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A Review of Current Challenges and Case Study toward Optimizing Micro-Computed X-Ray Tomography of Carbon Fabric Composites. MATERIALS 2020; 13:ma13163606. [PMID: 32824047 PMCID: PMC7475927 DOI: 10.3390/ma13163606] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 07/17/2020] [Accepted: 08/12/2020] [Indexed: 11/29/2022]
Abstract
X-ray computed tomography provides qualitative and quantitative structural and compositional information for a broad range of materials. Yet, its contribution to the field of advanced composites such as carbon fiber reinforced polymers is still limited by factors such as low imaging contrast, due to scarce X-ray attenuation features. This article, through a review of the state of the art, followed by an example case study on Micro-computed tomography (CT) analysis of low X-ray absorptive dry and prepreg carbon woven fabric composites, aims to highlight and address some challenges as well as best practices on performing scans that can capture key features of the material. In the case study, utilizing an Xradia Micro-CT-400, important aspects such as obtaining sufficient contrast, an examination of thin samples, sample size/resolution issues, and image-based modeling are discussed. The outcome of an optimized workflow in Micro-CT of composite fabrics can assist in further research efforts such as the generation of surface or volume meshes for the numerical modeling of underlying deformation mechanisms during their manufacturing processes.
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102
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Ziv O, Goldberg SN, Nissenbaum Y, Sosna J, Weiss N, Azhari H. In vivo noninvasive three-dimensional (3D) assessment of microwave thermal ablation zone using non-contrast-enhanced x-ray CT. Med Phys 2020; 47:4721-4734. [PMID: 32745257 DOI: 10.1002/mp.14428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 07/20/2020] [Accepted: 07/22/2020] [Indexed: 11/08/2022] Open
Abstract
PURPOSE To develop an image processing methodology for noninvasive three-dimensional (3D) quantification of microwave thermal ablation zones in vivo using x-ray computed tomography (CT) imaging without injection of a contrast enhancing material. METHODS Six microwave (MW) thermal ablation procedures were performed in three pigs. The ablations were performed with a constant heating duration of 8 min and power level of 30 W. During the procedure images from sixty 1 mm thick slices were acquired every 30 s. At the end of all ablation procedures for each pig, a contrast-enhanced scan was acquired for reference. Special algorithms for addressing challenges stemming from the 3D in vivo setup and processing the acquired images were prepared. The algorithms first rearranged the data to account for the oblique needle orientation and for breathing motion. Then, the gray level variance changes were analyzed, and optical flow analysis was applied to the treated volume in order to obtain the ablation contours and reconstruct the ablation zone in 3D. The analysis also included a special correction algorithm for eliminating artifacts caused by proximal major blood vessels and blood flow. Finally, 3D reference reconstructions from the contrast-enhanced scan were obtained for quantitative comparison. RESULTS For four ablations located >3 mm from a large blood vessel, the mean dice similarity coefficient (DSC) and the mean absolute radial discrepancy between the contours obtained from the reference contrast-enhanced images and the contours produced by the algorithm were 0.82 ± 0.03 and 1.92 ± 1.47 mm, respectively. In two cases of ablation adjacent to large blood vessels, the average DSC and discrepancy were: 0.67 ± 0.6 and 2.96 ± 2.15 mm, respectively. The addition of the special correction algorithm utilizing blood vessels mapping improved the mean DSC and the mean absolute discrepancy to 0.85 ± 0.02 and 1.19 ± 1.00 mm, respectively. CONCLUSIONS The developed algorithms provide highly accurate detailed contours in vivo (average error < 2.5 mm) and cope well with the challenges listed above. Clinical implementation of the developed methodology could potentially provide real time noninvasive 3D accurate monitoring of MW thermal ablation in-vivo, provided that the radiation dose can be reduced.
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Affiliation(s)
- Omri Ziv
- Department of Biomedical Engineering, Technion - IIT, Haifa, 32000, Israel
| | - S Nahum Goldberg
- Department of Radiology, Hadassah Medical Center, Hebrew University, Jerusalem, 91120, Israel.,Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA
| | - Yitzhak Nissenbaum
- Department of Radiology, Hadassah Medical Center, Hebrew University, Jerusalem, 91120, Israel
| | - Jacob Sosna
- Department of Radiology, Hadassah Medical Center, Hebrew University, Jerusalem, 91120, Israel.,Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA
| | - Noam Weiss
- Department of Biomedical Engineering, Technion - IIT, Haifa, 32000, Israel
| | - Haim Azhari
- Department of Biomedical Engineering, Technion - IIT, Haifa, 32000, Israel
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103
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Deep Learning Versus Iterative Reconstruction for CT Pulmonary Angiography in the Emergency Setting: Improved Image Quality and Reduced Radiation Dose. Diagnostics (Basel) 2020; 10:diagnostics10080558. [PMID: 32759874 PMCID: PMC7460033 DOI: 10.3390/diagnostics10080558] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 08/01/2020] [Accepted: 08/02/2020] [Indexed: 12/13/2022] Open
Abstract
To compare image quality and the radiation dose of computed tomography pulmonary angiography (CTPA) subjected to the first deep learning-based image reconstruction (DLR) (50%) algorithm, with images subjected to the hybrid-iterative reconstruction (IR) technique (50%). One hundred forty patients who underwent CTPA for suspected pulmonary embolism (PE) between 2018 and 2019 were retrospectively reviewed. Image quality was assessed quantitatively (image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)) and qualitatively (on a 5-point scale). Radiation dose parameters (CT dose index, CTDIvol; and dose-length product, DLP) were also recorded. Ninety-three patients were finally analyzed, 48 with hybrid-IR and 45 with DLR images. The image noise was significantly lower and the SNR (24.4 ± 5.9 vs. 20.7 ± 6.1) and CNR (21.8 ± 5.8 vs. 18.6 ± 6.0) were significantly higher on DLR than hybrid-IR images (p < 0.01). DLR images received a significantly higher score than hybrid-IR images for image quality, with both soft (4.4 ± 0.7 vs. 3.8 ± 0.8) and lung (4.1 ± 0.7 vs. 3.6 ± 0.9) filters (p < 0.01). No difference in diagnostic confidence level for PE between both techniques was found. CTDIvol (4.8 ± 1.4 vs. 4.0 ± 1.2 mGy) and DLP (157.9 ± 44.9 vs. 130.8 ± 41.2 mGy∙cm) were lower on DLR than hybrid-IR images. DLR both significantly improved the image quality and reduced the radiation dose of CTPA examinations as compared to the hybrid-IR technique.
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104
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Juntunen MAK, Sepponen P, Korhonen K, Pohjanen VM, Ketola J, Kotiaho A, Nieminen MT, Inkinen SI. Interior photon counting computed tomography for quantification of coronary artery calcium: pre-clinical phantom study. Biomed Phys Eng Express 2020; 6:055011. [PMID: 33444242 DOI: 10.1088/2057-1976/aba133] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Computed tomography (CT) is the reference method for cardiac imaging, but concerns have been raised regarding the radiation dose of CT examinations. Recently, photon counting detectors (PCDs) and interior tomography, in which the radiation beam is limited to the organ-of-interest, have been suggested for patient dose reduction. In this study, we investigated interior PCD-CT (iPCD-CT) for non-enhanced quantification of coronary artery calcium (CAC) using an anthropomorphic torso phantom and ex vivo coronary artery samples. We reconstructed the iPCD-CT measurements with filtered back projection (FBP), iterative total variation (TV) regularization, padded FBP, and adaptively detruncated FBP and adaptively detruncated TV. We compared the organ doses between conventional CT and iPCD-CT geometries, assessed the truncation and cupping artifacts with iPCD-CT, and evaluated the CAC quantification performance of iPCD-CT. With approximately the same effective dose between conventional CT geometry (0.30 mSv) and interior PCD-CT with 10.2 cm field-of-view (0.27 mSv), the organ dose of the heart was increased by 52.3% with interior PCD-CT when compared to CT. Conversely, the organ doses to peripheral and radiosensitive organs, such as the stomach (55.0% reduction), were often reduced with interior PCD-CT. FBP and TV did not sufficiently reduce the truncation artifact, whereas padded FBP and adaptively detruncated FBP and TV yielded satisfactory truncation artifact reduction. Notably, the adaptive detruncation algorithm reduced truncation artifacts effectively when it was combined with reconstruction detrending. With this approach, the CAC quantification accuracy was good, and the coronary artery disease grade reclassification rate was particularly low (5.6%). Thus, our results confirm that CAC quantification can be performed with the interior CT geometry, that the artifacts are effectively reduced with suitable interior reconstruction methods, and that interior tomography provides efficient patient dose reduction.
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Affiliation(s)
- Mikael A K Juntunen
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland. Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
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105
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Lim WH, Choi YH, Park JE, Cho YJ, Lee S, Cheon JE, Kim WS, Kim IO, Kim JH. Application of Vendor-Neutral Iterative Reconstruction Technique to Pediatric Abdominal Computed Tomography. Korean J Radiol 2020; 20:1358-1367. [PMID: 31464114 PMCID: PMC6715563 DOI: 10.3348/kjr.2018.0715] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 06/05/2019] [Indexed: 02/06/2023] Open
Abstract
Objective To compare image qualities between vendor-neutral and vendor-specific hybrid iterative reconstruction (IR) techniques for abdominopelvic computed tomography (CT) in young patients. Materials and Methods In phantom study, we used an anthropomorphic pediatric phantom, age-equivalent to 5-year-old, and reconstructed CT data using traditional filtered back projection (FBP), vendor-specific and vendor-neutral IR techniques (ClariCT; ClariPI) in various radiation doses. Noise, low-contrast detectability and subjective spatial resolution were compared between FBP, vendor-specific (i.e., iDose1 to 5; Philips Healthcare), and vendor-neutral (i.e., ClariCT1 to 5) IR techniques in phantom. In 43 patients (median, 14 years; age range 1–19 years), noise, contrast-to-noise ratio (CNR), and qualitative image quality scores of abdominopelvic CT were compared between FBP, iDose level 4 (iDose4), and ClariCT level 2 (ClariCT2), which showed most similar image quality to clinically used vendor-specific IR images (i.e., iDose4) in phantom study. Noise, CNR, and qualitative imaging scores were compared using one-way repeated measure analysis of variance. Results In phantom study, ClariCT2 showed noise level similar to iDose4 (14.68–7.66 Hounsfield unit [HU] vs. 14.78–6.99 HU at CT dose index volume range of 0.8–3.8 mGy). Subjective low-contrast detectability and spatial resolution were similar between ClariCT2 and iDose4. In clinical study, ClariCT2 was equivalent to iDose4 for noise (14.26–17.33 vs. 16.01–18.90) and CNR (3.55–5.24 vs. 3.20–4.60) (p > 0.05). For qualitative imaging scores, the overall image quality ([reader 1, reader 2]; 2.74 vs. 2.07, 3.02 vs. 2.28) and noise (2.88 vs. 2.23, 2.93 vs. 2.33) of ClariCT2 were superior to those of FBP (p < 0.05), and not different from those of iDose4 (2.74 vs. 2.72, 3.02 vs. 2.98; 2.88 vs. 2.77, 2.93 vs. 2.86) (p > 0.05). Conclusion Vendor-neutral IR technique shows image quality similar to that of clinically used vendor-specific hybrid IR technique for abdominopelvic CT in young patients.
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Affiliation(s)
- Woo Hyeon Lim
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Young Hun Choi
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
| | - Ji Eun Park
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Yeon Jin Cho
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Seunghyun Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Jung Eun Cheon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Woo Sun Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - In One Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Jong Hyo Kim
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.,Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea.,Advanced Institute of Convergence Technology, Suwon, Korea
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106
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Solomon J, Lyu P, Marin D, Samei E. Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm. Med Phys 2020; 47:3961-3971. [PMID: 32506661 DOI: 10.1002/mp.14319] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 05/01/2020] [Accepted: 05/26/2020] [Indexed: 12/22/2022] Open
Abstract
PURPOSE To characterize the noise and spatial resolution properties of a commercially available deep learning-based computed tomography (CT) reconstruction algorithm. METHODS Two phantom experiments were performed. The first used a multisized image quality phantom (Mercury v3.0, Duke University) imaged at five radiation dose levels (CTDIvol : 0.9, 1.2, 3.6, 7.0, and 22.3 mGy) with a fixed tube current technique on a commercial CT scanner (GE Revolution CT). Images were reconstructed with conventional (FBP), iterative (GE ASiR-V), and deep learning-based (GE True Fidelity) reconstruction algorithms. Noise power spectrum (NPS), high-contrast (air-polyethylene interface), and intermediate-contrast (water-polyethylene interface) task transfer functions (TTF) were measured for each dose level and phantom size and summarized in terms of average noise frequency (fav ) and frequency at which the TTF was reduced to 50% (f50% ), respectively. The second experiment used a custom phantom with low-contrast rods and lung texture sections for the assessment of low-contrast TTF and noise spatial distribution. The phantom was imaged at five dose levels (CTDIvol : 1.0, 2.1, 3.0, 6.0, and 10.0 mGy) with 20 repeated scans at each dose, and images reconstructed with the same reconstruction algorithms. The local noise stationarity was assessed by generating spatial noise maps from the ensemble of repeated images and computing a noise inhomogeneity index, η , following AAPM TG233 methods. All measurements were compared among the algorithms. RESULTS Compared to FBP, noise magnitude was reduced on average (± one standard deviation) by 74 ± 6% and 68 ± 4% for ASiR-V (at "100%" setting) and True Fidelity (at "High" setting), respectively. The noise texture from ASiR-V had substantially lower noise frequency content with 55 ± 4% lower NPS fav compared to FBP while True Fidelity had only marginally different noise frequency content with 9 ± 5% lower NPS fav compared to FBP. Both ASiR-V and True Fidelity demonstrated locally nonstationary noise in a lung texture background at all radiation dose levels, with higher noise near high-contrast edges of vessels and lower noise in uniform regions. At the 1.0 mGy dose level η values were 314% and 271% higher in ASiR-V and True Fidelity compared to FBP, respectively. High-contrast spatial resolution was similar between all algorithms for all dose levels and phantom sizes (<3% difference in TTF f50% ). Compared to FBP, low-contrast spatial resolution was lower for ASiR-V and True Fidelity with a reduction of TTF f50% of up to 42% and 36%, respectively. CONCLUSIONS The deep learning-based CT reconstruction demonstrated a strong noise magnitude reduction compared to FBP while maintaining similar noise texture and high-contrast spatial resolution. However, the algorithm resulted in images with a locally nonstationary noise in lung textured backgrounds and had somewhat degraded low-contrast spatial resolution similar to what has been observed in currently available iterative reconstruction techniques.
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Affiliation(s)
- Justin Solomon
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA
| | - Peijei Lyu
- Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Daniele Marin
- Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA
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107
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Van De Looverbosch T, Rahman Bhuiyan MH, Verboven P, Dierick M, Van Loo D, De Beenbouwer J, Sijbers J, Nicolaï B. Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning. Food Control 2020. [DOI: 10.1016/j.foodcont.2020.107170] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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108
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Gao Y, Liang Z, Zhang H, Yang J, Ferretti J, Bilfinger T, Yaddanapudi K, Schweitzer M, Bhattacharji P, Moore W. A Task-dependent Investigation on Dose and Texture in CT Image Reconstruction. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2020; 4:441-449. [PMID: 33907724 PMCID: PMC8075295 DOI: 10.1109/trpms.2019.2957459] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Localizing and characterizing clinically-significant lung nodules, a potential precursor to lung cancer, at the lowest achievable radiation dose is demanded to minimize the stochastic radiation effects from x-ray computed tomography (CT). A minimal dose level is heavily dependent on the image reconstruction algorithms and clinical task, in which the tissue texture always plays an important role. This study aims to investigate the dependence through a task-based evaluation at multiple dose levels and variable textures in reconstructions with prospective patient studies. 133 patients with a suspicious pulmonary nodule scheduled for biopsy were recruited and the data was acquired at120kVp with three different dose levels of 100, 40 and 20mAs. Three reconstruction algorithms were implemented: analytical filtered back-projection (FBP) with optimal noise filtering; statistical Markov random field (MRF) model with optimal Huber weighting (MRF-H) for piecewise smooth reconstruction; and tissue-specific texture model (MRF-T) for texture preserved statistical reconstruction. Experienced thoracic radiologists reviewed and scored all images at random, blind to the CT dose and reconstruction algorithms. The radiologists identified the nodules in each image including the 133 biopsy target nodules and 66 other non-target nodules. For target nodule characterization, only MRF-T at 40mAs showed no statistically significant difference from FBP at 100mAs. For localizing both the target nodules and the non-target nodules, some as small as 3mm, MRF-T at 40 and 20mAs levels showed no statistically significant difference from FBP at 100mAs, respectively. MRF-H and FBP at 40 and 20mAs levels performed statistically differently from FBP at 100mAs. This investigation concluded that (1) the textures in the MRF-T reconstructions improves both the tasks of localizing and characterizing nodules at low dose CT and (2) the task of characterizing nodules is more challenging than the task of localizing nodules and needs more dose or enhanced textures from reconstruction.
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Affiliation(s)
- Yongfeng Gao
- Department of Radiology, Stony Brook University, Stony Brook, NY
11794, USA
| | - Zhengrong Liang
- Departments of Radiology, Biomedical Engineering, Computer Science,
and Electrical Engineering, Stony Brook University, Stony Brook, NY 11794,
USA
| | - Hao Zhang
- Departments of Radiology and Biomedical Engineering, Stony Brook
University, Stony Brook, NY 11794, USA and now with the Department of
Radiation Oncology, Stanford University, Stanford, CA 94035, USA
| | - Jie Yang
- Department of Family, Population and Preventive Medicine, Stony
Brook University, Stony Brook, NY 11794, USA
| | - John Ferretti
- Department of Radiology, Stony Brook University, Stony Brook, NY
11794, USA
| | - Thomas Bilfinger
- Department of Surgery, Stony Brook University, Stony Brook, NY
11794, USA)
| | | | - Mark Schweitzer
- Department of Radiology, Stony Brook University, Stony Brook, NY
11794, USA
| | - Priya Bhattacharji
- Department of Radiology, Stony Brook University, Stony Brook, NY
11794, USA, and now with the Department of Radiology, New York University,
New York, NY 10016, USA
| | - William Moore
- Department of Radiology, Stony Brook University, Stony Brook, NY
11794, USA, and now with the Department of Radiology, New York University,
New York, NY 10016, USA
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109
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Rampado O, Depaoli A, Marchisio F, Gatti M, Racine D, Ruggeri V, Ruggirello I, Darvizeh F, Fonio P, Ropolo R. Effects of different levels of CT iterative reconstruction on low-contrast detectability and radiation dose in patients of different sizes: an anthropomorphic phantom study. Radiol Med 2020; 126:55-62. [PMID: 32495272 DOI: 10.1007/s11547-020-01228-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 05/12/2020] [Indexed: 01/19/2023]
Abstract
PURPOSE The purpose of this study was to verify the maintenance of low-contrast detectability at different CT dose reduction levels, in patients of different sizes, as a consequence of the application of iterative reconstruction at different strengths combined with tube current modulation. METHODS Anthropomorphic abdominal phantoms of two sizes (small and large) were imaged at a fixed noise with iterative algorithm ASIR-V percentages in the range between 0 and 70% and corresponding dose reductions in the range of 0-83%. A total of 1400 images with and without liver low-contrast simulated lesions were evaluated by five radiologists, using the receiver operating characteristics (ROC) paradigm and evaluating the area under the ROC curve (AUC). The human observer results were then compared with AUC obtained with a channelized Hotelling observer (CHO). CNR values were also calculated. RESULTS For the small phantom, the AUC values lie between 0.90 and 0.93 for human evaluations of images acquired without iterative reconstruction, with 30% ASIR-V and with 50% ASIR-V. The AUC decreased significantly to 0.81 (p = 0.0001) at 70% ASIR-V. The CHO results were in coherence with human observer scores. Also, similar results were observed for the large size phantom. CNR values were stable for the different ASIR-V percentages. CONCLUSIONS The iterative algorithm maintained the low-contrast detectability up to a dose reduction of about 70%, following application of a 50% ASIR-V combined with automatic tube current modulation, regardless of the phantom size. At further dose reductions using greater iterative percentages, a significant decrease in detectability was observed.
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Affiliation(s)
- Osvaldo Rampado
- Medical Physics Unit, S.C. Fisica Sanitaria, A.O.U. Città della Salute e della Scienza di Torino, Corso Bramante 88, 10126, Turin, Italy.
| | - Alessandro Depaoli
- University Radiodiagnostic Unit, A.O.U. Città della Salute e della Scienza di Torino, Corso Bramante 88, 10126, Turin, Italy
| | - Filippo Marchisio
- University Radiodiagnostic Unit, A.O.U. Città della Salute e della Scienza di Torino, Corso Bramante 88, 10126, Turin, Italy
| | - Marco Gatti
- University Radiodiagnostic Unit, A.O.U. Città della Salute e della Scienza di Torino, Corso Bramante 88, 10126, Turin, Italy
| | - Damien Racine
- Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand-Pré 1, 1007, Lausanne, Switzerland
| | - Valeria Ruggeri
- University Radiodiagnostic Unit, A.O.U. Città della Salute e della Scienza di Torino, Corso Bramante 88, 10126, Turin, Italy
| | - Irene Ruggirello
- University Radiodiagnostic Unit, A.O.U. Città della Salute e della Scienza di Torino, Corso Bramante 88, 10126, Turin, Italy
| | - Fatemeh Darvizeh
- University Radiodiagnostic Unit, A.O.U. Città della Salute e della Scienza di Torino, Corso Bramante 88, 10126, Turin, Italy
| | - Paolo Fonio
- University Radiodiagnostic Unit, A.O.U. Città della Salute e della Scienza di Torino, Corso Bramante 88, 10126, Turin, Italy
| | - Roberto Ropolo
- Medical Physics Unit, S.C. Fisica Sanitaria, A.O.U. Città della Salute e della Scienza di Torino, Corso Bramante 88, 10126, Turin, Italy
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Abstract
We present a new plenoptic microscopy configuration for 3D snapshot imaging, which is dual telecentric and can directly record true projection images corresponding with different viewing angles. It also allows blocking high-angle stray rays without sacrificing the light collection efficiency. This configuration named as snapshot projection optical tomography (SPOT) arranges an objective lens and a microlens array (MLA) in a 4-f telecentric configuration and places an aperture stop at the back focal plane of a relay lens. We develop a forward imaging model for SPOT, which can also be applied to existing light field microscopy techniques using an MLA as tube lens. Using the developed system, we demonstrate snapshot 3D imaging of various fluorescent beads and a biological cell, which confirms the capability of SPOT for imaging specimens with an extended fluorophore distribution as well as isolated fluorochromes. The transverse and vertical resolutions are measured to be 0.8 μm and 1.6 μm, respectively.
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Affiliation(s)
- Yongjin Sung
- College of Engineering & Applied Science, University of Wisconsin, Milwaukee, WI 53211, USA
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111
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I S, C A, H S, P T, T F. Comparisons of Hounsfield Unit Linearity between Images Reconstructed using an Adaptive Iterative Dose Reduction (AIDR) and a Filter Back-Projection (FBP) Techniques. J Biomed Phys Eng 2020; 10:215-224. [PMID: 32337189 PMCID: PMC7166214 DOI: 10.31661/jbpe.v0i0.1912-1013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 12/09/2019] [Indexed: 12/13/2022]
Abstract
Background: The HU linearity is an essential parameter in a quantitative imaging and the treatment planning systems of radiotherapy. Objective: This study aims to evaluate the linearity of Hounsfield unit (HU) in applying the adaptive iterative dose reduction (AIDR)
on CT scanner and its comparison to the filtered back-projection (FBP). Material and Methods: In this experimental phantom study, a TOS-phantom was scanned using a Toshiba Alexion 6 CT scanner. The images were reconstructed
using the FBP and AIDR. Measurements of HU and noise values were performed on images of the “HU linearity” module of the TOS-phantom.
The module had five embedded objects, i.e., air, polypropylene, nylon, acrylic, and Delrin. On each object, a circle area of 4.32
cm2 was drawn and used to measure HU and noise values. The R2 of the relation between mass densities vs. HU values was used to
measure HU linearities at four different tube voltages. The Mann-Whitney U test was used to compare unpaired data and p-value < 0.05 was considered statistically significant. Results: The AIDR method produced a significant smaller image noise than the FBP method (p-value < 0.05).
There were no significant differences in HU values of images reconstructed using FBP and AIDR methods (p-value > 0.05).
The HU values acquired by the methods showed the same linearity marked by coinciding linear lines with the same R2 value (> 0.999). Conclusion: AIDR methods produce the HU linearity as FBP methods with a smaller image noise level.
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Affiliation(s)
- Suyudi I
- BSc, Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Indonesia
| | - Anam C
- PhD, Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Indonesia
| | - Sutanto H
- PhD, Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Indonesia
| | - Triadyaksa P
- PhD, Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Indonesia
| | - Fujibuchi T
- PhD, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Japan
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112
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Rossignol J, Turtos RM, Gundacker S, Gaudreault D, Auffray E, Lecoq P, Bérubé-Lauzière Y, Fontaine R. Time-of-flight computed tomography - proof of principle. Phys Med Biol 2020; 65:085013. [PMID: 32084652 DOI: 10.1088/1361-6560/ab78bf] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Computed tomography has greatly improved over the last decade, especially through x-ray dose exposure reduction while maintaining image quality. Herein, a new concept is proposed to improve the contrast-to-noise ratio (CNR) by including the time-of-flight (TOF) information of individual photons to obtain further insight on the photon's trajectory and to reject scatter contribution. The proof of the concept relies on both simulation and experimental measurements in a cone-beam computed tomography arrangement. Results show a statistical difference between the TOF of scattered and primary photons exploitable in TOF computed tomography. For a large volume of the size of a human abdomen, a scatter reduction from 296% to 4% is achieved in our simulation setup with perfect timing measurements which yields a 110% better CNR, or a dose reduction by a factor of four. Cup artifacts are also reduced from 24.7% to 0.8%, and attenuation inaccuracies are improved from -26.3% to -0.8%. With 100 ps and 10 ps FWHM timing jitters, respectively 75% and 95% of the scatter contribution can be removed with marginal gains below 10 ps. Experimental measurements confirm the feasibility of measuring statistical differences between the TOF of scattered and primary photons.
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Affiliation(s)
- J Rossignol
- Institut Interdisciplinaire d'Innovation Technologique (3IT), Université de Sherbrooke, Sherbrooke, Québec, Canada. Département de Génie Électrique et Génie Informatique, Université de Sherbrooke, Sherbrooke, Québec, Canada
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Zhang T, Xing Y, Zhang L, Jin X, Gao H, Chen Z. Stationary computed tomography with source and detector in linear symmetric geometry: Direct filtered backprojection reconstruction. Med Phys 2020; 47:2222-2236. [PMID: 32009236 DOI: 10.1002/mp.14058] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 12/13/2019] [Accepted: 01/16/2020] [Indexed: 01/08/2023] Open
Abstract
PURPOSE Inverse-geometry computed tomography (IGCT) could have great potential in medical applications and security inspections, and has been actively investigated in recent years. In this work, we explore a special architecture of IGCT in a stationary configuration: symmetric-geometry computed tomography (SGCT), where the x-ray source and detector are linearly distributed in a symmetric design. A direct filtered backprojection (FBP)-type algorithm is developed to analytically reconstruct images from the SGCT projections. METHODS In our proposed SGCT system, a big number of x-ray source points equally distributed along a straight-line trajectory will sequentially fire in an ultra-fast manner in one side, and an equispaced detector whose total length is comparable to that of the source will continuously collect data in the opposite side, as the object to be scanned moves into the imaging plane. We firstly present the overall design of SGCT. An FBP-type reconstruction algorithm is then derived for this unique imaging configuration. With finite length of x-ray source and detector arrays, projection data from one segment of SGCT scan are insufficient for an exact reconstruction. As a result, in practical applications, dual-SGCT scan whose detector segments are placed perpendicular to each other, is of particular interest and is proposed. Two segments of SGCT together can make sure that the passing rays cover at least 180 degrees for each and every point if carefully designed. In general, however, there exists a data redundancy problem for a dual-SGCT. So a weighting strategy is developed to maximize the use of projection data collected while avoid image artifacts. In addition, we further extend the fan-beam SGCT to cone beam and obtain a Feldkamp-Davis-Kress (FDK)-type reconstruction algorithm. Finally, we conduct a set of experimental studies both in simulation and on a prototype SGCT system and validate our proposed methods. RESULTS A simulation study using the Shepp-Logan head phantom confirms that CT images can be exactly reconstructed from dual-SGCT scan and that our proposed weighting strategy is able to handle the data redundancy properly. Compared with the rebinning-to-parallel-beam method using the forward projection of an abdominal CT dataset, our proposed method is seen to be less sensitive to data truncation. Our algorithm can achieve 10.64 lp/cm of spatial resolution at 50% modulation transfer functions point, higher than that of the rebinning method which can only reach at 9.42 lp/cm even with extremely fine interpolation. Real experiments of a cylindrical object on a prototype SGCT further prove the effectiveness and practicability of the direct FBP method proposed, with similar level of noise performance to rebinning algorithm. CONCLUSIONS A new concept of SGCT with linearly distributed source and detector is investigated in this work, in which spinning of sources and detectors is no longer needed during data acquisition, simplifying its system design, development, and manufacturing. A direct FBP-type algorithm is developed for analytical reconstruction from SGCT projection data. Numerical and real experiments validate our method and show that exact CT image can be reconstructed from dual-SGCT scan, where data redundancy problem can be solved by our proposed weighting function.
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Affiliation(s)
- Tao Zhang
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, China.,Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, 100084, China
| | - Yuxiang Xing
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, China.,Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, 100084, China
| | - Li Zhang
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, China.,Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, 100084, China
| | - Xin Jin
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, China.,Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, 100084, China
| | - Hewei Gao
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, China.,Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, 100084, China
| | - Zhiqiang Chen
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, China.,Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, 100084, China
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114
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Helical tomotherapy: Comparison of Hi-ART and Radixact clinical patient treatments at the Technical University of Munich. Sci Rep 2020; 10:4928. [PMID: 32188899 PMCID: PMC7080845 DOI: 10.1038/s41598-020-61499-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 02/26/2020] [Indexed: 12/14/2022] Open
Abstract
The helical tomotherapy (HT) Hi-ART system was installed at our department in April 2007. In July 2018 the first Radixact system in Germany has been launched for clinical use. We present differences, advantages and disadvantages and show future perspectives in patient treatment using two HT devices. We investigate patient characteristics, image quality, radiotherapy treatment specifications and analyze the time effort for treatments with the Hi-ART system from April 2010 until May 2017 and compare it to the data acquired in the first nine months of usage of the Radixact system. Comparing the Hi-ART and Radixact system, the unique option of integrated MVCT image acquisition has experienced distinct improvement in image quality. Time effort for irradiation treatment could be improved resulting in a mean beam on time for craniospinal axis treatment of 636.2 s for the Radixact system compared to 915.9 s for the Hi-ART system. The beneficial use of tomotherapy for complex target volumes is demonstrated by a head and neck tumor case and craniospinal axis treatment. With the Radixact system MVCT image quality has been improved allowing for fast and precise interfraction dose adaptation. The improved time effort for patient treatment could increase the accessibility for clinical usage.
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115
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Rubert N, Southard R, Hamman SM, Robison R. Evaluation of low-contrast detectability for iterative reconstruction in pediatric abdominal computed tomography: a phantom study. Pediatr Radiol 2020; 50:345-356. [PMID: 31705156 DOI: 10.1007/s00247-019-04561-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 09/23/2019] [Accepted: 10/16/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND Iterative reconstruction is offered by all vendors to achieve similar or better CT image quality at lower doses than images reconstructed with filtered back-projection. OBJECTIVE The purpose of this study was to investigate the dose-reduction potential for pediatric abdominal CT imaging when using either a commercially available hybrid or a commercially available model-based iterative reconstruction algorithm from a single manufacturer. MATERIALS AND METHODS A phantom containing four low-contrast inserts and a uniform background with total attenuation equivalent to the abdomen of an average 8-year-old child was imaged on a CT scanner (IQon; Philips Healthcare, Cleveland, OH). We reconstructed images using both hybrid iterative reconstruction (iDose4) and model-based iterative reconstruction (Iterative Model Reconstruction). The four low-contrast inserts had circular cross-section with diameters of 3 mm, 5 mm, 7 mm and 10 mm and contrasts of 14 Hounsfield units (HU), 7 HU, 5 HU and 3 HU, respectively. Helical scans with identical kilovoltage (kV), pitch, rotation time, and collimation were repeated on the phantom at volume CT dose index (CTDIvol) of 2.0 milligrays (mGy), 3.0 mGy, 4.5 mGy and 6.0 mGy. We measured the contrast-to-noise ratio (CNR) in each rod across scans. Additionally, we collected sub-images containing each rod and sub-images containing the background and used them in two-alternative forced choice observer experiments with four observers (two radiologists and two physicists). We calculated the dose-reduction potential of both iterative reconstruction algorithms relative to a scan performed at 6 mGy and reconstructed with filtered back-projection. RESULTS We calculated dose-reduction potential by either matching average equal observer performance in the two-alternative forced choice experiments or matching CNR. When matching CNR, the dose-reduction potential was 34% to 45% for hybrid iterative reconstruction and 89% to 95% for model-based iterative reconstruction. When matching average observer performance, the dose-reduction potential was 9% to 30% for hybrid iterative reconstruction and 57% to 74% for model-based iterative reconstruction. The range in dose-reduction potential depended on the rod size and contrast level. CONCLUSION Observer performance in this phantom study indicates that the dose-reduction potential indicated by an observer study is less than that of CNR; extrapolating the results to clinical studies suggests that the dose-reduction potential would also be less.
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Affiliation(s)
- Nicholas Rubert
- Department of Radiology, Phoenix Children's Hospital, 1919 E. Thomas Road, Phoenix, AZ, 85016, USA.
| | - Richard Southard
- Department of Radiology, Phoenix Children's Hospital, 1919 E. Thomas Road, Phoenix, AZ, 85016, USA.,College of Medicine, University of Arizona, Phoenix, AZ, USA
| | - Susan M Hamman
- Section of Pediatric Radiology, C. S. Mott Children's Hospital, Department of Radiology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Ryan Robison
- Department of Radiology, Phoenix Children's Hospital, 1919 E. Thomas Road, Phoenix, AZ, 85016, USA.,College of Nursing and Health Innovation, Arizona State University, Phoenix, AZ, USA
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Ye S, Ravishankar S, Long Y, Fessler JA. SPULTRA: Low-Dose CT Image Reconstruction With Joint Statistical and Learned Image Models. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:729-741. [PMID: 31425021 PMCID: PMC7170173 DOI: 10.1109/tmi.2019.2934933] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Low-dose CT image reconstruction has been a popular research topic in recent years. A typical reconstruction method based on post-log measurements is called penalized weighted-least squares (PWLS). Due to the underlying limitations of the post-log statistical model, the PWLS reconstruction quality is often degraded in low-dose scans. This paper investigates a shifted-Poisson (SP) model based likelihood function that uses the pre-log raw measurements that better represents the measurement statistics, together with a data-driven regularizer exploiting a Union of Learned TRAnsforms (SPULTRA). Both the SP induced data-fidelity term and the regularizer in the proposed framework are nonconvex. The proposed SPULTRA algorithm uses quadratic surrogate functions for the SP induced data-fidelity term. Each iteration involves a quadratic subproblem for updating the image, and a sparse coding and clustering subproblem that has a closed-form solution. The SPULTRA algorithm has a similar computational cost per iteration as its recent counterpart PWLS-ULTRA that uses post-log measurements, and it provides better image reconstruction quality than PWLS-ULTRA, especially in low-dose scans.
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Liu Z, Bicer T, Kettimuthu R, Gursoy D, De Carlo F, Foster I. TomoGAN: low-dose synchrotron x-ray tomography with generative adversarial networks: discussion. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2020; 37:422-434. [PMID: 32118926 DOI: 10.1364/josaa.375595] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 01/08/2020] [Indexed: 06/10/2023]
Abstract
Synchrotron-based x-ray tomography is a noninvasive imaging technique that allows for reconstructing the internal structure of materials at high spatial resolutions from tens of micrometers to a few nanometers. In order to resolve sample features at smaller length scales, however, a higher radiation dose is required. Therefore, the limitation on the achievable resolution is set primarily by noise at these length scales. We present TomoGAN, a denoising technique based on generative adversarial networks, for improving the quality of reconstructed images for low-dose imaging conditions. We evaluate our approach in two photon-budget-limited experimental conditions: (1) sufficient number of low-dose projections (based on Nyquist sampling), and (2) insufficient or limited number of high-dose projections. In both cases, the angular sampling is assumed to be isotropic, and the photon budget throughout the experiment is fixed based on the maximum allowable radiation dose on the sample. Evaluation with both simulated and experimental datasets shows that our approach can significantly reduce noise in reconstructed images, improving the structural similarity score of simulation and experimental data from 0.18 to 0.9 and from 0.18 to 0.41, respectively. Furthermore, the quality of the reconstructed images with filtered back projection followed by our denoising approach exceeds that of reconstructions with the simultaneous iterative reconstruction technique, showing the computational superiority of our approach.
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118
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Rayudu NM, Anitha DP, Mei K, Zoffl F, Kopp FK, Sollmann N, Löffler MT, Kirschke JS, Noël PB, Subburaj K, Baum T. Low-dose and sparse sampling MDCT-based femoral bone strength prediction using finite element analysis. Arch Osteoporos 2020; 15:17. [PMID: 32088769 DOI: 10.1007/s11657-020-0708-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 01/06/2020] [Indexed: 02/03/2023]
Abstract
UNLABELLED This study aims to evaluate the impact of dose reduction through tube current and sparse sampling on multi-detector computed tomography (MDCT)-based femoral bone strength prediction using finite element (FE) analysis. FE-predicted femoral failure load obtained from MDCT scan data was not significantly affected by 50% dose reductions through sparse sampling. Further decrease in dose through sparse sampling (25% of original projections) and virtually reduced tube current (50% and 25% of the original dose) showed significant effects on the FE-predicted failure load results. PURPOSE To investigate the effect of virtually reduced tube current and sparse sampling on multi-detector computed tomography (MDCT)-based femoral bone strength prediction using finite element (FE) analysis. METHODS Routine MDCT data covering the proximal femur of 21 subjects (17 males; 4 females; mean age, 71.0 ± 8.8 years) without any bone diseases aside from osteoporosis were included in this study. Fifty percent and 75% dose reductions were achieved by virtually reducing tube current and by applying a sparse sampling strategy from the raw image data. Images were then reconstructed with a statistically iterative reconstruction algorithm. FE analysis was performed on all reconstructed images and the failure load was calculated. The root mean square coefficient of variation (RMSCV) and coefficient of correlation (R2) were calculated to determine the variation in the FE-predicted failure load data for dose reductions, using original-dose MDCT scan as the standard of reference. RESULTS Fifty percent dose reduction through sparse sampling showed lower RMSCV and higher correlations when compared with virtually reduced tube current method (RMSCV = 5.70%, R2 = 0.96 vs. RMSCV = 20.78%, R2 = 0.79). Seventy-five percent dose reduction achieved through both methods (RMSCV = 22.38%, R2 = 0.80 for sparse sampling; RMSCV = 24.58%, R2 = 0.73 for reduced tube current) could not predict the failure load accurately. CONCLUSION Our simulations indicate that up to 50% reduction in radiation dose through sparse sampling can be used for FE-based prediction of femoral failure load. Sparse-sampled MDCT may allow fracture risk prediction and treatment monitoring in osteoporosis with less radiation exposure in the future.
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Affiliation(s)
- Nithin Manohar Rayudu
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore
| | - D Praveen Anitha
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore
| | - Kai Mei
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Florian Zoffl
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Felix K Kopp
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Maximilian T Löffler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Peter B Noël
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Karupppasamy Subburaj
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore.
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
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Huang L, Jiang H, Li S, Bai Z, Zhang J. Two stage residual CNN for texture denoising and structure enhancement on low dose CT image. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 184:105115. [PMID: 31627148 DOI: 10.1016/j.cmpb.2019.105115] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 09/27/2019] [Accepted: 10/02/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE X-ray computed tomography (CT) plays an important role in modern medical science. Human health problems caused by CT radiation have attracted the attention of the academic community widely. Reducing radiation dose results in a deterioration in image quality and further affects doctor's diagnosis. Therefore, this paper introduces a new denoise method for low dose CT (LDCT) images, called two stage residual convolutional neural network (TS-RCNN). METHODS There are two important parts with respect to our network. 1) The first stage RCNN is proposed for texture denoising via the stationary wavelet transform (SWT) and the perceptual loss. Specifically, SWT is performed on each normal dose CT (NDCT) image and generated four wavelet images are considered as the labels. 2) The second stage RCNN is established for structure enhancement via the average NDCT model on the basis of the first network's result. Finally, the denoised CT image is obtained via inverse SWT. RESULTS Our proposed TS-RCNN is trained on three groups of simulated LDCT images in 1123 images per group and evaluated on 129 simulated LDCT images for each group. Besides, to demonstrate the clinical application of TS-RCNN, we also test our method on the 2016 Low Dose CT Grand Challenge dataset. Quantitative results show that TS-RCNN almost achieves the best results in terms of MSE, SSIM and PSNR compared to other methods. CONCLUSIONS The experimental results and comparisons demonstrate that TS-RCNN not only preserves more texture information, but also enhances structural information on LDCT images.
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Affiliation(s)
- Liangliang Huang
- Software College, Northeastern University, Shenyang 110819, China
| | - Huiyan Jiang
- Software College, Northeastern University, Shenyang 110819, China.
| | - Shaojie Li
- Sino-Dutch Biomedical and Information Engineering College, Northeastern University, Shenyang 110819, China
| | - Zhiqi Bai
- Software College, Northeastern University, Shenyang 110819, China
| | - Jitong Zhang
- Sino-Dutch Biomedical and Information Engineering College, Northeastern University, Shenyang 110819, China
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120
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Pediatric head computed tomography with advanced modeled iterative reconstruction: focus on image quality and reduction of radiation dose. Pediatr Radiol 2020; 50:242-251. [PMID: 31630218 DOI: 10.1007/s00247-019-04532-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Revised: 07/21/2019] [Accepted: 09/10/2019] [Indexed: 01/11/2023]
Abstract
BACKGROUND Iterative reconstruction has become the standard method for reconstructing computed tomography (CT) scans and needs to be verified for adaptation. OBJECTIVE To assess the image quality after adapting advanced modeled iterative reconstruction (ADMIRE) for pediatric head CT. MATERIALS AND METHODS We included image sets with filtered back projection reconstruction (the cFBP group, n=105) and both filtered back projection and ADMIRE reconstruction (the lower-dose group, n=109) after dose reduction. All five strength levels of ADMIRE and filtered back projection were adapted for the lower-dose group and compared with the cFBP group. Quantitative parameters including noise, signal-to-noise ratio and contrast-to-noise ratio and qualitative parameters including noise, white matter and gray matter differentiation of the supra- and infratentorial levels, sharpness, artifact, and diagnostic accuracy were also evaluated and compared with interobserver agreement. RESULTS There was a mean dose reduction of 30.6% in CT dose index volume, 32.1% in dose length product, and 32.1% in effective dose after tube current reduction. There was gradual reduction of noise in air, cerebrospinal fluid and white matter with strength levels of ADMIRE from 1 to 5 (P<0.001). Signal-to-noise ratio and contrast-to-noise ratio in all age groups increased among strength levels of ADMIRE, in sequence from 1 to 5, with statistical significance (P<0.001). Gradual reduction of qualitative parameters was noted among strength levels of ADMIRE in sequence from 1 to 5 (P<0.001). CONCLUSION Use of ADMIRE for pediatric head CT can reduce radiation dose without degrading image quality.
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121
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GPU acceleration of a model-based iterative method for Digital Breast Tomosynthesis. Sci Rep 2020; 10:43. [PMID: 31913333 PMCID: PMC6949234 DOI: 10.1038/s41598-019-56920-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 12/13/2019] [Indexed: 11/08/2022] Open
Abstract
Digital Breast Tomosynthesis (DBT) is a modern 3D Computed Tomography X-ray technique for the early detection of breast tumors, which is receiving growing interest in the medical and scientific community. Since DBT performs incomplete sampling of data, the image reconstruction approaches based on iterative methods are preferable to the classical analytic techniques, such as the Filtered Back Projection algorithm, providing fewer artifacts. In this work, we consider a Model-Based Iterative Reconstruction (MBIR) method well suited to describe the DBT data acquisition process and to include prior information on the reconstructed image. We propose a gradient-based solver named Scaled Gradient Projection (SGP) for the solution of the constrained optimization problem arising in the considered MBIR method. Even if the SGP algorithm exhibits fast convergence, the time required on a serial computer for the reconstruction of a real DBT data set is too long for the clinical needs. In this paper we propose a parallel SGP version designed to perform the most expensive computations of each iteration on Graphics Processing Unit (GPU). We apply the proposed parallel approach on three different GPU boards, with computational performance comparable with that of the boards usually installed in commercial DBT systems. The numerical results show that the proposed GPU-based MBIR method provides accurate reconstructions in a time suitable for clinical trials.
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Shi L, Liu B, Yu H, Wei C, Wei L, Zeng L, Wang G. Review of CT image reconstruction open source toolkits. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2020; 28:619-639. [PMID: 32390648 DOI: 10.3233/xst-200666] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Computed tomography (CT) has been widely applied in medical diagnosis, nondestructive evaluation, homeland security, and other science and engineering applications. Image reconstruction is one of the core CT imaging technologies. In this review paper, we systematically reviewed the currently publicly available CT image reconstruction open source toolkits in the aspects of their environments, object models, imaging geometries, and algorithms. In addition to analytic and iterative algorithms, deep learning reconstruction networks and open codes are also reviewed as the third category of reconstruction algorithms. This systematic summary of the publicly available software platforms will help facilitate CT research and development.
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Affiliation(s)
- Liu Shi
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Baodong Liu
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, USA
| | - Cunfeng Wei
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Long Wei
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Li Zeng
- College of Mathematics and Statistics, Chongqing University, Chongqing, China
- Engineering Research Center of Industrial Computed Tomography Nondestructive Testing of the Education Ministry of China, Chongqing University, Chongqing, China
| | - Ge Wang
- Biomedical Imaging Center, AI-based X-ray Imaging System (AXIS) Lab, Rensselaer Polytechnic Institute, Troy, NY, USA
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Image Quality Measured From Ultra-Low Dose Chest Computed Tomography Examination Protocols Using 6 Different Iterative Reconstructions From 4 Vendors, a Phantom Study. J Comput Assist Tomogr 2020; 44:95-101. [DOI: 10.1097/rct.0000000000000947] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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124
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Nielsen JS, Edmund JM, Van Leemput K. Magnetic resonance-based computed tomography metal artifact reduction using Bayesian modelling. Phys Med Biol 2019; 64:245012. [PMID: 31766033 DOI: 10.1088/1361-6560/ab5b70] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Metal artifact reduction (MAR) algorithms reduce the errors caused by metal implants in x-ray computed tomography (CT) images and are an important part of error management in radiotherapy. A promising MAR approach is to leverage the information in magnetic resonance (MR) images that can be acquired for organ or tumor delineation. This is however complicated by the ambiguous relationship between CT values and conventional-sequence MR intensities as well as potential co-registration issues. In order to address these issues, this paper proposes a self-tuning Bayesian model for MR-based MAR that combines knowledge of the MR image intensities in local spatial neighborhoods with the information in an initial, corrupted CT reconstructed using filtered back projection. We demonstrate the potential of the resulting model in three widely-used MAR scenarios: image inpainting, sinogram inpainting and model-based iterative reconstruction. Compared to conventional alternatives in a retrospective study on nine head-and-neck patients with CT and T1-weighted MR scans, we find improvements in terms of image quality and quantitative CT value accuracy within each scenario. We conclude that the proposed model provides a versatile way to use the anatomical information in a co-acquired MR scan to boost the performance of MAR algorithms.
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Affiliation(s)
- Jonathan Scharff Nielsen
- Department of Health Technology, Technical University of Denmark, 2820 Lyngby, Denmark. Radiotherapy Research Unit, Department of Oncology, Gentofte and Herlev Hospital, University of Copenhagen, 2730 Herlev, Denmark
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Shin JB, Yoon DK, Pak S, Kwon YH, Suh TS. Comparative performance analysis for abdominal phantom ROI detectability according to CT reconstruction algorithm: ADMIRE. J Appl Clin Med Phys 2019; 21:136-143. [PMID: 31729832 PMCID: PMC6964754 DOI: 10.1002/acm2.12765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 10/09/2019] [Accepted: 10/11/2019] [Indexed: 01/10/2023] Open
Abstract
Purpose We compared and analyzed the detectability performance pertaining to an abdominal phantom including a region of interest (ROI) according to a computed tomography (CT) reconstruction algorithm. Methods Three types of reconstruction algorithms (FBP, SAFIRE, and ADMIRE) were used to evaluate the detectability performance using the abdominal phantom (phantom size: 25 × 18 × 28 cm3). The vendor default settings for routine multi‐detector computed tomography abdominal scans were used. As the quantitative evaluation method, the contrast‐to‐noise ratio (CNR), difference in coefficient of variation (COV) with the normalization based on the FBP data, and the noise power spectrum (NPS) were measured. Results The characteristic of the ADMIRE‐3 reconstructed image was higher than those of the FBP and SAFIRE‐3 reconstructed images. The CNR values of the SAFIRE and ADMIRE images were much higher than the corresponding values of the FBP images. The difference in COV values for the ADMIRE images was ~1.2 times lower than the corresponding values of the SAFIRE images. Conclusion The comparative analysis of the abdominal phantom low‐contrast resolution differences for each CT exposure parameters showed that ADMIRE demonstrated better results than SAFIRE and FBP in terms of contrast, CNR, COV difference, and 1D NPS. This indicates that ADMIRE can provide a clearer observation even with the same number of contrast objects as compared to SAFIRE and FBP owing to its better contrast resolution in the central part of the contrast hole at low kV.
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Affiliation(s)
- Jun-Bong Shin
- Department of Biomedical Engineering and Research Institute of Biomedical Engineering, College of Medicine, Catholic University of Korea, Seoul, Korea
| | - Do-Kun Yoon
- Department of Biomedical Engineering and Research Institute of Biomedical Engineering, College of Medicine, Catholic University of Korea, Seoul, Korea
| | | | | | - Tae Suk Suh
- Department of Biomedical Engineering and Research Institute of Biomedical Engineering, College of Medicine, Catholic University of Korea, Seoul, Korea
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Moloney F, Twomey M, James K, Kavanagh RG, Fama D, O'Neill S, Grey TM, Moore N, Murphy MJ, O'Connor OJ, Maher MM. A phantom study of the performance of model-based iterative reconstruction in low-dose chest and abdominal CT: When are benefits maximized? Radiography (Lond) 2019; 24:345-351. [PMID: 30292504 DOI: 10.1016/j.radi.2018.04.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Revised: 03/26/2018] [Accepted: 04/23/2018] [Indexed: 01/09/2023]
Abstract
INTRODUCTION The aim of this study was to assess and compare the effects of CT image reconstruction techniques on low-dose CT image quality using phantoms. METHODS Anthropomorphic torso and spatial/contrast-resolution phantoms were scanned at decreasing tube currents between 400 and 10 mA. CT thorax and abdomen/pelvis series were reconstructed with filtered back projection (FBP) alone, combined 40% adaptive statistical iterative reconstruction & FBP (ASIR40), and model-based iterative reconstruction (MBIR) [(resolution-preference 05 (RP05) and RP20 in the thorax and RP05 and noise-reduction 05 (NR05) in the abdomen)]. Two readers rated image quality quantitatively and qualitatively. RESULTS In thoracic CT, objective image noise on MBIR RP05 data sets outperformed FBP at 200, 100, 50 and 10 mA and outperformed ASIR40 at 50 and 10 mA (p < 0.001). MBIR RP20 outperformed FBP at 50 and 10 mA and outperformed ASIR40 at 10 mA (p < 0.001). Compared with both FBP and ASIR40, MBIR RP05 demonstrated significantly better signal-to-noise ratio (SNR) at 10 mA. In abdomino-pelvic CT, MBIR RP05 and NR05 outperformed FBP and ASIR at all tube current levels for objective image noise. NR05 demonstrated greater SNR at 200, 100, 50 and 10 mA and RP05 demonstrated greater SNR at 50 and 10 mA compared with both FBP and ASIR. MBIR images demonstrated better subjective image quality scores. Spatial resolution, low-contrast detectability and contrast-to-noise ratio (CNR) were comparable between image reconstruction techniques. CONCLUSION CTs reconstructed with MBIR have lower image noise and improved image quality compared with FBP and ASIR. These effects increase with reduced radiation exposure confirming optimal use for low-dose CT imaging.
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Affiliation(s)
- F Moloney
- Department of Radiology, Cork University Hospital, Cork, Ireland; College of Medicine & Health, University College Cork, Cork, Ireland
| | - M Twomey
- Department of Radiology, Cork University Hospital, Cork, Ireland; College of Medicine & Health, University College Cork, Cork, Ireland
| | - K James
- Department of Radiology, Cork University Hospital, Cork, Ireland; College of Medicine & Health, University College Cork, Cork, Ireland
| | - R G Kavanagh
- Department of Radiology, Cork University Hospital, Cork, Ireland; College of Medicine & Health, University College Cork, Cork, Ireland.
| | - D Fama
- Department of Radiology, Cork University Hospital, Cork, Ireland
| | - S O'Neill
- Department of Radiology, Cork University Hospital, Cork, Ireland; College of Medicine & Health, University College Cork, Cork, Ireland
| | - T M Grey
- Department of Radiology, Cork University Hospital, Cork, Ireland
| | - N Moore
- Department of Radiology, Cork University Hospital, Cork, Ireland; College of Medicine & Health, University College Cork, Cork, Ireland
| | - M J Murphy
- Department of Radiology, Cork University Hospital, Cork, Ireland
| | - O J O'Connor
- Department of Radiology, Cork University Hospital, Cork, Ireland; College of Medicine & Health, University College Cork, Cork, Ireland; APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - M M Maher
- Department of Radiology, Cork University Hospital, Cork, Ireland; College of Medicine & Health, University College Cork, Cork, Ireland; APC Microbiome Ireland, University College Cork, Cork, Ireland
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Sumida I, Magome T, Kitamori H, Das IJ, Yamaguchi H, Kizaki H, Aboshi K, Yamashita K, Yamada Y, Seo Y, Isohashi F, Ogawa K. Deep convolutional neural network for reduction of contrast-enhanced region on CT images. JOURNAL OF RADIATION RESEARCH 2019; 60:586-594. [PMID: 31125068 PMCID: PMC6805976 DOI: 10.1093/jrr/rrz030] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Revised: 03/30/2019] [Indexed: 06/09/2023]
Abstract
This study aims to produce non-contrast computed tomography (CT) images using a deep convolutional neural network (CNN) for imaging. Twenty-nine patients were selected. CT images were acquired without and with a contrast enhancement medium. The transverse images were divided into 64 × 64 pixels. This resulted in 14 723 patches in total for both non-contrast and contrast-enhanced CT image pairs. The proposed CNN model comprises five two-dimensional (2D) convolution layers with one shortcut path. For comparison, the U-net model, which comprises five 2D convolution layers interleaved with pooling and unpooling layers, was used. Training was performed in 24 patients and, for testing of trained models, another 5 patients were used. For quantitative evaluation, 50 regions of interest (ROIs) were selected on the reference contrast-enhanced image of the test data, and the mean pixel value of the ROIs was calculated. The mean pixel values of the ROIs at the same location on the reference non-contrast image and the predicted non-contrast image were calculated and those values were compared. Regarding the quantitative analysis, the difference in mean pixel value between the reference contrast-enhanced image and the predicted non-contrast image was significant (P < 0.0001) for both models. Significant differences in pixels (P < 0.0001) were found using the U-net model; in contrast, there was no significant difference using the proposed CNN model when comparing the reference non-contrast images and the predicted non-contrast images. Using the proposed CNN model, the contrast-enhanced region was satisfactorily reduced.
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Affiliation(s)
- Iori Sumida
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, 2-2 Yamada-oka, Suita, Osaka, Japan
| | - Taiki Magome
- Department of Radiological Sciences, Faculty of Health Sciences, Komazawa University, 1-23-1 Komazawa, Setagaya-ku, Tokyo, Japan
| | - Hideki Kitamori
- Department of Health Sciences, Graduate School of Medicine Science, Kyusyu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Japan
- Department of Oral and Maxillofacial Radiology, Osaka University Graduate School of Dentistry, 1-8 Yamada-oka Suita, Japan
| | - Indra J Das
- Department of Radiation Oncology, New York University Langone Medical Center, Laura & Isaac Perlmutter Cancer Center, 160 E 34th Street, New York, NY, USA
| | - Hajime Yamaguchi
- Department of Radiation Oncology, NTT West Osaka hospital, 2-6-40 Karasugatsuji, Tennoji-ku, Osaka, Japan
| | - Hisao Kizaki
- Department of Radiation Oncology, NTT West Osaka hospital, 2-6-40 Karasugatsuji, Tennoji-ku, Osaka, Japan
| | - Keiko Aboshi
- Department of Radiation Oncology, NTT West Osaka hospital, 2-6-40 Karasugatsuji, Tennoji-ku, Osaka, Japan
| | - Kyohei Yamashita
- Department of Radiation Oncology, NTT West Osaka hospital, 2-6-40 Karasugatsuji, Tennoji-ku, Osaka, Japan
| | - Yuji Yamada
- Department of Radiation Oncology, NTT West Osaka hospital, 2-6-40 Karasugatsuji, Tennoji-ku, Osaka, Japan
| | - Yuji Seo
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, 2-2 Yamada-oka, Suita, Osaka, Japan
| | - Fumiaki Isohashi
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, 2-2 Yamada-oka, Suita, Osaka, Japan
| | - Kazuhiko Ogawa
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, 2-2 Yamada-oka, Suita, Osaka, Japan
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Magnusson M, Björnfot M, Tedgren ÅC, Carlsson GA, Sandborg M, Malusek A. DIRA-3D—a model-based iterative algorithm for accurate dual-energy dual-source 3D helical CT. Biomed Phys Eng Express 2019. [DOI: 10.1088/2057-1976/ab42ee] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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129
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Hempel JM, Niklas Bongers M, Braun K, Ernemann U, Bier G. Noise reduction and image quality in ultra-high resolution computed tomography of the temporal bone using advanced modeled iterative reconstruction. Acta Radiol 2019; 60:1135-1143. [PMID: 30621442 DOI: 10.1177/0284185118820699] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Johann-Martin Hempel
- Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls University, Tübingen, Germany
| | - Malte Niklas Bongers
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tübingen, Germany
| | - Katharina Braun
- Department of Otolaryngology and Head and Neck Surgery, Eberhard Karls University, Tübingen, Germany
| | - Ulrike Ernemann
- Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls University, Tübingen, Germany
| | - Georg Bier
- Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls University, Tübingen, Germany
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130
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Guo Y, Aveyard R, Rieger B. A Multichannel Cross-Modal Fusion Framework for Electron Tomography. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2019; 28:4206-4218. [PMID: 30908226 DOI: 10.1109/tip.2019.2907461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In this paper, we present a multichannel cross-modal fusion algorithm to combine two complementary modalities in electron tomography: X-ray spectroscopy and scanning transmission electron microscopy (STEM). The former reveals compositions with high elemental specificity but low signal-to-noise ratio (SNR), while the latter characterizes the structure with high SNR but little chemical information. We use a multivariate regression to build a cross-modal fusion framework for these two modalities to simultaneously achieve high elemental specificity and high SNR for a target element chosen from the sample under study. Specifically, we first compute three-dimensional tomograms from tilt-series datasets of X-ray and STEM using different reconstruction algorithms. Then, we generate many feature images from each tomogram. Finally, we adopt partial least squares regression to assess the connection between these feature images and the reconstruction of the target element. Based on the simulated and experimental datasets of semiconductor devices, we demonstrate that our algorithm can not only produce continuous edges, homogeneous foreground, and clean background in its element-specific reconstructions but also can more accurately preserve fine structures than state-of-the-art tomography techniques. Moreover, we show that it can deliver results with high fidelity even for X-ray datasets with limited tilts or low counts. This property is highly desired in the semiconductor industry where acquisition time and sample damage are essential.
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131
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Muckley MJ, Chen B, Vahle T, O'Donnell T, Knoll F, Sodickson AD, Sodickson DK, Otazo R. Image reconstruction for interrupted-beam x-ray CT on diagnostic clinical scanners. Phys Med Biol 2019; 64:155007. [PMID: 31258151 DOI: 10.1088/1361-6560/ab2df1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Low-dose x-ray CT is a major research area with high clinical impact. Compressed sensing using view-based sparse sampling and sparsity-promoting regularization has shown promise in simulations, but these methods can be difficult to implement on diagnostic clinical CT scanners since the x-ray beam cannot be switched on and off rapidly enough. An alternative to view-based sparse sampling is interrupted-beam sparse sampling. SparseCT is a recently-proposed interrupted-beam scheme that achieves sparse sampling by blocking a portion of the beam using a multislit collimator (MSC). The use of an MSC necessitates a number of modifications to the standard compressed sensing reconstruction pipeline. In particular, we find that SparseCT reconstruction is feasible within a model-based image reconstruction framework that incorporates data fidelity weighting to consider penumbra effects and source jittering to consider the effect of partial source obstruction. Here, we present these modifications and demonstrate their application in simulations and real-world prototype scans. In simulations compared to conventional low-dose acquisitions, SparseCT is able to achieve smaller normalized root-mean square differences and higher structural similarity measures on two reduction factors. In prototype experiments, we successfully apply our reconstruction modifications and maintain image resolution at quarter-dose reduction level. The SparseCT design requires only small hardware modifications to current diagnostic clinical scanners, opening up new possibilities for CT dose reduction.
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Affiliation(s)
- Matthew J Muckley
- New York University School of Medicine, New York, NY, United States of America
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132
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Tube Current Reduction in CT Angiography: How Low Can We Go in Imaging of Patients With Suspected Acute Stroke? AJR Am J Roentgenol 2019; 213:410-416. [DOI: 10.2214/ajr.18.20954] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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133
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Application of iterative reconstruction algorithms to mitigate CT-artefacts when measuring fiber reinforced polymer materials. POLYMER 2019. [DOI: 10.1016/j.polymer.2019.06.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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134
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Abstract
CLINICAL/METHODICAL ISSUE Computed tomography (CT) acquisition should be performed following the ALARA principle: keeping patient radiation exposure as low as reasonably achievable. STANDARD RADIOLOGICAL METHODS Reconstruction with filtered backprojection is still the standard in CT. METHODICAL INNOVATIONS Recently, iterative reconstruction techniques have become available, using a different approach for image reconstruction. A similar approach is used for iterative metal artifact reduction. PERFORMANCE Compared to filtered backprojection, iterative reconstruction yields improvements in image quality and reduces image noise. ACHIEVEMENTS Using iterative reconstruction allows the reduction of patient radiation exposure by up to 80%, depending on the used algorithm and the clinical task at hand. With the help of iterative metal artifact reduction, images of diagnostic quality can be acquired despite metal implants. PRACTICAL RECOMMENDATIONS Iterative reconstruction should be used to reduce patient radiation exposure in accordance with the clinical requirements. The use of iterative metal artifact reduction is recommended.
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135
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Muhanna N, Douglas CM, Daly MJ, Chan HHL, Weersink R, Qiu J, Townson J, de Almeida JR, Goldstein D, Gilbert R, Yu E, Kucharczyk W, Jaffray DA, Irish JC. The image-guided operating room-Utility and impact on surgeon's performance in the head and neck surgery. Head Neck 2019; 41:3372-3382. [PMID: 31287216 DOI: 10.1002/hed.25864] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 05/17/2019] [Accepted: 06/18/2019] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND The image-guided operating room (OR) is an emerging standard for dealing with complex cases in many surgical disciplines including neurosurgery, thoracic surgery, maxillofacial trauma, and orthopedic surgery. Its use in head and neck oncological surgery is not well established. The primary aim of this study was to assess the image quality of cone-beam CT (CBCT) under real clinical conditions. The secondary aim was to assess the effect on surgical performance and decision making. METHODS Intraoperative 3D imaging was performed using a CBCT capable C-Arm mounted on a multi-axis robot (Siemens Zeego) in the image-guided OR. All patients had immediate preoperative imaging taken with further intraoperative imaging performed as required. Ten initial patients, comprising 28 intraoperative scans, were used for questionnaire-based image reviews conducted with experienced head and neck clinicians. Scans were assessed for aspects of both image quality and clinical utility, on separate 5-point Likert scales (1-5). RESULTS The median rating for bony detail was 4 out of 5. Vascular detail was increased (P < 10-8 ) from 1 to 3 with the use of IV contrast (region of interest CT# was 284 HU [SD, 47 HU]). Images were rated as 4 for freedom from artifact. Soft tissue definition was 2, with no significant improvement (P = .2) with the addition of IV iodinated contrast. Surgeons rated the greatest clinical utility (4) for the CBCT when assessing postreconstruction imaging of a complex case. CONCLUSIONS The image quality of CBCT in the image-guided OR is good for bony detail and complex oncological reconstructions in the head and neck setting but probably has limited benefit for intraoperative soft tissue delineation. Future studies must also focus on clinical outcomes to help demonstrate the value of the image-guided OR.
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Affiliation(s)
- Nidal Muhanna
- Department of Otolaryngology-Head and Neck Surgery, University of Toronto, Toronto, Ontario, Canada.,Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Otolaryngology, Head and Neck and Maxillofacial Surgery, Tel-Aviv Sourasky Medical Center - Tel Aviv University, Tel Aviv, Israel.,Guided Therapeutics (GTx) Program, TECHNA Institute, University Health Network, Toronto, Ontario, Canada
| | - Catriona M Douglas
- Department of Otolaryngology-Head and Neck Surgery, University of Toronto, Toronto, Ontario, Canada.,Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Michael J Daly
- Guided Therapeutics (GTx) Program, TECHNA Institute, University Health Network, Toronto, Ontario, Canada
| | - Harley H L Chan
- Guided Therapeutics (GTx) Program, TECHNA Institute, University Health Network, Toronto, Ontario, Canada
| | - Robert Weersink
- Guided Therapeutics (GTx) Program, TECHNA Institute, University Health Network, Toronto, Ontario, Canada.,Department of Medical Physics, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Jimmy Qiu
- Guided Therapeutics (GTx) Program, TECHNA Institute, University Health Network, Toronto, Ontario, Canada
| | - Jason Townson
- Guided Therapeutics (GTx) Program, TECHNA Institute, University Health Network, Toronto, Ontario, Canada
| | - John R de Almeida
- Department of Otolaryngology-Head and Neck Surgery, University of Toronto, Toronto, Ontario, Canada.,Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - David Goldstein
- Department of Otolaryngology-Head and Neck Surgery, University of Toronto, Toronto, Ontario, Canada.,Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Ralph Gilbert
- Department of Otolaryngology-Head and Neck Surgery, University of Toronto, Toronto, Ontario, Canada.,Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Eugene Yu
- Toronto Joint Department of Medical Imaging, University Health Network/Mt. Sinai Hospital, Toronto, Ontario, Canada
| | - Walter Kucharczyk
- Toronto Joint Department of Medical Imaging, University Health Network/Mt. Sinai Hospital, Toronto, Ontario, Canada
| | - David A Jaffray
- Guided Therapeutics (GTx) Program, TECHNA Institute, University Health Network, Toronto, Ontario, Canada.,Department of Medical Physics, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Toronto Joint Department of Medical Imaging, University Health Network/Mt. Sinai Hospital, Toronto, Ontario, Canada
| | - Jonathan C Irish
- Department of Otolaryngology-Head and Neck Surgery, University of Toronto, Toronto, Ontario, Canada.,Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Guided Therapeutics (GTx) Program, TECHNA Institute, University Health Network, Toronto, Ontario, Canada
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Demer LL, Tintut Y, Nguyen KL, Hsiai T, Lee JT. Rigor and Reproducibility in Analysis of Vascular Calcification. Circ Res 2019; 120:1240-1242. [PMID: 28408452 DOI: 10.1161/circresaha.116.310326] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Linda L Demer
- From the Department of Medicine (L.L.D., Y.T., K.L.N., T.H.), Department of Physiology (L.L.D., Y.T.), Department of Bioengineering (L.L.D., T.H.), Department of Orthopaedic Surgery (Y.T.), Department of Molecular & Medical Pharmacology (J.T.L.), and Crump Institute for Molecular Imaging (J.T.L.), UCLA and the Greater Los Angeles Veterans Administration.
| | - Yin Tintut
- From the Department of Medicine (L.L.D., Y.T., K.L.N., T.H.), Department of Physiology (L.L.D., Y.T.), Department of Bioengineering (L.L.D., T.H.), Department of Orthopaedic Surgery (Y.T.), Department of Molecular & Medical Pharmacology (J.T.L.), and Crump Institute for Molecular Imaging (J.T.L.), UCLA and the Greater Los Angeles Veterans Administration
| | - Kim-Lien Nguyen
- From the Department of Medicine (L.L.D., Y.T., K.L.N., T.H.), Department of Physiology (L.L.D., Y.T.), Department of Bioengineering (L.L.D., T.H.), Department of Orthopaedic Surgery (Y.T.), Department of Molecular & Medical Pharmacology (J.T.L.), and Crump Institute for Molecular Imaging (J.T.L.), UCLA and the Greater Los Angeles Veterans Administration
| | - Tzung Hsiai
- From the Department of Medicine (L.L.D., Y.T., K.L.N., T.H.), Department of Physiology (L.L.D., Y.T.), Department of Bioengineering (L.L.D., T.H.), Department of Orthopaedic Surgery (Y.T.), Department of Molecular & Medical Pharmacology (J.T.L.), and Crump Institute for Molecular Imaging (J.T.L.), UCLA and the Greater Los Angeles Veterans Administration
| | - Jason T Lee
- From the Department of Medicine (L.L.D., Y.T., K.L.N., T.H.), Department of Physiology (L.L.D., Y.T.), Department of Bioengineering (L.L.D., T.H.), Department of Orthopaedic Surgery (Y.T.), Department of Molecular & Medical Pharmacology (J.T.L.), and Crump Institute for Molecular Imaging (J.T.L.), UCLA and the Greater Los Angeles Veterans Administration
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137
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Intraosteal Behavior of Porous Scaffolds: The mCT Raw-Data Analysis as a Tool for Better Understanding. Symmetry (Basel) 2019. [DOI: 10.3390/sym11040532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The aim of the study is to determine the existing correlation between high-resolution 3D imaging technique obtained through Micro Computed Tomography (mCT) and histological-histomorphometric images to determine in vivo bone osteogenic behavior of bioceramic scaffolds. A Ca-Si-P scaffold ceramic doped and non-doped (control) with a natural demineralized bone matrix (DBM) were implanted in rabbit tibias for 1, 3, and 5 months. A progressive disorganization and disintegration of scaffolds and bone neoformation occurs, from the periphery to the center of the implants, without any differences between histomorphometric and radiological analysis. However, significant differences (p < 0.05) between DMB-doped and non-doped materials where only detected through mathematical analysis of mCT. In this way, average attenuation coefficient for DMB-doped decreased from 0.99 ± 0.23 Hounsfield Unit (HU) (3 months) to 0.86 ± 0.32 HU (5 months). Average values for non-doped decreased from 0.86 ± 0.25 HU (3 months) to 0.66 ± 0.33 HU. Combination of radiological analysis and mathematical mCT seems to provide an adequate in vivo analysis of bone-implanted biomaterials after surgery, obtaining similar results to the one provided by histomorphometric analysis. Mathematical analysis of Computed Tomography (CT) would allow the conducting of long-term duration in vivo studies, without the need for animal sacrifice, and the subsequent reduction in variability.
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138
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Sollmann N, Mei K, Hedderich DM, Maegerlein C, Kopp FK, Löffler MT, Zimmer C, Rummeny EJ, Kirschke JS, Baum T, Noël PB. Multi-detector CT imaging: impact of virtual tube current reduction and sparse sampling on detection of vertebral fractures. Eur Radiol 2019; 29:3606-3616. [PMID: 30903337 PMCID: PMC6554251 DOI: 10.1007/s00330-019-06090-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 01/30/2019] [Accepted: 02/08/2019] [Indexed: 12/14/2022]
Abstract
PURPOSE To systematically evaluate the effects of virtual tube current reduction and sparse sampling on image quality and vertebral fracture diagnostics in multi-detector computed tomography (MDCT). MATERIALS AND METHODS In routine MDCT scans of 35 patients (80.0% females, 70.6 ± 14.2 years, 65.7% showing vertebral fractures), reduced radiation doses were retrospectively simulated by virtually lowering tube currents and applying sparse sampling, considering 50%, 25%, and 10% of the original tube current and projections, respectively. Two readers evaluated items of image quality and presence of vertebral fractures. Readout between the evaluations in the original images and those with virtually lowered tube currents or sparse sampling were compared. RESULTS A significant difference was revealed between the evaluations of image quality between MDCT with virtually lowered tube current and sparse-sampled MDCT (p < 0.001). Sparse-sampled data with only 25% of original projections still showed good to very good overall image quality and contrast of vertebrae as well as minimal artifacts. There were no missed fractures in sparse-sampled MDCT with 50% reduction of projections, and clinically acceptable determination of fracture age was possible in MDCT with 75% reduction of projections, in contrast to MDCT with 50% or 75% virtual tube current reduction, respectively. CONCLUSION Sparse-sampled MDCT provides adequate image quality and diagnostic accuracy for vertebral fracture detection with 50% of original projections in contrast to corresponding MDCT with lowered tube current. Thus, sparse sampling is a promising technique for dose reductions in MDCT that could be introduced in future generations of scanners. KEY POINTS • MDCT with a reduction of projection numbers of 50% still showed high diagnostic accuracy without any missed vertebral fractures. • Clinically acceptable determination of vertebral fracture age was possible in MDCT with a reduction of projection numbers of 75%. • With sparse sampling, higher reductions in radiation exposure can be achieved without compromised image or diagnostic quality in routine MDCT of the spine as compared to MDCT with reduced tube currents.
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Affiliation(s)
- Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany.
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
| | - Kai Mei
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675 Munich, Germany
| | - Dennis M Hedderich
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Christian Maegerlein
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Felix K Kopp
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675 Munich, Germany
| | - Maximilian T Löffler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Ernst J Rummeny
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675 Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Peter B Noël
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675 Munich, Germany
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, One Silverstein, Philadelphia, PA 19104, USA
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139
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Abstract
Cardiac SPECT continues to play a critical role in detecting and managing cardiovascular disease, in particularly coronary artery disease (CAD) (Jaarsma et al 2012 J. Am. Coll. Cardiol. 59 1719-28), (Agostini et al 2016 Eur. J. Nucl. Med. Mol. Imaging 43 2423-32). While conventional dual-head SPECT scanners using parallel-hole collimators and scintillation crystals with photomultiplier tubes are still the workhorse of cardiac SPECT, they have the limitations of low photon sensitivity (~130 count s-1 MBq-1), poor image resolution (~15 mm) (Imbert et al 2012 J. Nucl. Med. 53 1897-903), relatively long acquisition time, inefficient use of the detector, high radiation dose, etc. Recently our field observed an exciting growth of new developments of dedicated cardiac scanners and collimators, as well as novel imaging algorithms for quantitative cardiac SPECT. These developments have opened doors to new applications with potential clinical impact, including ultra-low-dose imaging, absolute quantification of myocardial blood flow (MBF) and coronary flow reserve (CFR), multi-radionuclide imaging, and improved image quality as a result of attenuation, scatter, motion, and partial volume corrections (PVCs). In this article, we review the recent advances in cardiac SPECT instrumentation and imaging methods. This review mainly focuses on the most recent developments published since 2012 and points to the future of cardiac SPECT from an imaging physics perspective.
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Affiliation(s)
- Jing Wu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, United States of America
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140
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Miller AR, Jackson D, Hui C, Deshpande S, Kuo E, Hamilton GS, Lau KK. Lung nodules are reliably detectable on ultra-low-dose CT utilising model-based iterative reconstruction with radiation equivalent to plain radiography. Clin Radiol 2019; 74:409.e17-409.e22. [PMID: 30832990 DOI: 10.1016/j.crad.2019.02.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Accepted: 02/06/2019] [Indexed: 12/21/2022]
Abstract
AIM To determine if ultra-low-dose (ULD) computed tomography (CT) utilising model-based iterative reconstruction (MBIR) with radiation equivalent to plain radiography allows the detection of lung nodules. MATERIALS AND METHODS Ninety-nine individuals undergoing surveillance of solid pulmonary nodules undertook a low-dose (LD) and ULD CT during the same sitting. Image pairs were read blinded, in random order, and independently by two experienced thoracic radiologists. With LD-CT as the reference standard, the number, size, and location of nodules was compared, and inter-rater agreement was established. RESULTS There was very good inter-rater agreement with regards nodules ≥4mm for both the LD- (k=0.931) and ULD-CT (k=0.869). One hundred and ninety-nine nodules were reported on the LD-CT by both radiologists and 196 reported on the ULD-CT, with no nodules reported only on the ULD-CT. This gives a sensitivity of 98.5% and specificity of 100% for ULD-CT with MBIR. The effective dose of radiation was significantly different between the two scans (p<0.0001), 1.67 mSv for the LD-CT and 0.13 mSv for the ULD-CT. CONCLUSION ULD-CT utilising MBIR and delivering radiation equivalent to plain radiography, allows detection of lung nodules with high sensitivity. The attendant 10-fold reduction in radiation may allow for dramatic reductions in cumulative radiation exposure.
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Affiliation(s)
- A R Miller
- Monash Lung and Sleep, Monash Health, Clayton, Victoria, Australia; Monash University, Clayton, Victoria, Australia; General Medicine, Monash Health, Clayton, Victoria, Australia.
| | - D Jackson
- Monash Imaging, Monash Health, Clayton, Victoria, Australia
| | - C Hui
- Monash Imaging, Monash Health, Clayton, Victoria, Australia
| | - S Deshpande
- Monash Lung and Sleep, Monash Health, Clayton, Victoria, Australia
| | - E Kuo
- Monash Lung and Sleep, Monash Health, Clayton, Victoria, Australia
| | - G S Hamilton
- Monash Lung and Sleep, Monash Health, Clayton, Victoria, Australia; Monash University, Clayton, Victoria, Australia
| | - K K Lau
- General Medicine, Monash Health, Clayton, Victoria, Australia; Monash Imaging, Monash Health, Clayton, Victoria, Australia
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141
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Micieli D, Minniti T, Evans LM, Gorini G. Accelerating Neutron Tomography experiments through Artificial Neural Network based reconstruction. Sci Rep 2019; 9:2450. [PMID: 30792423 PMCID: PMC6385317 DOI: 10.1038/s41598-019-38903-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 12/18/2018] [Indexed: 11/19/2022] Open
Abstract
Neutron Tomography (NT) is a non-destructive technique to investigate the inner structure of a wide range of objects and, in some cases, provides valuable results in comparison to the more common X-ray imaging techniques. However, NT is time consuming and scanning a set of similar objects during a beamtime leads to data redundancy and long acquisition times. Nowadays NT is unfeasible for quality checking study of large quantities of similar objects. One way to decrease the total scan time is to reduce the number of projections. Analytical reconstruction methods are very fast but under this condition generate streaking artifacts in the reconstructed images. Iterative algorithms generally provide better reconstruction for limited data problems, but at the expense of longer reconstruction time. In this study, we propose the recently introduced Neural Network Filtered Back-Projection (NN-FBP) method to optimize the time usage in NT experiments. Simulated and real neutron data were used to assess the performance of the NN-FBP method as a function of the number of projections. For the first time a machine learning based algorithm is applied and tested for NT image reconstruction problem. We demonstrate that the NN-FBP method can reliably reduce acquisition and reconstruction times and it outperforms conventional reconstruction methods used in NT, providing high image quality for limited datasets.
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Affiliation(s)
- Davide Micieli
- Università della Calabria, Dipartimento di Fisica, Arcavacata di Rende (Cosenza), 87036, Italy.
- Università degli Studi Milano-Bicocca, Dipartimento di Fisica "G. Occhialini", Milano, 20126, Italy.
| | - Triestino Minniti
- STFC, Rutherford Appleton Laboratory, ISIS Facility, Harwell, United Kingdom
| | - Llion Marc Evans
- Culham Centre for Fusion Energy, Culham Science Centre, Abingdon, Oxfordshire, United Kingdom
- College of Engineering, Swansea University, Bay Campus, Fabian Way, Swansea, United Kingdom
| | - Giuseppe Gorini
- Università degli Studi Milano-Bicocca, Dipartimento di Fisica "G. Occhialini", Milano, 20126, Italy
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142
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Shanblatt ER, Sung Y, Gupta R, Nelson BJ, Leng S, Graves WS, McCollough CH. Forward model for propagation-based x-ray phase contrast imaging in parallel- and cone-beam geometry. OPTICS EXPRESS 2019; 27:4504-4521. [PMID: 30876068 DOI: 10.1364/oe.27.004504] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 01/21/2019] [Indexed: 06/09/2023]
Abstract
We demonstrate a fast, flexible, and accurate paraxial wave propagation model to serve as a forward model for propagation-based X-ray phase contrast imaging (XPCI) in parallel-beam or cone-beam geometry. This model incorporates geometric cone-beam effects into the multi-slice beam propagation method. It enables rapid prototyping and is well suited to serve as a forward model for propagation-based X-ray phase contrast tomographic reconstructions. Furthermore, it is capable of modeling arbitrary objects, including those that are strongly or multi-scattering. Simulation studies were conducted to compare our model to other forward models in the X-ray regime, such as the Mie and full-wave Rytov solutions.
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143
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Polat A, Hassan S, Yildirim I, Oliver LE, Mostafaei M, Kumar S, Maharjan S, Bourguet L, Cao X, Ying G, Eyvazi Hesar M, Zhang YS. A miniaturized optical tomography platform for volumetric imaging of engineered living systems. LAB ON A CHIP 2019; 19:550-561. [PMID: 30657153 PMCID: PMC6391727 DOI: 10.1039/c8lc01190g] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Volumetric optical microscopy approaches that enable acquisition of three-dimensional (3D) information from a biological sample are attractive for numerous non-invasive imaging applications. The unprecedented structural details that these techniques provide have helped in our understanding of different aspects of architecture of cells, tissues, and organ systems as they occur in their natural states. Nonetheless, the instrumentation for most of these techniques is sophisticated, bulky, and costly, and is less affordable to most laboratory settings. Several miniature imagers based on webcams or low-cost sensors featuring easy assembly have been reported, for in situ imaging of biological structures at low costs. However, they have not been able to achieve the ability of 3D imaging throughout the entire volumes for spatiotemporal analyses of the structural changes in these specimens. Here we present a miniaturized optical tomography (mini-Opto) platform for low-cost, volumetric characterization of engineered living systems through hardware optimizations as well as applications of an optimized algebraic algorithm for image reconstruction.
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Affiliation(s)
- Adem Polat
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 65 Landsdowne St, Cambridge, MA 02139, USA.
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144
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Hickethier T, Baeßler B, Kroeger JR, Müller D, Maintz D, Michels G, Bunck AC. Knowledge-based iterative reconstructions for imaging of coronary artery stents: first in-vitro experience and comparison of different radiation dose levels and kernel settings. Acta Radiol 2019; 60:160-167. [PMID: 29807442 DOI: 10.1177/0284185118778875] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND Advanced knowledge-based iterative model reconstructions (IMR) became recently available for routine computed tomography (CT). Using more realistic physical models it promises improved image quality and potential radiation dose reductions, both possibly beneficial for non-invasive assessment of coronary stents. PURPOSE To evaluate the influence of different IMR settings at different radiation doses on stent lumen visualization in comparison to filtered back projection (FBP) and first-generation (hybrid) iterative reconstruction (HIR). MATERIAL AND METHODS Ten coronary stents in a coronary phantom were examined at four different dose settings (120 kV/125 mAs, 120 kV/75 mAs, 100 kV/125 mAs, 100 kV/75 mAs). Images were reconstructed with stent-specific FBP and HIR kernels and with IMR using CardiacRoutine (CR) and CardiacSharp (CS) settings at three different iteration levels. Image quality was evaluated using established parameters: image noise; in-stent attenuation difference; and visible lumen diameter. RESULTS Image noise was significantly lower in IMR than in corresponding HIR and FBP images. At lower radiation doses, image noise increased significantly except with IMR CR3 and IMR CS3. Visible lumen diameters were significantly larger with IMR CS than with FBP, HIR, and IMR CR. IMR CR showed the smallest attenuation difference, while attenuation was artificially decreased extensively with IMR CS. FBP and HIR showed moderately increased in-stent attenuations. No relevant influence of used radiation doses on visible lumen diameters or attenuation differences was found. CONCLUSION IMR CR reduces image noise significantly while offering comparable stent-specific image quality in comparison to FBP and HIR and therefore potentially facilitates stent lumen delineation. Utilization of IMR CS for stent evaluation seems unfavorable due to artificial image alterations.
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Affiliation(s)
- Tilman Hickethier
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
| | - Bettina Baeßler
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
| | - Jan Robert Kroeger
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
| | - Dirk Müller
- Clinical Science CT, Philips Germany GmbH, Hamburg, Germany
| | - David Maintz
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
| | - Guido Michels
- Department III of Internal Medicine, University Hospital of Cologne, Cologne, Germany
| | - Alexander C Bunck
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
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145
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Anitha D, Subburaj K, Kopp FK, Mei K, Foehr P, Burgkart R, Sollmann N, Maegerlein C, Kirschke JS, Noel PB, Baum T. Effect of Statistically Iterative Image Reconstruction on Vertebral Bone Strength Prediction Using Bone Mineral Density and Finite Element Modeling: A Preliminary Study. J Comput Assist Tomogr 2019; 43:61-65. [PMID: 30211797 DOI: 10.1097/rct.0000000000000788] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Statistical iterative reconstruction (SIR) using multidetector computed tomography (MDCT) is a promising alternative to standard filtered back projection (FBP), because of lower noise generation while maintaining image quality. Hence, we investigated the feasibility of SIR in predicting MDCT-based bone mineral density (BMD) and vertebral bone strength from finite element (FE) analysis. The BMD and FE-predicted bone strength derived from MDCT images reconstructed using standard FBP (FFBP) and SIR with (FSIR) and without regularization (FSIRB0) were validated against experimental failure loads (Fexp). Statistical iterative reconstruction produced the best quality images with regard to noise, signal-to-noise ratio, and contrast-to-noise ratio. Fexp significantly correlated with FFBP, FSIR, and FSIRB0. FFBP had a significant correlation with FSIRB0 and FSIR. The BMD derived from FBP, SIRB0, and SIR were significantly correlated. Effects of regularization should be further investigated with FE and BMD analysis to allow for an optimal iterative reconstruction algorithm to be implemented in an in vivo scenario.
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Affiliation(s)
| | | | | | | | - Peter Foehr
- Department of Orthopaedic Surgery, Biomechanical Laboratory, and
| | - Rainer Burgkart
- Department of Orthopaedic Surgery, Biomechanical Laboratory, and
| | - Nico Sollmann
- Department of Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Christian Maegerlein
- Department of Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan S Kirschke
- Department of Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | - Thomas Baum
- Department of Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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146
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Vasconcelos KDF, Codari M, Queiroz PM, Nicolielo LFP, Freitas DQ, Sforza C, Jacobs R, Haiter-Neto F. The performance of metal artifact reduction algorithms in cone beam computed tomography images considering the effects of materials, metal positions, and fields of view. Oral Surg Oral Med Oral Pathol Oral Radiol 2019; 127:71-76. [DOI: 10.1016/j.oooo.2018.09.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 09/11/2018] [Accepted: 09/18/2018] [Indexed: 01/08/2023]
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147
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Hernandez-Giron I, den Harder JM, Streekstra GJ, Geleijns J, Veldkamp WJ. Development of a 3D printed anthropomorphic lung phantom for image quality assessment in CT. Phys Med 2019; 57:47-57. [DOI: 10.1016/j.ejmp.2018.11.015] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 10/31/2018] [Accepted: 11/21/2018] [Indexed: 11/26/2022] Open
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148
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Wang W, Gang GJ, Siewerdsen JH, Stayman JW. Predicting image properties in penalized-likelihood reconstructions of flat-panel CBCT. Med Phys 2019; 46:65-80. [PMID: 30372536 PMCID: PMC6904934 DOI: 10.1002/mp.13249] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 09/17/2018] [Accepted: 10/09/2018] [Indexed: 12/18/2022] Open
Abstract
PURPOSE Model-based iterative reconstruction (MBIR) algorithms such as penalized-likelihood (PL) methods exhibit data-dependent and shift-variant properties. Image quality predictors have been derived to prospectively estimate local noise and spatial resolution, facilitating both system hardware design and tuning of reconstruction methods. However, current MBIR image quality predictors rely on idealized system models, ignoring physical blurring effects and noise correlations found in real systems. In this work, we develop and validate a new set of predictors using a physical system model specific to flat-panel cone-beam CT (FP-CBCT). METHODS Physical models appropriate for integration with MBIR analysis are developed and parameterized to represent nonidealities in FP projection data including focal spot blur, scintillator blur, detector aperture effect, and noise correlations. Flat-panel-specific predictors for local spatial resolution and local noise properties in PL reconstructions are developed based on these realistic physical models. Estimation accuracy of conventional (idealized) and FP-specific predictors is investigated and validated against experimental CBCT measurements using specialized phantoms. RESULTS Validation studies show that flat-panel-specific predictors can accurately estimate the local spatial resolution and noise properties, while conventional predictors show significant deviations in the magnitude and scale of the spatial resolution and local noise. The proposed predictors show accurate estimations over a range of imaging conditions including varying x-ray technique and regularization strength. The conventional spatial resolution prediction is sharper than ground truth. Using conventional spatial resolution predictor, the full width at half maximum (FWHM) of local point spread function (PSF) is underestimated by 0.2 mm. This mismatch is mostly eliminated in FP-specific prediction. The general shape and amplitude of local noise power spectrum (NPS) FP-specific predictions are consistent with measurement, while the conventional predictions underestimated the noise level by 70%. CONCLUSION The proposed image quality predictors permit accurate estimation of local spatial resolution and noise properties for PL reconstruction, accounting for dependencies on the system geometry, x-ray technique, and patient-specific anatomy in real FP-CBCT. Such tools enable prospective analysis of image quality for a range of goals including novel system and acquisition design, adaptive and task-driven imaging, and tuning of MBIR for robust and reliable behavior.
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Affiliation(s)
- Wenying Wang
- Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreMD21205USA
| | - Grace J. Gang
- Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreMD21205USA
| | | | - J. Webster Stayman
- Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreMD21205USA
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149
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Generative Low-Dose CT Image Denoising. DEEP LEARNING AND CONVOLUTIONAL NEURAL NETWORKS FOR MEDICAL IMAGING AND CLINICAL INFORMATICS 2019. [DOI: 10.1007/978-3-030-13969-8_14] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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150
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Effective Radiation Dose Reduction in Computed Tomography With Iterative Reconstruction in Patients With Urinary Stone. J Comput Assist Tomogr 2019; 43:877-883. [DOI: 10.1097/rct.0000000000000921] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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