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Fujita N, Yasaka K, Hatano S, Sakamoto N, Kurokawa R, Abe O. Deep learning reconstruction for high-resolution computed tomography images of the temporal bone: comparison with hybrid iterative reconstruction. Neuroradiology 2024; 66:1105-1112. [PMID: 38514472 DOI: 10.1007/s00234-024-03330-1] [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: 12/07/2023] [Accepted: 03/04/2024] [Indexed: 03/23/2024]
Abstract
PURPOSE We investigated whether the quality of high-resolution computed tomography (CT) images of the temporal bone improves with deep learning reconstruction (DLR) compared with hybrid iterative reconstruction (HIR). METHODS This retrospective study enrolled 36 patients (15 men, 21 women; age, 53.9 ± 19.5 years) who had undergone high-resolution CT of the temporal bone. Axial and coronal images were reconstructed using DLR, HIR, and filtered back projection (FBP). In qualitative image analyses, two radiologists independently compared the DLR and HIR images with FBP in terms of depiction of structures, image noise, and overall quality, using a 5-point scale (5 = better than FBP, 1 = poorer than FBP) to evaluate image quality. The other two radiologists placed regions of interest on the tympanic cavity and measured the standard deviation of CT attenuation (i.e., quantitative image noise). Scores from the qualitative and quantitative analyses of the DLR and HIR images were compared using, respectively, the Wilcoxon signed-rank test and the paired t-test. RESULTS Qualitative and quantitative image noise was significantly reduced in DLR images compared with HIR images (all comparisons, p ≤ 0.016). Depiction of the otic capsule, auditory ossicles, and tympanic membrane was significantly improved in DLR images compared with HIR images (both readers, p ≤ 0.003). Overall image quality was significantly superior in DLR images compared with HIR images (both readers, p < 0.001). CONCLUSION Compared with HIR, DLR provided significantly better-quality high-resolution CT images of the temporal bone.
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Affiliation(s)
- Nana Fujita
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Koichiro Yasaka
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan.
| | - Sosuke Hatano
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Naoya Sakamoto
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Ryo Kurokawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
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Li J, Meng T, Zhang G, Yu X, Lu Z, Zhang W. Artificial intelligence iterative reconstruction in abdominal CT of patients with irregular arm positioning: a case-by-case evaluation. Acta Radiol 2024:2841851241258845. [PMID: 38873726 DOI: 10.1177/02841851241258845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
BACKGROUND Streak artifacts induced by irregular arm positioning have been an issue in diagnosing the abdomen. PURPOSE To illustrate the risk of misdiagnosis in abdominal computed tomography (CT) of patients with irregular arm positioning through a case-by-case evaluation and to test if it can be solved by the artificial intelligence iterative reconstruction (AIIR) algorithm. MATERIAL AND METHODS By reviewing 5220 cases of chest and thoracoabdominal CT, 64 patients with irregular arm positioning were enrolled, whose image data were reconstructed using AIIR in addition to routine hybrid iterative reconstruction (HIR). Lesion detection for livers, spleens, kidneys, gallbladders, and pancreas on AIIR images, performed by two radiologists, was compared with those on HIR images. Discrepancies arising from AIIR images included both cases with additional abnormalities and those with corrections made on previous detections. For cases with discrepancies, artifact scores for organs where discrepancies were found, and contrast-to-noise ratios (CNRs) of cysts with discrepancies were compared between two image sets. RESULTS Additional abnormalities were detected for 15 cases: additional liver cirrhosis (n=2); additional gallbladder stone (n=1); additional cholecystitis (n=1), additional spleen nodule (n=1); additional kidney cysts (n=8); additional liver cysts (3); and additional spleen cyst (n=1). A spleen contusion was corrected for one case. All involved artifact scores were improved on AIIR images. CNRs of involved liver, kidney, and spleen cysts were improved by up to 539.7%, 538.5%, and 245.5%, respectively. CONCLUSION Irregular arm positioning may induce a variety of misdiagnoses in abdominal CT, which is almost totally avoidable by the AIIR algorithm.
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Affiliation(s)
- Jin Li
- Department of Radiology, The Fourth Affiliated Hospital of Soochow University, Suzhou, PR China
| | - Ting Meng
- Central Research Institute, United Imaging Healthcare, Shanghai, PR China
| | - Guozhi Zhang
- Central Research Institute, United Imaging Healthcare, Shanghai, PR China
| | - Xiang Yu
- Department of Radiology, The Fourth Affiliated Hospital of Soochow University, Suzhou, PR China
| | - Zhihua Lu
- Department of Radiology, The Fourth Affiliated Hospital of Soochow University, Suzhou, PR China
| | - Weiguo Zhang
- Department of Radiology, The Fourth Affiliated Hospital of Soochow University, Suzhou, PR China
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Hamada A, Yasaka K, Hatano S, Kurokawa M, Inui S, Kubo T, Watanabe Y, Abe O. Deep-Learning Reconstruction of High-Resolution CT Improves Interobserver Agreement for the Evaluation of Pulmonary Fibrosis. Can Assoc Radiol J 2024:8465371241228468. [PMID: 38293802 DOI: 10.1177/08465371241228468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024] Open
Abstract
Objective: This study aimed to investigate whether deep-learning reconstruction (DLR) improves interobserver agreement in the evaluation of honeycombing for patients with interstitial lung disease (ILD) who underwent high-resolution computed tomography (CT) compared with hybrid iterative reconstruction (HIR). Methods: In this retrospective study, 35 consecutive patients suspected of ILD who underwent CT including the chest region were included. High-resolution CT images of the unilateral lung with DLR and HIR were reconstructed for the right and left lungs. A radiologist placed regions of interest on the lung and measured standard deviation of CT attenuation (i.e., quantitative image noise). In the qualitative image analyses, 5 blinded readers assessed the presence of honeycombing and reticulation, qualitative image noise, artifacts, and overall image quality using a 5-point scale (except for artifacts which was evaluated using a 3-point scale). Results: The quantitative and qualitative image noise in DLR was remarkably reduced compared to that in HIR (P < .001). Artifacts and overall DLR quality were significantly improved compared to those of HIR (P < .001 for 4 out of 5 readers). Interobserver agreement in the evaluations of honeycombing and reticulation for DLR (0.557 [0.450-0.693] and 0.525 [0.470-0.541], respectively) were higher than those for HIR (0.321 [0.211-0.520] and 0.470 [0.354-0.533], respectively). A statistically significant difference was found for honeycombing (P = .014). Conclusions: DLR improved interobserver agreement in the evaluation of honeycombing in patients with ILD on CT compared to HIR.
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Affiliation(s)
- Akiyoshi Hamada
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Koichiro Yasaka
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Sosuke Hatano
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Mariko Kurokawa
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Shohei Inui
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Takatoshi Kubo
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Yusuke Watanabe
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
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Okimoto N, Yasaka K, Fujita N, Watanabe Y, Kanzawa J, Abe O. Deep learning reconstruction for improving the visualization of acute brain infarct on computed tomography. Neuroradiology 2024; 66:63-71. [PMID: 37991522 PMCID: PMC10761512 DOI: 10.1007/s00234-023-03251-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 11/13/2023] [Indexed: 11/23/2023]
Abstract
PURPOSE This study aimed to investigate the impact of deep learning reconstruction (DLR) on acute infarct depiction compared with hybrid iterative reconstruction (Hybrid IR). METHODS This retrospective study included 29 (75.8 ± 13.2 years, 20 males) and 26 (64.4 ± 12.4 years, 18 males) patients with and without acute infarction, respectively. Unenhanced head CT images were reconstructed with DLR and Hybrid IR. In qualitative analyses, three readers evaluated the conspicuity of lesions based on five regions and image quality. A radiologist placed regions of interest on the lateral ventricle, putamen, and white matter in quantitative analyses, and the standard deviation of CT attenuation (i.e., quantitative image noise) was recorded. RESULTS Conspicuity of acute infarct in DLR was superior to that in Hybrid IR, and a statistically significant difference was observed for two readers (p ≤ 0.038). Conspicuity of acute infarct with time from onset to CT imaging at < 24 h in DLR was significantly improved compared with Hybrid IR for all readers (p ≤ 0.020). Image noise in DLR was significantly reduced compared with Hybrid IR with both the qualitative and quantitative analyses (p < 0.001 for all). CONCLUSION DLR in head CT helped improve acute infarct depiction, especially those with time from onset to CT imaging at < 24 h.
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Affiliation(s)
- Naomasa Okimoto
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
- Department of Radiology, Tokyo Metropolitan Bokutoh Hospital, 4-23-15 Kotobashi, Sumida-Ku, Tokyo, 130-8575, Japan
| | - Koichiro Yasaka
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan.
| | - Nana Fujita
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Yusuke Watanabe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Jun Kanzawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
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