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Kitera N, Fujioka C, Higaki T, Nishimaru E, Yokomachi K, Matsumoto Y, Kiguchi M, Ohashi K, Kasai H, Awai K. [Validation of Optimal Imaging Conditions for Coronary Computed Tomography Angiography Using High-definition Mode and Deep Learning Image Reconstruction Algorithm]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2024; 80:499-509. [PMID: 38508756 DOI: 10.6009/jjrt.2024-1353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
PURPOSE To verify the optimal imaging conditions for coronary computed tomography angiography (CCTA) examinations when using high-definition (HD) mode and deep learning image reconstruction (DLIR) in combination. METHOD A chest phantom and an in-house phantom using 3D printer were scanned with a 256-row detector CT scanner. The scan parameters were as follows - acquisition mode: ON (HD mode) and OFF (normal resolution [NR] mode), rotation time: 0.28 s/rotation, beam coverage width: 160 mm, and the radiation dose was adjusted based on CT-AEC. Image reconstruction was performed using ASiR-V (Hybrid-IR), TrueFidelity Image (DLIR), and HD-Standard (HD mode) and Standard (NR mode) reconstruction kernels. The task-based transfer function (TTF) and noise power spectrum (NPS) were measured for image evaluation, and the detectability index (d') was calculated. Visual evaluation was also performed on an in-house coronary phantom. RESULT The in-plane TTF was better for the HD mode than for the NR mode, while the z-axis TTF was lower for DLIR than for Hybrid-IR. The NPS values in the high-frequency region were higher for the HD mode compared to those for the NR mode, and the NPS was lower for DLIR than for Hybrid-IR. The combination of HD mode and DLIR showed the best value for in-plane d', whereas the combination of NR mode and DLIR showed the best value for z-axis d'. In the visual evaluation, the combination of NR mode and DLIR showed the best values from a noise index of 45 HU. CONCLUSION The optimal combination of HD mode and DLIR depends on the image noise level, and the combination of NR mode and DLIR was the best imaging condition under noisy conditions.
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Affiliation(s)
- Nobuo Kitera
- Department of Radiology, Hiroshima University Hospital
| | | | - Toru Higaki
- Graduate School of Advanced Science and Engineering, Hiroshima University
| | | | | | | | - Masao Kiguchi
- Department of Radiology, Hiroshima University Hospital
| | - Kazuya Ohashi
- Department of Radiology, Nagoya City University Hospital
| | - Harumasa Kasai
- Department of Radiology, Nagoya City University Hospital
| | - Kazuo Awai
- Graduate School of Biomedical and Health Sciences, Hiroshima University
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Kang HJ, Lee JM, Park SJ, Lee SM, Joo I, Yoon JH. Image Quality Improvement of Low-dose Abdominal CT using Deep Learning Image Reconstruction Compared with the Second Generation Iterative Reconstruction. Curr Med Imaging 2024; 20:e250523217310. [PMID: 37231764 DOI: 10.2174/1573405620666230525104809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 03/23/2023] [Accepted: 04/06/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND Whether deep learning-based CT reconstruction could improve lesion conspicuity on abdominal CT when the radiation dose is reduced is controversial. OBJECTIVES To determine whether DLIR can provide better image quality and reduce radiation dose in contrast-enhanced abdominal CT compared with the second generation of adaptive statistical iterative reconstruction (ASiR-V). AIMS This study aims to determine whether deep-learning image reconstruction (DLIR) can improve image quality. METHOD In this retrospective study, a total of 102 patients were included, who underwent abdominal CT using a DLIR-equipped 256-row scanner and routine CT of the same protocol on the same vendor's 64-row scanner within four months. The CT data from the 256-row scanner were reconstructed into ASiR-V with three blending levels (AV30, AV60, and AV100), and DLIR images with three strength levels (DLIR-L, DLIR-M, and DLIR-H). The routine CT data were reconstructed into AV30, AV60, and AV100. The contrast-to-noise ratio (CNR) of the liver, overall image quality, subjective noise, lesion conspicuity, and plasticity in the portal venous phase (PVP) of ASiR-V from both scanners and DLIR were compared. RESULTS The mean effective radiation dose of PVP of the 256-row scanner was significantly lower than that of the routine CT (6.3±2.0 mSv vs. 2.4±0.6 mSv; p< 0.001). The mean CNR, image quality, subjective noise, and lesion conspicuity of ASiR-V images of the 256-row scanner were significantly lower than those of ASiR-V images at the same blending factor of routine CT, but significantly improved with DLIR algorithms. DLIR-H showed higher CNR, better image quality, and subjective noise than AV30 from routine CT, whereas plasticity was significantly better for AV30. CONCLUSION DLIR can be used for improving image quality and reducing radiation dose in abdominal CT, compared with ASIR-V.
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Affiliation(s)
- Hyo-Jin Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Sae Jin Park
- Department of Radiology, G&E alphadom medical center, Seongnam, Korea
| | - Sang Min Lee
- Department of Radiology, Cha Gangnam Medical Center, Seoul, Korea
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
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Performance of Ultra-High-Resolution Computed Tomography in Super High-Resolution Mode at the Routine Radiation Dose: Phantom Study. J Comput Assist Tomogr 2022; 46:900-905. [PMID: 35723670 DOI: 10.1097/rct.0000000000001351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Using a chest phantom, we compared the image quality of ultra-high-resolution computed tomography (U-HRCT) images acquired in super high-resolution (SHR) and normal resolution (NR) mode and at the routine radiation dose. The detector size was 0.25 and 0.5 mm, respectively. METHODS A chest phantom was scanned on a U-HRCT scanner. The scan parameters were tube voltage 120 kV and volume CT dose index 13.0 mGy, the routine radiation dose for conventional scans. The rotation time was 0.5 s/rot, the number of matrices was 512 in NR and 1024 in SHR mode. For physical evaluation, the modulation transfer function was measured on the spherical simulated nodule, and the noise power spectrum on the cylindrical water phantom. A CT value profile curve was created using an in-house simulated bronchial phantom. For visual evaluation, 3 radiologists and 3 radiology technologists evaluated overall image quality using a 4-grade scale (grade 1, poor; and grade 4, excellent). RESULTS The 10% of modulation transfer function was 13.5 lp/cm in NR and 14.9 lp/cm in SHR mode (P<0.01). ƒpeak was 5.6 lp/cm in NR and 8.8 lp/cm in SHR mode (P<0.01), and the peak of noise power spectrum shifted. On the profile curves, the CT value at the edge changed in NR but not in SHR mode. The overall image quality was grade 3.0 ± 0.7 in SHR and grade 2.0 ± 0.7 in NR mode (P<0.01). CONCLUSIONS The image quality of SHR mode with U-HRCT was superior to that of NR mode at the routine radiation dose.
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Cheng Y, Han Y, Li J, Fan G, Cao L, Li J, Jia X, Yang J, Guo J. Low-dose CT urography using deep learning image reconstruction: a prospective study for comparison with conventional CT urography. Br J Radiol 2021; 94:20201291. [PMID: 33571034 DOI: 10.1259/bjr.20201291] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
OBJECTIVES To compare the image quality of low-dose CT urography (LD-CTU) using deep learning image reconstruction (DLIR) with conventional CTU (C-CTU) using adaptive statistical iterative reconstruction (ASIR-V). METHODS This was a prospective, single-institutional study using the excretory phase CTU images for analysis. Patients were assigned to the LD-DLIR group (100kV and automatic mA modulation for noise index (NI) of 23) and C-ASIR-V group (100kV and NI of 10) according to the scan protocols in the excretory phase. Two radiologists independently assessed the overall image quality, artifacts, noise and sharpness of urinary tracts. Additionally, the mean CT attenuation, signal-to-noise ratio (SNR) and contrast-to-noise (CNR) in the urinary tracts were evaluated. RESULTS 26 patients each were included in the LD-DLIR group (10 males and 16 females; mean age: 57.23 years, range: 33-76 years) and C-ASIR-V group (14 males and 12 females; mean age: 60 years, range: 33-77 years). LD-DLIR group used a significantly lower effective radiation dose compared with the C-ASIR-V group (2.01 ± 0.44 mSv vs 6.9 ± 1.46 mSv, p < 0.001). LD-DLIR group showed good overall image quality with average score >4 and was similar to that of the C-ASIR-V group. Both groups had adequate and similar attenuation value, SNR and CNR in most segments of urinary tracts. CONCLUSION It is feasibility to provide comparable image quality while reducing 71% radiation dose in low-dose CTU with a deep learning image reconstruction algorithm compared to the conventional CTU with ASIR-V. ADVANCES IN KNOWLEDGE (1) CT urography with deep learning reconstruction algorithm can reduce the radiation dose by 71% while still maintaining image quality.
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Affiliation(s)
- Yannan Cheng
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Yangyang Han
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Jianying Li
- GE Healthcare, Computed Tomography Research Center, Beijing, 100176, PR China
| | - Ganglian Fan
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Le Cao
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Junjun Li
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Xiaoqian Jia
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Jian Yang
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Jianxin Guo
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
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Diagnostic value of deep learning reconstruction for radiation dose reduction at abdominal ultra-high-resolution CT. Eur Radiol 2021; 31:4700-4709. [PMID: 33389036 DOI: 10.1007/s00330-020-07566-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 10/01/2020] [Accepted: 11/26/2020] [Indexed: 10/22/2022]
Abstract
OBJECTIVES We evaluated lower dose (LD) hepatic dynamic ultra-high-resolution computed tomography (U-HRCT) images reconstructed with deep learning reconstruction (DLR), hybrid iterative reconstruction (hybrid-IR), or model-based IR (MBIR) in comparison with standard-dose (SD) U-HRCT images reconstructed with hybrid-IR as the reference standard to identify the method that allowed for the greatest radiation dose reduction while preserving the diagnostic value. METHODS Evaluated were 72 patients who had undergone hepatic dynamic U-HRCT; 36 were scanned with the standard radiation dose (SD group) and 36 with 70% of the SD (lower dose [LD] group). Hepatic arterial and equilibrium phase (HAP, EP) images were reconstructed with hybrid-IR in the SD group, and with hybrid-IR, MBIR, and DLR in the LD group. One radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise. The overall image quality was assessed by 3 other radiologists; they used a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). Superiority and equivalence with prespecified margins were assessed. RESULTS With respect to the image noise, in the HAP and EP, LD DLR and LD MBIR images were superior to SD hybrid-IR images; LD hybrid-IR images were neither superior nor equivalent to SD hybrid-IR images. With respect to the quality scores, only LD DLR images were superior to SD hybrid-IR images. CONCLUSIONS DLR preserved the quality of abdominal U-HRCT images even when scanned with a reduced radiation dose. KEY POINTS • Lower dose DLR images were superior to the standard-dose hybrid-IR images quantitatively and qualitatively at abdominal U-HRCT. • Neither hybrid-IR nor MBIR may allow for a radiation dose reduction at abdominal U-HRCT without compromising the image quality. • Because DLR allows for a reduction in the radiation dose and maintains the image quality even at the thinnest slice section, DLR should be applied to abdominal U-HRCT scans.
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Akagi M, Nakamura Y, Higaki T, Narita K, Honda Y, Awai K. Deep learning reconstruction of equilibrium phase CT images in obese patients. Eur J Radiol 2020; 133:109349. [PMID: 33152626 DOI: 10.1016/j.ejrad.2020.109349] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 10/07/2020] [Accepted: 10/11/2020] [Indexed: 12/27/2022]
Abstract
PURPOSE To compare abdominal equilibrium phase (EP) CT images of obese and non-obese patients to identify the reconstruction method that preserves the diagnostic value of images obtained in obese patients. METHODS We compared EP images of 50 obese patients whose body mass index (BMI) exceeded 25 (group 1) with EP images of 50 non-obese patients (BMI < 25, group 2). Group 1 images were subjected to deep learning reconstruction (DLR), hybrid iterative reconstruction (hybrid-IR), and model-based IR (MBIR), group 2 images to hybrid-IR; group 2 hybrid-IR images served as the reference standard. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise. The overall image quality was assessed by 3 other radiologists; they used a confidence scale ranging from 1 (unacceptable) to 5 (excellent). Non-inferiority and potential superiority were assessed. RESULTS With respect to the image noise, group 1 DLR- were superior to group 2 hybrid-IR images; group 1 hybrid-IR- and MBIR images were neither superior nor non-inferior to group 2 hybrid-IR images. The quality scores of only DLR images in group 1 were superior to hybrid-IR images of group 2 while the quality scores of group 1 hybrid-IR- and MBIR images were neither superior nor non-inferior to group 2 hybrid-IR images. CONCLUSIONS DLR preserved the quality of EP images obtained in obese patients.
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Affiliation(s)
- Motonori Akagi
- Diagnostic Radiology, Hiroshima University, Diagnostic Radiology, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
| | - Yuko Nakamura
- Diagnostic Radiology, Hiroshima University, Diagnostic Radiology, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
| | - Toru Higaki
- Diagnostic Radiology, Hiroshima University, Diagnostic Radiology, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
| | - Keigo Narita
- Diagnostic Radiology, Hiroshima University, Diagnostic Radiology, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
| | - Yukiko Honda
- Diagnostic Radiology, Hiroshima University, Diagnostic Radiology, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
| | - Kazuo Awai
- Diagnostic Radiology, Hiroshima University, Diagnostic Radiology, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
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Narita K, Nakamura Y, Higaki T, Akagi M, Honda Y, Awai K. Deep learning reconstruction of drip-infusion cholangiography acquired with ultra-high-resolution computed tomography. Abdom Radiol (NY) 2020; 45:2698-2704. [PMID: 32248261 DOI: 10.1007/s00261-020-02508-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
PURPOSE Deep learning reconstruction (DLR) introduces deep convolutional neural networks into the reconstruction flow. We examined the clinical applicability of drip-infusion cholangiography (DIC) acquired on an ultra-high-resolution CT (U-HRCT) scanner reconstructed with DLR in comparison to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR). METHODS This retrospective, single-institution study included 30 patients seen between January 2018 and November 2019. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and calculated the contrast-to-noise ratio (CNR) in the common bile duct. The overall visual image quality of the bile duct on thick-slab maximum intensity projections was assessed by two other radiologists and graded on a 5-point confidence scale ranging from 1 (not delineated) to 5 (clearly delineated). The difference among hybrid-IR, MBIR, and DLR images was compared. RESULTS The image noise was significantly lower on DLR than hybrid-IR and MBIR images and the CNR and the overall visual image quality of the bile duct were significantly higher on DLR than on hybrid-IR and MBIR images (all: p < 0.001). CONCLUSION DLR resulted in significant quantitative and qualitative improvement of DIC acquired with U-HRCT.
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Affiliation(s)
- Keigo Narita
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Yuko Nakamura
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
| | - Toru Higaki
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Motonori Akagi
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Yukiko Honda
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Kazuo Awai
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
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Chu JS, Wang ZJ. Protocol Optimization for Renal Mass Detection and Characterization. Radiol Clin North Am 2020; 58:851-873. [PMID: 32792119 DOI: 10.1016/j.rcl.2020.05.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Renal masses increasingly are found incidentally, largely due to the frequent use of medical imaging. Computed tomography (CT) and MR imaging are mainstays for renal mass characterization, presurgical planning of renal tumors, and surveillance after surgery or systemic therapy for advanced renal cell carcinomas. CT protocols should be tailored to different clinical indications, balancing diagnostic accuracy and radiation exposure. MR imaging protocols should take advantage of the improved soft tissue contrast for renal tumor diagnosis and staging. Optimized imaging protocols enable analysis of imaging features that help narrow the differential diagnoses and guide management in patients with renal masses.
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Affiliation(s)
- Jason S Chu
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, Box 0628, San Francisco, CA 94143, USA
| | - Zhen J Wang
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, Box 0628, San Francisco, CA 94143, USA.
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Possibility of Deep Learning in Medical Imaging Focusing Improvement of Computed Tomography Image Quality. J Comput Assist Tomogr 2020; 44:161-167. [PMID: 31789682 DOI: 10.1097/rct.0000000000000928] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Deep learning (DL), part of a broader family of machine learning methods, is based on learning data representations rather than task-specific algorithms. Deep learning can be used to improve the image quality of clinical scans with image noise reduction. We review the ability of DL to reduce the image noise, present the advantages and disadvantages of computed tomography image reconstruction, and examine the potential value of new DL-based computed tomography image reconstruction.
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Akagi M, Nakamura Y, Higaki T, Narita K, Honda Y, Zhou J, Yu Z, Akino N, Awai K. Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol 2019; 29:6163-6171. [PMID: 30976831 DOI: 10.1007/s00330-019-06170-3] [Citation(s) in RCA: 218] [Impact Index Per Article: 43.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Revised: 02/22/2019] [Accepted: 03/14/2019] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Deep learning reconstruction (DLR) is a new reconstruction method; it introduces deep convolutional neural networks into the reconstruction flow. This study was conducted in order to examine the clinical applicability of abdominal ultra-high-resolution CT (U-HRCT) exams reconstructed with a new DLR in comparison to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR). METHODS Our retrospective study included 46 patients seen between December 2017 and April 2018. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and calculated the contrast-to-noise ratio (CNR) for the aorta, portal vein, and liver. The overall image quality was assessed by two other radiologists and graded on a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). The difference between CT images subjected to hybrid-IR, MBIR, and DLR was compared. RESULTS The image noise was significantly lower and the CNR was significantly higher on DLR than hybrid-IR and MBIR images (p < 0.01). DLR images received the highest and MBIR images the lowest scores for overall image quality. CONCLUSIONS DLR improved the quality of abdominal U-HRCT images. KEY POINTS • The potential degradation due to increased noise may prevent implementation of ultra-high-resolution CT in the abdomen. • Image noise and overall image quality for hepatic ultra-high-resolution CT images improved with deep learning reconstruction as compared to hybrid- and model-based iterative reconstruction.
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Affiliation(s)
- Motonori Akagi
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Yuko Nakamura
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan.
| | - Toru Higaki
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Keigo Narita
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Yukiko Honda
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Jian Zhou
- Canon Medical Research USA, Inc., Vernon Hills, IL, USA
| | - Zhou Yu
- Canon Medical Research USA, Inc., Vernon Hills, IL, USA
| | | | - Kazuo Awai
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
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Hajdu SD, Daniel RT, Meuli RA, Zerlauth JB, Dunet V. Impact of model-based iterative reconstruction (MBIR) on image quality in cerebral CT angiography before and after intracranial aneurysm treatment. Eur J Radiol 2018; 102:109-114. [PMID: 29685523 DOI: 10.1016/j.ejrad.2018.03.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Revised: 03/04/2018] [Accepted: 03/05/2018] [Indexed: 12/25/2022]
Abstract
PURPOSE To subjectively and objectively assess the impact of model-based iterative reconstruction(MBIR) on image quality in cerebral computed tomography angiography compared to adaptive statistical iterative reconstruction (ASIR). METHODS 107 patients (mean age: 58 ± 14 years) were included prior to (n = 38) and after (n = 69) intracranial aneurysm treatment. Images were acquired using a routine protocol and reconstructed with MBIR and ASIR. Image noise, signal-to-noise (SNR) and contrast-to-noise (CNR) ratios in the internal carotid and middle cerebral arteries were compared between MBIR and ASIR using the Wilcoxon signed-rank test. Additionally, two neuroradiologists subjectively assessed noise, artefacts, vessel sharpness and overall quality using a semi-quantitative assessment scale. RESULTS Objective assessment revealed that MBIR reduced noise (p < 0.0001) and additionally improved SNR (p < 0.0001) and CNR (p < 0.0001) compared to ASIR in untreated and treated patients. Subjective assessment revealed that in untreated patients, MBIR improved noise reduction, artefacts, vessel sharpness and overall quality relative to ASIR (p < 0.0001). In the treated groups, noise and vessel sharpness were improved (p < 0.0001) with no change in artefacts on images reconstructed with MBIR compared to ASIR. CONCLUSION MBIR significantly improves noise, SNR, CNR and vessel sharpness in untreated and treated patients with intracranial aneurysms. MBIR does not reduce artefacts generated by metallic devices following intracranial aneurysm treatment.
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Affiliation(s)
- Steven David Hajdu
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, Lausanne, Switzerland.
| | - Roy Thomas Daniel
- Department of Neurosurgery, Lausanne University Hospital, Lausanne, Switzerland
| | - Reto Antoine Meuli
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Jean-Baptiste Zerlauth
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Vincent Dunet
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, Lausanne, Switzerland
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Telesmanich ME, Jensen CT, Enriquez JL, Wagner-Bartak NA, Liu X, Le O, Wei W, Chandler AG, Tamm EP. Third version of vendor-specific model-based iterativereconstruction (Veo 3.0): evaluation of CT image quality in the abdomen using new noise reduction presets and varied slice optimization. Br J Radiol 2017; 90:20170188. [PMID: 28707531 DOI: 10.1259/bjr.20170188] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To qualitatively and quantitatively compare abdominal CT images reconstructed with a newversion of model-based iterative reconstruction (Veo 3.0; GE Healthcare Waukesha, WI) utilizing varied presetsof resolution preference, noise reduction and slice optimization. METHODS This retrospective study was approved by our Institutional Review Board and was Health Insurance Portability and Accountability Act compliant. The raw datafrom 30 consecutive patients who had undergone CT abdomen scanning were used to reconstructfour clinical presets of 3.75mm axial images using Veo 3.0: 5% resolution preference (RP05n), 5%noise reduction (NR05) and 40% noise reduction (NR40) with new 3.75mm "sliceoptimization," as well as one set using RP05 with conventional 0.625mm "slice optimization" (RP05c). The images were reviewed by two independent readers in a blinded, randomized manner using a 5-point Likert scale as well as a 5-point comparative scale. Multiple two-dimensional circular regions of interest were defined for noise and contrast-to-noise ratio measurements. Line profiles were drawn across the 7 lp cm-1 bar pattern of the Catphan 600 phantom for evaluation of spatial resolution. RESULTS The NR05 image set was ranked as the best series in overall image quality (mean difference inrank 0.48, 95% CI [0.081-0.88], p = 0.01) and with specific reference to liver evaluation (meandifference 0.46, 95% CI [0.030-0.89], p = 0.03), when compared with the secondbest series ineach category. RP05n was ranked as the best for bone evaluation. NR40 was ranked assignificantly inferior across all assessed categories. Although the NR05 and RP05c image setshad nearly the same contrast-to-noise ratio and spatial resolution, NR05 was generally preferred. Image noise and spatial resolution increased along a spectrum with RP05n the highest and NR40the lowest. Compared to RP05n, the average noise was 21.01% lower for NR05, 26.88%lower for RP05c and 50.86% lower for NR40. CONCLUSION Veo 3.0 clinical presets allow for selection of image noise and spatial resolution balance; for contrast-enhanced CT evaluation of the abdomen, the 5% noise reduction preset with 3.75 mm slice optimization (NR05) was generally ranked superior qualitatively and, relative to other series, was in the middle of the spectrum with reference to image noise and spatial resolution. Advances in knowledge: To our knowledge, this is the first study of Veo 3.0 noise reduction presets and varied slice optimization. This study provides insight into the behaviour of slice optimization and documents the degree of noise reduction and spatial resolution changes that users can expect across various Veo 3.0 clinical presets. These results provide important parameters to guide preset selection for both clinical and research purposes.
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Affiliation(s)
- Morgan E Telesmanich
- 1 Department of Diagnostic Radiology, Baylor College of Medicine , Houston , USA
| | - Corey T Jensen
- 2 Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center , Houston , USA
| | - Jose L Enriquez
- 1 Department of Diagnostic Radiology, Baylor College of Medicine , Houston , USA
| | - Nicolaus A Wagner-Bartak
- 2 Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center , Houston , USA
| | - Xinming Liu
- 3 Department of Imaging Physics, The University of Texas MD Anderson Cancer Center , Houston , USA
| | - Ott Le
- 2 Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center , Houston , USA
| | - Wei Wei
- 4 Department of Biostatistics, The University of Texas MD Anderson Cancer Center , Houston , USA
| | - Adam G Chandler
- 3 Department of Imaging Physics, The University of Texas MD Anderson Cancer Center , Houston , USA.,5 Department of Molecular Imaging and Computed Tomography Research, GE Healthcare , Waukesha , USA
| | - Eric P Tamm
- 2 Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center , Houston , USA
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Li K, Gomez-Cardona D, Hsieh J, Lubner MG, Pickhardt PJ, Chen GH. Statistical model based iterative reconstruction in clinical CT systems. Part III. Task-based kV/mAs optimization for radiation dose reduction. Med Phys 2016; 42:5209-21. [PMID: 26328971 DOI: 10.1118/1.4927722] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
PURPOSE For a given imaging task and patient size, the optimal selection of x-ray tube potential (kV) and tube current-rotation time product (mAs) is pivotal in achieving the maximal radiation dose reduction while maintaining the needed diagnostic performance. Although contrast-to-noise (CNR)-based strategies can be used to optimize kV/mAs for computed tomography (CT) imaging systems employing the linear filtered backprojection (FBP) reconstruction method, a more general framework needs to be developed for systems using the nonlinear statistical model-based iterative reconstruction (MBIR) method. The purpose of this paper is to present such a unified framework for the optimization of kV/mAs selection for both FBP- and MBIR-based CT systems. METHODS The optimal selection of kV and mAs was formulated as a constrained optimization problem to minimize the objective function, Dose(kV,mAs), under the constraint that the achievable detectability index d'(kV,mAs) is not lower than the prescribed value of d'R for a given imaging task. Since it is difficult to analytically model the dependence of d' on kV and mAs for the highly nonlinear MBIR method, this constrained optimization problem is solved with comprehensive measurements of Dose(kV,mAs) and d'(kV,mAs) at a variety of kV-mAs combinations, after which the overlay of the dose contours and d' contours is used to graphically determine the optimal kV-mAs combination to achieve the lowest dose while maintaining the needed detectability for the given imaging task. As an example, d' for a 17 mm hypoattenuating liver lesion detection task was experimentally measured with an anthropomorphic abdominal phantom at four tube potentials (80, 100, 120, and 140 kV) and fifteen mA levels (25 and 50-700) with a sampling interval of 50 mA at a fixed rotation time of 0.5 s, which corresponded to a dose (CTDIvol) range of [0.6, 70] mGy. Using the proposed method, the optimal kV and mA that minimized dose for the prescribed detectability level of d'R=16 were determined. As another example, the optimal kV and mA for an 8 mm hyperattenuating liver lesion detection task were also measured using the developed framework. Both an in vivo animal and human subject study were used as demonstrations of how the developed framework can be applied to the clinical work flow. RESULTS For the first task, the optimal kV and mAs were measured to be 100 and 500, respectively, for FBP, which corresponded to a dose level of 24 mGy. In comparison, the optimal kV and mAs for MBIR were 80 and 150, respectively, which corresponded to a dose level of 4 mGy. The topographies of the iso-d' map and the iso-CNR map were the same for FBP; thus, the use of d'- and CNR-based optimization methods generated the same results for FBP. However, the topographies of the iso-d' and iso-CNR map were significantly different in MBIR; the CNR-based method overestimated the performance of MBIR, predicting an overly aggressive dose reduction factor. For the second task, the developed framework generated the following optimization results: for FBP, kV = 140, mA = 350, dose = 37.5 mGy; for MBIR, kV = 120, mA = 250, dose = 18.8 mGy. Again, the CNR-based method overestimated the performance of MBIR. Results of the preliminary in vivo studies were consistent with those of the phantom experiments. CONCLUSIONS A unified and task-driven kV/mAs optimization framework has been developed in this work. The framework is applicable to both linear and nonlinear CT systems such as those using the MBIR method. As expected, the developed framework can be reduced to the conventional CNR-based kV/mAs optimization frameworks if the system is linear. For MBIR-based nonlinear CT systems, however, the developed task-based kV/mAs optimization framework is needed to achieve the maximal dose reduction while maintaining the desired diagnostic performance.
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Affiliation(s)
- Ke Li
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, 1111 Highland Avenue, Madison, Wisconsin 53705 and Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, 600 Highland Avenue, Madison, Wisconsin 53792
| | - Daniel Gomez-Cardona
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, 1111 Highland Avenue, Madison, Wisconsin 53705
| | - Jiang Hsieh
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, 1111 Highland Avenue, Madison, Wisconsin 53705 and GE Healthcare, 3000 N Grandview Boulevard, Waukesha, Wisconsin 53188
| | - Meghan G Lubner
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, 600 Highland Avenue, Madison, Wisconsin 53792
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, 600 Highland Avenue, Madison, Wisconsin 53792
| | - Guang-Hong Chen
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, 1111 Highland Avenue, Madison, Wisconsin 53705 and Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, 600 Highland Avenue, Madison, Wisconsin 53792
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Abstract
1. CT radiation dose optimization is one of the major concerns for the scientific community. 2. CT image quality is dependent on the selected image reconstruction algorithm. 3. Iterative reconstruction algorithms have reemerged with the potential of radiation dose optimization by lowering image noise. 4. Tube current is the most common parameter used to reduce radiation dose along with iterative reconstruction. 5. Tube potential (kV) is also used for dose optimization with iterative reconstruction in CT angiography protocols and small patients.
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CT Liver Imaging: What is New? CURRENT RADIOLOGY REPORTS 2015. [DOI: 10.1007/s40134-015-0088-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Low-Dose Pelvic Computed Tomography Using Adaptive Iterative Dose Reduction 3-Dimensional Algorithm. J Comput Assist Tomogr 2015; 39:629-34. [DOI: 10.1097/rct.0000000000000242] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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