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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; 105:379-385. [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] [MESH Headings] [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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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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Cao J, Mroueh N, Mercaldo N, Lennartz S, Kongboonvijit S, Srinivas Rao S, Pisuchpen N, Baliyan V, Pierce TT, Anderson MA, Sertic M, Shenoy-Bhangle AS, Kambadakone AR. Detectability of Hypoattenuating Liver Lesions with Deep Learning CT Reconstruction: A Phantom and Patient Study. Radiology 2024; 313:e232749. [PMID: 39377679 DOI: 10.1148/radiol.232749] [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: 10/09/2024]
Abstract
Background CT deep learning image reconstruction (DLIR) improves image quality by reducing noise compared with adaptive statistical iterative reconstruction-V (ASIR-V). However, objective assessment of low-contrast lesion detectability is lacking. Purpose To investigate low-contrast detectability of hypoattenuating liver lesions on CT scans reconstructed with DLIR compared with CT scans reconstructed with ASIR-V in a patient and a phantom study. Materials and Methods This single-center retrospective study included patients undergoing portal venous phase abdominal CT between February and May 2021 and a low-contrast-resolution phantom scanned with the same protocol. Four reconstructions (ASIR-V at 40% strength [ASIR-V 40] and DLIR at three strengths) were generated. Five radiologists qualitatively assessed the images using the five-point Likert scale for image quality, lesion diagnostic confidence, conspicuity, and small lesion (≤1 cm) visibility. Up to two key lesions per patient, confirmed at histopathologic testing or at prior or follow-up imaging studies, were included. Lesion-to-background contrast-to-noise ratio was calculated. Interreader variability was analyzed. Intergroup qualitative and quantitative metrics were compared between DLIR and ASIR-V 40 using proportional odds logistic regression models. Results Eighty-six liver lesions (mean size, 15 mm ± 9.5 [SD]) in 50 patients (median age, 62 years [IQR, 57-73 years]; 27 [54%] female patients) were included. Differences were not detected for various qualitative low-contrast detectability metrics between ASIR-V 40 and DLIR (P > .05). Quantitatively, medium-strength DLIR and high-strength DLIR yielded higher lesion-to-background contrast-to-noise ratios than ASIR-V 40 (medium-strength DLIR vs ASIR-V 40: odds ratio [OR], 1.96 [95% CI: 1.65, 2.33]; high-strength DLIR vs ASIR-V 40: OR, 5.36 [95% CI: 3.68, 7.82]; P < .001). Low-contrast lesion attenuation was reduced by 2.8-3.6 HU with DLIR. Interreader agreement was moderate to very good for the qualitative metrics. Subgroup analysis based on lesion size of larger than 1 cm and 1 cm or smaller yielded similar results (P > .05). Qualitatively, phantom study results were similar to those in patients (P > .05). Conclusion The detectability of low-contrast liver lesions was similar on CT scans reconstructed with low-, medium-, and high-strength DLIR and ASIR-V 40 in both patient and phantom studies. Lesion-to-background contrast-to-noise ratios were higher for DLIR medium- and high-strength reconstructions compared with ASIR-V 40. © RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
- Jinjin Cao
- From the Department of Radiology, Division of Abdominal Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270, Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P., V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Cologne, University Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Nayla Mroueh
- From the Department of Radiology, Division of Abdominal Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270, Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P., V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Cologne, University Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Nathaniel Mercaldo
- From the Department of Radiology, Division of Abdominal Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270, Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P., V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Cologne, University Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Simon Lennartz
- From the Department of Radiology, Division of Abdominal Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270, Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P., V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Cologne, University Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Sasiprang Kongboonvijit
- From the Department of Radiology, Division of Abdominal Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270, Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P., V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Cologne, University Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Shravya Srinivas Rao
- From the Department of Radiology, Division of Abdominal Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270, Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P., V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Cologne, University Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Nisanard Pisuchpen
- From the Department of Radiology, Division of Abdominal Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270, Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P., V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Cologne, University Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Vinit Baliyan
- From the Department of Radiology, Division of Abdominal Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270, Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P., V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Cologne, University Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Theodore T Pierce
- From the Department of Radiology, Division of Abdominal Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270, Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P., V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Cologne, University Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Mark A Anderson
- From the Department of Radiology, Division of Abdominal Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270, Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P., V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Cologne, University Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Madeleine Sertic
- From the Department of Radiology, Division of Abdominal Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270, Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P., V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Cologne, University Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Anuradha S Shenoy-Bhangle
- From the Department of Radiology, Division of Abdominal Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270, Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P., V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Cologne, University Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Avinash R Kambadakone
- From the Department of Radiology, Division of Abdominal Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270, Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P., V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Cologne, University Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
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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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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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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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Tanahashi Y, Kubota K, Nomura T, Ikeda T, Kutsuna M, Funayama S, Kobayashi T, Ozaki K, Ichikawa S, Goshima S. Improved vascular depiction and image quality through deep learning reconstruction of CT hepatic arteriography during transcatheter arterial chemoembolization. Jpn J Radiol 2024:10.1007/s11604-024-01614-3. [PMID: 38888853 DOI: 10.1007/s11604-024-01614-3] [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: 03/29/2024] [Accepted: 06/04/2024] [Indexed: 06/20/2024]
Abstract
PURPOSE To evaluate the effect of deep learning reconstruction (DLR) on vascular depiction, tumor enhancement, and image quality of computed tomography hepatic arteriography (CTHA) images acquired during transcatheter arterial chemoembolization (TACE). METHODS Institutional review board approval was obtained. Twenty-seven patients (18 men and 9 women, mean age, 75.7 years) who underwent CTHA immediately before TACE were enrolled. All images were reconstructed using three reconstruction algorithms: hybrid-iterative reconstruction (hybrid-IR), DLR with mild strength (DLR-M), and DLR with strong strength (DLR-S). Vascular depiction, tumor enhancement, feeder visualization, and image quality of CTHA were quantitatively and qualitatively assessed by two radiologists and compared between the three reconstruction algorithms. RESULTS The mean signal-to-noise ratios (SNR) of sub-segmental arteries and sub-sub-segmental arteries, and the contrast-to-noise ratio (CNR) of tumors, were significantly higher on DLR-S than on DLR-M and hybrid-IR (P < 0.001). The mean qualitative score for sharpness of sub-segmental and sub-sub-segmental arteries was significantly better on DLR-S than on DLR-M and hybrid-IR (P < 0.001). There was no significant difference in the feeder artery detection rate of automated feeder artery detection software among three reconstruction algorithms (P = 0.102). The contrast, continuity, and confidence level of feeder artery detection was significantly better on DLR-S than on DLR-M (P = 0.013, 0.005, and 0.001) and hybrid-IR (P < 0.001, P = 0.002, and P < 0.001). The weighted kappa values between two readers for qualitative scores of feeder artery visualization were 0.807-0.874. The mean qualitative scores for sharpness, granulation, and diagnostic acceptability of CTHA were better on DLR-S than on DLR-M and hybrid-IR (P < 0.001). CONCLUSIONS DLR significantly improved the SNR of small hepatic arteries, the CNR of tumor, and feeder artery visualization on CTHA images. DLR-S seems to be better suited to routine CTHA in TACE than does hybrid-IR.
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Affiliation(s)
- Yukichi Tanahashi
- Department of Radiology, Hamamatsu University School of Medicine, 1-20-1 Handayama, Chuo-ku, Hamamatsu City, Shizuoka, 431-3192, Japan.
| | - Koh Kubota
- Department of Radiology, Hamamatsu University School of Medicine, 1-20-1 Handayama, Chuo-ku, Hamamatsu City, Shizuoka, 431-3192, Japan
| | - Takayuki Nomura
- Radiology Service, Hamamatsu University Hospital, Hamamatsu City, Shizuoka, Japan
| | - Takanobu Ikeda
- Department of Radiology, Hamamatsu University School of Medicine, 1-20-1 Handayama, Chuo-ku, Hamamatsu City, Shizuoka, 431-3192, Japan
| | - Masaya Kutsuna
- Department of Radiology, Hamamatsu University School of Medicine, 1-20-1 Handayama, Chuo-ku, Hamamatsu City, Shizuoka, 431-3192, Japan
| | - Satoshi Funayama
- Department of Radiology, Hamamatsu University School of Medicine, 1-20-1 Handayama, Chuo-ku, Hamamatsu City, Shizuoka, 431-3192, Japan
| | - Tatsunori Kobayashi
- Department of Radiology, Hamamatsu University School of Medicine, 1-20-1 Handayama, Chuo-ku, Hamamatsu City, Shizuoka, 431-3192, Japan
| | - Kumi Ozaki
- Department of Radiology, Hamamatsu University School of Medicine, 1-20-1 Handayama, Chuo-ku, Hamamatsu City, Shizuoka, 431-3192, Japan
| | - Shintaro Ichikawa
- Department of Radiology, Hamamatsu University School of Medicine, 1-20-1 Handayama, Chuo-ku, Hamamatsu City, Shizuoka, 431-3192, Japan
| | - Satoshi Goshima
- Department of Radiology, Hamamatsu University School of Medicine, 1-20-1 Handayama, Chuo-ku, Hamamatsu City, Shizuoka, 431-3192, Japan
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Kawai N, Noda Y, Nakamura F, Kaga T, Suzuki R, Miyoshi T, Mori F, Hyodo F, Kato H, Matsuo M. Low-tube-voltage whole-body CT angiography with extremely low iodine dose: a comparison between hybrid-iterative reconstruction and deep-learning image-reconstruction algorithms. Clin Radiol 2024; 79:e791-e798. [PMID: 38403540 DOI: 10.1016/j.crad.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 02/27/2024]
Abstract
AIM To evaluate arterial enhancement, its depiction, and image quality in low-tube potential whole-body computed tomography (CT) angiography (CTA) with extremely low iodine dose and compare the results with those obtained by hybrid-iterative reconstruction (IR) and deep-learning image-reconstruction (DLIR) methods. MATERIALS AND METHODS This prospective study included 34 consecutive participants (27 men; mean age, 74.2 years) who underwent whole-body CTA at 80 kVp for evaluating aortic diseases between January and July 2020. Contrast material (240 mg iodine/ml) with simultaneous administration of its quarter volume of saline, which corresponded to 192 mg iodine/ml, was administered. CT raw data were reconstructed using adaptive statistical IR-Veo of 40% (hybrid-IR), DLIR with medium- (DLIR-M), and high-strength level (DLIR-H). A radiologist measured CT attenuation of the arteries and background noise, and the signal-to-noise ratio (SNR) was then calculated. Two reviewers qualitatively evaluated the arterial depictions and diagnostic acceptability on axial, multiplanar-reformatted (MPR), and volume-rendered (VR) images. RESULTS Mean contrast material volume and iodine weight administered were 64.1 ml and 15.4 g, respectively. The SNRs of the arteries were significantly higher in the following order of the DLIR-H, DLIR-M, and hybrid-IR (p<0.001). Depictions of six arteries on axial, three arteries on MPR, and four arteries on VR images were significantly superior in the DLIR-M or hybrid-IR than in the DLIR-H (p≤0.009 for each). Diagnostic acceptability was significantly better in the DLIR-M and DLIR-H than in the hybrid-IR (p<0.001-0.005). CONCLUSION DLIR-M showed well-balanced arterial depictions and image quality compared with the hybrid-IR and DLIR-H.
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Affiliation(s)
- N Kawai
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - Y Noda
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - F Nakamura
- Department of Radiology, Gifu Municipal Hospital, 7-1 Kashima, Gifu 500-8513, Japan
| | - T Kaga
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - R Suzuki
- Department of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu 501-1194, Japan
| | - T Miyoshi
- Department of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu 501-1194, Japan
| | - F Mori
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - F Hyodo
- Department of Pharmacology, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan; Center for One Medicine Innovative Translational Research (COMIT), Institute for Advanced Study, Gifu University, Japan
| | - H Kato
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - M Matsuo
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
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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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Liu H, Zhou Y, Gou S, Luo Z. Tumor conspicuity enhancement-based segmentation model for liver tumor segmentation and RECIST diameter measurement in non-contrast CT images. Comput Biol Med 2024; 174:108420. [PMID: 38613896 DOI: 10.1016/j.compbiomed.2024.108420] [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: 10/25/2023] [Revised: 04/04/2024] [Accepted: 04/04/2024] [Indexed: 04/15/2024]
Abstract
BACKGROUND AND OBJECTIVE Liver tumor segmentation (LiTS) accuracy on contrast-enhanced computed tomography (CECT) images is higher than that on non-contrast computed tomography (NCCT) images. However, CECT requires contrast medium and repeated scans to obtain multiphase enhanced CT images, which is time-consuming and cost-increasing. Therefore, despite the lower accuracy of LiTS on NCCT images, which still plays an irreplaceable role in some clinical settings, such as guided brachytherapy, ablation, or evaluation of patients with renal function damage. In this study, we intend to generate enhanced high-contrast pseudo-color CT (PCCT) images to improve the accuracy of LiTS and RECIST diameter measurement on NCCT images. METHODS To generate high-contrast CT liver tumor region images, an intensity-based tumor conspicuity enhancement (ITCE) model was first developed. In the ITCE model, a pseudo color conversion function from an intensity distribution of the tumor was established, and it was applied in NCCT to generate enhanced PCCT images. Additionally, we design a tumor conspicuity enhancement-based liver tumor segmentation (TCELiTS) model, which was applied to improve the segmentation of liver tumors on NCCT images. The TCELiTS model consists of three components: an image enhancement module based on the ITCE model, a segmentation module based on a deep convolutional neural network, and an attention loss module based on restricted activation. Segmentation performance was analyzed using the Dice similarity coefficient (DSC), sensitivity, specificity, and RECIST diameter error. RESULTS To develop the deep learning model, 100 patients with histopathologically confirmed liver tumors (hepatocellular carcinoma, 64 patients; hepatic hemangioma, 36 patients) were randomly divided into a training set (75 patients) and an independent test set (25 patients). Compared with existing tumor automatic segmentation networks trained on CECT images (U-Net, nnU-Net, DeepLab-V3, Modified U-Net), the DSCs achieved on the enhanced PCCT images are both improved compared with those on NCCT images. We observe improvements of 0.696-0.713, 0.715 to 0.776, 0.748 to 0.788, and 0.733 to 0.799 in U-Net, nnU-Net, DeepLab-V3, and Modified U-Net, respectively, in terms of DSC values. In addition, an observer study including 5 doctors was conducted to compare the segmentation performance of enhanced PCCT images with that of NCCT images and showed that enhanced PCCT images are more advantageous for doctors to segment tumor regions. The results showed an accuracy improvement of approximately 3%-6%, but the time required to segment a single CT image was reduced by approximately 50 %. CONCLUSIONS Experimental results show that the ITCE model can generate high-contrast enhanced PCCT images, especially in liver regions, and the TCELiTS model can improve LiTS accuracy in NCCT images.
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Affiliation(s)
- Haofeng Liu
- School of Artificial Intelligence, Xidian University, Xi'An, 710071, China
| | - Yanyan Zhou
- Department of Interventional Radiology, Tangdu Hospital, Airforce Medical University, Xi'an, 710038, China
| | - Shuiping Gou
- School of Artificial Intelligence, Xidian University, Xi'An, 710071, China
| | - Zhonghua Luo
- Department of Interventional Radiology, Tangdu Hospital, Airforce Medical University, Xi'an, 710038, China.
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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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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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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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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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Cao L, Liu X, Qu T, Cheng Y, Li J, Li Y, Chen L, Niu X, Tian Q, Guo J. Improving spatial resolution and diagnostic confidence with thinner slice and deep learning image reconstruction in contrast-enhanced abdominal CT. Eur Radiol 2023; 33:1603-1611. [PMID: 36190531 DOI: 10.1007/s00330-022-09146-y] [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: 05/24/2022] [Revised: 08/22/2022] [Accepted: 09/05/2022] [Indexed: 12/01/2022]
Abstract
OBJECTIVE To evaluate image quality and diagnostic confidence improvement using a thin slice and a deep learning image reconstruction (DLIR) in contrast-enhanced abdominal CT. METHODS Forty patients with hepatic lesions in enhanced abdominal CT were retrospectively analyzed. Images in the portal phase were reconstructed at 5 mm and 1.25 mm slice thickness using the 50% adaptive statistical iterative reconstruction (ASIR-V) (ASIR-V50%) and at 1.25 mm using DLIR at medium (DLIR-M) and high (DLIR-H) settings. CT number and standard deviation of the hepatic parenchyma, spleen, portal vein, and subcutaneous fat were measured, and contrast-to-noise ratio (CNR) was calculated. Edge-rise-slope (ERS) was measured on the portal vein to reflect spatial resolution and the CT number skewness on liver parenchyma was calculated to reflect image texture. Two radiologists blindly assessed the overall image quality including subjective noise, image contrast, visibility of small structures using a 5-point scale, and object sharpness and lesion contour using a 4-point scale. RESULTS For the 1.25-mm images, DLIR significantly reduced image noise, improved CNR and overall subjective image quality compared to ASIR-V50%. Compared to the 5-mm ASIR-V50% images, DLIR images had significantly higher scores in the visibility and contour for small structures and lesions; as well as significantly higher ERS and lower CT number skewness. At a quarter of the signal strength, the 1.25-mm DLIR-H images had a similar subjective noise score as the 5-mm ASIR-V50% images. CONCLUSION DLIR significantly reduces image noise and maintains a more natural image texture; image spatial resolution and diagnostic confidence can be improved using thin slice images and DLIR in abdominal CT. KEY POINTS • DLIR further reduces image noise compared with ASIR-V while maintaining favorable image texture. • In abdominal CT, thinner slice images improve image spatial resolution and small object visualization but suffer from higher image noise. • Thinner slice images combined with DLIR in abdominal CT significantly suppress image noise for detecting low-density lesions while significantly improving image spatial resolution and overall image quality.
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Affiliation(s)
- Le Cao
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China
| | - Xiang Liu
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China
| | - Tingting Qu
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China
| | - Yannan Cheng
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China
| | - Jianying Li
- CT Research Center, GE Healthcare China, Beijing, 100176, China
| | - Yanan Li
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China
| | - Lihong Chen
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China
| | - Xinyi Niu
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China
| | - Qian Tian
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China
| | - Jianxin Guo
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, People's Republic of China.
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Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in dual-energy CT of the abdomen: a phantom and clinical study. Eur Radiol 2023; 33:1388-1399. [PMID: 36114848 DOI: 10.1007/s00330-022-09127-1] [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: 07/21/2022] [Revised: 07/21/2022] [Accepted: 08/19/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES To investigate the effect of deep learning image reconstruction (DLIR) on the accuracy of iodine quantification and image quality of dual-energy CT (DECT) compared to that of other reconstruction algorithms in a phantom experiment and an abdominal clinical study. METHODS An elliptical phantom with five different iodine concentrations (1-12 mgI/mL) was imaged five times with fast-kilovoltage-switching DECT for three target volume CT dose indexes. All images were reconstructed using filtered back-projection, iterative reconstruction (two levels), and DLIR algorithms. Measured and nominal iodine concentrations were compared among the algorithms. Contrast-enhanced CT of the abdomen with the same scanner was acquired in clinical patients. In arterial and portal venous phase images, iodine concentration, image noise, and coefficients of variation for four locations were retrospectively compared among the algorithms. One-way repeated-measures analyses of variance were used to evaluate differences in the iodine concentrations, standard deviations, coefficients of variation, and percentages of error among the algorithms. RESULTS In the phantom study, the measured iodine concentrations were equivalent among the algorithms: within ± 8% of the nominal values, with root-mean-square deviations of 0.08-0.36 mgI/mL, regardless of radiation dose. In the clinical study (50 patients; 35 men; mean age, 68 ± 11 years), iodine concentrations were equivalent among the algorithms for each location (all p > .99). Image noise and coefficients of variation were lower with DLIR than with the other algorithms (all p < .01). CONCLUSIONS The DLIR algorithm reduced image noise and variability of iodine concentration values compared with other reconstruction algorithms in the fast-kilovoltage-switching dual-energy CT. KEY POINTS • In the phantom study, standard deviations and coefficients of variation in iodine quantification were lower on images with the deep learning image reconstruction algorithm than on those with other algorithms. • In the clinical study, iodine concentrations of measurement location in the upper abdomen were consistent across four reconstruction algorithms, while image noise and variability of iodine concentrations were lower on images with the deep learning image reconstruction algorithm.
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A Review of Deep Learning CT Reconstruction: Concepts, Limitations, and Promise in Clinical Practice. CURRENT RADIOLOGY REPORTS 2022. [DOI: 10.1007/s40134-022-00399-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
Abstract
Purpose of Review
Deep Learning reconstruction (DLR) is the current state-of-the-art method for CT image formation. Comparisons to existing filter back-projection, iterative, and model-based reconstructions are now available in the literature. This review summarizes the prior reconstruction methods, introduces DLR, and then reviews recent findings from DLR from a physics and clinical perspective.
Recent Findings
DLR has been shown to allow for noise magnitude reductions relative to filtered back-projection without suffering from “plastic” or “blotchy” noise texture that was found objectionable with most iterative and model-based solutions. Clinically, early reader studies have reported increases in subjective quality scores and studies have successfully implemented DLR-enabled dose reductions.
Summary
The future of CT image reconstruction is bright; deep learning methods have only started to tackle problems in this space via addressing noise reduction. Artifact mitigation and spectral applications likely be future candidates for DLR applications.
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Noda Y, Kawai N, Kawamura T, Kobori A, Miyase R, Iwashima K, Kaga T, Miyoshi T, Hyodo F, Kato H, Matsuo M. Radiation and iodine dose reduced thoraco-abdomino-pelvic dual-energy CT at 40 keV reconstructed with deep learning image reconstruction. Br J Radiol 2022; 95:20211163. [PMID: 35230135 PMCID: PMC10996425 DOI: 10.1259/bjr.20211163] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 02/15/2022] [Accepted: 02/21/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To evaluate the feasibility of a simultaneous reduction of radiation and iodine doses in dual-energy thoraco-abdomino-pelvic CT reconstructed with deep learning image reconstruction (DLIR). METHODS Thoraco-abdomino-pelvic CT was prospectively performed in 111 participants; 52 participants underwent a standard-dose single-energy CT with a standard iodine dose (600 mgI/kg; SD group), while 59 underwent a low-dose dual-energy CT with a reduced iodine dose [300 mgI/kg; double low-dose (DLD) group]. CT data were reconstructed with a hybrid iterative reconstruction in the SD group and a high-strength level of DLIR at 40 keV in the DLD group. Two radiologists measured the CT numbers of the descending and abdominal aorta, portal vein, hepatic vein, inferior vena cava, liver, pancreas, spleen, and kidney, and background noise. Two other radiologists assessed diagnostic acceptability using a 5-point scale. The CT dose-index volume (CTDIvol), iodine weight, CT numbers of anatomical structures, background noise, and diagnostic acceptability were compared between the two groups using Mann-Whitney U test. RESULTS The median CTDIvol [10 mGy; interquartile range (IQR), 9-13 mGy vs 4 mGy; IQR, 4-5 mGy] and median iodine weight (35 g; IQR, 31-38 g vs 16 g; IQR, 14-18 g) were lower in the DLD group than in the SD group (p < 0.001 for each). The CT numbers of all anatomical structures and background noise were higher in the DLD group than in the SD group (p < 0.001 for all). The diagnostic image quality was obtained in 100% (52/52) of participants in the SD group and 95% (56/59) of participants in the DLD group. CONCLUSION Virtual monochromatic images at 40 keV reconstructed with DLIR could achieve half doses of radiation and iodine while maintaining diagnostic image quality. ADVANCES IN KNOWLEDGE Virtual monochromatic images at 40 keV reconstructed with DLIR algorithm allowed to reduce the doses of radiation and iodine while maintaining diagnostic image quality.
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Affiliation(s)
| | | | | | | | - Rena Miyase
- Department of Radiology, Gifu University,
Gifu, Japan
| | - Ken Iwashima
- Department of Radiology, Gifu University,
Gifu, Japan
| | - Tetsuro Kaga
- Department of Radiology, Gifu University,
Gifu, Japan
| | - Toshiharu Miyoshi
- Department of Radiology Services, Gifu University
Hospital, Gifu,
Japan
| | - Fuminori Hyodo
- Department of Radiology, Frontier Science for Imaging, Gifu
University, Gifu,
Japan
| | - Hiroki Kato
- Department of Radiology, Gifu University,
Gifu, Japan
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21
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Kaga T, Noda Y, Mori T, Kawai N, Miyoshi T, Hyodo F, Kato H, Matsuo M. Unenhanced abdominal low-dose CT reconstructed with deep learning-based image reconstruction: image quality and anatomical structure depiction. Jpn J Radiol 2022; 40:703-711. [PMID: 35286578 PMCID: PMC9252942 DOI: 10.1007/s11604-022-01259-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 02/23/2022] [Indexed: 11/25/2022]
Abstract
Purpose To evaluate the utility of deep learning-based image reconstruction (DLIR) algorithm in unenhanced abdominal low-dose CT (LDCT). Materials and methods Two patient groups were included in this prospective study: 58 consecutive patients who underwent unenhanced abdominal standard-dose CT reconstructed with hybrid iterative reconstruction (SDCT group) and 48 consecutive patients who underwent unenhanced abdominal LDCT reconstructed with high strength level of DLIR (LDCT group). The background noise and signal-to-noise ratio (SNR) of the liver, pancreas, spleen, kidney, abdominal aorta, inferior vena cava, and portal vein were calculated. Two radiologists qualitatively assessed the overall image noise, overall image quality, and abdominal anatomical structures depiction. Quantitative and qualitative parameters and size-specific dose estimates (SSDE) were compared between SDCT and LDCT groups. Results The background noise was lower in LDCT group than in SDCT group (P = 0.02). SNRs were higher in LDCT group than in SDCT group (P < 0.001–0.004) except for the liver. Overall image noise was superior in LDCT group than in SDCT group (P < 0.001). Overall image quality was not different between SDCT and LDCT groups (P = 0.25–0.26). Depiction of almost all abdominal anatomical structures was equal to or better in LDCT group than in SDCT group (P < 0.001–0.88). The SSDE was lower in LDCT group (4.0 mGy) than in SDCT group (20.6 mGy) (P < 0.001). Conclusions DLIR facilitates substantial radiation dose reduction of > 75% and significantly reduces background noise. DLIR can maintain image quality and anatomical structure depiction in unenhanced abdominal LDCT.
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Affiliation(s)
- Tetsuro Kaga
- 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.
| | - Takayuki Mori
- 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
| | - Toshiharu Miyoshi
- Department of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Fuminori Hyodo
- Department of Radiology, Frontier Science for Imaging, 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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22
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Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen. Eur Radiol 2022; 32:5499-5507. [PMID: 35238970 DOI: 10.1007/s00330-022-08647-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 02/05/2022] [Accepted: 02/11/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To evaluate the usefulness of deep learning image reconstruction (DLIR) to improve the image quality of dual-energy computed tomography (DECT) of the abdomen, compared to hybrid iterative reconstruction (IR). METHODS This study included 40 patients who underwent contrast-enhanced DECT of the abdomen. Virtual monochromatic 40-, 50-, and 70-keV and iodine density images were reconstructed using three reconstruction algorithms, including hybrid IR (ASiR-V50%) and DLIR (TrueFidelity) at medium- and high-strength level (DLIR-M and DLIR-H, respectively). The standard deviation of attenuation in liver parenchyma was measured as image noise. The contrast-to-noise ratio (CNR) for the portal vein on portal venous phase CT was calculated. The vessel conspicuity and overall image quality were graded on a 5-point scale ranging from 1 (poor) to 5 (excellent). The comparative scale of lesion conspicuity in 47 abdominal solid lesions was evaluated on a 5-point scale ranging from 0 (best) to -4 (markedly inferior). RESULTS The image noise of virtual monochromatic 40-, 50 -, and 70-keV and iodine density images was significantly decreased by DLIR compared to hybrid IR (p < 0.0001). The CNR was significantly higher in DLIR-H and DLIR-M than in hybrid IR (p < 0.0001). The vessel conspicuity and overall image quality scores were also significantly greater in DLIR-H and DLIR-M than in hybrid IR (p < 0.05). The lesion conspicuity scores for DLIR-M and DLIR-H were significantly higher than those for hybrid IR in the virtual monochromatic image of all energy levels (p ≤ 0.001). CONCLUSIONS DLIR improves vessel conspicuity, CNR, and lesion conspicuity of virtual monochromatic and iodine density images in abdominal contrast-enhanced DECT, compared to hybrid IR. KEY POINTS • Deep learning image reconstruction (DLIR) is useful for reducing image noise and improving the CNR of visual monochromatic 40-, 50-, and 70-keV images in dual-energy CT. • DLIR can improve lesion conspicuity of abdominal solid lesions on virtual monochromatic images compared to hybrid iterative reconstruction. • DLIR can also be applied to iodine density maps and significantly improves their image quality.
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The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis. Eur Radiol 2021; 32:2921-2929. [PMID: 34913104 PMCID: PMC9038933 DOI: 10.1007/s00330-021-08438-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 09/23/2021] [Accepted: 10/25/2021] [Indexed: 10/27/2022]
Abstract
OBJECTIVE To determine the difference in CT values and image quality of abdominal CT images reconstructed by filtered back-projection (FBP), hybrid iterative reconstruction (IR), and deep learning reconstruction (DLR). METHODS PubMed and Embase were systematically searched for articles regarding CT densitometry in the abdomen and the image reconstruction techniques FBP, hybrid IR, and DLR. Mean differences in CT values between reconstruction techniques were analyzed. A comparison between signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of FBP, hybrid IR, and DLR was made. A comparison of diagnostic confidence between hybrid IR and DLR was made. RESULTS Sixteen articles were included, six being suitable for meta-analysis. In the liver, the mean difference between hybrid IR and DLR was - 0.633 HU (p = 0.483, SD ± 0.902 HU). In the spleen, the mean difference between hybrid IR and DLR was - 0.099 HU (p = 0.925, SD ± 1.061 HU). In the pancreas, the mean difference between hybrid IR and DLR was - 1.372 HU (p = 0.353, SD ± 1.476 HU). In 14 articles, CNR was described. In all cases, DLR showed a significantly higher CNR. In 9 articles, SNR was described. In all cases but one, DLR showed a significantly higher SNR. In all cases, DLR showed a significantly higher diagnostic confidence. CONCLUSIONS There were no significant differences in CT values reconstructed by FBP, hybrid IR, and DLR in abdominal organs. This shows that these reconstruction techniques are consistent in reconstructing CT values. DLR images showed a significantly higher SNR and CNR, compared to FBP and hybrid IR. KEY POINTS CT values of abdominal CT images are similar between deep learning reconstruction (DLR), filtered back-projection (FBP), and hybrid iterative reconstruction (IR). DLR results in improved image quality in terms of SNR and CNR compared to FBP and hybrid IR images. DLR can thus be safely implemented in the clinical setting resulting in improved image quality without affecting CT values.
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Noda Y, Nakamura F, Kawamura T, Kawai N, Kaga T, Miyoshi T, Kato H, Hyodo F, Matsuo M. Deep-learning image-reconstruction algorithm for dual-energy CT angiography with reduced iodine dose: preliminary results. Clin Radiol 2021; 77:e138-e146. [PMID: 34782114 DOI: 10.1016/j.crad.2021.10.014] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/15/2021] [Indexed: 01/24/2023]
Abstract
AIM To evaluate the computed tomography (CT) attenuation values, background noise, arterial depiction, and image quality in whole-body dual-energy CT angiography (DECTA) at 40 keV with a reduced iodine dose using deep-learning image reconstruction (DLIR) and compare them with hybrid iterative reconstruction (IR). MATERIAL AND METHODS Whole-body DECTA with a reduced iodine dose (200 mg iodine/kg) was performed in 22 patients, and DECTA data at 1.25-mm section thickness with 50% overlap were reconstructed at 40 keV using 40% adaptive statistical iterative reconstruction with Veo (hybrid-IR group), and DLIR at medium and high levels (DLIR-M and DLIR-H groups). The CT attenuation values of the thoracic and abdominal aortas and iliac artery and background noise were measured. Arterial depiction and image quality on axial, multiplanar reformatted (MPR), and volume-rendered (VR) images were assessed by two readers. Quantitative and qualitative parameters were compared between the hybrid-IR, DLIR-M, and DLIR-H groups. RESULTS The vascular CT attenuation values were almost comparable between the three groups (p=0.013-0.97), but the background noise was significantly lower in the DLIR-H group than in the hybrid-IR and DLIR-M groups (p<0.001). The arterial depictions on axial and MPR images and in almost all arteries on VR images were comparable (p=0.14-1). The image quality of axial, MPR, and VR images was significantly better in the DLIR-H group (p<0.001-0.015). CONCLUSION DLIR significantly reduced background noise and improved image quality in DECTA at 40 keV compared with hybrid-IR, while maintaining the arterial depiction in almost all arteries.
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Affiliation(s)
- Y Noda
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - F Nakamura
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - T Kawamura
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - N Kawai
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - T Kaga
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - T Miyoshi
- Department of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu 501-1194, Japan
| | - H Kato
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - F Hyodo
- Department of Radiology, Frontier Science for Imaging, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - M Matsuo
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
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