% lower than that of VMI reconstruction. The highest CNR and SNR values were measured in VMI DLM images. The mean CNR in endoleak in 40 keV was accounted for as 1.83 ± 1.2; 2.07 ± 2.02; 3.6 ± 3.26 in VMI, VMI IR, and VMI DLM, respectively. The DLM algorithm significantly reduced noise and increased lesion conspicuity, resulting in higher objective and subjective image quality compared to other reconstruction techniques. The application of DLM algorithms to low-energy VMIs significantly enhances the diagnostic value of DECTA in evaluating endoleaks. DLM reconstructions surpass traditional VMIs and IR in terms of image quality.
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Affiliation(s)
- Wojciech Kazimierczak
- Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067, Bydgoszcz, Poland.
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009, Bydgoszcz, Poland.
- University Hospital No 1 in Bydgoszcz, Marii Skłodowskiej - Curie 9, 85-094, Bydgoszcz, Poland.
| | - Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009, Bydgoszcz, Poland
| | - Justyna Wilamowska
- Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067, Bydgoszcz, Poland
- University Hospital No 1 in Bydgoszcz, Marii Skłodowskiej - Curie 9, 85-094, Bydgoszcz, Poland
| | - Olaf Wojtowicz
- Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067, Bydgoszcz, Poland
- University Hospital No 1 in Bydgoszcz, Marii Skłodowskiej - Curie 9, 85-094, Bydgoszcz, Poland
| | - Ewa Nowak
- University Hospital No 1 in Bydgoszcz, Marii Skłodowskiej - Curie 9, 85-094, Bydgoszcz, Poland
| | - Zbigniew Serafin
- Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067, Bydgoszcz, Poland
- University Hospital No 1 in Bydgoszcz, Marii Skłodowskiej - Curie 9, 85-094, Bydgoszcz, Poland
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Mück J, Reiter E, Klingert W, Bertolani E, Schenk M, Nikolaou K, Afat S, Brendlin AS. Towards safer imaging: A comparative study of deep learning-based denoising and iterative reconstruction in intraindividual low-dose CT scans using an in-vivo large animal model. Eur J Radiol 2024; 171:111267. [PMID: 38169217 DOI: 10.1016/j.ejrad.2023.111267] [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: 11/01/2023] [Revised: 12/04/2023] [Accepted: 12/14/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE Computed tomography (CT) scans are a significant source of medically induced radiation exposure. Novel deep learning-based denoising (DLD) algorithms have been shown to enable diagnostic image quality at lower radiation doses than iterative reconstruction (IR) methods. However, most comparative studies employ low-dose simulations due to ethical constraints. We used real intraindividual animal scans to investigate the dose-reduction capabilities of a DLD algorithm in comparison to IR. MATERIALS AND METHODS Fourteen veterinarian-sedated alive pigs underwent 2 CT scans on the same 3rd generation dual-source scanner with two months between each scan. Four additional scans ensued each time, with mAs reduced to 50 %, 25 %, 10 %, and 5 %. All scans were reconstructed ADMIRE levels 2 (IR2) and a novel DLD algorithm, resulting in 280 datasets. Objective image quality (CT numbers stability, noise, and contrast-to-noise ratio) was measured via consistent regions of interest. Three radiologists independently rated all possible dataset combinations per time point for subjective image quality (-1 = inferior, 0 = equal, 1 = superior). The points were averaged for a semiquantitative score, and inter-rater agreement was measured using Spearman's correlation coefficient and adequately corrected mixed-effects modeling analyzed objective and subjective image quality. RESULTS Neither dose-reduction nor reconstruction method negatively impacted CT number stability (p > 0.999). In objective image quality assessment, the lowest radiation dose achievable by DLD when comparing noise (p = 0.544) and CNR (p = 0.115) to 100 % IR2 was 25 %. Overall, inter-rater agreement of the subjective image quality ratings was strong (r ≥ 0.69, mean 0.93 ± 0.05, 95 % CI 0.92-0.94; each p < 0.001), and subjective assessments corroborated that DLD at 25 % radiation dose was comparable to 100 % IR2 in image quality, sharpness, and contrast (p ≥ 0.281). CONCLUSIONS The DLD algorithm can achieve image quality comparable to the standard IR method but with a significant dose reduction of up to 75%. This suggests a promising avenue for lowering patient radiation exposure without sacrificing diagnostic quality.
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Affiliation(s)
- Jonas Mück
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Elisa Reiter
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Wilfried Klingert
- Department of General, Visceral and Transplant Surgery, Eberhard-Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Elisa Bertolani
- Department of General, Visceral and Transplant Surgery, Eberhard-Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Martin Schenk
- Department of General, Visceral and Transplant Surgery, Eberhard-Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany.
| | - Andreas S Brendlin
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
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Demircioğlu A, Bos D, Demircioğlu E, Qaadan S, Glasmachers T, Bruder O, Umutlu L, Nassenstein K. Deep learning-based scan range optimization can reduce radiation exposure in coronary CT angiography. Eur Radiol 2024; 34:411-421. [PMID: 37552254 PMCID: PMC10791769 DOI: 10.1007/s00330-023-09971-9] [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: 12/27/2022] [Revised: 04/24/2023] [Accepted: 05/28/2023] [Indexed: 08/09/2023]
Abstract
OBJECTIVES Cardiac computed tomography (CT) is essential in diagnosing coronary heart disease. However, a disadvantage is the associated radiation exposure to the patient which depends in part on the scan range. This study aimed to develop a deep neural network to optimize the delimitation of scan ranges in CT localizers to reduce the radiation dose. METHODS On a retrospective training cohort of 1507 CT localizers randomly selected from calcium scoring and angiography scans and acquired between 2010 and 2017, optimized scan ranges were delimited by two radiologists in consensus. A neural network was trained to reproduce the scan ranges and was tested on two randomly selected and independent validation cohorts: an internal cohort of 233 CT localizers (January 2018-June 2020) and an external cohort from a nearby hospital of 298 CT localizers (July 2020-December 2020). Localizers where a bypass surgery was visible were excluded. The effective radiation dose to the patient was simulated using a Monte Carlo simulation. Scan ranges of radiographers, radiologists, and the network were compared using an equivalence test; likewise, the reduction in effective dose was tested using a superior test. RESULTS The network replicated the radiologists' scan ranges with a Dice score of 96.5 ± 0.02 (p < 0.001, indicating equivalence). The generated scan ranges resulted in an effective dose reduction of 10.0% (p = 0.002) in the internal cohort and 12.6% (p < 0.001) in the external cohort compared to the scan ranges delimited by radiographers in clinical routine. CONCLUSIONS Automatic delimitation of the scan range can result in a radiation dose reduction to the patient. CLINICAL RELEVANCE STATEMENT Fully automated delimitation of the scan range using a deep neural network enables a significant reduction in radiation exposure during CT coronary angiography compared to manual examination planning. It can also reduce the workload of the radiographers. KEY POINTS • Scan range delimitation for coronary computed tomography angiography could be performed with high accuracy by a deep neural network. • Automated scan ranges showed a high agreement of 96.5% with the scan ranges of radiologists. • Using a Monte Carlo simulation, automated scan ranges reduced the effective dose to the patient by up to 12.6% (0.9 mSv) compared to the scan ranges of radiographers in clinical routine.
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Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany.
| | - Denise Bos
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany
| | - Ender Demircioğlu
- Department of Thoracic and Cardiovascular Surgery, West German Heart and Vascular Centre Essen, University Hospital Essen, 45147, Essen, Germany
| | - Sahar Qaadan
- Department of Mechatronics and Artificial Intelligence Engineering, German Jordanian University, Madaba, JO-11180, Jordan
| | - Tobias Glasmachers
- Faculty of Computer Science, Ruhr-University Bochum, 44801, Bochum, Germany
| | - Oliver Bruder
- Department of Cardiology and Angiology, Contilia Heart and Vascular Center, Elisabeth-Krankenhaus Essen, 45138, Essen, Germany
| | - Lale Umutlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany
| | - Kai Nassenstein
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany
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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: 10] [Impact Index Per Article: 10.0] [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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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: 9] [Impact Index Per Article: 9.0] [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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Chu B, Gan L, Shen Y, Song J, Liu L, Li J, Liu B. A Deep Learning Image Reconstruction Algorithm for Improving Image Quality and Hepatic Lesion Detectability in Abdominal Dual-Energy Computed Tomography: Preliminary Results. J Digit Imaging 2023; 36:2347-2355. [PMID: 37580484 PMCID: PMC10584787 DOI: 10.1007/s10278-023-00893-y] [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: 06/29/2023] [Revised: 06/29/2023] [Accepted: 07/27/2023] [Indexed: 08/16/2023] Open
Abstract
This study aimed to compare the performance of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) in improving image quality and diagnostic performance using virtual monochromatic spectral images in abdominal dual-energy computed tomography (DECT). Sixty-two patients [mean age ± standard deviation (SD): 56 years ± 13; 30 men] who underwent abdominal DECT were prospectively included in this study. The 70-keV DECT images in the portal phase were reconstructed at 5-mm and 1.25-mm slice thicknesses with 40% ASIR-V (ASIR-V40%) and at 1.25-mm slice with deep learning image reconstruction at medium (DLIR-M) and high (DLIR-H) levels and then compared. Computed tomography (CT) attenuation, SD values, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured in the liver, spleen, erector spinae, and intramuscular fat. The lesions in each reconstruction group at 1.25-mm slice thickness were counted. The image quality and diagnostic confidence were subjectively evaluated by two radiologists using a 5-point scale. For the 1.25-mm images, DLIR-M and DLIR-H had lower SD, higher SNR and CNR, and better subjective image quality compared with ASIR-V40%; DLIR-H performed the best (all P values < 0.001). Furthermore, the 1.25-mm DLIR-H images had similar SD, SNR, and CNR values as the 5-mm ASIR-V40% images (all P > 0.05). Three image groups had similar lesion detection rates, but DLIR groups exhibited higher confidence in diagnosing lesions. Compared with ASIR-V40% at 70 keV, 70-keV DECT with DLIR-H further reduced image noise and improved image quality. Additionally, it improved diagnostic confidence while ensuring a consistent lesion detection rate of liver lesions.
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Affiliation(s)
- Bingqian Chu
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Heifei 230022, People's Republic of China
| | - Lu Gan
- Department of Radiology, Huainan Oriental Guangji Hospital, Huainan 232101, People's Republic of China
| | - Yi Shen
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Heifei 230022, People's Republic of China
| | - Jian Song
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Heifei 230022, People's Republic of China
| | - Ling Liu
- CT Research Center, GE Healthcare China, Shanghai 210000, People's Republic of China
| | - Jianying Li
- CT Research Center, GE Healthcare China, Shanghai 210000, People's Republic of China
| | - Bin Liu
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Heifei 230022, People's Republic of China.
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21
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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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Tatsugami F, Nakaura T, Yanagawa M, Fujita S, Kamagata K, Ito R, Kawamura M, Fushimi Y, Ueda D, Matsui Y, Yamada A, Fujima N, Fujioka T, Nozaki T, Tsuboyama T, Hirata K, Naganawa S. Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction. Diagn Interv Imaging 2023; 104:521-528. [PMID: 37407346 DOI: 10.1016/j.diii.2023.06.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/07/2023]
Abstract
Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis prediction in patients with cardiovascular disease. Deep learning, a type of machine learning, has revolutionized radiology by enabling automatic feature extraction and learning from large datasets, particularly in image-based applications. Thus, AI-driven techniques have enabled a faster analysis of cardiac CT examinations than when they are analyzed by humans, while maintaining reproducibility. However, further research and validation are required to fully assess the diagnostic performance, radiation dose-reduction capabilities, and clinical correctness of these AI-driven techniques in cardiac CT. This review article presents recent advances of AI in the field of cardiac CT, including deep-learning-based image reconstruction, coronary artery motion correction, automatic calcium scoring, automatic epicardial fat measurement, coronary artery stenosis diagnosis, fractional flow reserve prediction, and prognosis prediction, analyzes current limitations of these techniques and discusses future challenges.
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Affiliation(s)
- Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo Chuo-ku, Kumamoto, 860-8556, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Shohei Fujita
- Departmen of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyoku, Kyoto, 606-8507, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital N15, W5, Kita-Ku, Sapporo 060-8638, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-0016, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15 Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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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: 4] [Impact Index Per Article: 4.0] [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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Nishii T, Kobayashi T, Saito T, Kotoku A, Ohta Y, Kitahara S, Umehara K, Ota J, Horinouchi H, Morita Y, Noguchi T, Ishida T, Fukuda T. Deep Learning-based Post Hoc CT Denoising for the Coronary Perivascular Fat Attenuation Index. Acad Radiol 2023; 30:2505-2513. [PMID: 36868878 DOI: 10.1016/j.acra.2023.01.023] [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: 12/08/2022] [Revised: 01/06/2023] [Accepted: 01/17/2023] [Indexed: 03/05/2023]
Abstract
RATIONALE AND OBJECTIVES Coronary inflammation related to high-risk hemorrhagic plaques can be captured by the perivascular fat attenuation index (FAI) using coronary computed tomography angiography (CCTA). Since the FAI is susceptible to image noise, we believe deep learning (DL)-based post hoc noise reduction can improve diagnostic capability. We aimed to assess the diagnostic performance of the FAI in DL-based denoised high-fidelity CCTA images compared with coronary plaque magnetic resonance imaging (MRI) delivered high-intensity hemorrhagic plaques (HIPs). MATERIALS AND METHODS We retrospectively reviewed 43 patients who underwent CCTA and coronary plaque MRI. We generated high-fidelity CCTA images by denoising the standard CCTA images using a residual dense network that supervised the denoising task by averaging three cardiac phases with nonrigid registration. We measured the FAIs as the mean CT value of all voxels (range of -190 to -30 HU) located within a radial distance from the outer proximal right coronary artery wall. The diagnostic reference standard was defined as HIPs (high-risk hemorrhagic plaques) using MRI. The diagnostic performance of the FAI in the original and denoised images was assessed using receiver operating characteristic curves. RESULTS Of 43 patients, 13 had HIPs. The denoised CCTA improved the area under the curve (0.89 [95% confidence interval (CI) 0.78-0.99]) of the FAI compared with that in the original image (0.77 [95% CI, 0.62-0.91], p = 0.008). The optimal cutoff value for predicting HIPs in denoised CCTA was -69 HU with 0.85 (11/13) sensitivity, 0.79 (25/30) specificity, and 0.80 (36/43) accuracy. CONCLUSION DL-based denoised high-fidelity CCTA improved the AUC and specificity of the FAI for predicting HIPs.
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Affiliation(s)
- Tatsuya Nishii
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan.
| | - Takuma Kobayashi
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan; Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Tatsuya Saito
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Akiyuki Kotoku
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Yasutoshi Ohta
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Satoshi Kitahara
- Department of Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Kensuke Umehara
- Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, Suita, Japan; Medical Informatics Section, QST Hospital, National Institutes for Quantum Science and Technology, Inage-ku, Chiba, Japan; Applied MRI Research, Department of Molecular Imaging and Theranostics, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, Inage-ku, Chiba, Japan
| | - Junko Ota
- Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, Suita, Japan; Medical Informatics Section, QST Hospital, National Institutes for Quantum Science and Technology, Inage-ku, Chiba, Japan; Applied MRI Research, Department of Molecular Imaging and Theranostics, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, Inage-ku, Chiba, Japan
| | - Hiroki Horinouchi
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Yoshiaki Morita
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Teruo Noguchi
- Department of Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Takayuki Ishida
- Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Tetsuya Fukuda
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
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25
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Ren J, Zhao J, Wang Y, Xu M, Liu XY, Jin ZY, He YL, Li Y, Xue HD. Value of deep-learning image reconstruction at submillisievert CT for evaluation of the female pelvis. Clin Radiol 2023; 78:e881-e888. [PMID: 37620170 DOI: 10.1016/j.crad.2023.07.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/26/2023] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
AIM To assess the value of deep-learning reconstruction (DLR) at submillisievert computed tomography (CT) for the evaluation of the female pelvis, with standard dose (SD) hybrid iterative reconstruction (IR) images as reference. MATERIALS AND METHODS The present study enrolled 50 female patients consecutively who underwent contrast-enhanced abdominopelvic CT for clinically indicated reasons. Submillisievert pelvic images were acquired using a noise index of 15 for low-dose (LD) scans, which were reconstructed with DLR (body and body sharp), hybrid-IR, and model-based IR (MBIR). Additionally, SD scans were reconstructed with a noise index of 7.5 using hybrid-IR. Radiation dose, quantitative image quality, overall image quality, image appearance using a five-point Likert scale (1-5: worst to best), and lesion evaluation in both SD and LD images were analysed and compared. RESULTS The submillisievert pelvic CT examinations showed a 61.09 ± 4.13% reduction in the CT dose index volume compared to SD examinations. Among the LD images, DLR (body sharp) had the highest quantitative quality, followed by DLR (body), MBIR, and hybrid-IR. LD DLR (body) had overall image quality comparable to the reference (p=0.084) and favourable image appearance (p=0.209). In total, 40 pelvic lesions were detected in both SD and LD images. LD DLR (body and body sharp) exhibited similar diagnostic confidence (p=0.317 and 0.096) compared with SD hybrid-IR. CONCLUSION DLR algorithms, providing comparable image quality and diagnostic confidence, are feasible in submillisievert abdominopelvic CT. The DLR (body) algorithm with favourable image appearance is recommended in clinical settings.
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Affiliation(s)
- J Ren
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
| | - J Zhao
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
| | - Y Wang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
| | - M Xu
- Cannon Medical System, Beijing, PR China
| | - X-Y Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
| | - Z-Y Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China
| | - Y-L He
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
| | - Y Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing, PR China.
| | - H-D Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
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Yao X, Zhong S, Xu M, Zhang G, Yuan Y, Shuai T, Li Z. Deep learning-based motion correction algorithm for coronary CT angiography: Lowering the phase requirement for morphological and functional evaluation. J Appl Clin Med Phys 2023; 24:e14104. [PMID: 37485892 PMCID: PMC10476979 DOI: 10.1002/acm2.14104] [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: 03/13/2023] [Revised: 06/27/2023] [Accepted: 07/08/2023] [Indexed: 07/25/2023] Open
Abstract
PURPOSE To investigate the performance of a deep learning-based motion correction algorithm (MCA) at various cardiac phases of coronary computed tomography angiography (CCTA), and determine the extent to which it may allow for reliable morphological and functional evaluation. MATERIALS AND METHODS The acquired image data of 53 CCTA cases, where the patient heart rate (HR) was ≥75 bpm, were reconstructed at 0, ±2, ±4, ±6, and ±8% deviations from each optimal systolic phase, with and without the MCA, yielding a total of 954 images (53 cases × 9 phases × 2 reconstructions). The overall image quality and diagnostic confidence were graded by two radiologists using a 5-point scale, with scores ≥3 being deemed clinically interpretable. Signal-to-noise ratio, contrast-to-noise ratio, vessel sharpness, and circularity were measured. The CCTA-derived fractional flow reserve (CT-FFR) was calculated in 38 vessels on 24 patients to identify functionally significant stenosis, using the invasive fractional flow reserve (FFR) as reference. All metrics were compared between two reconstructions at various phases. RESULTS Inferior image quality was observed as the phase deviation was enlarged. However, MCA significantly improved the image quality at nonoptimal phases and the optimal phase. Coronary artery evaluation was feasible within 4% phase deviation using MCA, with interpretable overall image quality and high diagnostic confidence. With MCA, the performance of identifying functionally significant stenosis via CT-FFR was increased for images at various phase deviations. However, obvious decrease in accuracy, as compared to the image at the optimal phase, was found on those with deviations >4%. CONCLUSION The deep learning-based MCA allows up to 4% phase deviation in acquiring CCTA for reliable morphological and functional evaluation on patients with high HRs.
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Affiliation(s)
- Xiaoling Yao
- Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
| | | | - Maolan Xu
- Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
| | | | - Yuan Yuan
- Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
| | - Tao Shuai
- Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
| | - Zhenlin Li
- Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
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Nagata M, Ichikawa Y, Domae K, Yoshikawa K, Kanii Y, Yamazaki A, Nagasawa N, Ishida M, Sakuma H. Application of Deep Learning-Based Denoising Technique for Radiation Dose Reduction in Dynamic Abdominal CT: Comparison with Standard-Dose CT Using Hybrid Iterative Reconstruction Method. J Digit Imaging 2023; 36:1578-1587. [PMID: 36944812 PMCID: PMC10406991 DOI: 10.1007/s10278-023-00808-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: 12/15/2022] [Revised: 03/08/2023] [Accepted: 03/08/2023] [Indexed: 03/23/2023] Open
Abstract
The purpose is to evaluate whether deep learning-based denoising (DLD) algorithm provides sufficient image quality for abdominal computed tomography (CT) with a 30% reduction in radiation dose, compared to standard-dose CT reconstructed with conventional hybrid iterative reconstruction (IR). The subjects consisted of 50 patients who underwent abdominal CT with standard dose and reconstructed with hybrid IR (ASiR-V50%) and another 50 patients who underwent abdominal CT with approximately 30% less dose and reconstructed with ASiR-V50% and DLD at low-, medium- and high-strength (DLD-L, DLD-M and DLD-H, respectively). The standard deviation of attenuation in liver parenchyma was measured as image noise. Contrast-to-noise ratio (CNR) for portal vein on portal venous phase was calculated. Lesion conspicuity in 23 abdominal solid mass on the reduced-dose CT was rated on a 5-point scale: 0 (best) to -4 (markedly inferior). Compared with hybrid IR of standard-dose CT, DLD-H of reduced-dose CT provided significantly lower image noise (portal phase: 9.0 (interquartile range, 8.7-9.4) HU vs 12.0 (11.4-12.7) HU, P < 0.0001) and significantly higher CNR (median, 5.8 (4.4-7.4) vs 4.3 (3.3-5.3), P = 0.0019). As for DLD-M of reduced-dose CT, no significant difference was found in image noise and CNR compared to hybrid IR of standard-dose CT (P > 0.99). Lesion conspicuity scores for DLD-H and DLD-M were significantly better than hybrid IR (P < 0.05). Dynamic contrast-enhanced abdominal CT acquired with approximately 30% lower radiation dose and generated with the DLD algorithm exhibit lower image noise and higher CNR compared to standard-dose CT with hybrid IR.
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Affiliation(s)
- Motonori Nagata
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Yasutaka Ichikawa
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Kensuke Domae
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Kazuya Yoshikawa
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Yoshinori Kanii
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Akio Yamazaki
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Naoki Nagasawa
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Masaki Ishida
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Hajime Sakuma
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
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Hirairi T, Ichikawa K, Urikura A, Kawashima H, Tabata T, Matsunami T. Improvement of diagnostic performance of hyperacute ischemic stroke in head CT using an image-based noise reduction technique with non-black-boxed process. Phys Med 2023; 112:102646. [PMID: 37549457 DOI: 10.1016/j.ejmp.2023.102646] [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: 01/23/2023] [Revised: 06/05/2023] [Accepted: 07/28/2023] [Indexed: 08/09/2023] Open
Abstract
PURPOSE This study aims to investigate whether an image-based noise reduction (INR) technique with a conventional rule-based algorithm involving no black-boxed processes can outperform an existing hybrid-type iterative reconstruction (HIR) technique, when applied to brain CT images for diagnosis of early CT signs, which generally exhibit low-contrast lesions that are difficult to detect. METHODS The subjects comprised 27 patients having infarctions within 4.5 h of onset and 27 patients with no change in brain parenchyma. Images with thicknesses of 5 mm and 0.625 mm were reconstructed by HIR. Images with a thickness of 0.625 mm reconstructed by filter back projection (FBP) were processed by INR. The contrast-to-noise ratios (CNRs) were calculated between gray and white matters; lentiform nucleus and internal capsule; infarcted and non-infarcted areas. Two radiologists subjectively evaluated the presence of hyperdense artery signs (HASs) and infarctions and visually scored three properties regarding image quality (0.625-mm HIR images were excluded because of their notably worse noise appearances). RESULTS The CNRs of INR were significantly better than those of HIR with P < 0.001 for all the indicators. INR yielded significantly higher areas under the curve for both infarction and HAS detections than HIR (P < 0.001). Also, INR significantly improved the visual scores of all the three indicators. CONCLUSION The INR incorporating a simple and reproducible algorithm was more effective than HIR in detecting early CT signs and can be potentially applied to CT images from a large variety of CT systems.
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Affiliation(s)
- Tetsuya Hirairi
- Department of Radiological Technology, Juntendo University Shizuoka Hospital, 1129 Nagaoka, Izunokuni, Shizuoka, 410-2295, Japan.
| | - Katsuhiro Ichikawa
- Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa, 920-0942, Japan.
| | - Atsushi Urikura
- Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuuouku, Tokyo, 104-0045, Japan.
| | - Hiroki Kawashima
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa 920-0942, Japan.
| | - Takasumi Tabata
- Department of Radiology, Juntendo University Shizuoka Hospital, 1129 Nagaoka, Izunokuni, Shizuoka, 410-2295, Japan.
| | - Tamaki Matsunami
- Department of Radiology, Juntendo University Shizuoka Hospital, 1129 Nagaoka, Izunokuni, Shizuoka, 410-2295, Japan.
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Varga-Szemes A, Maurovich-Horvat P, Schoepf UJ, Zsarnoczay E, Pelberg R, Stone GW, Budoff MJ. Computed Tomography Assessment of Coronary Atherosclerosis: From Threshold-Based Evaluation to Histologically Validated Plaque Quantification. J Thorac Imaging 2023; 38:226-234. [PMID: 37115957 PMCID: PMC10287054 DOI: 10.1097/rti.0000000000000711] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Arterial plaque rupture and thrombosis is the primary cause of major cardiovascular and neurovascular events. The identification of atherosclerosis, especially high-risk plaques, is therefore crucial to identify high-risk patients and to implement preventive therapies. Computed tomography angiography has the ability to visualize and characterize vascular plaques. The standard methods for plaque evaluation rely on the assessment of plaque burden, stenosis severity, the presence of positive remodeling, napkin ring sign, and spotty calcification, as well as Hounsfield Unit (HU)-based thresholding for plaque quantification; the latter with multiple shortcomings. Semiautomated threshold-based segmentation techniques with predefined HU ranges identify and quantify limited plaque characteristics, such as low attenuation, non-calcified, and calcified plaque components. Contrary to HU-based thresholds, histologically validated plaque characterization, and quantification, an emerging Artificial intelligence-based approach has the ability to differentiate specific tissue types based on a biological correlate, such as lipid-rich necrotic core and intraplaque hemorrhage that determine plaque vulnerability. In this article, we review the relevance of plaque characterization and quantification and discuss the benefits and limitations of the currently available plaque assessment and classification techniques.
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Affiliation(s)
- Akos Varga-Szemes
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC
| | - Pal Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - U. Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC
| | - Emese Zsarnoczay
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC
- MTA-SE Cardiovascular Imaging Research Group, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Robert Pelberg
- Heart and Vascular Institute at The Christ Hospital Health Network, Cincinnati, OH
| | - Gregg W. Stone
- Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Matthew J. Budoff
- Department of Medicine, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA
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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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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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Nikolau EP, Toia GV, Nett B, Tang J, Szczykutowicz TP. A Characterization of Deep Learning Reconstruction Applied to Dual-Energy Computed Tomography Monochromatic and Material Basis Images. J Comput Assist Tomogr 2023; 47:437-444. [PMID: 36944100 DOI: 10.1097/rct.0000000000001442] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
OBJECTIVE Advancements in computed tomography (CT) reconstruction have enabled image quality improvements and dose reductions. Previous advancements have included iterative and model-based reconstruction. The latest image reconstruction advancement uses deep learning, which has been evaluated for polychromatic imaging only. This article characterizes a commercially available deep learning imaging reconstruction applied to dual-energy CT. METHODS Monochromatic, iodine basis, and water basis images were reconstructed with filtered back projection (FBP), iterative (ASiR-V), and deep learning (DLIR) methods in a phantom experiment. Slice thickness, contrast-to-noise ratio, modulation transfer function, and noise power spectrum metrics were used to characterize ASiR-V and DLIR relative to FBP over a range of dose levels, phantom sizes, and iodine concentrations. RESULTS Slice thicknesses for ASiR-V and DLIR demonstrated no statistically significant difference relative to FBP for all measurement conditions. Contrast-to-noise ratio performance for DLIR-high and ASiR-V 40% at 2 mg I/mL on 40-keV images were 162% and 30% higher than FBP, respectively. Task-based modulation transfer function measurements demonstrated no clinically significant change between FBP and ASiR-V and DLIR on monochromatic or iodine basis images. CONCLUSIONS Deep learning image reconstruction enabled better image quality at lower monochromatic energies and on iodine basis images where image contrast is maximized relative to polychromatic or high-energy monochromatic images. Deep learning image reconstruction did not demonstrate thicker slices, decreased spatial resolution, or poor noise texture (ie, "plastic") relative to FBP.
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Affiliation(s)
| | - Giuseppe V Toia
- Radiology University of Wisconsin Madison School of Medicine and Public Health
| | - Brian Nett
- GE Healthcare, Waukesha Wisconsin, Waukesha; and
| | - Jie Tang
- GE Healthcare, Waukesha Wisconsin, Waukesha; and
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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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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: 58] [Impact Index Per Article: 58.0] [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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Ahmed Z, Campeau D, Gong H, Rajendran K, Rajiah P, McCollough C, Leng S. High-pitch, high temporal resolution, multi-energy cardiac imaging on a dual-source photon-counting-detector CT. Med Phys 2023; 50:1428-1435. [PMID: 36427356 PMCID: PMC10033375 DOI: 10.1002/mp.16124] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 11/11/2022] [Accepted: 11/12/2022] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE To measure the accuracy of material decomposition using a dual-source photon-counting-detector (DS-PCD) CT operated in the high-pitch helical scanning mode and compare the results against dual-source energy-integrating-detector (DS-EID) CT, which requires use of a low-pitch value in dual-energy mode. METHODS A DS-PCD CT and a DS-EID CT were used to scan a cardiac motion phantom consisting of a 3-mm diameter iodine cylinder. Iodine maps were reconstructed using DS-PCD in high-pitch mode and DS-EID in low-pitch mode. Image-based circularity, diameter, and iodine concentration of the iodine cylinder were calculated and compared between the two scanners. With institutional review board approval, in vivo exams were performed with the DS-PCD CT in high-pitch mode. Images were qualitatively compared against patients with similar heart rates that were scanned with DS-EID CT in low-pitch dual-energy mode. RESULTS On iodine maps, the mean circularity was 0.97 ± 0.02 with DS-PCD in high-pitch mode and 0.95 ± 0.06 with DS-EID in low-pitch mode. The mean diameter was 2.9 ± 0.2 mm with DS-PCD and 3.1 ± 0.2 mm with DS-EID, both of which are close to the 3 mm ground truth. For DS-PCD, the mean iodine concentration was 9.6 ± 0.8 mg/ml and this was consistent with the 9.4 ± 0.6 mg/ml value obtained with the cardiac motion disabled. For DS-EID, the concentration was 12.7 ± 1.2 mg/ml with motion enabled and 11.7 ± 0.5 mg/ml disabled. The background noise in the iodine maps was 15.1 HU with DS-PCD and 14.4 HU with DS-EID, whereas the volume CT dose index (CTDIvol ) was 3 mGy with DS-PCD and 11 mGy with DS-EID. On comparison of six patients (three on PCD, three on EID) with similar heart rates, DS-PCD provided iodine maps with well-defined coronaries even at a high heart rate of 86 beats per minute. Meanwhile, there were substantial motion artifacts in iodine maps obtained with DS-EID for patients with similar heart rates. CONCLUSION In a cardiac motion phantom, DS-PCD CT can perform accurate material decomposition in high-pitch mode, providing iodine maps with excellent geometric accuracy and robustness to motion at approximately 38% of the dose for similar noise as DS-EID CT.
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Affiliation(s)
- Zaki Ahmed
- Department of Radiology, Mayo Clinic, Rochester, MN
| | - David Campeau
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN
| | - Hao Gong
- Department of Radiology, Mayo Clinic, Rochester, MN
| | | | | | | | - Shuai Leng
- Department of Radiology, Mayo Clinic, Rochester, MN
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Yang W, Chen C, Yang Y, Chen L, Yang C, Gong L, Wang J, Shi F, Wu D, Yan F. Diagnostic performance of deep learning-based vessel extraction and stenosis detection on coronary computed tomography angiography for coronary artery disease: a multi-reader multi-case study. LA RADIOLOGIA MEDICA 2023; 128:307-315. [PMID: 36800112 DOI: 10.1007/s11547-023-01606-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 02/03/2023] [Indexed: 02/18/2023]
Abstract
BACKGROUND Post-processing and interpretation of coronary CT angiography (CCTA) imaging are time-consuming and dependent on the reader's experience. An automated deep learning (DL)-based imaging reconstruction and diagnosis system was developed to improve diagnostic accuracy and efficiency. METHODS Our study including 374 cases from five sites, inviting 12 radiologists, assessed the DL-based system in diagnosing obstructive coronary disease with regard to diagnostic performance, imaging post-processing and reporting time of radiologists, with invasive coronary angiography as a standard reference. The diagnostic performance of DL system and DL-assisted human readers was compared with the traditional method of human readers without DL system. RESULTS Comparing the diagnostic performance of human readers without DL system versus with DL system, the AUC was improved from 0.81 to 0.82 (p < 0.05) at patient level and from 0.79 to 0.81 (p < 0.05) at vessel level. An increase in AUC was observed in inexperienced radiologists (p < 0.05), but was absent in experienced radiologists. Regarding diagnostic efficiency, comparing the DL system versus human reader, the average post-processing and reporting time was decreased from 798.60 s to 189.12 s (p < 0.05). The sensitivity and specificity of using DL system alone were 93.55% and 59.57% at patient level and 83.23% and 79.97% at vessel level, respectively. CONCLUSIONS With the DL system serving as a concurrent reader, the overall post-processing and reading time was substantially reduced. The diagnostic accuracy of human readers, especially for inexperienced readers, was improved. DL-assisted human reader had the potential of being the reading mode of choice in clinical routine.
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Affiliation(s)
- Wenjie Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chihua Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yanzhao Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lei Chen
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Changwei Yang
- Department of Radiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Lianggeng Gong
- Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jianing Wang
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
| | - Feng Shi
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Dijia Wu
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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De Santis D, Polidori T, Tremamunno G, Rucci C, Piccinni G, Zerunian M, Pugliese L, Del Gaudio A, Guido G, Barbato L, Laghi A, Caruso D. Deep learning image reconstruction algorithm: impact on image quality in coronary computed tomography angiography. LA RADIOLOGIA MEDICA 2023; 128:434-444. [PMID: 36847992 PMCID: PMC10119038 DOI: 10.1007/s11547-023-01607-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 02/03/2023] [Indexed: 03/01/2023]
Abstract
PURPOSE To perform a comprehensive intraindividual objective and subjective image quality evaluation of coronary CT angiography (CCTA) reconstructed with deep learning image reconstruction (DLIR) and to assess correlation with routinely applied hybrid iterative reconstruction algorithm (ASiR-V). MATERIAL AND METHODS Fifty-one patients (29 males) undergoing clinically indicated CCTA from April to December 2021 were prospectively enrolled. Fourteen datasets were reconstructed for each patient: three DLIR strength levels (DLIR_L, DLIR_M, and DLIR_H), ASiR-V from 10% to 100% in 10%-increment, and filtered back-projection (FBP). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) determined objective image quality. Subjective image quality was assessed with a 4-point Likert scale. Concordance between reconstruction algorithms was assessed by Pearson correlation coefficient. RESULTS DLIR algorithm did not impact vascular attenuation (P ≥ 0.374). DLIR_H showed the lowest noise, comparable with ASiR-V 100% (P = 1) and significantly lower than other reconstructions (P ≤ 0.021). DLIR_H achieved the highest objective quality, with SNR and CNR comparable to ASiR-V 100% (P = 0.139 and 0.075, respectively). DLIR_M obtained comparable objective image quality with ASiR-V 80% and 90% (P ≥ 0.281), while achieved the highest subjective image quality (4, IQR: 4-4; P ≤ 0.001). DLIR and ASiR-V datasets returned a very strong correlation in the assessment of CAD (r = 0.874, P = 0.001). CONCLUSION DLIR_M significantly improves CCTA image quality and has very strong correlation with routinely applied ASiR-V 50% dataset in the diagnosis of CAD.
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Affiliation(s)
- Domenico De Santis
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Tiziano Polidori
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Giuseppe Tremamunno
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Carlotta Rucci
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Giulia Piccinni
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Marta Zerunian
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Luca Pugliese
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Antonella Del Gaudio
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Gisella Guido
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Luca Barbato
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Andrea Laghi
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
| | - Damiano Caruso
- Radiology Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
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Yang L, Liu H, Han J, Xu S, Zhang G, Wang Q, Du Y, Yang F, Zhao X, Shi G. Ultra-low-dose CT lung screening with artificial intelligence iterative reconstruction: evaluation via automatic nodule-detection software. Clin Radiol 2023:S0009-9260(23)00031-4. [PMID: 36948944 DOI: 10.1016/j.crad.2023.01.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 01/04/2023] [Accepted: 01/15/2023] [Indexed: 02/05/2023]
Abstract
AIM To test the feasibility of ultra-low-dose (ULD) computed tomography (CT) combined with an artificial intelligence iterative reconstruction (AIIR) algorithm for screening pulmonary nodules using computer-assisted diagnosis (CAD). MATERIALS AND METHODS A chest phantom with artificial pulmonary nodules was first scanned using the routine protocol and the ULD protocol (3.28 versus 0.18 mSv) to compare the image quality and to test the acceptability of the ULD CT protocol. Next, 147 lung-screening patients were enrolled prospectively, undergoing an additional ULD CT immediately after their routine CT examination for clinical validation. Images were reconstructed with filtered back-projection (FBP), hybrid iterative reconstruction (HIR), the AIIR, and were imported to the CAD software for preliminary nodule detection. Subjective image quality on the phantom was scored using a five-point scale and compared using the Mann-Whitney U-test. Nodule detection using CAD was evaluated for ULD HIR and AIIR images using the routine dose image as reference. RESULTS Higher image quality was scored for AIIR than for FBP and HIR at ULD (p<0.001). As reported by CAD, 107 patients were presented with more than five nodules on routine dose images and were chosen to represent the challenging cases at an early stage of pulmonary disease. Among such, the performance of nodule detection by CAD on ULD HIR and AIIR images was 75.2% and 92.2% of the routine dose image, respectively. CONCLUSION Combined with AIIR, it was feasible to use an ULD CT protocol with 95% dose reduction for CAD-based screening of pulmonary nodules.
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Affiliation(s)
- L Yang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - H Liu
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - J Han
- United Imaging Healthcare, Shanghai, China
| | - S Xu
- United Imaging Healthcare, Shanghai, China
| | - G Zhang
- United Imaging Healthcare, Shanghai, China
| | - Q Wang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Y Du
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - F Yang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - X Zhao
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - G Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
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Chu M, Wu P, Li G, Yang W, Gutiérrez-Chico JL, Tu S. Advances in Diagnosis, Therapy, and Prognosis of Coronary Artery Disease Powered by Deep Learning Algorithms. JACC. ASIA 2023; 3:1-14. [PMID: 36873752 PMCID: PMC9982227 DOI: 10.1016/j.jacasi.2022.12.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/29/2022] [Accepted: 12/01/2022] [Indexed: 02/17/2023]
Abstract
Percutaneous coronary intervention has been a standard treatment strategy for patients with coronary artery disease with continuous ebullient progress in technology and techniques. The application of artificial intelligence and deep learning in particular is currently boosting the development of interventional solutions, improving the efficiency and objectivity of diagnosis and treatment. The ever-growing amount of data and computing power together with cutting-edge algorithms pave the way for the integration of deep learning into clinical practice, which has revolutionized the interventional workflow in imaging processing, interpretation, and navigation. This review discusses the development of deep learning algorithms and their corresponding evaluation metrics together with their clinical applications. Advanced deep learning algorithms create new opportunities for precise diagnosis and tailored treatment with a high degree of automation, reduced radiation, and enhanced risk stratification. Generalization, interpretability, and regulatory issues are remaining challenges that need to be addressed through joint efforts from multidisciplinary community.
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Affiliation(s)
- Miao Chu
- Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Peng Wu
- Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Guanyu Li
- Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark
| | - Wei Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | | | - Shengxian Tu
- Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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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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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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Zhou Z, Gao Y, Zhang W, Bo K, Zhang N, Wang H, Wang R, Du Z, Firmin D, Yang G, Zhang H, Xu L. Artificial intelligence-based full aortic CT angiography imaging with ultra-low-dose contrast medium: a preliminary study. Eur Radiol 2023; 33:678-689. [PMID: 35788754 DOI: 10.1007/s00330-022-08975-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 05/16/2022] [Accepted: 06/20/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To further reduce the contrast medium (CM) dose of full aortic CT angiography (ACTA) imaging using the augmented cycle-consistent adversarial framework (Au-CycleGAN) algorithm. METHODS We prospectively enrolled 150 consecutive patients with suspected aortic disease. All received ACTA scans of ultra-low-dose CM (ULDCM) protocol and low-dose CM (LDCM) protocol. These data were randomly assigned to the training datasets (n = 100) and the validation datasets (n = 50). The ULDCM images were reconstructed by the Au-CycleGAN algorithm. Then, the AI-based ULDCM images were compared with LDCM images in terms of image quality and diagnostic accuracy. RESULTS The mean image quality score of each location in the AI-based ULDCM group was higher than that in the ULDCM group but a little lower than that in the LDCM group (all p < 0.05). All AI-based ULDCM images met the diagnostic requirements (score ≥ 3). Except for the image noise, the AI-based ULDCM images had higher attenuation value than the ULDCM and LDCM images as well as higher SNR and CNR in all locations of the aorta analyzed (all p < 0.05). Similar results were also seen in obese patients (BMI > 25, all p < 0.05). Using the findings of LDCM images as the reference, the AI-based ULDCM images showed good diagnostic parameters and no significant differences in any of the analyzed aortic disease diagnoses (all K-values > 0.80, p < 0.05). CONCLUSIONS The required dose of CM for full ACTA imaging can be reduced to one-third of the CM dose of the LDCM protocol while maintaining image quality and diagnostic accuracy using the Au-CycleGAN algorithm. KEY POINTS • The required dose of contrast medium (CM) for full ACTA imaging can be reduced to one-third of the CM dose of the low-dose contrast medium (LDCM) protocol using the Au-CycleGAN algorithm. • Except for the image noise, the AI-based ultra-low-dose contrast medium (ULDCM) images had better quantitative image quality parameters than the ULDCM and LDCM images. • No significant diagnostic differences were noted between the AI-based ULDCM and LDCM images regarding all the analyzed aortic disease diagnoses.
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Affiliation(s)
- Zhen Zhou
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Yifeng Gao
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Weiwei Zhang
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Kairui Bo
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Nan Zhang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Hui Wang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Rui Wang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Zhiqiang Du
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - David Firmin
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK.,National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK.,National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Heye Zhang
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Lei Xu
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China.
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Lee N, Cho HH, Lee SM, You SK. Adaptation of Deep Learning Image Reconstruction for Pediatric Head CT: A Focus on the Image Quality. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2023; 84:240-252. [PMID: 36818715 PMCID: PMC9935960 DOI: 10.3348/jksr.2021.0073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 08/06/2021] [Accepted: 05/27/2022] [Indexed: 11/18/2022]
Abstract
Purpose To assess the effect of deep learning image reconstruction (DLIR) for head CT in pediatric patients. Materials and Methods We collected 126 pediatric head CT images, which were reconstructed using filtered back projection, iterative reconstruction using adaptive statistical iterative reconstruction (ASiR)-V, and all three levels of DLIR (TrueFidelity; GE Healthcare). Each image set group was divided into four subgroups according to the patients' ages. Clinical and dose-related data were reviewed. Quantitative parameters, including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and qualitative parameters, including noise, gray matter-white matter (GM-WM) differentiation, sharpness, artifact, acceptability, and unfamiliar texture change were evaluated and compared. Results The SNR and CNR of each level in each age group increased among strength levels of DLIR. High-level DLIR showed a significantly improved SNR and CNR (p < 0.05). Sequential reduction of noise, improvement of GM-WM differentiation, and improvement of sharpness was noted among strength levels of DLIR. Those of high-level DLIR showed a similar value as that with ASiR-V. Artifact and acceptability did not show a significant difference among the adapted levels of DLIR. Conclusion Adaptation of high-level DLIR for the pediatric head CT can significantly reduce image noise. Modification is needed while processing artifacts.
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Affiliation(s)
- Nim Lee
- Department of Radiology, Medical Research Institute, College of Medicine, Ewha Womans University Mokdong Hospital, Seoul, Korea
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University, College of Medicine, Seoul, Korea
| | - Hyun-Hae Cho
- Department of Radiology, Medical Research Institute, College of Medicine, Ewha Womans University Mokdong Hospital, Seoul, Korea
| | - So Mi Lee
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Korea
| | - Sun Kyoung You
- Department of Radiology, Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon, Korea
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Impact of deep learning image reconstructions (DLIR) on coronary artery calcium quantification. Eur Radiol 2022; 33:3832-3838. [PMID: 36480026 PMCID: PMC10181951 DOI: 10.1007/s00330-022-09287-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 08/03/2022] [Accepted: 11/06/2022] [Indexed: 12/13/2022]
Abstract
Abstract
Background
Deep learning image reconstructions (DLIR) have been recently introduced as an alternative to filtered back projection (FBP) and iterative reconstruction (IR) algorithms for computed tomography (CT) image reconstruction. The aim of this study was to evaluate the effect of DLIR on image quality and quantification of coronary artery calcium (CAC) in comparison to FBP.
Methods
One hundred patients were consecutively enrolled. Image quality–associated variables (noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)) as well as CAC-derived parameters (Agatston score, mass, and volume) were calculated from images reconstructed by using FBP and three different strengths of DLIR (low (DLIR_L), medium (DLIR_M), and high (DLIR_H)). Patients were stratified into 4 risk categories according to the Coronary Artery Calcium - Data and Reporting System (CAC-DRS) classification: 0 Agatston score (very low risk), 1–99 Agatston score (mildly increased risk), Agatston 100–299 (moderately increased risk), and ≥ 300 Agatston score (moderately-to-severely increased risk).
Results
In comparison to standard FBP, increasing strength of DLIR was associated with a significant and progressive decrease of image noise (p < 0.001) alongside a significant and progressive increase of both SNR and CNR (p < 0.001). The use of incremental levels of DLIR was associated with a significant decrease of Agatston CAC score and CAC volume (p < 0.001), while mass score remained unchanged when compared to FBP (p = 0.232). The underestimation of Agatston CAC led to a CAC-DRS misclassification rate of 8%.
Conclusion
DLIR systematically underestimates Agatston CAC score. Therefore, DLIR should be used cautiously for cardiovascular risk assessment.
Key Points
• In coronary artery calcium imaging, the implementation of deep learning image reconstructions improves image quality, by decreasing the level of image noise.
• Deep learning image reconstructions systematically underestimate Agatston coronary artery calcium score.
• Deep learning image reconstructions should be used cautiously in clinical routine to measure Agatston coronary artery calcium score for cardiovascular risk assessment.
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Jiang C, Jin D, Liu Z, Zhang Y, Ni M, Yuan H. Deep learning image reconstruction algorithm for carotid dual-energy computed tomography angiography: evaluation of image quality and diagnostic performance. Insights Imaging 2022; 13:182. [DOI: 10.1186/s13244-022-01308-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 09/24/2022] [Indexed: 11/28/2022] Open
Abstract
Abstract
Objectives
To evaluate image quality and diagnostic performance of carotid dual-energy computed tomography angiography (DECTA) using deep learning image reconstruction (DLIR) compared with images using adaptive statistical iterative reconstruction-Veo (ASIR-V).
Methods
Carotid DECTA datasets of 28 consecutive patients were reconstructed at 50 keV using DLIR at low, medium, and high levels (DLIR-L, DLIR-M, and DLIR-H) and 80% ASIR-V algorithms. Mean attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) at different levels of arteries were measured and calculated. Image quality for noise and texture, depiction of arteries, and diagnostic performance toward carotid plaques were assessed subjectively by two radiologists. Quantitative and qualitative parameters were compared between the ASIR-V, DLIR-L, DLIR-M, and DLIR-H groups.
Results
The image noise at aorta and common carotid artery, SNR, and CNR at all level arteries of DLIR-H images were significantly higher than those of ASIR-V images (p = 0.000–0.040). The quantitative analysis of DLIR-L and DLIR-M showed comparable denoise capability with ASIR-V. The overall image quality (p = 0.000) and image noise (p = 0.000–0.014) were significantly better in the DLIR-M and DLIR-H images. The image texture was improved by DLR at all level compared to ASIR-V images (p = 0.000–0.008). Depictions of head and neck arteries and diagnostic performance were comparable between four groups (p > 0.05).
Conclusions
Compared with 80% ASIR-V, we recommend DLIR-H for clinical carotid DECTA reconstruction, which can significantly improve the image quality of carotid DECTA at 50 keV but maintain a desirable diagnostic performance and arterial depiction.
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Xu C, Xu M, Yan J, Li YY, Yi Y, Guo YB, Wang M, Li YM, Jin ZY, Wang YN. The impact of deep learning reconstruction on image quality and coronary CT angiography-derived fractional flow reserve values. Eur Radiol 2022; 32:7918-7926. [PMID: 35596780 DOI: 10.1007/s00330-022-08796-2] [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: 09/22/2021] [Revised: 03/20/2022] [Accepted: 04/03/2022] [Indexed: 01/03/2023]
Abstract
OBJECTIVES To explore the impact of deep learning reconstruction (DLR) on image quality and machine learning-based coronary CT angiography (CTA)-derived fractional flow reserve (CT-FFRML) values. METHODS Thirty-three consecutive patients with known or suspected coronary artery disease who underwent coronary CTA and subsequent invasive coronary angiography were enrolled. DLR was compared with filtered back projection (FBP), statistical-based iterative reconstruction (SBIR), model-based iterative reconstruction (MBIR) Cardiac, and MBIR Cardiac sharp for objective image qualities of coronary CTA. Invasive fractional flow reserve (FFR) and quantitative flow ratio (QFR) were used as the reference standards. The diagnostic performances of different reconstruction approach-based CT-FFRML were calculated. RESULTS A total of 182 lesions in 33 patients were enrolled for analysis. The image quality of DLR was superior to the others. There were no significant differences in the CT-FFRML values among these five approaches (all p > 0.05). Of the 182 lesions, 17 had invasive FFR results, and 70 had QFR results. Using FFR as a reference, MBIR Cardiac, MBIR Cardiac sharp, and DLR achieved equal diagnostic performance, slightly higher than the other reconstruction approaches (MBIR Cardiac, MBIR Cardiac sharp, and DLR: AUC = 0.82, FBP and AIDR: AUC = 0.78, all p > 0.05). Using QFR as a reference, the AUCs of FBP, SBIR, MBIR Cardiac, MBIR Cardiac sharp, and DLR were 0.83, 0.81, 0.86, 0.84, and 0.83, respectively (all p > 0.05). CONCLUSIONS Our study showed that the DLR algorithm improved image quality, but there were no significant differences in the CT-FFRML values and diagnostic performance among different reconstruction approaches. KEY POINTS • Deep learning-based image reconstruction (DLR) improves the image quality of coronary CTA. • CT-FFRML values and diagnostic performance of DLR revealed no significant differences compared to other reconstruction approaches.
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Affiliation(s)
- Cheng Xu
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Min Xu
- Canon Medical System, Beijing, 100015, China
| | - Jing Yan
- Canon Medical System, Beijing, 100015, China
| | - Yan-Yu Li
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Yan Yi
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Yu-Bo Guo
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Ming Wang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Yu-Mei Li
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Zheng-Yu Jin
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Yi-Ning Wang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
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Hee Kim K, Choo KS, Jin Nam K, Lee K, Hwang JY, Park C, Jung Yang W. Cardiac CTA image quality of adaptive statistical iterative reconstruction-V versus deep learning reconstruction "TrueFidelity" in children with congenital heart disease. Medicine (Baltimore) 2022; 101:e31169. [PMID: 36281124 PMCID: PMC9592454 DOI: 10.1097/md.0000000000031169] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Several recent studies have reported that deep learning reconstruction "TrueFidelity" (TF) improves computed tomography (CT) image quality. However, no study has compared adaptive statistical repeated reconstruction (ASIR-V) using TF in pediatric cardiac CT angiography (CTA) with a low peak kilovoltage. OBJECTIVE This study aimed to determine whether ASIR-V or TF CTA image quality is superior in children with congenital heart disease (CHD). MATERIALS AND METHODS Fifty children (median age, 2 months; interquartile range, 0-5 months; 28 men) with CHD who underwent CTA were enrolled between June and September 2020. Images were reconstructed using 2 ASIR-V blending factors (80% and 100% [AV-100]) and 3 TF settings (low, medium, and high [TF-H] strength levels). For the quantitative analyses, 3 objective image qualities (attenuation, noise, and signal-to-noise ratio [SNR]) were measured of the great vessels and heart chambers. The contrast-to-noise ratio (CNR) was also evaluated between the left ventricle and the dial wall. For the qualitative analyses, the degree of quantum mottle and blurring at the upper level to the first branch of the main pulmonary artery was assessed independently by 2 radiologists. RESULTS When the ASIR-V blending factor level and TF strength were higher, the noise was lower, and the SNR was higher. The image noise and SNR of TF-H were significantly lower and higher than those of AV-100 (P < .01), except for noise in the right atrium and left pulmonary artery and SNR of the right ventricle. Regarding CNR, TF-H was significantly better than AV-100 (P < .01). In addition, in the objective assessment of the degree of quantum mottle and blurring, TF-H had the best score among all examined image sets (P < .01). CONCLUSION TF-H is superior to AV-100 in terms of objective and subjective image quality. Consequently, TF-H was the best image set for cardiac CTA in children with CHD.
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Affiliation(s)
- Kun Hee Kim
- Department of Radiology, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan-si, Gyeongsangnam-do, Korea
| | - Ki Seok Choo
- Department of Radiology, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan-si, Gyeongsangnam-do, Korea
- *Correspondence: Ki Seok Choo, Department of Radiology, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Beomeo-RI, Mulgeum-eup, Yangsan-si, Gyeongsangnam-do, 626-770, Korea (e-mail: )
| | - Kyoung Jin Nam
- Department of Radiology, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan-si, Gyeongsangnam-do, Korea
| | - Kyeyoung Lee
- Department of Radiology, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan-si, Gyeongsangnam-do, Korea
| | - Jae-Yeon Hwang
- Department of Radiology, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan-si, Gyeongsangnam-do, Korea
| | - ChanKue Park
- Department of Radiology, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan-si, Gyeongsangnam-do, Korea
| | - Woo Jung Yang
- Barunmom Rehabilitation Medicine, Busanjin-gu, Busan, Korea
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Tian Q, Li X, Li J, Cheng Y, Niu X, Zhu S, Xu W, Guo J. Image quality improvement in low-dose chest CT with deep learning image reconstruction. J Appl Clin Med Phys 2022; 23:e13796. [PMID: 36210060 PMCID: PMC9797160 DOI: 10.1002/acm2.13796] [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: 05/08/2022] [Revised: 07/10/2022] [Accepted: 09/06/2022] [Indexed: 01/01/2023] Open
Abstract
OBJECTIVES To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low-dose chest CT in comparison with 40% adaptive statistical iterative reconstruction-Veo (ASiR-V40%) algorithm. METHODS This retrospective study included 86 patients who underwent low-dose CT for lung cancer screening. Images were reconstructed with ASiR-V40% and DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) levels. CT value and standard deviation of lung tissue, erector spinae muscles, aorta, and fat were measured and compared across the four reconstructions. Subjective image quality was evaluated by two blind readers from three aspects: image noise, artifact, and visualization of small structures. RESULTS The effective dose was 1.03 ± 0.36 mSv. There was no significant difference in CT values of erector spinae muscles and aorta, whereas the maximum difference for lung tissue and fat was less than 5 HU among the four reconstructions. Compared with ASiR-V40%, the DLIR-L, DLIR-M, and DLIR-H reconstructions reduced the noise in aorta by 11.44%, 33.03%, and 56.1%, respectively, and had significantly higher subjective quality scores in image artifacts (all p < 0.001). ASiR-V40%, DLIR-L, and DLIR-M had equivalent score in visualizing small structures (all p > 0.05), whereas DLIR-H had slightly lower score. CONCLUSIONS Compared with ASiR-V40%, DLIR significantly reduces image noise in low-dose chest CT. DLIR strength is important and should be adjusted for different diagnostic needs in clinical application.
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Affiliation(s)
- Qian Tian
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiP. R. China
| | - Xinyu Li
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiP. R. China
| | - Jianying Li
- GE Healthcare, Computed Tomography Research CenterBeijingP. R. China
| | - Yannan Cheng
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiP. R. China
| | - Xinyi Niu
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiP. R. China
| | - Shumeng Zhu
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiP. R. China
| | - Wenting Xu
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiP. R. China
| | - Jianxin Guo
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiP. R. China
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49
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Is There Any Improvement in Image Quality in Obese Patients When Using a New X-ray Tube and Deep Learning Image Reconstruction in Coronary Computed Tomography Angiography? Life (Basel) 2022; 12:life12091428. [PMID: 36143464 PMCID: PMC9503813 DOI: 10.3390/life12091428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/29/2022] [Accepted: 09/06/2022] [Indexed: 11/30/2022] Open
Abstract
Deep learning image reconstruction (DLIR) is a technique that should reduce noise and improve image quality. This study assessed the impact of using both higher tube currents as well as DLIR on the image quality and diagnostic accuracy. The study consisted of 51 symptomatic obese (BMI > 30 kg/m2) patients with low to moderate risk of coronary artery disease (CAD). All patients underwent coronary computed tomography angiography (CCTA) twice, first with the Revolution CT scanner and then with the upgraded Revolution Apex scanner with the ability to increase tube current. Images were reconstructed using ASiR-V 50% and DLIR. The image quality was evaluated by an observer using a Likert score and by ROI measurements in aorta and the myocardium. Image quality was significantly improved with the Revolution Apex scanner and reconstruction with DLIR resulting in an odds ratio of 1.23 (p = 0.017), and noise was reduced by 41%. A total of 88% of the image sets performed with Revolution Apex + DLIR were assessed as good enough for diagnosis compared to 69% of the image sets performed with Revolution Apex/CT + ASiR-V. In obese patients, the combination of higher tube current and DLIR significantly improves the subjective image quality and diagnostic utility and reduces noise.
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50
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Chun SH, Suh YJ, Han K, Kwon Y, Kim AY, Choi BW. Deep learning-based reconstruction on cardiac CT yields distinct radiomic features compared to iterative and filtered back projection reconstructions. Sci Rep 2022; 12:15171. [PMID: 36071138 PMCID: PMC9452656 DOI: 10.1038/s41598-022-19546-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 08/30/2022] [Indexed: 11/09/2022] Open
Abstract
We aimed to determine the effects of deep learning-based reconstruction (DLR) on radiomic features obtained from cardiac computed tomography (CT) by comparing with iterative reconstruction (IR), and filtered back projection (FBP). A total of 284 consecutive patients with 285 cardiac CT scans that were reconstructed with DLR, IR, and FBP, were retrospectively enrolled. Radiomic features were extracted from the left ventricular (LV) myocardium, and from the periprosthetic mass if patients had cardiac valve replacement. Radiomic features of LV myocardium from each reconstruction were compared using a fitting linear mixed model. Radiomics models were developed to diagnose periprosthetic abnormality, and the performance was evaluated using the area under the receiver characteristics curve (AUC). Most radiomic features of LV myocardium (73 of 88) were significantly different in pairwise comparisons between all three reconstruction methods (P < 0.05). The radiomics model on IR exhibited the best diagnostic performance (AUC 0.948, 95% CI 0.880-1), relative to DLR (AUC 0.873, 95% CI 0.735-1) and FBP (AUC 0.875, 95% CI 0.731-1), but these differences did not reach significance (P > 0.05). In conclusion, applying DLR to cardiac CT scans yields radiomic features distinct from those obtained with IR and FBP, implying that feature robustness is not guaranteed when applying DLR.
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Affiliation(s)
- Sei Hyun Chun
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Young Joo Suh
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Kyunghwa Han
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Yonghan Kwon
- Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, Korea
| | | | - Byoung Wook Choi
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
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