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Higashigawa T, Ichikawa Y, Nakajima K, Kobayashi T, Domae K, Yamazaki A, Kato N, Ouchi T, Kato H, Sakuma H. Low energy virtual monochromatic CT with deep learning image reconstruction to improve delineation of endoleaks. Clin Radiol 2024; 79:e1260-e1267. [PMID: 39079807 DOI: 10.1016/j.crad.2024.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 09/09/2024]
Abstract
AIM This study aimed to investigate the utility of low-energy virtual monochromatic imaging (VMI) combined with deep-learning image reconstruction (DLIR) in improving the delineation of endoleaks (ELs) after endovascular aortic repair (EVAR) in contrast-enhanced dual-energy CT (DECT). METHODS A total of 61 consecutive patients (mean age, 77 years; 46 men) after EVAR who underwent contrast-enhanced DECT were enrolled. Virtual monochromatic 40- and 70-keV images were reconstructed using DLIR (TrueFidelity-H) and conventional hybrid iterative reconstruction (IR). Contrast-to-noise ratio (CNR) of the EL on the venous-phase CT was calculated. Four different reconstructed image series (hybrid IR and DLIR at two energy levels, 40- and 70-keV) were displayed side-by-side and visually assessed for EL conspicuity on a 5-point comparative scale from 0 (best) to -4 (significantly inferior). Two experienced radiologists independently conducted a qualitative evaluation of the CT images. RESULTS A total of 30 out of 61 patients presented with an EL. On both 40- and 70-keV images, the CNR of the EL was significantly higher in DLIR than in hybrid IR (40-keV, 14.5 ± 7.3 vs 8.6 ± 4.2, P<0.001; 70-keV, 8.7 ± 4.5 vs 5.5 ± 2.6, P<0.001). The comparative scale of EL conspicuity in the 40-keV DLIR images (Observer1, -0.2 ± 0.4; Observer2, 0.0 ± 0.0) was significantly higher than 40-keV hybrid IR (Observer1, -0.5 ± 0.5; Observer2, -1.0 ± 0.0; P<0.05), 70-keV DLIR (Observer1, -1.8 ± 0.4; Observer2, -2.0 ± 0.0; P<0.001) and 70-keV hybrid IR images (Observer1, -1.8 ± 0.4; Observer2, -2.4 ± 0.5; P<0.001), respectively. CONCLUSIONS Using 40-keV VMI in combination with DLIR improves EL delineation after EVAR compared with the 70-keV VMI with hybrid IR or DLIR.
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Affiliation(s)
- T Higashigawa
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie 514-8507, Japan.
| | - Y Ichikawa
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie 514-8507, Japan
| | - K Nakajima
- Department of Radiology, Ise Red Cross Hospital, 471-2 1-Chome Funae, Ise, Mie 516-8512, Japan
| | - T Kobayashi
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie 514-8507, Japan
| | - K Domae
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie 514-8507, Japan
| | - A Yamazaki
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie 514-8507, Japan
| | - N Kato
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie 514-8507, Japan
| | - T Ouchi
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie 514-8507, Japan
| | - H Kato
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie 514-8507, Japan
| | - H Sakuma
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie 514-8507, Japan
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Greffier J, Viry A, Robert A, Khorsi M, Si-Mohamed S. Photon-counting CT systems: A technical review of current clinical possibilities. Diagn Interv Imaging 2024:S2211-5684(24)00195-5. [PMID: 39304365 DOI: 10.1016/j.diii.2024.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2024] [Accepted: 09/03/2024] [Indexed: 09/22/2024]
Abstract
In recent years, computed tomography (CT) has undergone a number of developments to improve radiological care. The most recent major innovation has been the development of photon-counting detectors. By comparison with the energy-integrating detectors traditionally used in CT, these detectors offer better dose efficiency, eliminate electronic noise, improve spatial resolution and have intrinsic spectral sensitivity. These detectors also allow the energy of each photon to be counted, thus improving the sampling of the X-ray spectrum in multiple energy bins, to better distinguish between photoelectric and Compton attenuation coefficients, resulting in better spectral images and specific color K-edge images. The purpose of this article was to make the reader more familiar with the basic principles and techniques of new photon-counting CT systems equipped with photon-counting detectors and also to describe the currently available devices that could be used in clinical practice.
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Affiliation(s)
- Joël Greffier
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30900 Nîmes, France.
| | - Anaïs Viry
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, 1007 Lausanne, Switzerland
| | - Antoine Robert
- University of Lyon, INSA-Lyon, University Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, 69621 Villeurbanne, France
| | - Mouad Khorsi
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, 1007 Lausanne, Switzerland
| | - Salim Si-Mohamed
- University of Lyon, INSA-Lyon, University Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, 69621 Villeurbanne, France; Department of Radiology, Louis Pradel Hospital, Hospices Civils de Lyon, 69500 Bron, France
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Lin X, Gao Y, Zhu C, Song J, Liu L, Li J, Wu X. Improving diagnostic confidence in low-dose dual-energy CTE with low energy level and deep learning reconstruction. Eur J Radiol 2024; 178:111607. [PMID: 39033690 DOI: 10.1016/j.ejrad.2024.111607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 06/15/2024] [Accepted: 07/05/2024] [Indexed: 07/23/2024]
Abstract
OBJECTIVE To demonstrate the value of using 50 keV virtual monochromatic images with deep learning image reconstruction (DLIR) in low-dose dual-energy CT enterography (CTE). METHODS In this prospective study, 114 participants (62 % M; 41.9 ± 16 years) underwent dual-energy CTE. The early-enteric phase was performed using standard-dose (noise index (NI): 8) and images were reconstructed at 70 keV and 50 keV with 40 % strength ASIR-V (ASIR-V40%). The late-enteric phase used low-dose (NI: 12) and images were reconstructed at 50 keV with ASIR-V40%, and DLIR at medium (DLIR-M) and high strength (DLIR-H). Image standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge-rise-slope (ERS) were computed. The quantitative comb sign score was calculated for the 27 patients with Crohn's disease. The subjective noise, image contrast, display of rectus artery were scored using a 5-point scale by two radiologists blindly. RESULTS Effective dose was reduced by 50 % (P < 0.001) in the late-enteric phase to 3.26 mSv. The lower-dose 50 keV-DLIR-H images (SD:17.7 ± 0.5HU) had similar image noise (P = 0.97) as the standard-dose 70 keV-ASIR-V40% images (SD:17.7 ± 0.73HU), but with higher (P < 0.001) SNR, CNR, ERS and quantitative comb sign score (5.7 ± 0.17, 1.8 ± 0.12, 156.04 ± 5.21 and 5.05 ± 0.73, respectively). Furthermore, the lower-dose 50 keV-DLIR-H images obtained the highest score in the rectus artery visibility (4.27 ± 0.6). CONCLUSIONS The 50 keV images in dual-energy CTE with DLIR provides high-quality images, with a 50 % reduction in radiation dose. Images with high contrast and density resolutions significantly enhance the diagnostic confidence of Crohn's disease and are essential for the clinical development of individualized treatment plans.
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Affiliation(s)
- Xu Lin
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China
| | - Yankun Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China
| | - Chao Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China
| | - Jian Song
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China
| | - Ling Liu
- CT Research Center, GE Healthcare China, Shanghai 210000, China
| | - Jianying Li
- CT Research Center, GE Healthcare China, Shanghai 210000, China
| | - Xingwang Wu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China.
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Lin X, Gao Y, Zhu C, Song J, Liu L, Li J, Wu X. Improved overall image quality in low-dose dual-energy computed tomography enterography using deep-learning image reconstruction. Abdom Radiol (NY) 2024; 49:2979-2987. [PMID: 38480547 DOI: 10.1007/s00261-024-04221-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 01/21/2024] [Accepted: 01/24/2024] [Indexed: 08/22/2024]
Abstract
OBJECTIVE To demonstrate the clinical advantages of a deep-learning image reconstruction (DLIR) in low-dose dual-energy computed tomography enterography (DECTE) by comparing images with standard-dose adaptive iterative reconstruction-Veo (ASIR-V) images. METHODS In this Institutional review board approved prospective study, 86 participants who underwent DECTE were enrolled. The early-enteric phase scan was performed using standard-dose (noise index: 8) and images were reconstructed at 5 mm and 1.25 mm slice thickness with ASIR-V at a level of 40% (ASIR-V40%). The late-enteric phase scan used low-dose (noise index: 12) and images were reconstructed at 1.25 mm slice thickness with ASIR-V40%, and DLIR at medium (DLIR-M) and high (DLIR-H). The 70 keV monochromatic images were used for image comparison and analysis. For objective assessment, image noise, artifact index, SNR and CNR were measured. For subjective assessment, subjective noise, image contrast, bowel wall sharpness, mesenteric vessel clarity, and small structure visibility were scored by two radiologists blindly. Radiation dose was compared between the early- and late-enteric phases. RESULTS Radiation dose was reduced by 50% in the late-enteric phase [(6.31 ± 1.67) mSv] compared with the early-enteric phase [(3.01 ± 1.09) mSv]. For the 1.25 mm images, DLIR-M and DLIR-H significantly improved both objective and subjective image quality compared to those with ASIR-V40%. The low-dose 1.25 mm DLIR-H images had similar image noise, SNR, CNR values as the standard-dose 5 mm ASIR-V40% images, but significantly higher scores in image contrast [5(5-5), P < 0.05], bowel wall sharpness [5(5-5), P < 0.05], mesenteric vessel clarity [5(5-5), P < 0.05] and small structure visibility [5(5-5), P < 0.05]. CONCLUSIONS DLIR significantly reduces image noise at the same slice thickness, but significantly improves spatial resolution and lesion conspicuity with thinner slice thickness in DECTE, compared to conventional ASIR-V40% 5 mm images, all while providing 50% radiation dose reduction.
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Affiliation(s)
- Xu Lin
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Yankun Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Chao Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Jian Song
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Ling Liu
- CT Research Center, GE Healthcare China, Shanghai, 210000, China
| | - Jianying Li
- CT Research Center, GE Healthcare China, Shanghai, 210000, China
| | - Xingwang Wu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
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Jaruvongvanich V, Muangsomboon K, Teerasamit W, Suvannarerg V, Komoltri C, Thammakittiphan S, Lornimitdee W, Ritsamrej W, Chaisue P, Pongnapang N, Apisarnthanarak P. Optimizing computed tomography image reconstruction for focal hepatic lesions: Deep learning image reconstruction vs iterative reconstruction. Heliyon 2024; 10:e34847. [PMID: 39170325 PMCID: PMC11336302 DOI: 10.1016/j.heliyon.2024.e34847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/27/2024] [Accepted: 07/17/2024] [Indexed: 08/23/2024] Open
Abstract
Background Deep learning image reconstruction (DLIR) is a novel computed tomography (CT) reconstruction technique that minimizes image noise, enhances image quality, and enables radiation dose reduction. This study aims to compare the diagnostic performance of DLIR and iterative reconstruction (IR) in the evaluation of focal hepatic lesions. Methods We conducted a retrospective study of 216 focal hepatic lesions in 109 adult participants who underwent abdominal CT scanning at our institution. We used DLIR (low, medium, and high strength) and IR (0 %, 10 %, 20 %, and 30 %) techniques for image reconstruction. Four experienced abdominal radiologists independently evaluated focal hepatic lesions based on five qualitative aspects (lesion detectability, lesion border, diagnostic confidence level, image artifact, and overall image quality). Quantitatively, we measured and compared the level of image noise for each technique at the liver and aorta. Results There were significant differences (p < 0.001) among the seven reconstruction techniques in terms of lesion borders, image artifacts, and overall image quality. Low-strength DLIR (DLIR-L) exhibited the best overall image quality. Although high-strength DLIR (DLIR-H) had the least image noise and fewest artifacts, it also had the lowest scores for lesion borders and overall image quality. Image noise showed a weak to moderate positive correlation with participants' body mass index and waist circumference. Conclusions The optimal-strength DLIR significantly improved overall image quality for evaluating focal hepatic lesions compared to the IR technique. DLIR-L achieved the best overall image quality while maintaining acceptable levels of image noise and quality of lesion borders.
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Affiliation(s)
- Varin Jaruvongvanich
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Kobkun Muangsomboon
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Wanwarang Teerasamit
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Voraparee Suvannarerg
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Chulaluk Komoltri
- Division of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Sastrawut Thammakittiphan
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Wimonrat Lornimitdee
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Witchuda Ritsamrej
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Parinya Chaisue
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Napapong Pongnapang
- Department of Radiological Technology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Piyaporn Apisarnthanarak
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
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Zheng Z, Ai Z, Liang Y, Li Y, Wu Z, Wu M, Han Q, Ma K, Xiang Z. Clinical value of deep learning image reconstruction on the diagnosis of pulmonary nodule for ultra-low-dose chest CT imaging. Clin Radiol 2024; 79:628-636. [PMID: 38749827 DOI: 10.1016/j.crad.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/20/2024] [Accepted: 04/15/2024] [Indexed: 07/10/2024]
Abstract
PURPOSE To compare the image quality and pulmonary nodule detectability between deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) in ultra-low-dose CT (ULD-CT). METHODS 142 participants required lung examination who underwent simultaneously ULD-CT (UL-A, 0.57 ± 0.04 mSv or UL-B, 0.33 ± 0.03 mSv), and standard CT (SDCT, 4.32 ± 0.33 mSv) plain scans were included in this prospective study. SDCT was the reference standard using ASIR-V at 50% strength (50%ASIR-V). ULD-CT was reconstructed with 50%ASIR-V, DLIR at medium and high strength (DLIR-M, DLIR-H). The noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective scores were measured. The presence and accuracy of nodules were analyzed using a combination of a deep learning-based nodule evaluation system and a radiologist. RESULTS A total of 710 nodules were detected by SDCT, including 358 nodules in UL-A and 352 nodules in UL-B. DLIR-H exhibited superior noise, SNR, and CNR performance, and achieved comparable or even higher subjective scores compared to 50%ASIR-V in ULD-CT. Nodules sensitivity detection of 50%ASIR-V, DLIR-M, and DLIR-H in ULD-CT were identical (96.90%). In multivariate analysis, body mass index (BMI), nodule diameter, and type were independent predictors for the sensitivity of nodule detection (p<.001). DLIR-H provided a lower absolute percent error (APE) in volume (3.10% ± 95.11% vs 8.29% ± 99.14%) compared to 50%ASIR-V of ULD-CT (P<.001). CONCLUSIONS ULD-CT scanning has a high sensitivity for detecting pulmonary nodules. Compared with ASIR-V, DLIR can significantly reduce image noise, and improve image quality, and accuracy of the nodule measurement in ULD-CT.
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Affiliation(s)
- Z Zheng
- Postgraduate Cultivation Base of Guangzhou University of Chinese Medicine, Panyu Central Hospital, Guangzhou, China; Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - Z Ai
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - Y Liang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - Y Li
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - Z Wu
- Postgraduate Cultivation Base of Guangzhou University of Chinese Medicine, Panyu Central Hospital, Guangzhou, China; Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - M Wu
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - Q Han
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - K Ma
- CT Imaging Research Center, GE HealthCare China, Guangzhou, China.
| | - Z Xiang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
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Zhou Z, Gong H, Hsieh S, McCollough CH, Yu L. Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution. Med Phys 2024; 51:5399-5413. [PMID: 38555876 PMCID: PMC11321944 DOI: 10.1002/mp.17029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/19/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images. PURPOSE In this work, we aim to develop a patient-data-based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method. METHODS The patient-data-based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR-processed images from projection or image data; (4) creation of ensembles of DLIR-processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (-500, -100, -50, -20, -10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered-backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN-weak, DCNN-medium, and DCNN-strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion-present images from the lesion absent images. Modulation transfer functions (MTFs) both in-plane and along the z-axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t-test was used to compare the z-axis and in-plane spatial resolution of five algorithms for five different contrasts and three dose levels. RESULTS The in-plane and z-axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in-plane and z-axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (-10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in-plane and z-axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of -100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in-plane and cross-plane spatial resolution: FBP > DCNN-Weak > IR > DCNN-Medium > DCNN-Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels. CONCLUSIONS A patient-data-based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non-linear image reconstruction and post-processing techniques, the evaluated DCNN method degraded the in-plane and z-axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.
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Affiliation(s)
| | - Hao Gong
- Department of Radiology, Mayo Clinic, Rochester, MN, US
| | - Scott Hsieh
- Department of Radiology, Mayo Clinic, Rochester, MN, US
| | | | - Lifeng Yu
- Department of Radiology, Mayo Clinic, Rochester, MN, US
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Cao J, Mroueh N, Lennartz S, Mercaldo ND, Pisuchpen N, Kongboonvijit S, Srinivas Rao S, Yuenyongsinchai K, Pierce TT, Sertic M, Chung R, Kambadakone AR. Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques. Eur Radiol 2024:10.1007/s00330-024-10974-3. [PMID: 39046499 DOI: 10.1007/s00330-024-10974-3] [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: 02/19/2024] [Revised: 05/17/2024] [Accepted: 07/04/2024] [Indexed: 07/25/2024]
Abstract
OBJECTIVES To perform a multi-reader comparison of multiparametric dual-energy computed tomography (DECT) images reconstructed with deep-learning image reconstruction (DLIR) and standard-of-care adaptive statistical iterative reconstruction-V (ASIR-V). METHODS This retrospective study included 100 patients undergoing portal venous phase abdominal CT on a rapid kVp switching DECT scanner. Six reconstructed DECT sets (ASIR-V and DLIR, each at three strengths) were generated. Each DECT set included 65 keV monoenergetic, iodine, and virtual unenhanced (VUE) images. Using a Likert scale, three radiologists performed qualitative assessments for image noise, contrast, small structure visibility, sharpness, artifact, and image preference. Quantitative assessment was performed by measuring attenuation, image noise, and contrast-to-noise ratios (CNR). For the qualitative analysis, Gwet's AC2 estimates were used to assess agreement. RESULTS DECT images reconstructed with DLIR yielded better qualitative scores than ASIR-V images except for artifacts, where both groups were comparable. DLIR-H images were rated higher than other reconstructions on all parameters (p-value < 0.05). On quantitative analysis, there was no significant difference in the attenuation values between ASIR-V and DLIR groups. DLIR images had higher CNR values for the liver and portal vein, and lower image noise, compared to ASIR-V images (p-value < 0.05). The subgroup analysis of patients with large body habitus (weight ≥ 90 kg) showed similar results to the study population. Inter-reader agreement was good-to-very good overall. CONCLUSION Multiparametric post-processed DECT datasets reconstructed with DLIR were preferred over ASIR-V images with DLIR-H yielding the highest image quality scores. CLINICAL RELEVANCE STATEMENT Deep-learning image reconstruction in dual-energy CT demonstrated significant benefits in qualitative and quantitative image metrics compared to adaptive statistical iterative reconstruction-V. KEY POINTS Dual-energy CT (DECT) images reconstructed using deep-learning image reconstruction (DLIR) showed superior qualitative scores compared to adaptive statistical iterative reconstruction-V (ASIR-V) reconstructed images, except for artifacts where both reconstructions were rated comparable. While there was no significant difference in attenuation values between ASIR-V and DLIR groups, DLIR images showed higher contrast-to-noise ratios (CNR) for liver and portal vein, and lower image noise (p value < 0.05). Subgroup analysis of patients with large body habitus (weight ≥ 90 kg) yielded similar findings to the overall study population.
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Affiliation(s)
- Jinjin Cao
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Nayla Mroueh
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Simon Lennartz
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
- Institute for Diagnostic and Interventional Radiology, University Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | - Nathaniel D Mercaldo
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Nisanard Pisuchpen
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
- Department of Radiology, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Sasiprang Kongboonvijit
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
- Department of Radiology, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Shravya Srinivas Rao
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Kampon Yuenyongsinchai
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
- Department of Radiology, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Theodore T Pierce
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Madeleine Sertic
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Ryan Chung
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Avinash R Kambadakone
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA.
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9
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Kanan A, Pereira B, Hordonneau C, Cassagnes L, Pouget E, Tianhoun LA, Chauveau B, Magnin B. Deep learning CT reconstruction improves liver metastases detection. Insights Imaging 2024; 15:167. [PMID: 38971933 PMCID: PMC11227486 DOI: 10.1186/s13244-024-01753-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 06/17/2024] [Indexed: 07/08/2024] Open
Abstract
OBJECTIVES Detection of liver metastases is crucial for guiding oncological management. Computed tomography through iterative reconstructions is widely used in this indication but has certain limitations. Deep learning image reconstructions (DLIR) use deep neural networks to achieve a significant noise reduction compared to iterative reconstructions. While reports have demonstrated improvements in image quality, their impact on liver metastases detection remains unclear. Our main objective was to determine whether DLIR affects the number of detected liver metastasis. Our secondary objective was to compare metastases conspicuity between the two reconstruction methods. METHODS CT images of 121 patients with liver metastases were reconstructed using a 50% adaptive statistical iterative reconstruction (50%-ASiR-V), and three levels of DLIR (DLIR-low, DLIR-medium, and DLIR-high). For each reconstruction, two double-blinded radiologists counted up to a maximum of ten metastases. Visibility and contour definitions were also assessed. Comparisons between methods for continuous parameters were performed using mixed models. RESULTS A higher number of metastases was detected by one reader with DLIR-high: 7 (2-10) (median (Q₁-Q₃); total 733) versus 5 (2-10), respectively for DLIR-medium, DLIR-low, and ASiR-V (p < 0.001). Ten patents were detected with more metastases with DLIR-high simultaneously by both readers and a third reader for confirmation. Metastases visibility and contour definition were better with DLIR than ASiR-V. CONCLUSION DLIR-high enhanced the detection and visibility of liver metastases compared to ASiR-V, and also increased the number of liver metastases detected. CRITICAL RELEVANCE STATEMENT Deep learning-based reconstruction at high strength allowed an increase in liver metastases detection compared to hybrid iterative reconstruction and can be used in clinical oncology imaging to help overcome the limitations of CT. KEY POINTS Detection of liver metastases is crucial but limited with standard CT reconstructions. More liver metastases were detected with deep-learning CT reconstruction compared to iterative reconstruction. Deep learning reconstructions are suitable for hepatic metastases staging and follow-up.
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Affiliation(s)
- Achraf Kanan
- Department of Radiology, Estaing Hospital, Clermont University Hospital, Clermont-Ferrand, France
| | - Bruno Pereira
- Department of Biostatistics, DRCI, Clermont University Hospital, Clermont-Ferrand, France
| | - Constance Hordonneau
- Department of Radiology, Estaing Hospital, Clermont University Hospital, Clermont-Ferrand, France
| | - Lucie Cassagnes
- Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, Clermont-Ferrand, France
- Department of Radiology, Gabriel Montpied Hospital, Clermont University Hospital, Clermont-Ferrand, France
| | - Eléonore Pouget
- Department of Radiology, Estaing Hospital, Clermont University Hospital, Clermont-Ferrand, France
| | - Léon Appolinaire Tianhoun
- Department of Radiology, Estaing Hospital, Clermont University Hospital, Clermont-Ferrand, France
- Department of Radiology, Tengandogo' Ouagadougou University Hospital Center, Ouagadougou, Burkina Faso
| | - Benoît Chauveau
- Department of Radiology, Estaing Hospital, Clermont University Hospital, Clermont-Ferrand, France
| | - Benoît Magnin
- Department of Radiology, Estaing Hospital, Clermont University Hospital, Clermont-Ferrand, France.
- Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, Clermont-Ferrand, France.
- DI2AM, DRCI, Clermont University Hospital, Clermont-Ferrand, France.
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10
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Quaia E, Kiyomi Lanza de Cristoforis E, Agostini E, Zanon C. Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms. Tomography 2024; 10:912-921. [PMID: 38921946 PMCID: PMC11209234 DOI: 10.3390/tomography10060069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 06/05/2024] [Accepted: 06/06/2024] [Indexed: 06/27/2024] Open
Abstract
Deep learning image reconstruction (DLIR) algorithms employ convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at a low radiation dose. The aim of this study was to assess whether the DLIR algorithm reduces the CT effective dose (ED) and improves CT image quality in comparison with filtered back projection (FBP) and iterative reconstruction (IR) algorithms in intensive care unit (ICU) patients. We identified all consecutive patients referred to the ICU of a single hospital who underwent at least two consecutive chest and/or abdominal contrast-enhanced CT scans within a time period of 30 days using DLIR and subsequently the FBP or IR algorithm (Advanced Modeled Iterative Reconstruction [ADMIRE] model-based algorithm or Adaptive Iterative Dose Reduction 3D [AIDR 3D] hybrid algorithm) for CT image reconstruction. The radiation ED, noise level, and signal-to-noise ratio (SNR) were compared between the different CT scanners. The non-parametric Wilcoxon test was used for statistical comparison. Statistical significance was set at p < 0.05. A total of 83 patients (mean age, 59 ± 15 years [standard deviation]; 56 men) were included. DLIR vs. FBP reduced the ED (18.45 ± 13.16 mSv vs. 22.06 ± 9.55 mSv, p < 0.05), while DLIR vs. FBP and vs. ADMIRE and AIDR 3D IR algorithms reduced image noise (8.45 ± 3.24 vs. 14.85 ± 2.73 vs. 14.77 ± 32.77 and 11.17 ± 32.77, p < 0.05) and increased the SNR (11.53 ± 9.28 vs. 3.99 ± 1.23 vs. 5.84 ± 2.74 and 3.58 ± 2.74, p < 0.05). CT scanners employing DLIR improved the SNR compared to CT scanners using FBP or IR algorithms in ICU patients despite maintaining a reduced ED.
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Affiliation(s)
- Emilio Quaia
- Department of Radiology, University of Padova, Via Giustiniani 2, 35128 Padova, Italy
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11
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Zhang Y, Liu Z, Cheng Y, Wang Z, Li Z, Li J, Shuai T. Deep Learning Image Reconstruction for Transcatheter Aortic Valve Implantation Planning: Image Quality, Diagnostic Performance, Contrast volume and Radiation Dose Assessment. Acad Radiol 2024; 31:2268-2280. [PMID: 38472024 DOI: 10.1016/j.acra.2024.02.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 02/13/2024] [Accepted: 02/15/2024] [Indexed: 03/14/2024]
Abstract
RATIONALE AND OBJECTIVES To assess image quality, contrast volume and radiation dose reduction potential and diagnostic performance with the use of high-strength deep learning image reconstruction (DLIR-H) in transcatheter aortic valve implantation (TAVI) planning CT. METHODS We prospectively enrolled 128 patients referred to TAVI-planning CT. Patients were randomly divided into two groups: DLIR-H group (n = 64) and conventional group (n = 64). The DLIR-H group was scanned with tube voltage of 80kVp and body weighted-dependent contrast injection rate of 28mgI/kg/s, images reconstructed using DLIR-H; the conventional group was scanned with 100kVp and contrast injection rate of 40mgI/kg/s, and images reconstructed using adaptive statistical iterative reconstruction-V at 50% (ASIR-V 50%). Radiation dose, contrast volume, contrast injection rate, and image quality were compared between the two groups. The diagnostic performance of TAVI planning CT for coronary stenosis in 115 patients were calculated using invasive coronary angiography as golden standard. RESULTS DLIR-H group significantly reduced radiation dose (4.94 ± 0.39mSv vs. 7.93 ± 1.20mSv, p < 0.001), contrast dose (45.28 ± 5.38 mL vs. 63.26 ± 9.88 mL, p < 0.001), and contrast injection rate (3.1 ± 0.31 mL/s vs. 4.9 ± 0.2 mL/s, p < 0.001) compared to the conventional group. Images in DLIR-H group had significantly higher SNR and CNR (all p < 0.001). For the diagnostic performance on a per-patient basis, TAVI planning CT in the DLIR-H group provided 100% sensitivity, 92.1% specificity, 100% negative predictive value (NPV), and 84.2% positive predictive value for the detection of > 50% stenosis. In the conventional group, the corresponding results were 94.7%, 95.3%, 97.6%, and 90.0%, respectively. CONCLUSION DLIR-H in TAVI-planning CT provides improved image quality with reduced radiation and contrast doses, and enables satisfactory diagnostic performance for coronary arteries stenosis.
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Affiliation(s)
- Yu Zhang
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | - Zixuan Liu
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | - Yong Cheng
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | - Ziwei Wang
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | - Zhenlin Li
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | | | - Tao Shuai
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan 610041, China.
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12
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Lin YH, Su AC, Ng SH, Shen MR, Wu YJ, Chen AC, Lee CW, Lin YC. Insights about cervical lymph nodes: Evaluating deep learning-based reconstruction for head and neck computed tomography scan. Eur J Radiol Open 2024; 12:100534. [PMID: 39022614 PMCID: PMC467078 DOI: 10.1016/j.ejro.2023.100534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 10/14/2023] [Accepted: 10/20/2023] [Indexed: 07/20/2024] Open
Abstract
Purpose This study aimed to investigate differences in cervical lymph node image quality on dual-energy computed tomography (CT) scan with datasets reconstructed using filter back projection (FBP), hybrid iterative reconstruction (IR), and deep learning-based image reconstruction (DLIR) in patients with head and neck cancer. Method Seventy patients with head and neck cancer underwent follow-up contrast-enhanced dual-energy CT examinations. All datasets were reconstructed using FBP, hybrid IR with 30 % adaptive statistical IR (ASiR-V), and DLIR with three selectable levels (low, medium, and high) at 2.5- and 0.625-mm slice thicknesses. Herein, signal, image noise, signal-to-noise ratio, and contrast-to-noise ratio of lymph nodes and overall image quality, artifact, and noise of selected regions of interest were evaluated by two radiologists. Next, cervical lymph node sharpness was evaluated using full width at half maximum. Results DLIR exhibited significantly reduced noise, ranging from 3.8 % to 35.9 % with improved signal-to-noise ratio (11.5-105.6 %) and contrast-to-noise ratio (10.5-107.5 %) compared with FBP and ASiR-V, for cervical lymph nodes (p < 0.001). Further, 0.625-mm-thick images reconstructed using DLIR-medium and DLIR-high had a lower noise than 2.5-mm-thick images reconstructed using FBP and ASiR-V. The lymph node margins and vessels on DLIR-medium and DLIR-high were sharper than those on FBP and ASiR-V (p < 0.05). Both readers agreed that DLIR had a better image quality than the conventional reconstruction algorithms. Conclusion DLIR-medium and -high provided superior cervical lymph node image quality in head and neck CT. Improved image quality affords thin-slice DLIR images for dose-reduction protocols in the future.
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Affiliation(s)
- Yu-Han Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - An-Chi Su
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Shu-Hang Ng
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Min-Ru Shen
- Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Yu-Jie Wu
- Department of Radiology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | | | | | - Yu-Chun Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
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13
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Greffier J, Dabli D, Faby S, Pastor M, Croisille C, de Oliveira F, Erath J, Beregi JP. Abdominal image quality and dose reduction with energy-integrating or photon-counting detectors dual-source CT: A phantom study. Diagn Interv Imaging 2024:S2211-5684(24)00120-7. [PMID: 38760277 DOI: 10.1016/j.diii.2024.05.002] [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: 02/29/2024] [Revised: 05/02/2024] [Accepted: 05/03/2024] [Indexed: 05/19/2024]
Abstract
PURPOSE The purpose of this study was to assess image-quality and dose reduction potential using a photon-counting computed tomography (PCCT) system by comparison with two different dual-source CT (DSCT) systems using two phantoms. MATERIALS AND METHODS Acquisitions on phantoms were performed using two DSCT systems (DSCT1 [Somatom Force] and DSCT2 [Somatom Pro.Pulse]) and one PCCT system (Naeotom Alpha) at four dose levels (13/6/3.4/1.8 mGy). Noise power spectrum (NPS) and task-based transfer function (TTF) were computed to assess noise magnitude and noise texture and spatial resolution (f50), respectively. Detectability indexes (d') were computed to model the detection of abdominal lesions: one unenhanced high-contrast task, one contrast-enhanced high-contrast task and one unenhanced low-contrast task. Image quality was subjectively assessed on an anthropomorphic phantom by two radiologists. RESULTS For all dose levels, noise magnitude values were lower with PCCT than with DSCTs. For all CT systems, similar noise texture values were found at 13 and 6 mGy, but the greatest noise texture values were found for DSCT2 and the lowest for PCCT at 3.4 and 1.8 mGy. For high-contrast inserts, similar or lower f50 values were found with PCCT than with DSCT1 and the opposite pattern was found for the low-contrast insert. For the three simulated lesions, d' values were greater with PCCT than with DSCTs. Abdominal images were rated satisfactory for clinical use by the radiologists for all dose levels with PCCT and for 13 and 6 mGy with DSCTs. CONCLUSION By comparison with DSCTs, PCCT reduces image-noise and improves detectability of simulated abdominal lesions without altering the spatial resolution and image texture. Image-quality obtained with PCCT seem to indicate greater potential for dose optimization than those obtained with DSCTs.
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Affiliation(s)
- Joël Greffier
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30900 Nîmes, France.
| | - Djamel Dabli
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30900 Nîmes, France
| | - Sebastian Faby
- Department of Computed Tomography, Siemens Healthineers AG, 91301 Forchheim, Germany
| | - Maxime Pastor
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30900 Nîmes, France
| | - Cédric Croisille
- Department of Computed Tomography, Siemens Healthineers AG, 91301 Forchheim, Germany
| | - Fabien de Oliveira
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30900 Nîmes, France
| | - Julien Erath
- Department of Computed Tomography, Siemens Healthineers AG, 91301 Forchheim, Germany
| | - Jean Paul Beregi
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30900 Nîmes, France
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14
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Meng Z, Guo Y, Deng S, Xiang Q, Cao J, Zhang Y, Zhang K, Ma K, Xie S, Kang Z. Improving image quality of triple-low-protocol renal artery CT angiography with deep-learning image reconstruction: a comparative study with standard-dose single-energy and dual-energy CT with adaptive statistical iterative reconstruction. Clin Radiol 2024; 79:e651-e658. [PMID: 38433041 DOI: 10.1016/j.crad.2024.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 01/03/2024] [Accepted: 01/09/2024] [Indexed: 03/05/2024]
Abstract
AIM To investigate the improvement in image quality of triple-low-protocol (low radiation, low contrast medium dose, low injection speed) renal artery computed tomography (CT) angiography (RACTA) using deep-learning image reconstruction (DLIR), in comparison with standard-dose single- and dual-energy CT (DECT) using adaptive statistical iterative reconstruction-Veo (ASIR-V) algorithm. MATERIALS AND METHODS Ninety patients for RACTA were divided into different groups: standard-dose single-energy CT (S group) using ASIR-V at 60% strength (60%ASIR-V), DECT (DE group) with 60%ASIR-V including virtual monochromatic images at 40 keV (DE40 group) and 70 keV (DE70 group), and the triple-low protocol single-energy CT (L group) with DLIR at high level (DLIR-H). The effective dose (ED), contrast medium dose, injection speed, standard deviation (SD), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of abdominal aorta (AA), and left/right renal artery (LRA, RRA), and subjective scores were compared among the different groups. RESULTS The L group significantly reduced ED by 37.6% and 31.2%, contrast medium dose by 33.9% and 30.5%, and injection speed by 30% and 30%, respectively, compared to the S and DE groups. The L group had the lowest SD values for all arteries compared to the other groups (p<0.001). The SNR of RRA and LRA in the L group, and the CNR of all arteries in the DE40 group had highest value compared to others (p<0.05). The L group had the best comprehensive score with good consistency (p<0.05). CONCLUSIONS The triple-low protocol RACTA with DLIR-H significantly reduces the ED, contrast medium doses, and injection speed, while providing good comprehensive image quality.
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Affiliation(s)
- Z Meng
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China
| | - Y Guo
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China
| | - S Deng
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China
| | - Q Xiang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China
| | - J Cao
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China
| | - Y Zhang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China
| | - K Zhang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China
| | - K Ma
- CT Imaging Research Center, GE HealthCare China, Tianhe District, Huacheng Road 87, Guangzhou, 510623, China
| | - S Xie
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China.
| | - Z Kang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China.
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15
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Chandran M O, Pendem S, P S P, Chacko C, - P, Kadavigere R. Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review. F1000Res 2024; 13:274. [PMID: 38725640 PMCID: PMC11079581 DOI: 10.12688/f1000research.147345.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/26/2024] [Indexed: 05/12/2024] Open
Abstract
Background The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations. Methods We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Results Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations. Conclusions DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.
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Affiliation(s)
- Obhuli Chandran M
- Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Saikiran Pendem
- Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Priya P S
- Department of Radio Diagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Cijo Chacko
- Philips Research and Development, Philips Innovation Campus, Yelahanka, Karnataka, 560064, India
| | - Priyanka -
- Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Rajagopal Kadavigere
- Department of Radio Diagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
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Tachibana Y, Takaji R, Shiroo T, Asayama Y. Deep-learning reconstruction with low-contrast media and low-kilovoltage peak for CT of the liver. Clin Radiol 2024; 79:e546-e553. [PMID: 38238148 DOI: 10.1016/j.crad.2023.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 12/13/2023] [Accepted: 12/19/2023] [Indexed: 03/09/2024]
Abstract
AIM To compare images using reduced CM, low-kVp scanning and DLR reconstruction with conventional images (no CM reduction, normal tube voltage, reconstructed with HBIR. To compare images using reduced contrast media (CM), low kilovoltage peak (kVp) scanning and deep-learning reconstruction (DLR) with conventional image quality (no CM reduction, normal tube voltage, reconstructed with hybrid-type iterative reconstruction method [HBIR protocol]). MATERIALS AND METHODS A retrospective analysis was performed on 70 patients with liver disease and three-phase dynamic imaging using computed tomography (CT) from April 2020 to March 2022 at Oita University Hospital. Of these cases, 39 were reconstructed using the DLR protocol at a tube voltage of 80 kVp and CM of 300 mg iodine/kg while 31 were imaged at a tube voltage of 120 kVp with CM of 600 mg iodine/kg and were reconstructed by the usual HBIR protocol. Images from the DLR and HBIR protocols were analysed and compared based on the contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), figure-of-merit (FOM), and visual assessment. The CT dose index (CTDI)vol and size-specific dose estimates (SSDE) were compared with respect to radiation dose. RESULTS The DLR protocol was superior, with significant differences in CNR, SNR, and FOM except hepatic parenchyma in the arterial phase. For visual assessment, the DLR protocol had better values for vascular visualisation for the portal vein, image noise, and contrast enhancement of the hepatic parenchyma. Regarding comparison of the radiation dose, the DLR protocol was superior for all values of CTDIvol and SSDE, with significant differences (p<0.01; max. 52%). CONCLUSION Protocols using DLR with reduced CM and low kVp have better image quality and lower radiation dose compared to protocols using conventional HBIR.
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Affiliation(s)
- Y Tachibana
- Graduate School of Medicine, Oita University, 1-1 Idaigaoka, Hasama-machi, Yufu, 879-5593, Japan
| | - R Takaji
- Department of Radiology, Oita University Faculty of Medicine, 1-1 Idaigaoka, Hasama-machi, Yufu, 879-5593, Japan
| | - T Shiroo
- Radiology Department, Division of Medical Technology, Oita University Hospital, 1-1 Idaigaoka, Hasama-machi, Yufu, 879-5593, Japan
| | - Y Asayama
- Department of Radiology, Oita University Faculty of Medicine, 1-1 Idaigaoka, Hasama-machi, Yufu, 879-5593, Japan.
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You Y, Zhong S, Zhang G, Wen Y, Guo D, Li W, Li Z. Exploring the Low-Dose Limit for Focal Hepatic Lesion Detection with a Deep Learning-Based CT Reconstruction Algorithm: A Simulation Study on Patient Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01080-3. [PMID: 38502435 DOI: 10.1007/s10278-024-01080-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 03/21/2024]
Abstract
This study aims to investigate the maximum achievable dose reduction for applying a new deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in computed tomography (CT) for hepatic lesion detection. A total of 40 patients with 98 clinically confirmed hepatic lesions were retrospectively included. The mean volume CT dose index was 13.66 ± 1.73 mGy in routine-dose portal venous CT examinations, where the images were originally obtained with hybrid iterative reconstruction (HIR). Low-dose simulations were performed in projection domain for 40%-, 20%-, and 10%-dose levels, followed by reconstruction using both HIR and AIIR. Two radiologists were asked to detect hepatic lesion on each set of low-dose image in separate sessions. Qualitative metrics including lesion conspicuity, diagnostic confidence, and overall image quality were evaluated using a 5-point scale. The contrast-to-noise ratio (CNR) for lesion was also calculated for quantitative assessment. The lesion CNR on AIIR at reduced doses were significantly higher than that on routine-dose HIR (all p < 0.05). Lower qualitative image quality was observed as the radiation dose reduced, while there were no significant differences between 40%-dose AIIR and routine-dose HIR images. The lesion detection rate was 100%, 98% (96/98), and 73.5% (72/98) on 40%-, 20%-, and 10%-dose AIIR, respectively, whereas it was 98% (96/98), 73.5% (72/98), and 40% (39/98) on the corresponding low-dose HIR, respectively. AIIR outperformed HIR in simulated low-dose CT examinations of the liver. The use of AIIR allows up to 60% dose reduction for lesion detection while maintaining comparable image quality to routine-dose HIR.
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Affiliation(s)
- Yongchun You
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | | | | | - Yuting Wen
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Dian Guo
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Wanjiang Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
| | - Zhenlin Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
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18
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Gong H, Peng L, Du X, An J, Peng R, Guo R, Ma X, Xiong S, Ma Q, Zhang G, Ma J. Artificial Intelligence Iterative Reconstruction in Computed Tomography Angiography: An Evaluation on Pulmonary Arteries and Aorta With Routine Dose Settings. J Comput Assist Tomogr 2024; 48:244-250. [PMID: 37657068 DOI: 10.1097/rct.0000000000001542] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/03/2023]
Abstract
OBJECTIVE The objective of this study is to investigate whether a newly introduced deep learning-based iterative reconstruction algorithm, namely, the artificial intelligence iterative reconstruction (AIIR), has a clinical value in computed tomography angiography (CTA), especially for visualizing vascular structures and related lesions, with routine dose settings. METHODS A total of 63 patients were retrospectively collected from the triple rule-out CTA examinations, where both pulmonary and aortic data were available for each patient and were taken as the example for investigation. The images were reconstructed using the filtered back projection (FBP), hybrid iterative reconstruction (HIR), and the AIIR. The visibility of vasculature and pulmonary emboli and the general image quality were assessed. RESULTS Artificial intelligence iterative reconstruction resulted in significantly ( P < 0.001) lower noise as well as higher signal-to-noise ratio and contrast-to-noise ratio compared with FBP and HIR. Besides, AIIR achieved the highest subjective scores on general image quality ( P < 0.05). For the vasculature visibility, AIIR offered the best vessel conspicuity, especially for the small vessels ( P < 0.05). Also, >90% of emboli on the AIIR images were graded as sharp (score 5), whereas <15% of emboli on FBP and HIR images were scored 5. CONCLUSION As demonstrated for pulmonary and aortic CTAs, AIIR improves the image quality and offers a better depiction for vascular structures compared with FBP and HIR. The visibility of the pulmonary emboli was also increased by AIIR.
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Affiliation(s)
- Huan Gong
- From the Department of Radiology, The Second Affiliated Hospital of Shihezi University School of Medicine, Urumqi
| | | | - Xiangdong Du
- From the Department of Radiology, The Second Affiliated Hospital of Shihezi University School of Medicine, Urumqi
| | - Jiajia An
- From the Department of Radiology, The Second Affiliated Hospital of Shihezi University School of Medicine, Urumqi
| | - Rui Peng
- From the Department of Radiology, The Second Affiliated Hospital of Shihezi University School of Medicine, Urumqi
| | - Rui Guo
- From the Department of Radiology, The Second Affiliated Hospital of Shihezi University School of Medicine, Urumqi
| | - Xu Ma
- From the Department of Radiology, The Second Affiliated Hospital of Shihezi University School of Medicine, Urumqi
| | - Sining Xiong
- From the Department of Radiology, The Second Affiliated Hospital of Shihezi University School of Medicine, Urumqi
| | - Qin Ma
- From the Department of Radiology, The Second Affiliated Hospital of Shihezi University School of Medicine, Urumqi
| | | | - Jing Ma
- From the Department of Radiology, The Second Affiliated Hospital of Shihezi University School of Medicine, Urumqi
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19
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Lee DH, Lee JM, Lee CH, Afat S, Othman A. Image Quality and Diagnostic Performance of Low-Dose Liver CT with Deep Learning Reconstruction versus Standard-Dose CT. Radiol Artif Intell 2024; 6:e230192. [PMID: 38231025 PMCID: PMC10982822 DOI: 10.1148/ryai.230192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 11/13/2023] [Accepted: 01/02/2024] [Indexed: 01/18/2024]
Abstract
Purpose To compare the image quality and diagnostic capability in detecting malignant liver tumors of low-dose CT (LDCT, 33% dose) with deep learning-based denoising (DLD) and standard-dose CT (SDCT, 100% dose) with model-based iterative reconstruction (MBIR). Materials and Methods In this prospective, multicenter, noninferiority study, individuals referred for liver CT scans were enrolled from three tertiary referral hospitals between February 2021 and August 2022. All liver CT scans were conducted using a dual-source scanner with the dose split into tubes A (67% dose) and B (33% dose). Blended images from tubes A and B were created using MBIR to produce SDCT images, whereas LDCT images used data from tube B and were reconstructed with DLD. The noise in liver images was measured and compared between imaging techniques. The diagnostic performance of each technique in detecting malignant liver tumors was evaluated by three independent radiologists using jackknife alternative free-response receiver operating characteristic analysis. Noninferiority of LDCT compared with SDCT was declared when the lower limit of the 95% CI for the difference in figure of merit (FOM) was greater than -0.10. Results A total of 296 participants (196 men, 100 women; mean age, 60.5 years ± 13.3 [SD]) were included. The mean noise level in the liver was significantly lower for LDCT (10.1) compared with SDCT (10.7) (P < .001). Diagnostic performance was assessed in 246 participants (108 malignant tumors in 90 participants). The reader-averaged FOM was 0.880 for SDCT and 0.875 for LDCT (P = .35). The difference fell within the noninferiority margin (difference, -0.005 [95% CI: -0.024, 0.012]). Conclusion Compared with SDCT with MBIR, LDCT using 33% of the standard radiation dose had reduced image noise and comparable diagnostic performance in detecting malignant liver tumors. Keywords: CT, Abdomen/GI, Liver, Comparative Studies, Diagnosis, Reconstruction Algorithms Clinical trial registration no. NCT05804799 © RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
- Dong Ho Lee
- From the Departments of Radiology of Seoul National University
Hospital, Seoul, South Korea (D.H.L., J.M.L.); Seoul National University
Hospital, Seoul National University College of Medicine, 101 Daehak-ro,
Jongno-gu, Seoul 03080, South Korea (D.H.L., J.M.L.); Korea University Guro
Hospital, Korea University Medicine, Seoul, South Korea (C.H.L.); and
Tübingen University Hospital, Tübingen, Germany (S.A.,
A.O.)
| | - Jeong Min Lee
- From the Departments of Radiology of Seoul National University
Hospital, Seoul, South Korea (D.H.L., J.M.L.); Seoul National University
Hospital, Seoul National University College of Medicine, 101 Daehak-ro,
Jongno-gu, Seoul 03080, South Korea (D.H.L., J.M.L.); Korea University Guro
Hospital, Korea University Medicine, Seoul, South Korea (C.H.L.); and
Tübingen University Hospital, Tübingen, Germany (S.A.,
A.O.)
| | - Chang Hee Lee
- From the Departments of Radiology of Seoul National University
Hospital, Seoul, South Korea (D.H.L., J.M.L.); Seoul National University
Hospital, Seoul National University College of Medicine, 101 Daehak-ro,
Jongno-gu, Seoul 03080, South Korea (D.H.L., J.M.L.); Korea University Guro
Hospital, Korea University Medicine, Seoul, South Korea (C.H.L.); and
Tübingen University Hospital, Tübingen, Germany (S.A.,
A.O.)
| | - Saif Afat
- From the Departments of Radiology of Seoul National University
Hospital, Seoul, South Korea (D.H.L., J.M.L.); Seoul National University
Hospital, Seoul National University College of Medicine, 101 Daehak-ro,
Jongno-gu, Seoul 03080, South Korea (D.H.L., J.M.L.); Korea University Guro
Hospital, Korea University Medicine, Seoul, South Korea (C.H.L.); and
Tübingen University Hospital, Tübingen, Germany (S.A.,
A.O.)
| | - Ahmed Othman
- From the Departments of Radiology of Seoul National University
Hospital, Seoul, South Korea (D.H.L., J.M.L.); Seoul National University
Hospital, Seoul National University College of Medicine, 101 Daehak-ro,
Jongno-gu, Seoul 03080, South Korea (D.H.L., J.M.L.); Korea University Guro
Hospital, Korea University Medicine, Seoul, South Korea (C.H.L.); and
Tübingen University Hospital, Tübingen, Germany (S.A.,
A.O.)
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Li J, Zhu J, Zou Y, Zhang G, Zhu P, Wang N, Xie P. Diagnostic CT of colorectal cancer with artificial intelligence iterative reconstruction: A clinical evaluation. Eur J Radiol 2024; 171:111301. [PMID: 38237522 DOI: 10.1016/j.ejrad.2024.111301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 12/26/2023] [Accepted: 01/07/2024] [Indexed: 02/10/2024]
Abstract
OBJECTIVES To investigate the clinical value of a novel deep-learning based CT reconstruction algorithm, artificial intelligence iterative reconstruction (AIIR), in diagnostic imaging of colorectal cancer (CRC). METHODS This study retrospectively enrolled 217 patients with pathologically confirmed CRC. CT images were reconstructed with the AIIR algorithm and compared with those originally obtained with hybrid iterative reconstruction (HIR). Objective image quality was evaluated in terms of the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Subjective image quality was graded on the conspicuity of tumor margin and enhancement pattern as well as the certainty in diagnosing organ invasion and regional lymphadenopathy. In patients with surgical pathology (n = 116), the performance of diagnosing visceral peritoneum invasion was characterized using receiver operating characteristic (ROC) analysis. Changes of diagnostic thinking in diagnosing hepatic metastases were assessed through lesion classification confidence. RESULTS The SNRs and CNRs on AIIR images were significantly higher than those on HIR images (all p < 0.001). The AIIR was scored higher for all subjective metrics (all p < 0.001) except for the certainty of diagnosing regional lymphadenopathy (p = 0.467). In diagnosing visceral peritoneum invasion, higher area under curve (AUC) of the ROC was found for AIIR than HIR (0.87 vs 0.77, p = 0.001). In assessing hepatic metastases, AIIR was found capable of correcting the misdiagnosis and improving the diagnostic confidence provided by HIR (p = 0.01). CONCLUSIONS Compared to HIR, AIIR offers better image quality, improves the diagnostic performance regarding CRC, and thus has the potential for application in routine abdominal CT.
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Affiliation(s)
- Jiao Li
- Department of Radiology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
| | - Junying Zhu
- Department of Radiology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
| | - Yixuan Zou
- United Imaging Healthcare, Shanghai 201800, China.
| | - Guozhi Zhang
- United Imaging Healthcare, Shanghai 201800, China.
| | - Pan Zhu
- Department of Radiology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
| | - Ning Wang
- Department of Radiology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
| | - Peiyi Xie
- Department of Radiology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
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21
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Sadia RT, Chen J, Zhang J. CT image denoising methods for image quality improvement and radiation dose reduction. J Appl Clin Med Phys 2024; 25:e14270. [PMID: 38240466 PMCID: PMC10860577 DOI: 10.1002/acm2.14270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 12/15/2023] [Accepted: 12/28/2023] [Indexed: 02/13/2024] Open
Abstract
With the ever-increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in low-dose CT images. Numerous noise reduction algorithms have emerged, such as iterative reconstruction and most recently, deep learning (DL)-based approaches. Given the rapid advancements in Artificial Intelligence techniques, we recognize the need for a comprehensive review that emphasizes the most recently developed methods. Hence, we have performed a thorough analysis of existing literature to provide such a review. Beyond directly comparing the performance, we focus on pivotal aspects, including model training, validation, testing, generalizability, vulnerability, and evaluation methods. This review is expected to raise awareness of the various facets involved in CT image denoising and the specific challenges in developing DL-based models.
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Affiliation(s)
- Rabeya Tus Sadia
- Department of Computer ScienceUniversity of KentuckyLexingtonKentuckyUSA
| | - Jin Chen
- Department of Medicine‐NephrologyUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Jie Zhang
- Department of RadiologyUniversity of KentuckyLexingtonKentuckyUSA
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22
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Lyu P, Li Z, Chen Y, Wang H, Liu N, Liu J, Zhan P, Liu X, Shang B, Wang L, Gao J. Deep learning reconstruction CT for liver metastases: low-dose dual-energy vs standard-dose single-energy. Eur Radiol 2024; 34:28-38. [PMID: 37532899 DOI: 10.1007/s00330-023-10033-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 05/28/2023] [Accepted: 06/05/2023] [Indexed: 08/04/2023]
Abstract
OBJECTIVES To assess image quality and liver metastasis detection of reduced-dose dual-energy CT (DECT) with deep learning image reconstruction (DLIR) compared to standard-dose single-energy CT (SECT) with DLIR or iterative reconstruction (IR). METHODS In this prospective study, two groups of 40 participants each underwent abdominal contrast-enhanced scans with full-dose SECT (120-kVp images, DLIR and IR algorithms) or reduced-dose DECT (40- to 60-keV virtual monochromatic images [VMIs], DLIR algorithm), with 122 and 106 metastases, respectively. Groups were matched by age, sex ratio, body mass index, and cross-sectional area. Noise power spectrum of liver images and task-based transfer function of metastases were calculated to assess the noise texture and low-contrast resolution. The image noise, signal-to-noise ratios (SNR) of liver and portal vein, liver-to-lesion contrast-to-noise ratio (LLR), lesion conspicuity, lesion detection rate, and the subjective image quality metrics were compared between groups on 1.25-mm reconstructed images. RESULTS Compared to 120-kVp images with IR, 40- and 50-keV VMIs with DLIR showed similar noise texture and LLR, similar or higher image noise and low-contrast resolution, improved SNR and lesion conspicuity, and similar or better perceptual image quality. When compared to 120-kVp images with DLIR, 50-keV VMIs with DLIR had similar low-contrast resolution, SNR, LLR, lesion conspicuity, and perceptual image quality but lower frequency noise texture and higher image noise. For the detection of hepatic metastases, reduced-dose DECT by 34% maintained observer lesion detection rates. CONCLUSION DECT assisted with DLIR enables a 34% dose reduction for detecting hepatic metastases while maintaining comparable perceptual image quality to full-dose SECT. CLINICAL RELEVANCE STATEMENT Reduced-dose dual-energy CT with deep learning image reconstruction is as accurate as standard-dose single-energy CT for the detection of liver metastases and saves more than 30% of the radiation dose. KEY POINTS • The 40- and 50-keV virtual monochromatic images (VMIs) with deep learning image reconstruction (DLIR) improved lesion conspicuity compared with 120-kVp images with iterative reconstruction while providing similar or better perceptual image quality. • The 50-keV VMIs with DLIR provided comparable perceptual image quality and lesion conspicuity to 120-kVp images with DLIR. • The reduction of radiation by 34% by DLIR in low-keV VMIs is clinically sufficient for detecting low-contrast hepatic metastases.
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Affiliation(s)
- Peijie Lyu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Zhen Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Yan Chen
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Huixia Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Nana Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Jie Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Pengchao Zhan
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Xing Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Bo Shang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Luotong Wang
- CT Imaging Research Center, GE Healthcare China, Beijing, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China.
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Kawashima H. [[CT] 6. The Current Situation of AI Image Reconstruction in CT]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2024; 80:252-259. [PMID: 38382985 DOI: 10.6009/jjrt.2024-2321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Affiliation(s)
- Hiroki Kawashima
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University
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24
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Li S, Yuan L, Lu T, Yang X, Ren W, Wang L, Zhao J, Deng J, Liu X, Xue C, Sun Q, Zhang W, Zhou J. Deep learning imaging reconstruction of reduced-dose 40 keV virtual monoenergetic imaging for early detection of colorectal cancer liver metastases. Eur J Radiol 2023; 168:111128. [PMID: 37816301 DOI: 10.1016/j.ejrad.2023.111128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/07/2023] [Accepted: 09/28/2023] [Indexed: 10/12/2023]
Abstract
OBJECTIVE To explore whether reduced-dose (RD) gemstone spectral imaging (GSI) and deep learning image reconstruction (DLIR) of 40 keV virtual monoenergetic image (VMI) enhanced the early detection and diagnosis of colorectal cancer liver metastases (CRLM). METHODS Thirty-five participants with pathologically confirmed colorectal cancer were prospectively enrolled from March to August 2022 after routine care abdominal computed tomography (CT). GSI mode was used for contrast-enhanced CT, and two portal venous phase CT images were obtained [standard-dose (SD) CT dose index (CTDIvol) = 15.51 mGy, RD CTDIvol = 7.95 mGy]. The 40 keV-VMI were reconstructed via filtered back projection (FBP) and iterative reconstruction (ASIR-V 60 %, AV60) of both SD and RD images. RD medium-strength deep learning image reconstruction (DLIR-M) and RD high-strength deep learning image reconstruction (DLIR-H) were used to reconstruct the 40 keV-VMI. The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the liver and the lesions were objectively evaluated. The overall image quality, lesion conspicuity, and diagnostic confidence were subjectively evaluated, to compare the differences in evaluation results among the different images. RESULTS All 35 participants (mean age: 59.51 ± 11.01 years; 14 females) underwent SD and RD GSI portal venous-phase CT scans. The dose-length product of the RD GSI scan was reduced by 49-53 % lower than that of the SD GSI scan (420.22 ± 31.95) vs (817.58 ± 60.56). A total of 219 lesions were identified, including 55 benign lesions and 164 metastases, with an average size of 7.37 ± 4.14 mm. SD-FBP detected 207 lesions, SD-AV60 detected 201 lesions, and DLIR-M and DLIR-H detected 199 and 190 lesions, respectively. For lesions ≤ 5 mm, there was no statistical difference between SD-FBP vs DLIR-M (χ2McNemar = 1.00, P = 0.32) and SD-AV60 vs DLIR-M (χ2McNemar = 0.33, P = 0.56) in the detection rate. The CNR, SNR, and noise of DLIR-M and DLIR-H 40 keV-VMI images were better than those of SD-FBP images (P < 0.01) but did not differ significantly from those of SD-AV60 images (P > 0.05). When the lesions ≤ 5 mm, there were statistical differences in the overall diagnostic sensitivity of lesions compared with SD-FBP, SD-AV60, DLIR-M and DLIR-H (P<0.01). There were no statistical differences in the sensitivity of lesions diagnosis between SD-FBP, SD-AV60 and DLIR-M (both P>0.05). However, the DLIR-M subjective image quality and lesion diagnostic confidence were higher for SD-FBP (both P < 0.01). CONCLUSION Reduced dose DLIR-M of 40 keV-VMI can be used for routine follow-up care of colorectal cancer patients, to optimize evaluations and ensure CT image quality. Meanwhile, the detection rate and diagnostic sensitivity and specificity of small lesions, early liver metastases is not obviously reduced.
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Affiliation(s)
- Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Long Yuan
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Ting Lu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Xinmei Yang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Wei Ren
- CT Imaging Research Center, GE Healthcare China, Beijing, 100176, China.
| | - Luotong Wang
- CT Imaging Research Center, GE Healthcare China, Beijing, 100176, China.
| | - Jun Zhao
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Qiu Sun
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China.
| | - Wenjuan Zhang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
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Jensen CT, Wong VK, Wagner-Bartak NA, Liu X, Padmanabhan Nair Sobha R, Sun J, Likhari GS, Gupta S. Accuracy of liver metastasis detection and characterization: Dual-energy CT versus single-energy CT with deep learning reconstruction. Eur J Radiol 2023; 168:111121. [PMID: 37806195 DOI: 10.1016/j.ejrad.2023.111121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 09/08/2023] [Accepted: 09/27/2023] [Indexed: 10/10/2023]
Abstract
PURPOSE To assess whether image quality differences between SECT (single-energy CT) and DECT (dual-energy CT 70 keV) with equivalent radiation doses result in altered detection and characterization accuracy of liver metastases when using deep learning image reconstruction (DLIR), and whether DECT spectral curve usage improves accuracy of indeterminate lesion characterization. METHODS In this prospective Health Insurance Portability and Accountability Act-compliant study (March through August 2022), adult men and non-pregnant adult women with biopsy-proven colorectal cancer and liver metastases underwent SECT (120 kVp) and a DECT (70 keV) portovenous abdominal CT scan using DLIR in the same breath-hold (Revolution CT ES; GE Healthcare). Participants were excluded if consent could not be obtained, if there were nonequivalent radiation doses between the two scans, or if the examination was cancelled/rescheduled. Three radiologists independently performed lesion detection and characterization during two separate sessions (SECT DLIRmedium and DECT DLIRhigh) as well as reported lesion confidence and overall image quality. Hounsfield units were measured. Spectral HU curves were provided for any lesions rated as indeterminate. McNemar's test was used to test the marginal homogeneity in terms of diagnostic sensitivity, accuracy and lesion detection. A generalized estimating equation method was used for categorical outcomes. RESULTS 30 participants (mean age, 58 years ± 11, 21 men) were evaluated. Mean CTDIvol was 34 mGy for both scans. 141 lesions (124 metastases, 17 benign) with a mean size of 0.8 cm ± 0.3 cm were identified. High scores for image quality (scores of 4 or 5) were not significantly different between DECT (N = 71 out of 90 total scores from the three readers) and SECT (N = 62) (OR, 2.01; 95% CI:0.89, 4.57; P = 0.093). Equivalent image noise to SECT DLIRmed (HU SD 10 ± 2) was obtained with DECT DLIRhigh (HU SD 10 ± 3) (P = 1). There was no significant difference in lesion detection between DECT and SECT (140/141 lesions) (99.3%; 95% CI:96.1%, 100%).The mean lesion confidence scores by each reader were 4.2 ± 1.3, 3.9 ± 1.0, and 4.8 ± 0.8 for SECT and 4.1 ± 1.4, 4.0 ± 1.0, and 4.7 ± 0.8 for DECT (odds ratio [OR], 0.83; 95% CI: 0.62, 1.11; P = 0.21). Small lesion (≤5mm) characterization accuracy on SECT and DECT was 89.1% (95% CI:76.4%, 96.4%; 41/46) and 84.8% (71.1%, 93.7%; 39/46), respectively (P = 0.41). Use of spectral HU lesion curves resulted in 34 correct changes in characterizations and no mischaracterizations. CONCLUSION DECT required a higher strength of DLIR to obtain equivalent noise compared to SECT DLIR. At equivalent radiation doses and image noise, there was no significant difference in subjective image quality or observer lesion performance between DECT (70 keV) and SECT. However, DECT spectral HU curves of indeterminate lesions improved characterization.
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Affiliation(s)
- Corey T Jensen
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, USA.
| | - Vincenzo K Wong
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, USA
| | - Nicolaus A Wagner-Bartak
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, USA
| | - Xinming Liu
- Department of Physics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, USA
| | - Renjith Padmanabhan Nair Sobha
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, USA
| | - Jia Sun
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, USA
| | - Gauruv S Likhari
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, USA
| | - Shiva Gupta
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, USA
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Brady SL. Implementation of AI image reconstruction in CT-how is it validated and what dose reductions can be achieved. Br J Radiol 2023; 96:20220915. [PMID: 37102695 PMCID: PMC10546449 DOI: 10.1259/bjr.20220915] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 03/10/2023] [Accepted: 03/15/2023] [Indexed: 04/28/2023] Open
Abstract
CT reconstruction has undergone a substantial change over the last decade with the introduction of iterative reconstruction (IR) and now with deep learning reconstruction (DLR). In this review, DLR will be compared to IR and filtered back-projection (FBP) reconstructions. Comparisons will be made using image quality metrics such as noise power spectrum, contrast-dependent task-based transfer function, and non-prewhitening filter detectability index (dNPW'). Discussion on how DLR has impacted CT image quality, low-contrast detectability, and diagnostic confidence will be provided. DLR has shown the ability to improve in areas that IR is lacking, namely: noise magnitude reduction does not alter noise texture to the degree that IR did, and the noise texture found in DLR is more aligned with noise texture of an FBP reconstruction. Additionally, the dose reduction potential for DLR is shown to be greater than IR. For IR, the consensus was dose reduction should be limited to no more than 15-30% to preserve low-contrast detectability. For DLR, initial phantom and patient observer studies have shown acceptable dose reduction between 44 and 83% for both low- and high-contrast object detectability tasks. Ultimately, DLR is able to be used for CT reconstruction in place of IR, making it an easy "turnkey" upgrade for CT reconstruction. DLR for CT is actively being improved as more vendor options are being developed and current DLR options are being enhanced with second generation algorithms being released. DLR is still in its developmental early stages, but is shown to be a promising future for CT reconstruction.
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Cao J, Mroueh N, Pisuchpen N, Parakh A, Lennartz S, Pierce TT, Kambadakone AR. Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT? Abdom Radiol (NY) 2023; 48:3253-3264. [PMID: 37369922 DOI: 10.1007/s00261-023-03992-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023]
Abstract
BACKGROUND CT image reconstruction has evolved from filtered back projection to hybrid- and model-based iterative reconstruction. Deep learning-based image reconstruction is a relatively new technique that uses deep convolutional neural networks to improve image quality. OBJECTIVE To evaluate and compare 1.25 mm thin-section abdominal CT images reconstructed with deep learning image reconstruction (DLIR) with 5 mm thick images reconstructed with adaptive statistical iterative reconstruction (ASIR-V). METHODS This retrospective study included 52 patients (31 F; 56.9±16.9 years) who underwent abdominal CT scans between August-October 2019. Image reconstruction was performed to generate 5 mm images at 40% ASIR-V and 1.25 mm DLIR images at three strengths (low [DLIR-L], medium [DLIR-M], and high [DLIR-H]). Qualitative assessment was performed to determine image noise, contrast, visibility of small structures, sharpness, and artifact based on a 5-point-scale. Image preference determination was based on a 3-point-scale. Quantitative assessment included measurement of attenuation, image noise, and contrast-to-noise ratios (CNR). RESULTS Thin-section images reconstructed with DLIR-M and DLIR-H yielded better image quality scores than 5 mm ASIR-V reconstructed images. Mean qualitative scores of DLIR-H for noise (1.77 ± 0.71), contrast (1.6 ± 0.68), small structure visibility (1.42 ± 0.66), sharpness (1.34 ± 0.55), and image preference (1.11 ± 0.34) were the best (p<0.05). DLIR-M yielded intermediate scores. All DLIR reconstructions showed superior ratings for artifacts compared to ASIR-V (p<0.05), whereas each DLIR group performed comparably (p>0.05, 0.405-0.763). In the quantitative assessment, there were no significant differences in attenuation values between all reconstructions (p>0.05). However, DLIR-H demonstrated the lowest noise (9.17 ± 3.11) and the highest CNR (CNRliver = 26.88 ± 6.54 and CNRportal vein = 7.92 ± 3.85) (all p<0.001). CONCLUSION DLIR allows generation of thin-section (1.25 mm) abdominal CT images, which provide improved image quality with higher inter-reader agreement compared to 5 mm thick images reconstructed with ASIR-V. CLINICAL IMPACT Improved image quality of thin-section CT images reconstructed with DLIR has several benefits in clinical practice, such as improved diagnostic performance without radiation dose penalties.
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Affiliation(s)
- Jinjin Cao
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Nayla Mroueh
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Nisanard Pisuchpen
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
- Department of Radiology, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Anushri Parakh
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Simon Lennartz
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | - Theodore T Pierce
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA
| | - Avinash R Kambadakone
- Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA.
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Nakamoto A, Onishi H, Tsuboyama T, Fukui H, Ota T, Ogawa K, Yano K, Kiso K, Honda T, Tatsumi M, Tomiyama N. Image Quality and Lesion Detectability of Pancreatic Phase Thin-Slice Computed Tomography Images With a Deep Learning-Based Reconstruction Algorithm. J Comput Assist Tomogr 2023; 47:698-703. [PMID: 37707398 DOI: 10.1097/rct.0000000000001485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
OBJECTIVE To evaluate the image quality and lesion detectability of pancreatic phase thin-slice computed tomography (CT) images reconstructed with a deep learning-based reconstruction (DLR) algorithm compared with filtered-back projection (FBP) and hybrid iterative reconstruction (IR) algorithms. METHODS Fifty-three patients who underwent dynamic contrast-enhanced CT including pancreatic phase were enrolled in this retrospective study. Pancreatic phase thin-slice (0.625 mm) images were reconstructed with each FBP, hybrid IR, and DLR. Objective image quality and signal-to-noise ratio of the pancreatic parenchyma, and contrast-to-noise ratio of pancreatic lesions were compared between the 3 reconstruction algorithms. Two radiologists independently assessed the image quality of all images. The diagnostic performance for the detection of pancreatic lesions was compared among the reconstruction algorithms using jackknife alternative free-response receiver operating characteristic analysis. RESULTS Deep learning-based reconstruction resulted in significantly lower image noise and higher signal-to-noise ratio and contrast-to-noise ratio than hybrid IR and FBP ( P < 0.001). Deep learning-based reconstruction also yielded significantly higher visual scores than hybrid IR and FBP ( P < 0.01). The diagnostic performance of DLR for detecting pancreatic lesions was highest for both readers, although a significant difference was found only between DLR and FBP in one reader ( P = 0.02). CONCLUSIONS Deep learning-based reconstruction showed improved objective and subjective image quality of pancreatic phase thin-slice CT relative to other reconstruction algorithms and has potential for improving lesion detectability.
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Affiliation(s)
- Atsushi Nakamoto
- From the Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
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Ludes G, Ohana M, Labani A, Meyer N, Moliére S, Roy C. Impact of a reduced iodine load with deep learning reconstruction on abdominal MDCT. Medicine (Baltimore) 2023; 102:e34579. [PMID: 37657067 PMCID: PMC10476859 DOI: 10.1097/md.0000000000034579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 07/13/2023] [Indexed: 09/03/2023] Open
Abstract
To evaluate the impact of a reduced iodine load using deep learning reconstruction (DLR) on the hepatic parenchyma compared to conventional iterative reconstruction (hybrid IR) and its consequence on the radiation dose and image quality. This retrospective monocentric intraindividual comparison study included 66 patients explored at the portal phase using different multidetector computed tomography parameters: Group A, hybrid IR algorithm (hybrid IR) and a nonionic low-osmolality contrast agent (350 mgI/mL); Group B, DLR algorithm (DLR) and a nonionic iso-osmolality contrast agent (270 mgI/mL). We recorded the attenuation of the liver parenchyma, image quality, and radiation dose parameters. The mean hounsfield units (HU) value of the liver parenchyma was significantly lower in group B, at 105.9 ± 10.9 HU versus 118.5 ± 14.6 HU in group A. However, the 90%IC of mean liver attenuation in the group B (DLR) was between 100.8 HU and 109.3 HU. The signal-to-noise ratio of the liver parenchyma was significantly higher on DLR images, increasing by 56%. However, for both the contrast-to-noise ratio (CNR) and CNR liver/PV no statistical difference was found, even if the CNR liver/PV ratio was slightly higher for group A. The mean dose-length product and computed tomography dose index volume values were significantly lower with DLR, corresponding to a radiation dose reduction of 36% for the DLR. Using a DLR algorithm for abdominal multidetector computed tomography with a low iodine load can provide sufficient enhancement of the liver parenchyma up to 100 HU in addition to the advantages of a higher image quality, a better signal-to-noise ratio and a lower radiation dose.
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Affiliation(s)
- Gaspard Ludes
- Department of Radiology B, University Hospital of Strasbourg – New Civil Hospital, Strasbourg, Cedex, France
| | - Mickael Ohana
- Department of Radiology B, University Hospital of Strasbourg – New Civil Hospital, Strasbourg, Cedex, France
| | - Aissam Labani
- Department of Radiology B, University Hospital of Strasbourg – New Civil Hospital, Strasbourg, Cedex, France
| | - Nicolas Meyer
- Department of Statistics, University Hospital of Strasbourg – New Civil Hospital, Strasbourg, Cedex, France
| | - Sébastien Moliére
- Department of Radiology B, University Hospital of Strasbourg – New Civil Hospital, Strasbourg, Cedex, France
| | - Catherine Roy
- Department of Radiology B, University Hospital of Strasbourg – New Civil Hospital, Strasbourg, Cedex, France
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Pula M, Kucharczyk E, Zdanowicz A, Guzinski M. Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination. Tomography 2023; 9:1485-1493. [PMID: 37624111 PMCID: PMC10459011 DOI: 10.3390/tomography9040118] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/14/2023] [Accepted: 07/19/2023] [Indexed: 08/26/2023] Open
Abstract
In this study, we assess image quality in computed tomography scans reconstructed via DLIR (Deep Learning Image Reconstruction) and compare it with iterative reconstruction ASIR-V (Adaptive Statistical Iterative Reconstruction) in CT (computed tomography) scans of the head. The CT scans of 109 patients were subjected to both objective and subjective evaluation of image quality. The objective evaluation was based on the SNR (signal-to-noise ratio) and CNR (contrast-to-noise ratio) of the brain's gray and white matter. The regions of interest for our study were set in the BGA (basal ganglia area) and PCF (posterior cranial fossa). Simultaneously, a subjective assessment of image quality, based on brain structure visibility, was conducted by experienced radiologists. In the assessed scans, we obtained up to a 54% increase in SNR for gray matter and a 60% increase for white matter using DLIR in comparison to ASIR-V. Moreover, we achieved a CNR increment of 58% in the BGA structures and 50% in the PCF. In the subjective assessment of the obtained images, DLIR had a mean rating score of 2.8, compared to the mean score of 2.6 for ASIR-V images. In conclusion, DLIR shows improved image quality compared to the standard iterative reconstruction of CT images of the head.
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Affiliation(s)
- Michal Pula
- Lower Silesian Oncology, Pulmonology and Hematology Center, Hirszfelda Square 12, 53-413 Wrocław, Poland;
| | - Emilia Kucharczyk
- Faculty of Medicine, Wroclaw Medical University, Ludwika Pasteura 1, 50-367 Wrocław, Poland;
| | - Agata Zdanowicz
- Department of General Radiology, Interventional Radiology and Neuroradiology, Wroclaw Medical University, Borowska 213, 50-556 Wrocław, Poland;
| | - Maciej Guzinski
- Department of General Radiology, Interventional Radiology and Neuroradiology, Wroclaw Medical University, Borowska 213, 50-556 Wrocław, Poland;
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Koh S, Lee NK, Kim S, Hong SB, Kim DU, Han SY. The efficacy of low-dose CT with deep learning image reconstruction in the surveillance of incidentally detected pancreatic cystic lesions. Abdom Radiol (NY) 2023; 48:2585-2595. [PMID: 37204510 DOI: 10.1007/s00261-023-03958-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 05/20/2023]
Abstract
PURPOSE To evaluate the efficacy of low-dose CT (LDCT) with deep learning image reconstruction (DLIR) for the surveillance of pancreatic cystic lesions (PCLs) compared with standard-dose CT (SDCT) with adaptive statistical iterative reconstruction (ASIR-V). METHODS The study enrolled 103 patients who underwent pancreatic CT for follow-up of incidentally detected PCLs. The CT protocol included LDCT in the pancreatic phase with 40% ASIR-V, DLIR at medium (DLIR-M) and high levels (DLIR-H), and SDCT in the portal-venous phase with 40% ASIR-V. The overall image quality and conspicuity of PCLs were qualitatively assessed using five-point scales by two radiologists. The size of PCLs, presence of thickened/enhancing walls, enhancing mural nodules, and main pancreatic duct dilatation were reviewed. CT noise and cyst-to-pancreas contrast-to-noise ratio (CNR) were measured. Qualitative and quantitative parameters were analyzed using the chi-squared test, one-way ANOVA, and t-test. Additionally, interobserver agreement was analyzed using the kappa and weighted-kappa statistics. RESULTS The volume CT dose-indexes in LDCT and SDCT were 3.0 ± 0.6 mGy and 8.4 ± 2.9 mGy, respectively. LDCT with DLIR-H showed the highest overall image quality, the lowest noise, and the highest CNR. The PCL conspicuity in LDCT with either DLIR-M or DLIR-H was not significantly different from that in SDCT with ASIR-V. Other findings depicting PCLs also revealed no significant differences between LDCT with DLIR and SDCT with ASIR-V. Moreover, the results revealed good or excellent interobserver agreement. CONCLUSION LDCT with DLIR has a comparable performance with SDCT for the follow-up of incidentally detected PCLs.
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Affiliation(s)
- Sungho Koh
- Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, #179, Gudeok-Ro, Seo-Gu, Busan, 49241, Republic of Korea
| | - Nam Kyung Lee
- Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, #179, Gudeok-Ro, Seo-Gu, Busan, 49241, Republic of Korea.
| | - Suk Kim
- Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, #179, Gudeok-Ro, Seo-Gu, Busan, 49241, Republic of Korea
| | - Seung Baek Hong
- Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, #179, Gudeok-Ro, Seo-Gu, Busan, 49241, Republic of Korea
| | - Dong Uk Kim
- Department of Internal Medicine, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, Busan, Republic of Korea
| | - Sung Yong Han
- Department of Internal Medicine, Biomedical Research Institute, Pusan National University Hospital, and Pusan National University School of Medicine, Pusan National University, Busan, Republic of Korea
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Shehata MA, Saad AM, Kamel S, Stanietzky N, Roman-Colon AM, Morani AC, Elsayes KM, Jensen CT. Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis. Abdom Radiol (NY) 2023; 48:2724-2756. [PMID: 37280374 DOI: 10.1007/s00261-023-03966-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/13/2023] [Accepted: 05/16/2023] [Indexed: 06/08/2023]
Abstract
OBJECTIVE To perform a systematic literature review and meta-analysis of the two most common commercially available deep-learning algorithms for CT. METHODS We used PubMed, Scopus, Embase, and Web of Science to conduct systematic searches for studies assessing the most common commercially available deep-learning CT reconstruction algorithms: True Fidelity (TF) and Advanced intelligent Clear-IQ Engine (AiCE) in the abdomen of human participants since only these two algorithms currently have adequate published data for robust systematic analysis. RESULTS Forty-four articles fulfilled inclusion criteria. 32 studies evaluated TF and 12 studies assessed AiCE. DLR algorithms produced images with significantly less noise (22-57.3% less than IR) but preserved a desirable noise texture with increased contrast-to-noise ratios and improved lesion detectability on conventional CT. These improvements with DLR were similarly noted in dual-energy CT which was only assessed for a single vendor. Reported radiation reduction potential was 35.1-78.5%. Nine studies assessed observer performance with the two dedicated liver lesion studies being performed on the same vendor reconstruction (TF). These two studies indicate preserved low contrast liver lesion detection (> 5 mm) at CTDIvol 6.8 mGy (BMI 23.5 kg/m2) to 12.2 mGy (BMI 29 kg/m2). If smaller lesion detection and improved lesion characterization is needed, a CTDIvol of 13.6-34.9 mGy is needed in a normal weight to obese population. Mild signal loss and blurring have been reported at high DLR reconstruction strengths. CONCLUSION Deep learning reconstructions significantly improve image quality in CT of the abdomen. Assessment of other dose levels and clinical indications is needed. Careful choice of radiation dose levels is necessary, particularly for small liver lesion assessment.
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Affiliation(s)
- Mostafa A Shehata
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA
| | | | - Serageldin Kamel
- Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA
| | - Nir Stanietzky
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA
| | | | - Ajaykumar C Morani
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA
| | - Khaled M Elsayes
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA
| | - Corey T Jensen
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA.
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Nagata M, Ichikawa Y, Domae K, Yoshikawa K, Kanii Y, Yamazaki A, Nagasawa N, Ishida M, Sakuma H. Application of Deep Learning-Based Denoising Technique for Radiation Dose Reduction in Dynamic Abdominal CT: Comparison with Standard-Dose CT Using Hybrid Iterative Reconstruction Method. J Digit Imaging 2023; 36:1578-1587. [PMID: 36944812 PMCID: PMC10406991 DOI: 10.1007/s10278-023-00808-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 03/08/2023] [Accepted: 03/08/2023] [Indexed: 03/23/2023] Open
Abstract
The purpose is to evaluate whether deep learning-based denoising (DLD) algorithm provides sufficient image quality for abdominal computed tomography (CT) with a 30% reduction in radiation dose, compared to standard-dose CT reconstructed with conventional hybrid iterative reconstruction (IR). The subjects consisted of 50 patients who underwent abdominal CT with standard dose and reconstructed with hybrid IR (ASiR-V50%) and another 50 patients who underwent abdominal CT with approximately 30% less dose and reconstructed with ASiR-V50% and DLD at low-, medium- and high-strength (DLD-L, DLD-M and DLD-H, respectively). The standard deviation of attenuation in liver parenchyma was measured as image noise. Contrast-to-noise ratio (CNR) for portal vein on portal venous phase was calculated. Lesion conspicuity in 23 abdominal solid mass on the reduced-dose CT was rated on a 5-point scale: 0 (best) to -4 (markedly inferior). Compared with hybrid IR of standard-dose CT, DLD-H of reduced-dose CT provided significantly lower image noise (portal phase: 9.0 (interquartile range, 8.7-9.4) HU vs 12.0 (11.4-12.7) HU, P < 0.0001) and significantly higher CNR (median, 5.8 (4.4-7.4) vs 4.3 (3.3-5.3), P = 0.0019). As for DLD-M of reduced-dose CT, no significant difference was found in image noise and CNR compared to hybrid IR of standard-dose CT (P > 0.99). Lesion conspicuity scores for DLD-H and DLD-M were significantly better than hybrid IR (P < 0.05). Dynamic contrast-enhanced abdominal CT acquired with approximately 30% lower radiation dose and generated with the DLD algorithm exhibit lower image noise and higher CNR compared to standard-dose CT with hybrid IR.
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Affiliation(s)
- Motonori Nagata
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Yasutaka Ichikawa
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Kensuke Domae
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Kazuya Yoshikawa
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Yoshinori Kanii
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Akio Yamazaki
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Naoki Nagasawa
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Masaki Ishida
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
| | - Hajime Sakuma
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, 514-8507 Tsu, Mie Japan
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Zhong J, Shen H, Chen Y, Xia Y, Shi X, Lu W, Li J, Xing Y, Hu Y, Ge X, Ding D, Jiang Z, Yao W. Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT. J Digit Imaging 2023; 36:1390-1407. [PMID: 37071291 PMCID: PMC10406981 DOI: 10.1007/s10278-023-00806-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 04/19/2023] Open
Abstract
This study is aimed to evaluate effects of deep learning image reconstruction (DLIR) on image quality in single-energy CT (SECT) and dual-energy CT (DECT), in reference to adaptive statistical iterative reconstruction-V (ASIR-V). The Gammex 464 phantom was scanned in SECT and DECT modes at three dose levels (5, 10, and 20 mGy). Raw data were reconstructed using six algorithms: filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) strength, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H), to generate SECT 120kVp images and DECT 120kVp-like images. Objective image quality metrics were computed, including noise power spectrum (NPS), task transfer function (TTF), and detectability index (d'). Subjective image quality evaluation, including image noise, texture, sharpness, overall quality, and low- and high-contrast detectability, was performed by six readers. DLIR-H reduced overall noise magnitudes from FBP by 55.2% in a more balanced way of low and high frequency ranges comparing to AV-40, and improved the TTF values at 50% for acrylic inserts by average percentages of 18.32%. Comparing to SECT 20 mGy AV-40 images, the DECT 10 mGy DLIR-H images showed 20.90% and 7.75% improvement in d' for the small-object high-contrast and large-object low-contrast tasks, respectively. Subjective evaluation showed higher image quality and better detectability. At 50% of the radiation dose level, DECT with DLIR-H yields a gain in objective detectability index compared to full-dose AV-40 SECT images used in daily practice.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Hailin Shen
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiao Tong University School of Medicine, Suzhou, 215028 China
| | - Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 China
| | - Yihan Xia
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 China
| | - Xiaomeng Shi
- Department of Materials, Imperial College London, South Kensington Campus, London, SW7 2AZ UK
| | - Wei Lu
- Computed Tomography Research Center, GE Healthcare, Shanghai, 201203 China
| | - Jianying Li
- Computed Tomography Research Center, GE Healthcare, Beijing, 100176 China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Zhenming Jiang
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
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Takai Y, Noda Y, Asano M, Kawai N, Kaga T, Tsuchida Y, Miyoshi T, Hyodo F, Kato H, Matsuo M. Deep-learning image reconstruction for 80-kVp pancreatic CT protocol: Comparison of image quality and pancreatic ductal adenocarcinoma visibility with hybrid-iterative reconstruction. Eur J Radiol 2023; 165:110960. [PMID: 37423016 DOI: 10.1016/j.ejrad.2023.110960] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/19/2023] [Accepted: 07/03/2023] [Indexed: 07/11/2023]
Abstract
PURPOSE To evaluate the image quality and visibility of pancreatic ductal adenocarcinoma (PDAC) in 80-kVp pancreatic CT protocol and compare them between hybrid-iterative reconstruction (IR) and deep-learning image reconstruction (DLIR) algorithms. METHOD A total of 56 patients who underwent 80-kVp pancreatic protocol CT for pancreatic disease evaluation from January 2022 to July 2022 were included in this retrospective study. Among them, 20 PDACs were observed. The CT raw data were reconstructed using 40% adaptive statistical IR-Veo (hybrid-IR group) and DLIR at medium- and high-strength levels (DLIR-M and DLIR-H groups, respectively). The CT attenuation of the abdominal aorta, pancreas, and PDAC (if present) at the pancreatic phase and those of the portal vein and liver at the portal venous phase; background noise; signal-to-noise ratio (SNR) of these anatomical structures; and tumor-to-pancreas contrast-to-noise ratio (CNR) were calculated. The confidence scores for the image noise, overall image quality, and visibility of PDAC were qualitatively assigned using a five-point scale. Quantitative and qualitative parameters were compared among the three groups using Friedman test. RESULTS The CT attenuation of all anatomical structures were comparable among the three groups (P = .26-.86), except that of the pancreas (P = .001). Background noise was lower (P <.001) and SNRs (P <.001) and tumor-to-pancreas CNR (P <.001) were higher in the DLIR-H group than those in the other two groups. The image noise, overall image quality, and visibility of PDAC were better in the DLIR-H group than in the other two groups (P <.001-.003). CONCLUSION In 80-kVp pancreatic CT protocol, DLIR at a high-strength level improved image quality and visibility of PDAC.
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Affiliation(s)
- Yukiko Takai
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Yoshifumi Noda
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Masashi Asano
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Nobuyuki Kawai
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Tetsuro Kaga
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Yuki Tsuchida
- Department of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Toshiharu Miyoshi
- Department of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Fuminori Hyodo
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan; Institute for Advanced Study, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Hiroki Kato
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Masayuki Matsuo
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
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Graafen D, Stoehr F, Halfmann MC, Emrich T, Foerster F, Yang Y, Düber C, Müller L, Kloeckner R. Quantum iterative reconstruction on a photon-counting detector CT improves the quality of hepatocellular carcinoma imaging. Cancer Imaging 2023; 23:69. [PMID: 37480062 PMCID: PMC10362630 DOI: 10.1186/s40644-023-00592-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 07/08/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND Excellent image quality is crucial for workup of hepatocellular carcinoma (HCC) in patients with liver cirrhosis because a signature tumor signal allows for non-invasive diagnosis without histologic proof. Photon-counting detector computed tomography (PCD-CT) can enhance abdominal image quality, especially in combination with a novel iterative reconstruction algorithm, quantum iterative reconstruction (QIR). The purpose of this study was to analyze the impact of different QIR levels on PCD-CT imaging of HCC in both phantom and patient scans. METHODS Virtual monoenergetic images at 50 keV were reconstructed using filtered back projection and all available QIR levels (QIR 1-4). Objective image quality properties were investigated in phantom experiments. The study also included 44 patients with triple-phase liver PCD-CT scans of viable HCC lesions. Quantitative image analysis involved assessing the noise, contrast, and contrast-to-noise ratio of the lesions. Qualitative image analysis was performed by three raters evaluating noise, artifacts, lesion conspicuity, and overall image quality using a 5-point Likert scale. RESULTS Noise power spectra in the phantom experiments showed increasing noise suppression with higher QIR levels without affecting the modulation transfer function. This pattern was confirmed in the in vivo scans, in which the lowest noise levels were found in QIR-4 reconstructions, with around a 50% reduction in median noise level compared with the filtered back projection images. As contrast does not change with QIR, QIR-4 also yielded the highest contrast-to-noise ratios. With increasing QIR levels, rater scores were significantly better for all qualitative image criteria (all p < .05). CONCLUSIONS Without compromising image sharpness, the best image quality of iodine contrast optimized low-keV virtual monoenergetic images can be achieved using the highest QIR level to suppress noise. Using these settings as standard reconstruction for HCC in PCD-CT imaging might improve diagnostic accuracy and confidence.
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Affiliation(s)
- Dirk Graafen
- Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
| | - Fabian Stoehr
- Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Moritz C Halfmann
- Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
- German Center for Cardiovascular Research (DZHK), Partner-Site Rhine-Main, Mainz, Germany
| | - Tilman Emrich
- Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
- German Center for Cardiovascular Research (DZHK), Partner-Site Rhine-Main, Mainz, Germany
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Friedrich Foerster
- Department of Medicine I, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Yang Yang
- Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Christoph Düber
- Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Lukas Müller
- Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Roman Kloeckner
- Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
- Present Address: Institute of Interventional Radiology, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
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Liao S, Mo Z, Zeng M, Wu J, Gu Y, Li G, Quan G, Lv Y, Liu L, Yang C, Wang X, Huang X, Zhang Y, Cao W, Dong Y, Wei Y, Zhou Q, Xiao Y, Zhan Y, Zhou XS, Shi F, Shen D. Fast and low-dose medical imaging generation empowered by hybrid deep-learning and iterative reconstruction. Cell Rep Med 2023; 4:101119. [PMID: 37467726 PMCID: PMC10394257 DOI: 10.1016/j.xcrm.2023.101119] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 05/16/2023] [Accepted: 06/19/2023] [Indexed: 07/21/2023]
Abstract
Fast and low-dose reconstructions of medical images are highly desired in clinical routines. We propose a hybrid deep-learning and iterative reconstruction (hybrid DL-IR) framework and apply it for fast magnetic resonance imaging (MRI), fast positron emission tomography (PET), and low-dose computed tomography (CT) image generation tasks. First, in a retrospective MRI study (6,066 cases), we demonstrate its capability of handling 3- to 10-fold under-sampled MR data, enabling organ-level coverage with only 10- to 100-s scan time; second, a low-dose CT study (142 cases) shows that our framework can successfully alleviate the noise and streak artifacts in scans performed with only 10% radiation dose (0.61 mGy); and last, a fast whole-body PET study (131 cases) allows us to faithfully reconstruct tumor-induced lesions, including small ones (<4 mm), from 2- to 4-fold-accelerated PET acquisition (30-60 s/bp). This study offers a promising avenue for accurate and high-quality image reconstruction with broad clinical value.
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Affiliation(s)
- Shu Liao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Zhanhao Mo
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Mengsu Zeng
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yuning Gu
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Guobin Li
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Guotao Quan
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Yang Lv
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Lin Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Chun Yang
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Xinglie Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Xiaoqian Huang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yang Zhang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Wenjing Cao
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Yun Dong
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Qing Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yongqin Xiao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yiqiang Zhan
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China.
| | - Dinggang Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China; School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China; Shanghai Clinical Research and Trial Center, Shanghai 200122, China.
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Zhou Z, Inoue A, McCollough CH, Yu L. Self-trained deep convolutional neural network for noise reduction in CT. J Med Imaging (Bellingham) 2023; 10:044008. [PMID: 37636895 PMCID: PMC10449263 DOI: 10.1117/1.jmi.10.4.044008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 08/04/2023] [Accepted: 08/08/2023] [Indexed: 08/29/2023] Open
Abstract
Purpose Supervised deep convolutional neural network (CNN)-based methods have been actively used in clinical CT to reduce image noise. The networks of these methods are typically trained using paired high- and low-quality data from a massive number of patients and/or phantom images. This training process is tedious, and the network trained under a given condition may not be generalizable to patient images acquired and reconstructed under different conditions. We propose a self-trained deep CNN (ST_CNN) method for noise reduction in CT that does not rely on pre-existing training datasets. Approach The ST_CNN training was accomplished using extensive data augmentation in the projection domain, and the inference was applied to the data itself. Specifically, multiple independent noise insertions were applied to the original patient projection data to generate multiple realizations of low-quality projection data. Then, rotation augmentation was adopted for both the original and low-quality projection data by applying the rotation angle directly on the projection data so that images were rotated at arbitrary angles without introducing additional bias. A large number of paired low- and high-quality images from the same patient were reconstructed and paired for training the ST_CNN model. Results No significant difference was found between the ST_CNN and conventional CNN models in terms of the peak signal-to-noise ratio and structural similarity index measure. The ST_CNN model outperformed the conventional CNN model in terms of noise texture and homogeneity in liver parenchyma as well as better subjective visualization of liver lesions. The ST_CNN may sacrifice the sharpness of vessels slightly compared to the conventional CNN model but without affecting the visibility of peripheral vessels or diagnosis of vascular pathology. Conclusions The proposed ST_CNN method trained from the data itself may achieve similar image quality in comparison with conventional deep CNN denoising methods pre-trained on external datasets.
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Affiliation(s)
- Zhongxing Zhou
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Akitoshi Inoue
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | | | - Lifeng Yu
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
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Funama Y, Nakaura T, Hasegawa A, Sakabe D, Oda S, Kidoh M, Nagayama Y, Hirai T. Noise power spectrum properties of deep learning-based reconstruction and iterative reconstruction algorithms: Phantom and clinical study. Eur J Radiol 2023; 165:110914. [PMID: 37295358 DOI: 10.1016/j.ejrad.2023.110914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 05/18/2023] [Accepted: 05/31/2023] [Indexed: 06/12/2023]
Abstract
PURPOSE To compare the noise power spectrum (NPS) properties and perform a qualitative analysis of hybrid iterative reconstruction (IR), model-based IR (MBIR), and deep learning-based reconstruction (DLR) at a similar noise level in clinical study and compare these outcomes with those in phantom study. METHODS A Catphan phantom with an external body ring was used in the phantom study. In the clinical study, computed tomography (CT) examination data of 34 patients were reviewed. NPS was calculated from DLR, hybrid IR, and MBIR images. The noise magnitude ratio (NMR) and the central frequency ratio (CFR) were calculated from DLR, hybrid IR, and MBIR images relative to filtered back-projection images using NPS. Clinical images were independently reviewed by two radiologists. RESULTS In the phantom study, DLR with a mild level had a similar noise level as hybrid IR and MBIR with strong levels. In the clinical study, DLR with a mild level had a similar noise level as hybrid IR with standard and MBIR with strong levels. The NMR and CFR were 0.40 and 0.76 for DLR, 0.42 and 0.55 for hybrid IR, and 0.48 and 0.62 for MBIR. The visual inspection of the clinical DLR image was superior to that of the hybrid IR and MBIR images. CONCLUSION Deep learning-based reconstruction improves overall image quality with substantial noise reduction while maintaining image noise texture compared with the CT reconstruction techniques.
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Affiliation(s)
- Yoshinori Funama
- Department of Medical Radiation Sciences, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan.
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Akira Hasegawa
- Department of Radiological Technology, National Cancer Center Japan, Tokyo, Japan; AlgoMedica, Inc., Sunnyvale, CA, USA
| | - Daisuke Sakabe
- Department of Radiology, Kumamoto University Hospital, Kumamoto, Japan
| | - Seitaro Oda
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Yasunori Nagayama
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
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Koo SA, Jung Y, Um KA, Kim TH, Kim JY, Park CH. Clinical Feasibility of Deep Learning-Based Image Reconstruction on Coronary Computed Tomography Angiography. J Clin Med 2023; 12:jcm12103501. [PMID: 37240607 DOI: 10.3390/jcm12103501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 04/24/2023] [Accepted: 05/14/2023] [Indexed: 05/28/2023] Open
Abstract
This study evaluated the feasibility of deep-learning-based image reconstruction (DLIR) on coronary computed tomography angiography (CCTA). By using a 20 cm water phantom, the noise reduction ratio and noise power spectrum were evaluated according to the different reconstruction methods. Then 46 patients who underwent CCTA were retrospectively enrolled. CCTA was performed using the 16 cm coverage axial volume scan technique. All CT images were reconstructed using filtered back projection (FBP); three model-based iterative reconstructions (MBIR) of 40%, 60%, and 80%; and three DLIR algorithms: low (L), medium (M), and high (H). Quantitative and qualitative image qualities of CCTA were compared according to the reconstruction methods. In the phantom study, the noise reduction ratios of MBIR-40%, MBIR-60%, MBIR-80%, DLIR-L, DLIR-M, and DLIR-H were 26.7 ± 0.2%, 39.5 ± 0.5%, 51.7 ± 0.4%, 33.1 ± 0.8%, 43.2 ± 0.8%, and 53.5 ± 0.1%, respectively. The pattern of the noise power spectrum of the DLIR images was more similar to FBP images than MBIR images. In a CCTA study, CCTA yielded a significantly lower noise index with DLIR-H reconstruction than with the other reconstruction methods. DLIR-H showed a higher SNR and CNR than MBIR (p < 0.05). The qualitative image quality of CCTA with DLIR-H was significantly higher than that of MBIR-80% or FBP. The DLIR algorithm was feasible and yielded a better image quality than the FBP or MBIR algorithms on CCTA.
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Affiliation(s)
- Seul Ah Koo
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
| | - Yunsub Jung
- Research Team, GE Healthcare Korea, Seoul 04637, Republic of Korea
| | - Kyoung A Um
- Research Team, GE Healthcare Korea, Seoul 04637, Republic of Korea
| | - Tae Hoon Kim
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
| | - Ji Young Kim
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
| | - Chul Hwan Park
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
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Cheng CT, Lin HS, Hsu CP, Chen HW, Huang JF, Fu CY, Hsieh CH, Yeh CN, Chung IF, Liao CH. The three-dimensional weakly supervised deep learning algorithm for traumatic splenic injury detection and sequential localization: an experimental study. Int J Surg 2023; 109:1115-1124. [PMID: 36999810 PMCID: PMC10389597 DOI: 10.1097/js9.0000000000000380] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 03/23/2023] [Indexed: 04/01/2023]
Abstract
BACKGROUND Splenic injury is the most common solid visceral injury in blunt abdominal trauma, and high-resolution abdominal computed tomography (CT) can adequately detect the injury. However, these lethal injuries sometimes have been overlooked in current practice. Deep learning (DL) algorithms have proven their capabilities in detecting abnormal findings in medical images. The aim of this study is to develop a three-dimensional, weakly supervised DL algorithm for detecting splenic injury on abdominal CT using a sequential localization and classification approach. MATERIAL AND METHODS The dataset was collected in a tertiary trauma center on 600 patients who underwent abdominal CT between 2008 and 2018, half of whom had splenic injuries. The images were split into development and test datasets at a 4 : 1 ratio. A two-step DL algorithm, including localization and classification models, was constructed to identify the splenic injury. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Grad-CAM (Gradient-weighted Class Activation Mapping) heatmaps from the test set were visually assessed. To validate the algorithm, we also collected images from another hospital to serve as external validation data. RESULTS A total of 480 patients, 50% of whom had spleen injuries, were included in the development dataset, and the rest were included in the test dataset. All patients underwent contrast-enhanced abdominal CT in the emergency room. The automatic two-step EfficientNet model detected splenic injury with an AUROC of 0.901 (95% CI: 0.836-0.953). At the maximum Youden index, the accuracy, sensitivity, specificity, PPV, and NPV were 0.88, 0.81, 0.92, 0.91, and 0.83, respectively. The heatmap identified 96.3% of splenic injury sites in true positive cases. The algorithm achieved a sensitivity of 0.92 for detecting trauma in the external validation cohort, with an acceptable accuracy of 0.80. CONCLUSIONS The DL model can identify splenic injury on CT, and further application in trauma scenarios is possible.
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Affiliation(s)
- Chi-Tung Cheng
- Department of Trauma and Emergency Surgery
- Chang Gung University, Taoyuan
| | - Hou-Shian Lin
- Department of Trauma and Emergency Surgery
- Chang Gung University, Taoyuan
| | - Chih-Po Hsu
- Department of Trauma and Emergency Surgery
- Chang Gung University, Taoyuan
| | - Huan-Wu Chen
- Department of Medical Imaging and Intervention
- Chang Gung University, Taoyuan
| | - Jen-Fu Huang
- Department of Trauma and Emergency Surgery
- Chang Gung University, Taoyuan
| | - Chih-Yuan Fu
- Department of Trauma and Emergency Surgery
- Chang Gung University, Taoyuan
| | - Chi-Hsun Hsieh
- Department of Trauma and Emergency Surgery
- Chang Gung University, Taoyuan
| | - Chun-Nan Yeh
- Department of General Surgery
- Chang Gung University, Taoyuan
| | - I-Fang Chung
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan, Republic of China
| | - Chien-Hung Liao
- Department of Trauma and Emergency Surgery
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Linkou
- Chang Gung University, Taoyuan
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Szczykutowicz TP, Ahmad M, Liu X, Pozniak MA, Lubner MG, Jensen CT. How Do Cancer-Specific Computed Tomography Protocols Compare With the American College of Radiology Dose Index Registry? An Analysis of Computed Tomography Dose at 2 Cancer Centers. J Comput Assist Tomogr 2023; 47:429-436. [PMID: 37185007 PMCID: PMC10199233 DOI: 10.1097/rct.0000000000001441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
BACKGROUND Little guidance exists on how to stratify radiation dose according to diagnostic task. Changing dose for different cancer types is currently not informed by the American College of Radiology Dose Index Registry dose survey. METHODS A total of 9602 patient examinations were pulled from 2 National Cancer Institute designated cancer centers. Computed tomography dose (CTDI vol ) was extracted, and patient water equivalent diameter was calculated. N-way analysis of variance was used to compare the dose levels between 2 protocols used at site 1, and three protocols used at site 2. RESULTS Sites 1 and 2 both independently stratified their doses according to cancer indications in similar ways. For example, both sites used lower doses ( P < 0.001) for follow-up of testicular cancer, leukemia, and lymphoma. Median dose at median patient size from lowest to highest dose level for site 1 were 17.9 (17.7-18.0) mGy (mean [95% confidence interval]) and 26.8 (26.2-27.4) mGy. For site 2, they were 12.1 (10.6-13.7) mGy, 25.5 (25.2-25.7) mGy, and 34.2 (33.8-34.5) mGy. Both sites had higher doses ( P < 0.001) between their routine and high-image-quality protocols, with an increase of 48% between these doses for site 1 and 25% for site 2. High-image-quality protocols were largely applied for detection of low-contrast liver lesions or subtle pelvic pathology. CONCLUSIONS We demonstrated that 2 cancer centers independently choose to stratify their cancer doses in similar ways. Sites 1 and 2 dose data were higher than the American College of Radiology Dose Index Registry dose survey data. We thus propose including a cancer-specific subset for the dose registry.
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Affiliation(s)
| | - Moiz Ahmad
- Department of Imaging Physics and Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Xinming Liu
- Department of Imaging Physics and Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Myron A Pozniak
- From the Department of Radiology, University of Wisconsin Madison School of Medicine and Public Health
| | - Meghan G Lubner
- From the Department of Radiology, University of Wisconsin Madison School of Medicine and Public Health
| | - Corey T Jensen
- Department of Imaging Physics and Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, TX
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Paudyal R, Shah AD, Akin O, Do RKG, Konar AS, Hatzoglou V, Mahmood U, Lee N, Wong RJ, Banerjee S, Shin J, Veeraraghavan H, Shukla-Dave A. Artificial Intelligence in CT and MR Imaging for Oncological Applications. Cancers (Basel) 2023; 15:cancers15092573. [PMID: 37174039 PMCID: PMC10177423 DOI: 10.3390/cancers15092573] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 05/15/2023] Open
Abstract
Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients.
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Affiliation(s)
- Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Akash D Shah
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Amaresha Shridhar Konar
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Usman Mahmood
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Nancy Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Richard J Wong
- Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | | | | | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
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Yang K, Cao J, Pisuchpen N, Kambadakone A, Gupta R, Marschall T, Li X, Liu B. CT image quality evaluation in the age of deep learning: trade-off between functionality and fidelity. Eur Radiol 2023; 33:2439-2449. [PMID: 36350391 DOI: 10.1007/s00330-022-09233-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/16/2022] [Accepted: 10/09/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE To quantitatively compare DLIR and ASiR-V with realistic anatomical images. METHODS CT scans of an anthropomorphic phantom were acquired using three routine protocols (brain, chest, and abdomen) at four dose levels, with images reconstructed at five levels of ASiR-V and three levels of DLIR. Noise power spectrum (NPS) was estimated using a difference image by subtracting two matching images from repeated scans. Using the max-dose FBP reconstruction as the ground truth, the structure similarity index (SSIM) and gradient magnitude (GM) of difference images were evaluated. Image noise magnitude (σ), frequency location of the NPS peak (fpeak), mean SSIM (MSSIM), and mean GM (MGM) were used as quantitative metrics to compare image quality, for each anatomical region, protocol, algorithm, dose level, and slice thickness. RESULTS Image noise had a strong (R2 > 0.99) power law relationship with dose for all algorithms. For the abdomen and chest, fpeak shifted from 0.3 (FBP) down to 0.15 mm-1 (ASiR-V 100%) with increasing ASiR-V strength but remained 0.3 mm-1 for all DLIR levels. fpeak shifted down for the brain protocol with increasing DLIR levels. Three levels of DLIR produced similar image noise levels as ASiR-V 40%, 80%, and 100%, respectively. DLIR had lower MSSIM but higher MGM than ASiR-V while matching imaging noise. CONCLUSION Compared to ASiR-V, DLIR presents trade-offs between functionality and fidelity: it has a noise texture closer to FBP and more edge enhancement, but reduced structure similarity. These trade-offs and unique protocol-dependent behaviors of DLIR should be considered during clinical implementation and deployment. KEY POINTS • DLIR reconstructed images demonstrate closer noise texture and lower structure similarity to FBP while producing equivalent noise levels comparable to ASiR-V. • DLIR has additional edge enhancement as compared to ASiR-V. • DLIR has unique protocol-dependent behaviors that should be considered for clinical implementation.
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Affiliation(s)
- Kai Yang
- Division of Diagnostic Imaging Physics, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
| | - Jinjin Cao
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA
| | - Nisanard Pisuchpen
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA
| | - Avinash Kambadakone
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA
| | - Rajiv Gupta
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA
| | - Theodore Marschall
- Division of Diagnostic Imaging Physics, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Xinhua Li
- Division of Diagnostic Imaging Physics, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Bob Liu
- Division of Diagnostic Imaging Physics, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
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Nikolau EP, Toia GV, Nett B, Tang J, Szczykutowicz TP. A Characterization of Deep Learning Reconstruction Applied to Dual-Energy Computed Tomography Monochromatic and Material Basis Images. J Comput Assist Tomogr 2023; 47:437-444. [PMID: 36944100 DOI: 10.1097/rct.0000000000001442] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
OBJECTIVE Advancements in computed tomography (CT) reconstruction have enabled image quality improvements and dose reductions. Previous advancements have included iterative and model-based reconstruction. The latest image reconstruction advancement uses deep learning, which has been evaluated for polychromatic imaging only. This article characterizes a commercially available deep learning imaging reconstruction applied to dual-energy CT. METHODS Monochromatic, iodine basis, and water basis images were reconstructed with filtered back projection (FBP), iterative (ASiR-V), and deep learning (DLIR) methods in a phantom experiment. Slice thickness, contrast-to-noise ratio, modulation transfer function, and noise power spectrum metrics were used to characterize ASiR-V and DLIR relative to FBP over a range of dose levels, phantom sizes, and iodine concentrations. RESULTS Slice thicknesses for ASiR-V and DLIR demonstrated no statistically significant difference relative to FBP for all measurement conditions. Contrast-to-noise ratio performance for DLIR-high and ASiR-V 40% at 2 mg I/mL on 40-keV images were 162% and 30% higher than FBP, respectively. Task-based modulation transfer function measurements demonstrated no clinically significant change between FBP and ASiR-V and DLIR on monochromatic or iodine basis images. CONCLUSIONS Deep learning image reconstruction enabled better image quality at lower monochromatic energies and on iodine basis images where image contrast is maximized relative to polychromatic or high-energy monochromatic images. Deep learning image reconstruction did not demonstrate thicker slices, decreased spatial resolution, or poor noise texture (ie, "plastic") relative to FBP.
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Affiliation(s)
| | - Giuseppe V Toia
- Radiology University of Wisconsin Madison School of Medicine and Public Health
| | - Brian Nett
- GE Healthcare, Waukesha Wisconsin, Waukesha; and
| | - Jie Tang
- GE Healthcare, Waukesha Wisconsin, Waukesha; and
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Greffier J, Durand Q, Serrand C, Sales R, de Oliveira F, Beregi JP, Dabli D, Frandon J. First Results of a New Deep Learning Reconstruction Algorithm on Image Quality and Liver Metastasis Conspicuity for Abdominal Low-Dose CT. Diagnostics (Basel) 2023; 13:diagnostics13061182. [PMID: 36980490 PMCID: PMC10047497 DOI: 10.3390/diagnostics13061182] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/07/2023] [Accepted: 03/17/2023] [Indexed: 03/30/2023] Open
Abstract
The study's aim was to assess the impact of a deep learning image reconstruction algorithm (Precise Image; DLR) on image quality and liver metastasis conspicuity compared with an iterative reconstruction algorithm (IR). This retrospective study included all consecutive patients with at least one liver metastasis having been diagnosed between December 2021 and February 2022. Images were reconstructed using level 4 of the IR algorithm (i4) and the Standard/Smooth/Smoother levels of the DLR algorithm. Mean attenuation and standard deviation were measured by placing the ROIs in the fat, muscle, healthy liver, and liver tumor. Two radiologists assessed the image noise and image smoothing, overall image quality, and lesion conspicuity using Likert scales. The study included 30 patients (mean age 70.4 ± 9.8 years, 17 men). The mean CTDIvol was 6.3 ± 2.1 mGy, and the mean dose-length product 314.7 ± 105.7 mGy.cm. Compared with i4, the HU values were similar in the DLR algorithm at all levels for all tissues studied. For each tissue, the image noise significantly decreased with DLR compared with i4 (p < 0.01) and significantly decreased from Standard to Smooth (-26 ± 10%; p < 0.01) and from Smooth to Smoother (-37 ± 8%; p < 0.01). The subjective image assessment confirmed that the image noise significantly decreased between i4 and DLR (p < 0.01) and from the Standard to Smoother levels (p < 0.01), but the opposite occurred for the image smoothing. The highest scores for overall image quality and conspicuity were found for the Smooth and Smoother levels.
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Affiliation(s)
- Joël Greffier
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France
| | - Quentin Durand
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France
| | - Chris Serrand
- Department of Biostatistics, Clinical Epidemiology, Public Health, and Innovation in Methodology (BESPIM), CHU Nimes, 30029 Nimes, France
| | - Renaud Sales
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France
| | - Fabien de Oliveira
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France
| | - Jean-Paul Beregi
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France
| | - Djamel Dabli
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France
| | - Julien Frandon
- IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France
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Koetzier LR, Mastrodicasa D, Szczykutowicz TP, van der Werf NR, Wang AS, Sandfort V, van der Molen AJ, Fleischmann D, Willemink MJ. Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects. Radiology 2023; 306:e221257. [PMID: 36719287 PMCID: PMC9968777 DOI: 10.1148/radiol.221257] [Citation(s) in RCA: 58] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/26/2022] [Accepted: 10/13/2022] [Indexed: 02/01/2023]
Abstract
Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications. However, with faster and more advanced CT scanners, FBP has become increasingly obsolete. Higher image noise and more artifacts are especially noticeable in lower-dose CT imaging using FBP. This performance gap was partly addressed by model-based iterative reconstruction (MBIR). Yet, its "plastic" image appearance and long reconstruction times have limited widespread application. Hybrid iterative reconstruction partially addressed these limitations by blending FBP with MBIR and is currently the state-of-the-art reconstruction technique. In the past 5 years, deep learning reconstruction (DLR) techniques have become increasingly popular. DLR uses artificial intelligence to reconstruct high-quality images from lower-dose CT faster than MBIR. However, the performance of DLR algorithms relies on the quality of data used for model training. Higher-quality training data will become available with photon-counting CT scanners. At the same time, spectral data would greatly benefit from the computational abilities of DLR. This review presents an overview of the principles, technical approaches, and clinical applications of DLR, including metal artifact reduction algorithms. In addition, emerging applications and prospects are discussed.
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Affiliation(s)
| | | | - Timothy P. Szczykutowicz
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Niels R. van der Werf
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Adam S. Wang
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Veit Sandfort
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Aart J. van der Molen
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Dominik Fleischmann
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
| | - Martin J. Willemink
- From the Department of Radiology (L.R.K., D.M., A.S.W., V.S., D.F.,
M.J.W.) and Stanford Cardiovascular Institute (D.M., D.F., M.J.W.), Stanford
University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105;
Department of Radiology, University of Wisconsin–Madison, School of
Medicine and Public Health, Madison, Wis (T.P.S.); Department of Radiology,
Erasmus Medical Center, Rotterdam, the Netherlands (N.R.v.d.W.); Clinical
Science Western Europe, Philips Healthcare, Best, the Netherlands (N.R.v.d.W.);
and Department of Radiology, Leiden University Medical Center, Leiden, the
Netherlands (A.J.v.d.M.)
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Abdominopelvic CT Image Quality: Evaluation of Thin (0.5-mm) Slices Using Deep Learning Reconstruction. AJR Am J Roentgenol 2023; 220:381-388. [PMID: 36259592 DOI: 10.2214/ajr.22.28319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
BACKGROUND. Because thick-section images (typically 3-5 mm) have low image noise, radiologists typically use them to perform clinical interpretation, although they may additionally refer to thin-section images (typically 0.5-0.625 mm) for problem solving. Deep learning reconstruction (DLR) can yield thin-section images with low noise. OBJECTIVE. The purpose of this study is to compare abdominopelvic CT image quality between thin-section DLR images and thin- and thick-section hybrid iterative reconstruction (HIR) images. METHODS. This retrospective study included 50 patients (31 men and 19 women; median age, 64 years) who underwent abdominopelvic CT between June 15, 2020, and July 29, 2020. Images were reconstructed at 0.5-mm section using DLR and at 0.5-mm and 3.0-mm sections using HIR. Five radiologists independently performed pairwise comparisons (0.5-mm DLR and either 0.5-mm or 3.0-mm HIR) and recorded the preferred image for subjective image quality measures (scale, -2 to 2). The pooled scores of readers were compared with a score of 0 (denoting no preference). Image noise was quantified using the SD of ROIs on regions of homogeneous liver. RESULTS. For comparison of 0.5-mm DLR images and 0.5-mm HIR images, the median pooled score was 2 (indicating a definite preference for DLR) for noise and overall image quality and 1 (denoting a slight preference for DLR) for sharpness and natural appearance. For comparison of 0.5-mm DLR and 3.0-mm HIR, the median pooled score was 1 for the four previously mentioned measures. These assessments were all significantly different (p < .001) from 0. For artifacts, the median pooled score for both comparisons was 0, which was not significant for comparison with 3.0-mm HIR (p = .03) but was significant for comparison with 0.5-mm HIR (p < .001) due to imbalance in scores of 1 (n = 28) and -1 (slight preference for HIR, n = 1). Noise for 0.5-mm DLR was lower by mean differences of 12.8 HU compared with 0.5-mm HIR and 4.4 HU compared with 3.0-mm HIR (both p < .001). CONCLUSION. Thin-section DLR improves subjective image quality and reduces image noise compared with currently used thin- and thick-section HIR, without causing additional artifacts. CLINICAL IMPACT. Although further diagnostic performance studies are warranted, the findings suggest the possibility of replacing current use of both thin- and thick-section HIR with the use of thin-section DLR only during clinical interpretations.
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Huang X, Zhao W, Wang G, Wang Y, Li J, Li Y, Zeng Q, Guo J. Improving image quality with deep learning image reconstruction in double-low-dose head CT angiography compared with standard dose and adaptive statistical iterative reconstruction. Br J Radiol 2023; 96:20220625. [PMID: 36606518 PMCID: PMC9975360 DOI: 10.1259/bjr.20220625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 01/07/2023] Open
Abstract
OBJECTIVE To demonstrate similar image quality with deep learning image reconstruction (DLIR) in reduced contrast medium (CM) and radiation dose (double-low-dose) head CT angiography (CTA), in comparison with standard-dose and adaptive statistical iterative reconstruction-Veo (ASIR-V). METHODS A prospective study was performed in 63 patients who under head CTA using 256-slice CT. Patients were randomized into either the standard-dose group (n = 38) with 40 ml of Iopromide (370 mgI ml-1 at 4.5 ml s-1); or a double-low-dose group (n = 25) with CM of 25 ml at 3.0 ml s-1. For image reconstruction, the double-low-dose group used DLIR-M and DLIR-H strength, and the standard-dose group used ASIR-V with 50% strength. The CT value and standard deviation, signal-to-noise ratio and contrast-to-noise ratio of posterior fossa, neck muscles, carotid, vertebral and middle cerebral arteries were measured. The image noise, vessel edge and structure blurring and overall image quality were assessed by using a 5-grade method. The double-low-dose group reduced CM dose by 37.5% and CT dose index by 41% compared with the standard-dose group. DLIR further reduced the standard deviation value of the middle cerebral artery and posterior fossa and provided better overall subjective image quality (p < 0.05). CONCLUSION DLIR significantly reduces image noise and provides higher overall image quality in the double-low-dose CTA. ADVANCES IN KNOWLEDGE It is feasible to reduce CM dose by 37.5% and volume CT dose index by 41% with the combination of 80 kVp and DLIR in head CTA. Compared with ASIR-V, DLIR further reduces image noise and achieves better image quality with reduced contrast and radiation dose.
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Affiliation(s)
- Xin Huang
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi province, China
| | - Wenzhe Zhao
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi province, China
| | - Geliang Wang
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi province, China
| | - Yiming Wang
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi province, China
| | - Jianying Li
- CT Research Center, GE Healthcare China, Beijing, China
| | - Yanshou Li
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi province, China
| | - Qiang Zeng
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi province, China
| | - Jianxin Guo
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi province, China
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Guido G, Polici M, Nacci I, Bozzi F, De Santis D, Ubaldi N, Polidori T, Zerunian M, Bracci B, Laghi A, Caruso D. Iterative Reconstruction: State-of-the-Art and Future Perspectives. J Comput Assist Tomogr 2023; 47:244-254. [PMID: 36728734 DOI: 10.1097/rct.0000000000001401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
ABSTRACT Image reconstruction processing in computed tomography (CT) has evolved tremendously since its creation, succeeding at optimizing radiation dose while maintaining adequate image quality. Computed tomography vendors have developed and implemented various technical advances, such as automatic noise reduction filters, automatic exposure control, and refined imaging reconstruction algorithms.Focusing on imaging reconstruction, filtered back-projection has represented the standard reconstruction algorithm for over 3 decades, obtaining adequate image quality at standard radiation dose exposures. To overcome filtered back-projection reconstruction flaws in low-dose CT data sets, advanced iterative reconstruction algorithms consisting of either backward projection or both backward and forward projections have been developed, with the goal to enable low-dose CT acquisitions with high image quality. Iterative reconstruction techniques play a key role in routine workflow implementation (eg, screening protocols, vascular and pediatric applications), in quantitative CT imaging applications, and in dose exposure limitation in oncologic patients.Therefore, this review aims to provide an overview of the technical principles and the main clinical application of iterative reconstruction algorithms, focusing on the strengths and weaknesses, in addition to integrating future perspectives in the new era of artificial intelligence.
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Affiliation(s)
- Gisella Guido
- From the Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit, Sant'Andrea University Hospital, Rome, Italy
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