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Mileto A, Yu L, Revels JW, Kamel S, Shehata MA, Ibarra-Rovira JJ, Wong VK, Roman-Colon AM, Lee JM, Elsayes KM, Jensen CT. State-of-the-Art Deep Learning CT Reconstruction Algorithms in Abdominal Imaging. Radiographics 2024; 44:e240095. [PMID: 39612283 PMCID: PMC11618294 DOI: 10.1148/rg.240095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/18/2023] [Accepted: 05/21/2023] [Indexed: 12/01/2024]
Abstract
The implementation of deep neural networks has spurred the creation of deep learning reconstruction (DLR) CT algorithms. DLR CT techniques encompass a spectrum of deep learning-based methodologies that operate during the different steps of the image creation, prior to or after the traditional image formation process (eg, filtered backprojection [FBP] or iterative reconstruction [IR]), or alternatively by fully replacing FBP or IR techniques. DLR algorithms effectively facilitate the reduction of image noise associated with low photon counts from reduced radiation dose protocols. DLR methods have emerged as an effective solution to ameliorate limitations observed with prior CT image reconstruction algorithms, including FBP and IR algorithms, which are not able to preserve image texture and diagnostic performance at low radiation dose levels. An additional advantage of DLR algorithms is their high reconstruction speed, hence targeting the ideal triad of features for a CT image reconstruction (ie, the ability to consistently provide diagnostic-quality images and achieve radiation dose imaging levels as low as reasonably possible, with high reconstruction speed). An accumulated body of evidence supports the clinical use of DLR algorithms in abdominal imaging across multiple CT imaging tasks. The authors explore the technical aspects of DLR CT algorithms and examine various approaches to image synthesis in DLR creation. The clinical applications of DLR algorithms are highlighted across various abdominal CT imaging domains, with emphasis on the supporting evidence for diverse clinical tasks. An overview of the current limitations of and outlook for DLR algorithms for CT is provided. ©RSNA, 2024.
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Affiliation(s)
- Achille Mileto
- From the Department of Radiology, University of Washington School of
Medicine, Seattle, Wash (A.M.); Department of Radiology, Mayo Clinic, Rochester,
Minn (L.Y.); Department of Radiology, New York University Grossman School of
Medicine, NYU Langone Health, New York, NY (J.W.R.); Departments of Radiation
Oncology (S.K.) and Abdominal Imaging (M.A.S., J.J.I.R., V.K.W., K.M.E.,
C.T.J.), The University of Texas MD Anderson Cancer Center, 1400 Pressler St,
Unit 1473, Houston, TX 77030-4009; Department of Radiology, Texas
Children's Hospital, Houston, Tex (A.M.R.C.); and Department of
Radiology, Seoul National University College of Medicine, Seoul, South Korea
(J.M.L.)
| | - Lifeng Yu
- From the Department of Radiology, University of Washington School of
Medicine, Seattle, Wash (A.M.); Department of Radiology, Mayo Clinic, Rochester,
Minn (L.Y.); Department of Radiology, New York University Grossman School of
Medicine, NYU Langone Health, New York, NY (J.W.R.); Departments of Radiation
Oncology (S.K.) and Abdominal Imaging (M.A.S., J.J.I.R., V.K.W., K.M.E.,
C.T.J.), The University of Texas MD Anderson Cancer Center, 1400 Pressler St,
Unit 1473, Houston, TX 77030-4009; Department of Radiology, Texas
Children's Hospital, Houston, Tex (A.M.R.C.); and Department of
Radiology, Seoul National University College of Medicine, Seoul, South Korea
(J.M.L.)
| | - Jonathan W. Revels
- From the Department of Radiology, University of Washington School of
Medicine, Seattle, Wash (A.M.); Department of Radiology, Mayo Clinic, Rochester,
Minn (L.Y.); Department of Radiology, New York University Grossman School of
Medicine, NYU Langone Health, New York, NY (J.W.R.); Departments of Radiation
Oncology (S.K.) and Abdominal Imaging (M.A.S., J.J.I.R., V.K.W., K.M.E.,
C.T.J.), The University of Texas MD Anderson Cancer Center, 1400 Pressler St,
Unit 1473, Houston, TX 77030-4009; Department of Radiology, Texas
Children's Hospital, Houston, Tex (A.M.R.C.); and Department of
Radiology, Seoul National University College of Medicine, Seoul, South Korea
(J.M.L.)
| | - Serageldin Kamel
- From the Department of Radiology, University of Washington School of
Medicine, Seattle, Wash (A.M.); Department of Radiology, Mayo Clinic, Rochester,
Minn (L.Y.); Department of Radiology, New York University Grossman School of
Medicine, NYU Langone Health, New York, NY (J.W.R.); Departments of Radiation
Oncology (S.K.) and Abdominal Imaging (M.A.S., J.J.I.R., V.K.W., K.M.E.,
C.T.J.), The University of Texas MD Anderson Cancer Center, 1400 Pressler St,
Unit 1473, Houston, TX 77030-4009; Department of Radiology, Texas
Children's Hospital, Houston, Tex (A.M.R.C.); and Department of
Radiology, Seoul National University College of Medicine, Seoul, South Korea
(J.M.L.)
| | - Mostafa A. Shehata
- From the Department of Radiology, University of Washington School of
Medicine, Seattle, Wash (A.M.); Department of Radiology, Mayo Clinic, Rochester,
Minn (L.Y.); Department of Radiology, New York University Grossman School of
Medicine, NYU Langone Health, New York, NY (J.W.R.); Departments of Radiation
Oncology (S.K.) and Abdominal Imaging (M.A.S., J.J.I.R., V.K.W., K.M.E.,
C.T.J.), The University of Texas MD Anderson Cancer Center, 1400 Pressler St,
Unit 1473, Houston, TX 77030-4009; Department of Radiology, Texas
Children's Hospital, Houston, Tex (A.M.R.C.); and Department of
Radiology, Seoul National University College of Medicine, Seoul, South Korea
(J.M.L.)
| | - Juan J. Ibarra-Rovira
- From the Department of Radiology, University of Washington School of
Medicine, Seattle, Wash (A.M.); Department of Radiology, Mayo Clinic, Rochester,
Minn (L.Y.); Department of Radiology, New York University Grossman School of
Medicine, NYU Langone Health, New York, NY (J.W.R.); Departments of Radiation
Oncology (S.K.) and Abdominal Imaging (M.A.S., J.J.I.R., V.K.W., K.M.E.,
C.T.J.), The University of Texas MD Anderson Cancer Center, 1400 Pressler St,
Unit 1473, Houston, TX 77030-4009; Department of Radiology, Texas
Children's Hospital, Houston, Tex (A.M.R.C.); and Department of
Radiology, Seoul National University College of Medicine, Seoul, South Korea
(J.M.L.)
| | - Vincenzo K. Wong
- From the Department of Radiology, University of Washington School of
Medicine, Seattle, Wash (A.M.); Department of Radiology, Mayo Clinic, Rochester,
Minn (L.Y.); Department of Radiology, New York University Grossman School of
Medicine, NYU Langone Health, New York, NY (J.W.R.); Departments of Radiation
Oncology (S.K.) and Abdominal Imaging (M.A.S., J.J.I.R., V.K.W., K.M.E.,
C.T.J.), The University of Texas MD Anderson Cancer Center, 1400 Pressler St,
Unit 1473, Houston, TX 77030-4009; Department of Radiology, Texas
Children's Hospital, Houston, Tex (A.M.R.C.); and Department of
Radiology, Seoul National University College of Medicine, Seoul, South Korea
(J.M.L.)
| | - Alicia M. Roman-Colon
- From the Department of Radiology, University of Washington School of
Medicine, Seattle, Wash (A.M.); Department of Radiology, Mayo Clinic, Rochester,
Minn (L.Y.); Department of Radiology, New York University Grossman School of
Medicine, NYU Langone Health, New York, NY (J.W.R.); Departments of Radiation
Oncology (S.K.) and Abdominal Imaging (M.A.S., J.J.I.R., V.K.W., K.M.E.,
C.T.J.), The University of Texas MD Anderson Cancer Center, 1400 Pressler St,
Unit 1473, Houston, TX 77030-4009; Department of Radiology, Texas
Children's Hospital, Houston, Tex (A.M.R.C.); and Department of
Radiology, Seoul National University College of Medicine, Seoul, South Korea
(J.M.L.)
| | - Jeong Min Lee
- From the Department of Radiology, University of Washington School of
Medicine, Seattle, Wash (A.M.); Department of Radiology, Mayo Clinic, Rochester,
Minn (L.Y.); Department of Radiology, New York University Grossman School of
Medicine, NYU Langone Health, New York, NY (J.W.R.); Departments of Radiation
Oncology (S.K.) and Abdominal Imaging (M.A.S., J.J.I.R., V.K.W., K.M.E.,
C.T.J.), The University of Texas MD Anderson Cancer Center, 1400 Pressler St,
Unit 1473, Houston, TX 77030-4009; Department of Radiology, Texas
Children's Hospital, Houston, Tex (A.M.R.C.); and Department of
Radiology, Seoul National University College of Medicine, Seoul, South Korea
(J.M.L.)
| | - Khaled M. Elsayes
- From the Department of Radiology, University of Washington School of
Medicine, Seattle, Wash (A.M.); Department of Radiology, Mayo Clinic, Rochester,
Minn (L.Y.); Department of Radiology, New York University Grossman School of
Medicine, NYU Langone Health, New York, NY (J.W.R.); Departments of Radiation
Oncology (S.K.) and Abdominal Imaging (M.A.S., J.J.I.R., V.K.W., K.M.E.,
C.T.J.), The University of Texas MD Anderson Cancer Center, 1400 Pressler St,
Unit 1473, Houston, TX 77030-4009; Department of Radiology, Texas
Children's Hospital, Houston, Tex (A.M.R.C.); and Department of
Radiology, Seoul National University College of Medicine, Seoul, South Korea
(J.M.L.)
| | - Corey T. Jensen
- From the Department of Radiology, University of Washington School of
Medicine, Seattle, Wash (A.M.); Department of Radiology, Mayo Clinic, Rochester,
Minn (L.Y.); Department of Radiology, New York University Grossman School of
Medicine, NYU Langone Health, New York, NY (J.W.R.); Departments of Radiation
Oncology (S.K.) and Abdominal Imaging (M.A.S., J.J.I.R., V.K.W., K.M.E.,
C.T.J.), The University of Texas MD Anderson Cancer Center, 1400 Pressler St,
Unit 1473, Houston, TX 77030-4009; Department of Radiology, Texas
Children's Hospital, Houston, Tex (A.M.R.C.); and Department of
Radiology, Seoul National University College of Medicine, Seoul, South Korea
(J.M.L.)
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Zhong J, Hu Y, Xing Y, Wang L, Li J, Lu W, Shi X, Ding D, Ge X, Zhang H, Yao W. Deep learning image reconstruction for low-kiloelectron volt virtual monoenergetic images in abdominal dual-energy CT: medium strength provides higher lesion conspicuity. Acta Radiol 2024; 65:1133-1146. [PMID: 39033390 DOI: 10.1177/02841851241262765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
Abstract
BACKGROUND The best settings of deep learning image reconstruction (DLIR) algorithm for abdominal low-kiloelectron volt (keV) virtual monoenergetic imaging (VMI) have not been determined. PURPOSE To determine the optimal settings of the DLIR algorithm for abdominal low-keV VMI. MATERIAL AND METHODS The portal-venous phase computed tomography (CT) scans of 109 participants with 152 lesions were reconstructed into four image series: VMI at 50 keV using adaptive statistical iterative reconstruction (Asir-V) at 50% blending (AV-50); and VMI at 40 keV using AV-50 and DLIR at medium (DLIR-M) and high strength (DLIR-H). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of nine anatomical sites were calculated. Noise power spectrum (NPS) using homogenous region of liver, and edge rise slope (ERS) at five edges were measured. Five radiologists rated image quality and diagnostic acceptability, and evaluated the lesion conspicuity. RESULTS The SNR and CNR values, and noise and noise peak in NPS measurements, were significantly lower in DLIR images than AV-50 images in all anatomical sites (all P < 0.001). The ERS values were significantly higher in 40-keV images than 50-keV images at all edges (all P < 0.001). The differences of the peak and average spatial frequency among the four reconstruction algorithms were significant but relatively small. The 40-keV images were rated higher with DLIR-M than DLIR-H for diagnostic acceptance (P < 0.001) and lesion conspicuity (P = 0.010). CONCLUSION DLIR provides lower noise, higher sharpness, and more natural texture to allow 40 keV to be a new standard for routine VMI reconstruction for the abdomen and DLIR-M gains higher diagnostic acceptance and lesion conspicuity rating than DLIR-H.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Lingyun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Jianying Li
- Computed Tomography Research Center, GE Healthcare, Beijing, PR China
| | - Wei Lu
- Computed Tomography Research Center, GE Healthcare, Shanghai, PR China
| | - Xiaomeng Shi
- Department of Materials, Imperial College London, London, UK
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
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Jiang C, Zhang J, Li W, Li Y, Ni M, Jin D, Zhang Y, Jiang L, Yuan H. Deep Learning Imaging Reconstruction Algorithm for Carotid Dual Energy CT Angiography: Opportunistic Evaluation of Cervical Intervertebral Discs-A Preliminary Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1960-1968. [PMID: 38429560 PMCID: PMC11300736 DOI: 10.1007/s10278-024-01016-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/22/2023] [Accepted: 12/22/2023] [Indexed: 03/03/2024]
Abstract
Thus, the aim of this study is to evaluate the performance of deep learning imaging reconstruction (DLIR) algorithm in different image sets derived from carotid dual-energy computed tomography angiography (DECTA) for evaluating cervical intervertebral discs (IVDs) and compare them with those reconstructed using adaptive statistical iterative reconstruction-Veo (ASiR-V). Forty-two patients who underwent carotid DECTA were included in this retrospective analysis. Three types of image sets (70 keV, water-iodine, and water-calcium) were reconstructed using 50% ASiR-V and DLIR at medium and high levels (DLIR-M and DLIR-H). The diagnostic acceptability and conspicuity of IVDs were assessed using a 5-point scale. Hounsfield Units (HU) and water concentration (WC) values of the IVDs; standard deviation (SD); and coefficient of variation (CV) were calculated. Measurement parameters of the 50% ASIR-V, DLIR-M, and DLIR-H groups were compared. The DLIR-H group showed higher scores for diagnostic acceptability and conspicuity, as well as lower SD values for HU and WC than the ASiR-V and DLIR-M groups for the 70 keV and water-iodine image sets (all p < .001). However, there was no significant difference in scores and SD among the three groups for the water-calcium image set (all p > .005). The water-calcium image set showed better diagnostic accuracy for evaluating IVDs compared to the other image sets. The inter-rater agreement using ASiR-V, DLIR-M, and DLIR-H was good for the 70 keV image set, excellent for the water-iodine and water-calcium image sets. DLIR improved the visualization of IVDs in the 70 keV and water-iodine image sets. However, its improvement on color-coded water-calcium image set was limited.
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Affiliation(s)
- Chenyu Jiang
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Jingxin Zhang
- Department of Integration of Chinese and Western Medicine, School of Basic Medical Sciences, Peking University, Beijin, China
| | - Wenhuan Li
- CT Research Center, GE Healthcare China, 1 South Tongji Road, Beijing, China
| | - Yali Li
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Ming Ni
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Dan Jin
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Yan Zhang
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Liang Jiang
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China.
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Zhong J, Wang L, Yan C, Xing Y, Hu Y, Ding D, Ge X, Li J, Lu W, Shi X, Yuan F, Yao W, Zhang H. Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT. BMC Med Imaging 2024; 24:159. [PMID: 38926711 PMCID: PMC11201298 DOI: 10.1186/s12880-024-01334-0] [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: 01/04/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND To assess the improvement of image quality and diagnostic acceptance of thinner slice iodine maps enabled by deep learning image reconstruction (DLIR) in abdominal dual-energy CT (DECT). METHODS This study prospectively included 104 participants with 136 lesions. Four series of iodine maps were generated based on portal-venous scans of contrast-enhanced abdominal DECT: 5-mm and 1.25-mm using adaptive statistical iterative reconstruction-V (Asir-V) with 50% blending (AV-50), and 1.25-mm using DLIR with medium (DLIR-M), and high strength (DLIR-H). The iodine concentrations (IC) and their standard deviations of nine anatomical sites were measured, and the corresponding coefficient of variations (CV) were calculated. Noise-power-spectrum (NPS) and edge-rise-slope (ERS) were measured. Five radiologists rated image quality in terms of image noise, contrast, sharpness, texture, and small structure visibility, and evaluated overall diagnostic acceptability of images and lesion conspicuity. RESULTS The four reconstructions maintained the IC values unchanged in nine anatomical sites (all p > 0.999). Compared to 1.25-mm AV-50, 1.25-mm DLIR-M and DLIR-H significantly reduced CV values (all p < 0.001) and presented lower noise and noise peak (both p < 0.001). Compared to 5-mm AV-50, 1.25-mm images had higher ERS (all p < 0.001). The difference of the peak and average spatial frequency among the four reconstructions was relatively small but statistically significant (both p < 0.001). The 1.25-mm DLIR-M images were rated higher than the 5-mm and 1.25-mm AV-50 images for diagnostic acceptability and lesion conspicuity (all P < 0.001). CONCLUSIONS DLIR may facilitate the thinner slice thickness iodine maps in abdominal DECT for improvement of image quality, diagnostic acceptability, and lesion conspicuity.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Lingyun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Chao Yan
- Department of Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Jianying Li
- Computed Tomography Research Center, GE Healthcare, Beijing, 100176, China
| | - Wei Lu
- Computed Tomography Research Center, GE Healthcare, Shanghai, 201203, China
| | - Xiaomeng Shi
- Department of Materials, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
| | - Fei Yuan
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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Bousse A, Kandarpa VSS, Rit S, Perelli A, Li M, Wang G, Zhou J, Wang G. Systematic Review on Learning-based Spectral CT. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2024; 8:113-137. [PMID: 38476981 PMCID: PMC10927029 DOI: 10.1109/trpms.2023.3314131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.
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Affiliation(s)
- Alexandre Bousse
- LaTIM, Inserm UMR 1101, Université de Bretagne Occidentale, 29238 Brest, France
| | | | - Simon Rit
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Étienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69373, Lyon, France
| | - Alessandro Perelli
- Department of Biomedical Engineering, School of Science and Engineering, University of Dundee, DD1 4HN, UK
| | - Mengzhou Li
- Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Guobao Wang
- Department of Radiology, University of California Davis Health, Sacramento, USA
| | - Jian Zhou
- CTIQ, Canon Medical Research USA, Inc., Vernon Hills, 60061, USA
| | - Ge Wang
- Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA
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Virarkar MK, Mileto A, Vulasala SSR, Ananthakrishnan L, Bhosale P. Dual-Energy Computed Tomography Applications in the Genitourinary Tract. Radiol Clin North Am 2023; 61:1051-1068. [PMID: 37758356 DOI: 10.1016/j.rcl.2023.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
By virtue of material differentiation capabilities afforded through dedicated postprocessing algorithms, dual-energy CT (DECT) has been shown to provide benefit in the evaluation of various diseases. In this article, we review the diagnostic use of DECT in the assessment of genitourinary diseases, with emphasis on its role in renal stone characterization, incidental renal and adrenal lesion characterization, retroperitoneal trauma, reduction of radiation, and contrast dose and cost-effectiveness potential. We also discuss future perspectives of the DECT scanning mode, including the use of novel contrast injection strategies and photon-counting detector computed tomography.
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Affiliation(s)
- Mayur K Virarkar
- Department of Radiology, University of Florida College of Medicine, Clinical Center, C90, 2nd Floor, 655 West 8th Street, Jacksonville, FL 32209, USA
| | - Achille Mileto
- Department of Radiology, Mayo Clinic, Mayo Building West, 2nd Floor, 200 First Street SW, Rochester, MN, 55905, USA
| | - Sai Swarupa R Vulasala
- Department of radiology, University of Florida College of Medicine, Clinical Center, C90, 2nd Floor, 655 West 8th Street, Jacksonville, FL, 32209, USA.
| | - Lakshmi Ananthakrishnan
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, USA
| | - Priya Bhosale
- Department of Diagnostic Radiology, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 1479, Houston, TX 77030, USA
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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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8
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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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9
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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 PMCID: PMC11781595 DOI: 10.1007/s00261-023-03966-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [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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Zhong J, Wang L, Shen H, Li J, Lu W, Shi X, Xing Y, Hu Y, Ge X, Ding D, Yan F, Du L, Yao W, Zhang H. Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers. Eur Radiol 2023; 33:5331-5343. [PMID: 36976337 DOI: 10.1007/s00330-023-09556-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 02/07/2023] [Accepted: 02/17/2023] [Indexed: 03/29/2023]
Abstract
OBJECTIVES To evaluate image quality, diagnostic acceptability, and lesion conspicuity in abdominal dual-energy CT (DECT) using deep learning image reconstruction (DLIR) compared to those using adaptive statistical iterative reconstruction-V (Asir-V) at 50% blending (AV-50), and to identify potential factors impacting lesion conspicuity. METHODS The portal-venous phase scans in abdominal DECT of 47 participants with 84 lesions were prospectively included. The raw data were reconstructed to virtual monoenergetic image (VMI) at 50 keV using filtered back-projection (FBP), AV-50, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H). A noise power spectrum (NPS) was generated. CT number and standard deviation values of eight anatomical sites were measured. Signal-to-noise (SNR), and contrast-to-noise ratio (CNR) values were calculated. Five radiologists assessed image quality in terms of image contrast, image noise, image sharpness, artificial sensation, and diagnostic acceptability, and evaluated the lesion conspicuity. RESULTS DLIR further reduced image noise (p < 0.001) compared to AV-50 while better preserved the average NPS frequency (p < 0.001). DLIR maintained CT number values (p > 0.99) and improved SNR and CNR values compared to AV-50 (p < 0.001). DLIR-H and DLIR-M showed higher ratings in all image quality analyses than AV-50 (p < 0.001). DLIR-H provided significantly better lesion conspicuity than AV-50 and DLIR-M regardless of lesion size, relative CT attenuation to surrounding tissue, or clinical purpose (p < 0.05). CONCLUSIONS DLIR-H could be safely recommended for routine low-keV VMI reconstruction in daily contrast-enhanced abdominal DECT to improve image quality, diagnostic acceptability, and lesion conspicuity. KEY POINTS • DLIR is superior to AV-50 in noise reduction, with less shifts of the average spatial frequency of NPS towards low frequency, and larger improvements of NPS noise, noise peak, SNR, and CNR values. • DLIR-M and DLIR-H generate better image quality in terms of image contrast, noise, sharpness, artificial sensation, and diagnostic acceptability than AV-50, while DLIR-H provides better lesion conspicuity than AV-50 and DLIR-M. • DLIR-H could be safely recommended as a new standard for routine low-keV VMI reconstruction in contrast-enhanced abdominal DECT to provide better lesion conspicuity and better image quality than the standard AV-50.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Lingyun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Hailin Shen
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiao Tong University School of Medicine, Suzhou, 215028, China
| | - Jianying Li
- Computed Tomography Research Center, GE Healthcare, Beijing, 100176, China
| | - Wei Lu
- Computed Tomography Research Center, GE Healthcare, Shanghai, 201203, China
| | - Xiaomeng Shi
- Department of Materials, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lianjun Du
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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Dabli D, Durand Q, Frandon J, de Oliveira F, Pastor M, Beregi J, Greffier J. Impact of the automatic tube current modulation (ATCM) system on virtual monoenergetic image quality for dual-source CT: A phantom study. Phys Med 2023; 109:102574. [PMID: 37004360 DOI: 10.1016/j.ejmp.2023.102574] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/23/2023] [Accepted: 03/22/2023] [Indexed: 04/03/2023] Open
Abstract
PURPOSE To assess the impact of the automatic tube current modulation (ATCM) on virtual monoenergetic images (VMIs) quality in dual-source CT(DSCT). MATERIALS AND METHODS Acquisitions were performed on DSCT using the Mercury phantom. The acquisition parameters for an abdomen-pelvic examination with single-energy CT(SECT) and dual-energy CT(DECT) imaging were used. Acquisitions were performed for each imaging mode using fixed mAs and ATCM. The mAs value was set to obtain a volume CT dose index of 11 mGy in fixed mAs acquisitions. This value was used as the reference mAs in ATCM acquisitions. The noise power spectrum and task-based transfer function at 40,50,60 and 70 keV levels were computed on VMIs and SECT images. The detectability index (d') was calculated for a lesion with an iodine concentration of 10 mg/mL. RESULTS The noise magnitude on VMIs was higher with the ATCM system than with fixed mAs for all energy levels and section diameters of 21,26 and 31 cm. The noise texture and spatial resolution were similar between the fixed mAs and ATCM acquisitions for both imaging modes. The d' values were lower for all energy levels with ATCM than with fixed mAs acquisitions for 21 and 26 cm diameters by -39.82 ± 9.32%, similar at 31 cm diameter -4.13 ± 0.24% and higher at 36 cm diameter 10.40 ± 6.69%. It was higher on VMIs at all energy levels compared to SECT images. CONCLUSIONS The ATCM system could be used with DECT imaging to optimize patient exposure without changing the noise texture and spatial resolution of VMIs compared to fixed mAs and SECT.
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