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Nakaura T, Ito R, Ueda D, Nozaki T, Fushimi Y, Matsui Y, Yanagawa M, Yamada A, Tsuboyama T, Fujima N, Tatsugami F, Hirata K, Fujita S, Kamagata K, Fujioka T, Kawamura M, Naganawa S. The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI. Jpn J Radiol 2024; 42:685-696. [PMID: 38551772 PMCID: PMC11217134 DOI: 10.1007/s11604-024-01552-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 02/21/2024] [Indexed: 07/03/2024]
Abstract
The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs' potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor.
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Affiliation(s)
- Takeshi Nakaura
- Department of Central Radiology, Kumamoto University Hospital, Honjo 1-1-1, Kumamoto, 860-8556, Japan.
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1‑4‑3 Asahi‑Machi, Abeno‑ku, Osaka, 545‑8585, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, Shinjuku‑ku, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kita‑ku, Okayama, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Minami‑ku, Hiroshima, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita‑ku, Sapporo, Hokkaido, Japan
| | - Shohei Fujita
- Department of Radiology, University of Tokyo, Bunkyo‑ku, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo‑ku, Tokyo, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo‑ku, Tokyo, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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Yasaka K, Akai H, Kato S, Tajima T, Yoshioka N, Furuta T, Kageyama H, Toda Y, Akahane M, Ohtomo K, Abe O, Kiryu S. Iterative Motion Correction Technique with Deep Learning Reconstruction for Brain MRI: A Volunteer and Patient Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01184-w. [PMID: 38942939 DOI: 10.1007/s10278-024-01184-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/03/2024] [Accepted: 06/18/2024] [Indexed: 06/30/2024]
Abstract
The aim of this study was to investigate the effect of iterative motion correction (IMC) on reducing artifacts in brain magnetic resonance imaging (MRI) with deep learning reconstruction (DLR). The study included 10 volunteers (between September 2023 and December 2023) and 30 patients (between June 2022 and July 2022) for quantitative and qualitative analyses, respectively. Volunteers were instructed to remain still during the first MRI with fluid-attenuated inversion recovery sequence (FLAIR) and to move during the second scan. IMCoff DLR images were reconstructed from the raw data of the former acquisition; IMCon and IMCoff DLR images were reconstructed from the latter acquisition. After registration of the motion images, the structural similarity index measure (SSIM) was calculated using motionless images as reference. For qualitative analyses, IMCon and IMCoff FLAIR DLR images of the patients were reconstructed and evaluated by three blinded readers in terms of motion artifacts, noise, and overall quality. SSIM for IMCon images was 0.952, higher than that for IMCoff images (0.949) (p < 0.001). In qualitative analyses, although noise in IMCon images was rated as increased by two of the three readers (both p < 0.001), all readers agreed that motion artifacts and overall quality were significantly better in IMCon images than in IMCoff images (all p < 0.001). In conclusion, IMC reduced motion artifacts in brain FLAIR DLR images while maintaining similarity to motionless images.
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Affiliation(s)
- Koichiro Yasaka
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan
| | - Hiroyuki Akai
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Shimpei Kato
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Taku Tajima
- Department of Radiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-ku, Tokyo, 108-8329, Japan
| | - Naoki Yoshioka
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan
| | - Toshihiro Furuta
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Hajime Kageyama
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Yui Toda
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan
| | - Masaaki Akahane
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan
| | - Kuni Ohtomo
- International University of Health and Welfare, 2600-1 Ktiakanemaru, Ohtawara, Tochigi, 324-8501, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Shigeru Kiryu
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan.
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Yasaka K, Kanzawa J, Nakaya M, Kurokawa R, Tajima T, Akai H, Yoshioka N, Akahane M, Ohtomo K, Abe O, Kiryu S. Super-resolution Deep Learning Reconstruction for 3D Brain MR Imaging: Improvement of Cranial Nerve Depiction and Interobserver Agreement in Evaluations of Neurovascular Conflict. Acad Radiol 2024:S1076-6332(24)00368-4. [PMID: 38897913 DOI: 10.1016/j.acra.2024.06.010] [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: 04/24/2024] [Revised: 05/28/2024] [Accepted: 06/04/2024] [Indexed: 06/21/2024]
Abstract
RATIONALE AND OBJECTIVES To determine if super-resolution deep learning reconstruction (SR-DLR) improves the depiction of cranial nerves and interobserver agreement when assessing neurovascular conflict in 3D fast asymmetric spin echo (3D FASE) brain MR images, as compared to deep learning reconstruction (DLR). MATERIALS AND METHODS This retrospective study involved reconstructing 3D FASE MR images of the brain for 37 patients using SR-DLR and DLR. Three blinded readers conducted qualitative image analyses, evaluating the degree of neurovascular conflict, structure depiction, sharpness, noise, and diagnostic acceptability. Quantitative analyses included measuring edge rise distance (ERD), edge rise slope (ERS), and full width at half maximum (FWHM) using the signal intensity profile along a linear region of interest across the center of the basilar artery. RESULTS Interobserver agreement on the degree of neurovascular conflict of the facial nerve was generally higher with SR-DLR (0.429-0.923) compared to DLR (0.175-0.689). SR-DLR exhibited increased subjective image noise compared to DLR (p ≥ 0.008). However, all three readers found SR-DLR significantly superior in terms of sharpness (p < 0.001); cranial nerve depiction, particularly of facial and acoustic nerves, as well as the osseous spiral lamina (p < 0.001); and diagnostic acceptability (p ≤ 0.002). The FWHM (mm)/ERD (mm)/ERS (mm-1) for SR-DLR and DLR was 3.1-4.3/0.9-1.1/8795.5-10,703.5 and 3.3-4.8/1.4-2.1/5157.9-7705.8, respectively, with SR-DLR's image sharpness being significantly superior (p ≤ 0.001). CONCLUSION SR-DLR enhances image sharpness, leading to improved cranial nerve depiction and a tendency for greater interobserver agreement regarding facial nerve neurovascular conflict.
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Affiliation(s)
- Koichiro Yasaka
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan; Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba 286-0124, Japan
| | - Jun Kanzawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Moto Nakaya
- 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
| | - Taku Tajima
- Department of Radiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-ku, Tokyo 108-8329, Japan
| | - Hiroyuki Akai
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba 286-0124, Japan; Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | - Naoki Yoshioka
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba 286-0124, Japan
| | - Masaaki Akahane
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba 286-0124, Japan
| | - Kuni Ohtomo
- International University of Health and Welfare, 2600-1 Ktiakanemaru, Ohtawara, Tochigi 324-8501, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Shigeru Kiryu
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba 286-0124, Japan.
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Sato Y, Ohkuma K. Verification of image quality improvement by deep learning reconstruction to 1.5 T MRI in T2-weighted images of the prostate gland. Radiol Phys Technol 2024:10.1007/s12194-024-00819-5. [PMID: 38850389 DOI: 10.1007/s12194-024-00819-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/16/2024] [Accepted: 06/04/2024] [Indexed: 06/10/2024]
Abstract
This study aimed to evaluate whether the image quality of 1.5 T magnetic resonance imaging (MRI) of the prostate is equal to or higher than that of 3 T MRI by applying deep learning reconstruction (DLR). To objectively analyze the images from the 13 healthy volunteers, we measured the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the images obtained by the 1.5 T scanner with and without DLR, as well as for images obtained by the 3 T scanner. In the subjective, T2W images of the prostate were visually evaluated by two board-certified radiologists. The SNRs and CNRs in 1.5 T images with DLR were higher than that in 3 T images. Subjective image scores were better for 1.5 T images with DLR than 3 T images. The use of the DLR technique in 1.5 T MRI substantially improved the SNR and image quality of T2W images of the prostate gland, as compared to 3 T MRI.
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Affiliation(s)
- Yoshiomi Sato
- Department of Radiology, Saitama City Hospital, Mimuro 2460, Saitama, 336-8522, Japan.
| | - Kiyoshi Ohkuma
- Department of Diagnostic Radiology, Saitama City Hospital, Mimuro 2460, Saitama, 336-8522, Japan
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Kim BK, You SH, Kim B, Shin JH. Deep Learning-Based High-Resolution Magnetic Resonance Angiography (MRA) Generation Model for 4D Time-Resolved Angiography with Interleaved Stochastic Trajectories (TWIST) MRA in Fast Stroke Imaging. Diagnostics (Basel) 2024; 14:1199. [PMID: 38893725 PMCID: PMC11171826 DOI: 10.3390/diagnostics14111199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 06/03/2024] [Accepted: 06/05/2024] [Indexed: 06/21/2024] Open
Abstract
PURPOSE The purpose of this study is to improve the qualitative and quantitative image quality of the time-resolved angiography with interleaved stochastic trajectories technique (4D-TWIST-MRA) using deep neural network (DNN)-based MR image reconstruction software. MATERIALS AND METHODS A total of 520 consecutive patients underwent 4D-TWIST-MRA for ischemic stroke or intracranial vessel stenosis evaluation. Four-dimensional DNN-reconstructed MRA (4D-DNR) was generated using commercially available software (SwiftMR v.3.0.0.0, AIRS Medical, Seoul, Republic of Korea). Among those evaluated, 397 (76.3%) patients received concurrent time-of-flight MRA (TOF-MRA) to compare the signal-to-noise ratio (SNR), image quality, noise, sharpness, vascular conspicuity, and degree of venous contamination with a 5-point Likert scale. Two radiologists independently evaluated the detection rate of intracranial aneurysm in TOF-MRA, 4D-TWIST-MRA, and 4D-DNR in separate sessions. The other 123 (23.7%) patients received 4D-TWIST-MRA due to a suspicion of acute ischemic stroke. The confidence level and decision time for large vessel occlusion were evaluated in these patients. RESULTS In qualitative analysis, 4D-DNR demonstrated better overall image quality, sharpness, vascular conspicuity, and noise reduction compared to 4D-TWIST-MRA. Moreover, 4D-DNR exhibited a higher SNR than 4D-TWIST-MRA. The venous contamination and aneurysm detection rates were not significantly different between the two MRA images. When compared to TOF-MRA, 4D-CE-MRA underestimated the aneurysm size (2.66 ± 0.51 vs. 1.75 ± 0.62, p = 0.029); however, 4D-DNR showed no significant difference in size compared to TOF-MRA (2.66 ± 0.51 vs. 2.10 ± 0.41, p = 0.327). In the diagnosis of large vessel occlusion, 4D-DNR showed a better confidence level and shorter decision time than 4D-TWIST-MRA. CONCLUSION DNN reconstruction may improve the qualitative and quantitative image quality of 4D-TWIST-MRA, and also enhance diagnostic performance for intracranial aneurysm and large vessel occlusion.
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Affiliation(s)
| | - Sung-Hye You
- Department of Radiology, Anam Hospital, Korea University College of Medicine, #126-1, 5-Ka Anam-dong, Sungbuk ku, Seoul 136-705, Republic of Korea; (B.K.K.); (B.K.); (J.H.S.)
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Brain ME, Amukotuwa S, Bammer R. Deep learning denoising reconstruction enables faster T2-weighted FLAIR sequence acquisition with satisfactory image quality. J Med Imaging Radiat Oncol 2024; 68:377-384. [PMID: 38577926 DOI: 10.1111/1754-9485.13649] [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: 01/10/2023] [Accepted: 03/21/2024] [Indexed: 04/06/2024]
Abstract
INTRODUCTION Deep learning reconstruction (DLR) technologies are the latest methods attempting to solve the enduring problem of reducing MRI acquisition times without compromising image quality. The clinical utility of this reconstruction technique is yet to be fully established. This study aims to assess whether a commercially available DLR technique applied to 2D T2-weighted FLAIR brain images allows a reduction in scan time, without compromising image quality and thus diagnostic accuracy. METHODS 47 participants (24 male, mean age 55.9 ± 18.7 SD years, range 20-89 years) underwent routine, clinically indicated brain MRI studies in March 2022, that included a standard-of-care (SOC) T2-weighted FLAIR sequence, and an accelerated acquisition that was reconstructed using the DLR denoising product. Overall image quality, lesion conspicuity, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artefacts for each sequence, and preferred sequence on direct comparison, were subjectively assessed by two readers. RESULTS There was a strong preference for SOC FLAIR sequence for overall image quality (P = 0.01) and head-to-head comparison (P < 0.001). No difference was observed for lesion conspicuity (P = 0.49), perceived SNR (P = 1.0), and perceived CNR (P = 0.84). There was no difference in motion (P = 0.57) nor Gibbs ringing (P = 0.86) artefacts. Phase ghosting (P = 0.038) and pseudolesions were significantly more frequent (P < 0.001) on DLR images. CONCLUSION DLR algorithm allowed faster FLAIR acquisition times with comparable image quality and lesion conspicuity. However, an increased incidence and severity of phase ghosting artefact and presence of pseudolesions using this technique may result in a reduction in reading speed, efficiency, and diagnostic confidence.
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Affiliation(s)
- Matthew E Brain
- Department of Diagnostic Imaging, Monash Health, Monash Medical Centre, Melbourne, Victoria, Australia
| | - Shalini Amukotuwa
- Department of Diagnostic Imaging, Monash Health, Monash Medical Centre, Melbourne, Victoria, Australia
| | - Roland Bammer
- Department of Diagnostic Imaging, Monash Health, Monash Medical Centre, Melbourne, Victoria, Australia
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Suh PS, Park JE, Roh YH, Kim S, Jung M, Koo YS, Lee SA, Choi Y, Kim HS. Improving Diagnostic Performance of MRI for Temporal Lobe Epilepsy With Deep Learning-Based Image Reconstruction in Patients With Suspected Focal Epilepsy. Korean J Radiol 2024; 25:374-383. [PMID: 38528695 PMCID: PMC10973740 DOI: 10.3348/kjr.2023.0842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/10/2023] [Accepted: 01/07/2024] [Indexed: 03/27/2024] Open
Abstract
OBJECTIVE To evaluate the diagnostic performance and image quality of 1.5-mm slice thickness MRI with deep learning-based image reconstruction (1.5-mm MRI + DLR) compared to routine 3-mm slice thickness MRI (routine MRI) and 1.5-mm slice thickness MRI without DLR (1.5-mm MRI without DLR) for evaluating temporal lobe epilepsy (TLE). MATERIALS AND METHODS This retrospective study included 117 MR image sets comprising 1.5-mm MRI + DLR, 1.5-mm MRI without DLR, and routine MRI from 117 consecutive patients (mean age, 41 years; 61 female; 34 patients with TLE and 83 without TLE). Two neuroradiologists evaluated the presence of hippocampal or temporal lobe lesions, volume loss, signal abnormalities, loss of internal structure of the hippocampus, and lesion conspicuity in the temporal lobe. Reference standards for TLE were independently constructed by neurologists using clinical and radiological findings. Subjective image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were analyzed. Performance in diagnosing TLE, lesion findings, and image quality were compared among the three protocols. RESULTS The pooled sensitivity of 1.5-mm MRI + DLR (91.2%) for diagnosing TLE was higher than that of routine MRI (72.1%, P < 0.001). In the subgroup analysis, 1.5-mm MRI + DLR showed higher sensitivity for hippocampal lesions than routine MRI (92.7% vs. 75.0%, P = 0.001), with improved depiction of hippocampal T2 high signal intensity change (P = 0.016) and loss of internal structure (P < 0.001). However, the pooled specificity of 1.5-mm MRI + DLR (76.5%) was lower than that of routine MRI (89.2%, P = 0.004). Compared with 1.5-mm MRI without DLR, 1.5-mm MRI + DLR resulted in significantly improved pooled accuracy (91.2% vs. 73.1%, P = 0.010), image quality, SNR, and CNR (all, P < 0.001). CONCLUSION The use of 1.5-mm MRI + DLR enhanced the performance of MRI in diagnosing TLE, particularly in hippocampal evaluation, because of improved depiction of hippocampal abnormalities and enhanced image quality.
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Affiliation(s)
- Pae Sun Suh
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
| | - Yun Hwa Roh
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Seonok Kim
- Department of Clinical Epidemiology and Biostatics, University of Ulsan college of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Mina Jung
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Yong Seo Koo
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Sang-Ahm Lee
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Yangsean Choi
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
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Hokamura M, Uetani H, Nakaura T, Matsuo K, Morita K, Nagayama Y, Kidoh M, Yamashita Y, Ueda M, Mukasa A, Hirai T. Exploring the impact of super-resolution deep learning on MR angiography image quality. Neuroradiology 2024; 66:217-226. [PMID: 38148334 DOI: 10.1007/s00234-023-03271-1] [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: 09/20/2023] [Accepted: 12/14/2023] [Indexed: 12/28/2023]
Abstract
PURPOSE The aim of this study is to assess the effect of super-resolution deep learning-based reconstruction (SR-DLR), which uses k-space properties, on image quality of intracranial time-of-flight (TOF) magnetic resonance angiography (MRA) at 3 T. METHODS This retrospective study involved 35 patients who underwent intracranial TOF-MRA using a 3-T MRI system with SR-DLR based on k-space properties in October and November 2022. We reconstructed MRA with SR-DLR (matrix = 1008 × 1008) and MRA without SR-DLR (matrix = 336 × 336). We measured the signal-to-noise ratio (SNR), contrast, and contrast-to-noise ratio (CNR) in the basilar artery (BA) and the anterior cerebral artery (ACA) and the sharpness of the posterior cerebral artery (PCA) using the slope of the signal intensity profile curve at the half-peak points. Two radiologists evaluated image noise, artifacts, contrast, sharpness, and overall image quality of the two image types using a 4-point scale. We compared quantitative and qualitative scores between images with and without SR-DLR using the Wilcoxon signed-rank test. RESULTS The SNRs, contrasts, and CNRs were all significantly higher in images with SR-DLR than those without SR-DLR (p < 0.001). The slope was significantly greater in images with SR-DLR than those without SR-DLR (p < 0.001). The qualitative scores in MRAs with SR-DLR were all significantly higher than MRAs without SR-DLR (p < 0.001). CONCLUSION SR-DLR with k-space properties can offer the benefits of increased spatial resolution without the associated drawbacks of longer scan times and reduced SNR and CNR in intracranial MRA.
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Affiliation(s)
- Masamichi Hokamura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Hiroyuki Uetani
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan.
| | - Kensei Matsuo
- Department of Central Radiology, Kumamoto University Hospital, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Kosuke Morita
- Department of Central Radiology, Kumamoto University Hospital, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Yuichi Yamashita
- Canon Medical Systems Corporation, 70-1, Yanagi-cho, Saiwai-ku, Kawasaki-shi, Kanagawa, 212-0015, Japan
| | - Mitsuharu Ueda
- Department of Neurology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Akitake Mukasa
- Department of Neurosurgery, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
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Nakaura T, Yoshida N, Kobayashi N, Shiraishi K, Nagayama Y, Uetani H, Kidoh M, Hokamura M, Funama Y, Hirai T. Preliminary assessment of automated radiology report generation with generative pre-trained transformers: comparing results to radiologist-generated reports. Jpn J Radiol 2024; 42:190-200. [PMID: 37713022 PMCID: PMC10811038 DOI: 10.1007/s11604-023-01487-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 08/29/2023] [Indexed: 09/16/2023]
Abstract
PURPOSE In this preliminary study, we aimed to evaluate the potential of the generative pre-trained transformer (GPT) series for generating radiology reports from concise imaging findings and compare its performance with radiologist-generated reports. METHODS This retrospective study involved 28 patients who underwent computed tomography (CT) scans and had a diagnosed disease with typical imaging findings. Radiology reports were generated using GPT-2, GPT-3.5, and GPT-4 based on the patient's age, gender, disease site, and imaging findings. We calculated the top-1, top-5 accuracy, and mean average precision (MAP) of differential diagnoses for GPT-2, GPT-3.5, GPT-4, and radiologists. Two board-certified radiologists evaluated the grammar and readability, image findings, impression, differential diagnosis, and overall quality of all reports using a 4-point scale. RESULTS Top-1 and Top-5 accuracies for the different diagnoses were highest for radiologists, followed by GPT-4, GPT-3.5, and GPT-2, in that order (Top-1: 1.00, 0.54, 0.54, and 0.21, respectively; Top-5: 1.00, 0.96, 0.89, and 0.54, respectively). There were no significant differences in qualitative scores about grammar and readability, image findings, and overall quality between radiologists and GPT-3.5 or GPT-4 (p > 0.05). However, qualitative scores of the GPT series in impression and differential diagnosis scores were significantly lower than those of radiologists (p < 0.05). CONCLUSIONS Our preliminary study suggests that GPT-3.5 and GPT-4 have the possibility to generate radiology reports with high readability and reasonable image findings from very short keywords; however, concerns persist regarding the accuracy of impressions and differential diagnoses, thereby requiring verification by radiologists.
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Affiliation(s)
- Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan.
| | - Naofumi Yoshida
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Naoki Kobayashi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Kaori Shiraishi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Hiroyuki Uetani
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Masamichi Hokamura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Yoshinori Funama
- Department of Medical Physics, Faculty of Life Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
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10
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Ueda D, Kakinuma T, Fujita S, Kamagata K, Fushimi Y, Ito R, Matsui Y, Nozaki T, Nakaura T, Fujima N, Tatsugami F, Yanagawa M, Hirata K, Yamada A, Tsuboyama T, Kawamura M, Fujioka T, Naganawa S. Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol 2024; 42:3-15. [PMID: 37540463 PMCID: PMC10764412 DOI: 10.1007/s11604-023-01474-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 07/17/2023] [Indexed: 08/05/2023]
Abstract
In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.
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Affiliation(s)
- Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-Machi, Abeno-ku, Osaka, 545-8585, Japan.
| | | | - Shohei Fujita
- Department of Radiology, University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kita-ku, Okayama, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Chuo-ku, Kumamoto, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Minami-ku, Hiroshima, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita-ku, Sapporo, Hokkaido, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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11
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Matsuo K, Nakaura T, Morita K, Uetani H, Nagayama Y, Kidoh M, Hokamura M, Yamashita Y, Shinoda K, Ueda M, Mukasa A, Hirai T. Feasibility study of super-resolution deep learning-based reconstruction using k-space data in brain diffusion-weighted images. Neuroradiology 2023; 65:1619-1629. [PMID: 37673835 DOI: 10.1007/s00234-023-03212-y] [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: 04/23/2023] [Accepted: 08/08/2023] [Indexed: 09/08/2023]
Abstract
PURPOSE The purpose of this study is to evaluate the influence of super-resolution deep learning-based reconstruction (SR-DLR), which utilizes k-space data, on the quality of images and the quantitation of the apparent diffusion coefficient (ADC) for diffusion-weighted images (DWI) in brain magnetic resonance imaging (MRI). METHODS A retrospective analysis was performed on 34 patients who had undergone DWI using a 3 T MRI system with SR-DLR reconstruction based on k-space data in August 2022. DWI was reconstructed with SR-DLR (Matrix = 684 × 684) and without SR-DLR (Matrix = 228 × 228). Measurements were made of the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) in white matter (WM) and grey matter (GM), and the full width at half maximum (FWHM) of the septum pellucidum. Two radiologists assessed image noise, contrast, artifacts, blur, and the overall quality of three image types using a four-point scale. Quantitative and qualitative scores between images with and without SR-DLR were compared using the Wilcoxon signed-rank test. RESULTS Images with SR-DLR showed significantly higher SNRs and CNRs than those without SR-DLR (p < 0.001). No statistically significant variances were found in the apparent diffusion coefficients (ADCs) in WM and GM between images with and without SR-DLR (ADC in WM, p = 0.945; ADC in GM, p = 0.235). Moreover, the FWHM without SR-DLR was notably lower compared to that with SR-DLR (p < 0.001). CONCLUSION SR-DLR has the potential to augment the quality of DWI in DL MRI scans without significantly impacting ADC quantitation.
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Affiliation(s)
- Kensei Matsuo
- Department of Central Radiology, Kumamoto University Hospital, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo, Kumamoto, 860-8556, Japan.
| | - Kosuke Morita
- Department of Central Radiology, Kumamoto University Hospital, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Hiroyuki Uetani
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo, Kumamoto, 860-8556, Japan
| | - Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo, Kumamoto, 860-8556, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo, Kumamoto, 860-8556, Japan
| | - Masamichi Hokamura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo, Kumamoto, 860-8556, Japan
| | - Yuichi Yamashita
- Canon Medical Systems Corporation, 70-1, Yanagi, Saiwai, Kawasaki, Kanagawa, 212-0015, Japan
| | - Kensuke Shinoda
- MRI Systems Division, Canon Medical Systems Corporation, 1385 Shimoishigami, Otawara, Tochigi, 324-8550, Japan
| | - Mitsuharu Ueda
- Department of Neurology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo, Kumamoto, 860-8556, Japan
| | - Akitake Mukasa
- Department of Neurosurgery, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo, Kumamoto, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo, Kumamoto, 860-8556, Japan
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12
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Tatsugami F, Nakaura T, Yanagawa M, Fujita S, Kamagata K, Ito R, Kawamura M, Fushimi Y, Ueda D, Matsui Y, Yamada A, Fujima N, Fujioka T, Nozaki T, Tsuboyama T, Hirata K, Naganawa S. Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction. Diagn Interv Imaging 2023; 104:521-528. [PMID: 37407346 DOI: 10.1016/j.diii.2023.06.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/07/2023]
Abstract
Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis prediction in patients with cardiovascular disease. Deep learning, a type of machine learning, has revolutionized radiology by enabling automatic feature extraction and learning from large datasets, particularly in image-based applications. Thus, AI-driven techniques have enabled a faster analysis of cardiac CT examinations than when they are analyzed by humans, while maintaining reproducibility. However, further research and validation are required to fully assess the diagnostic performance, radiation dose-reduction capabilities, and clinical correctness of these AI-driven techniques in cardiac CT. This review article presents recent advances of AI in the field of cardiac CT, including deep-learning-based image reconstruction, coronary artery motion correction, automatic calcium scoring, automatic epicardial fat measurement, coronary artery stenosis diagnosis, fractional flow reserve prediction, and prognosis prediction, analyzes current limitations of these techniques and discusses future challenges.
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Affiliation(s)
- Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo Chuo-ku, Kumamoto, 860-8556, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Shohei Fujita
- Departmen of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyoku, Kyoto, 606-8507, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital N15, W5, Kita-Ku, Sapporo 060-8638, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-0016, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15 Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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13
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Fujima N, Kamagata K, Ueda D, Fujita S, Fushimi Y, Yanagawa M, Ito R, Tsuboyama T, Kawamura M, Nakaura T, Yamada A, Nozaki T, Fujioka T, Matsui Y, Hirata K, Tatsugami F, Naganawa S. Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging. Magn Reson Med Sci 2023; 22:401-414. [PMID: 37532584 PMCID: PMC10552661 DOI: 10.2463/mrms.rev.2023-0047] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/09/2023] [Indexed: 08/04/2023] Open
Abstract
Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.
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Affiliation(s)
- Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Osaka, Japan
| | - Shohei Fujita
- Department of Radiology, University of Tokyo, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Kumamoto, Kumamoto, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Okayama, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Hiroshima, Hiroshima, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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14
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Yanagawa M, Ito R, Nozaki T, Fujioka T, Yamada A, Fujita S, Kamagata K, Fushimi Y, Tsuboyama T, Matsui Y, Tatsugami F, Kawamura M, Ueda D, Fujima N, Nakaura T, Hirata K, Naganawa S. New trend in artificial intelligence-based assistive technology for thoracic imaging. LA RADIOLOGIA MEDICA 2023; 128:1236-1249. [PMID: 37639191 PMCID: PMC10547663 DOI: 10.1007/s11547-023-01691-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 07/25/2023] [Indexed: 08/29/2023]
Abstract
Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements in computing power, algorithm development, and the use of big data. In recent years, the application and development of AI technology in the medical field have intensified internationally. There is no doubt that AI will be used in clinical practice to assist in diagnostic imaging in the future. In qualitative diagnosis, it is desirable to develop an explainable AI that at least represents the basis of the diagnostic process. However, it must be kept in mind that AI is a physician-assistant system, and the final decision should be made by the physician while understanding the limitations of AI. The aim of this article is to review the application of AI technology in diagnostic imaging from PubMed database while particularly focusing on diagnostic imaging in thorax such as lesion detection and qualitative diagnosis in order to help radiologists and clinicians to become more familiar with AI in thorax.
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Affiliation(s)
- Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita-City, Osaka, 565-0871, Japan.
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-0016, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-2621, Japan
| | - Shohei Fujita
- Department of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyoku, Kyoto, 606-8507, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita-City, Osaka, 565-0871, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-Machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N15, W5, Kita-ku, Sapporo, 060-8638, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo Chuo-ku, Kumamoto, 860-8556, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15 Nish I 7, Kita-ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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15
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Hagiwara A, Fujita S, Kurokawa R, Andica C, Kamagata K, Aoki S. Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics. Invest Radiol 2023; 58:548-560. [PMID: 36822661 PMCID: PMC10332659 DOI: 10.1097/rli.0000000000000962] [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: 12/01/2022] [Revised: 01/10/2023] [Indexed: 02/25/2023]
Abstract
ABSTRACT With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.
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Affiliation(s)
- Akifumi Hagiwara
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shohei Fujita
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryo Kurokawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Christina Andica
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
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16
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Kiryu S, Akai H, Yasaka K, Tajima T, Kunimatsu A, Yoshioka N, Akahane M, Abe O, Ohtomo K. Clinical Impact of Deep Learning Reconstruction in MRI. Radiographics 2023; 43:e220133. [PMID: 37200221 DOI: 10.1148/rg.220133] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Deep learning has been recognized as a paradigm-shifting tool in radiology. Deep learning reconstruction (DLR) has recently emerged as a technology used in the image reconstruction process of MRI, which is an essential procedure in generating MR images. Denoising, which is the first DLR application to be realized in commercial MRI scanners, improves signal-to-noise ratio. When applied to lower magnetic field-strength scanners, the signal-to-noise ratio can be increased without extending the imaging time, and image quality is comparable to that of higher-field-strength scanners. Shorter imaging times decrease patient discomfort and reduce MRI scanner running costs. The incorporation of DLR into accelerated acquisition imaging techniques, such as parallel imaging or compressed sensing, shortens the reconstruction time. DLR is based on supervised learning using convolutional layers and is divided into the following three categories: image domain, k-space learning, and direct mapping types. Various studies have reported other derivatives of DLR, and several have shown the feasibility of DLR in clinical practice. Although DLR efficiently reduces Gaussian noise from MR images, denoising makes image artifacts more prominent, and a solution to this problem is desired. Depending on the training of the convolutional neural network, DLR may change the imaging features of lesions and obscure small lesions. Therefore, radiologists may need to adopt the habit of questioning whether any information has been lost on images that appear clean. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.
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Affiliation(s)
- Shigeru Kiryu
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
| | - Hiroyuki Akai
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
| | - Koichiro Yasaka
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
| | - Taku Tajima
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
| | - Akira Kunimatsu
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
| | - Naoki Yoshioka
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
| | - Masaaki Akahane
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
| | - Osamu Abe
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
| | - Kuni Ohtomo
- From the Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita 286-0124, Japan (S.K., H.A., K.Y., T.T., A.K., N.Y., M.A.); Department of Radiology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan (H.A.); Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan (K.Y., O.A.); Department of Radiology, International University of Health and Welfare Mita Hospital, Tokyo, Japan (T.T., A.K.); and International University of Health and Welfare, Otawara, Japan (K.O.)
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Yamada A, Kamagata K, Hirata K, Ito R, Nakaura T, Ueda D, Fujita S, Fushimi Y, Fujima N, Matsui Y, Tatsugami F, Nozaki T, Fujioka T, Yanagawa M, Tsuboyama T, Kawamura M, Naganawa S. Clinical applications of artificial intelligence in liver imaging. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01638-1. [PMID: 37165151 DOI: 10.1007/s11547-023-01638-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 04/21/2023] [Indexed: 05/12/2023]
Abstract
This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.
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Affiliation(s)
- Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan.
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo, Japan
| | - Kenji Hirata
- Department of Nuclear Medicine, Hokkaido University Hospital, Sapporo, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Chuo-Ku, Kumamoto, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Abeno-Ku, Osaka, Japan
| | - Shohei Fujita
- Department of Radiology, University of Tokyo, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kita-Ku, Okayama, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Minami-Ku, Hiroshima City, Hiroshima, Japan
| | - Taiki Nozaki
- Department of Radiology, St. Luke's International Hospital, Tokyo, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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Detection of intracranial aneurysms using deep learning-based CAD system: usefulness of the scores of CNN's final layer for distinguishing between aneurysm and infundibular dilatation. Jpn J Radiol 2023; 41:131-141. [PMID: 36173510 PMCID: PMC9889446 DOI: 10.1007/s11604-022-01341-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 09/12/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE We evaluated the diagnostic performance of a clinically available deep learning-based computer-assisted diagnosis software for detecting unruptured aneurysms (UANs) using magnetic resonance angiography and assessed the functionality of the convolutional neural network (CNN) final layer score for distinguishing between UAN and infundibular dilatation (ID). MATERIALS AND METHODS EIRL brain aneurysm (EIRL_BA) was used in this study. The subjects were 117 UAN and/or ID cases including 100 UAN lesions (average sizes of 2.56 ± 1.45 mm) and 40 ID lesions (average sizes of 1.75 ± 0.41 mm) in any of internal carotid artery, middle cerebral artery, and anterior communicating artery, and 123 normal controls. The sensitivity, specificity, and accuracy of EIRL_BA were determined for UAN and ID or UAN only. Furthermore, the relationship between the lesion category and score was examined using a linear regression analysis model, and the receiver operating characteristic (ROC) analysis was used to assess whether the scores represent UAN-like characteristics. RESULTS EIRL_BA showed a total of 203 candidates (an average of 1.73/case) in UAN and/or ID cases and 98 candidates (an average of 0.80/case) in normal controls. For diagnosing either UAN/ID, EIRL_BA showed an overall sensitivity of 80%, specificity of 84.2%, and accuracy of 83.7%, resulting in the positive likelihood ratio of 5.0. For diagnosing UAN only, the overall sensitivity of 89.0, specificity of 82.6%, and accuracy of 83.2% resulting in the positive likelihood ratio of 5.1. In a linear regression analysis, the scores significantly increased in the candidates' first and second ranks in UAN (p < 0.05) but not in ID. An ROC analysis using the score for diagnosing UAN showed an area under the curve of 0.836. CONCLUSION EIRL_BA is applicable for detecting small UAN, and the CNN's final layer scores may be an effective index for discriminating UAN and ID and representing the likelihood of UAN.
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Kojima S. [[MRI] 3. Current Status of AI Image Reconstruction in Clinical MRI Systems]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2023; 79:1200-1209. [PMID: 37866905 DOI: 10.6009/jjrt.2023-2260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Affiliation(s)
- Shinya Kojima
- Department of Medical Radiology, Faculty of Medical Technology, Teikyo University
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Fujioka T, Satoh Y, Imokawa T, Mori M, Yamaga E, Takahashi K, Kubota K, Onishi H, Tateishi U. Proposal to Improve the Image Quality of Short-Acquisition Time-Dedicated Breast Positron Emission Tomography Using the Pix2pix Generative Adversarial Network. Diagnostics (Basel) 2022; 12:diagnostics12123114. [PMID: 36553120 PMCID: PMC9777139 DOI: 10.3390/diagnostics12123114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/26/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
This study aimed to evaluate the ability of the pix2pix generative adversarial network (GAN) to improve the image quality of low-count dedicated breast positron emission tomography (dbPET). Pairs of full- and low-count dbPET images were collected from 49 breasts. An image synthesis model was constructed using pix2pix GAN for each acquisition time with training (3776 pairs from 16 breasts) and validation data (1652 pairs from 7 breasts). Test data included dbPET images synthesized by our model from 26 breasts with short acquisition times. Two breast radiologists visually compared the overall image quality of the original and synthesized images derived from the short-acquisition time data (scores of 1−5). Further quantitative evaluation was performed using a peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In the visual evaluation, both readers revealed an average score of >3 for all images. The quantitative evaluation revealed significantly higher SSIM (p < 0.01) and PSNR (p < 0.01) for 26 s synthetic images and higher PSNR for 52 s images (p < 0.01) than for the original images. Our model improved the quality of low-count time dbPET synthetic images, with a more significant effect on images with lower counts.
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Affiliation(s)
- Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Yoko Satoh
- Yamanashi PET Imaging Clinic, Chuo City 409-3821, Japan
- Department of Radiology, University of Yamanashi, Chuo City 409-3898, Japan
- Correspondence:
| | - Tomoki Imokawa
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Mio Mori
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Emi Yamaga
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Kanae Takahashi
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Kazunori Kubota
- Department of Radiology, Dokkyo Medical University Saitama Medical Center, Koshigaya 343-8555, Japan
| | - Hiroshi Onishi
- Department of Radiology, University of Yamanashi, Chuo City 409-3898, Japan
| | - Ukihide Tateishi
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
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Deep learning reconstruction for the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI: comparison with 3T MRI without deep learning reconstruction. Neuroradiology 2022; 64:2077-2083. [PMID: 35918450 DOI: 10.1007/s00234-022-03024-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 07/23/2022] [Indexed: 10/16/2022]
Abstract
PURPOSE To compare image quality and interobserver agreement in evaluations of neuroforaminal stenosis between 1.5T cervical spine magnetic resonance imaging (MRI) with deep learning reconstruction (DLR) and 3T MRI without DLR. METHODS In this prospective study, 21 volunteers (mean age: 42.4 ± 11.9 years; 17 males) underwent cervical spine T2-weighted sagittal 1.5T and 3T MRI on the same day. The 1.5T and 3T MRI data were used to reconstruct images with (1.5T-DLR) and without (3T-nonDLR) DLR, respectively. Regions of interest were marked on the spinal cord to calculate non-uniformity (NU; standard deviation/signal intensity × 100), as an indicator of image noise. Two blinded radiologists evaluated the images in terms of the depiction of structures, artifacts, noise, overall image quality, and neuroforaminal stenosis. The NU value and the subjective image quality scores were compared between 1.5T-DLR and 3T-nonDLR using the Wilcoxon signed-rank test. Interobserver agreement in evaluations of neuroforaminal stenosis for 1.5T-DLR and 3T-nonDLR was evaluated using Cohen's weighted kappa analysis. RESULTS The NU value for 1.5T-DLR was 8.4, which was significantly better than that for 3T-nonDLR (10.3; p < 0.001). Subjective image scores were significantly better for 1.5T-DLR than 3T-nonDLR images (p < 0.037). Interobserver agreement (95% confidence intervals) in the evaluations of neuroforaminal stenosis was significantly superior for 1.5T-DLR (0.920 [0.916-0.924]) than 3T-nonDLR (0.894 [0.889-0.898]). CONCLUSION By using DLR, image quality and interobserver agreement in evaluations of neuroforaminal stenosis on 1.5T cervical spine MRI could be improved compared to 3T MRI without DLR.
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