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Choi Y, Ko JS, Park JE, Jeong G, Seo M, Jun Y, Fujita S, Bilgic B. Beyond the Conventional Structural MRI: Clinical Application of Deep Learning Image Reconstruction and Synthetic MRI of the Brain. Invest Radiol 2024:00004424-990000000-00248. [PMID: 39159333 DOI: 10.1097/rli.0000000000001114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/21/2024]
Abstract
ABSTRACT Recent technological advancements have revolutionized routine brain magnetic resonance imaging (MRI) sequences, offering enhanced diagnostic capabilities in intracranial disease evaluation. This review explores 2 pivotal breakthrough areas: deep learning reconstruction (DLR) and quantitative MRI techniques beyond conventional structural imaging. DLR using deep neural networks facilitates accelerated imaging with improved signal-to-noise ratio and spatial resolution, enhancing image quality with short scan times. DLR focuses on supervised learning applied to clinical implementation and applications. Quantitative MRI techniques, exemplified by 2D multidynamic multiecho, 3D quantification using interleaved Look-Locker acquisition sequences with T2 preparation pulses, and magnetic resonance fingerprinting, enable precise calculation of brain-tissue parameters and further advance diagnostic accuracy and efficiency. Potential DLR instabilities and quantification and bias limitations will be discussed. This review underscores the synergistic potential of DLR and quantitative MRI, offering prospects for improved brain imaging beyond conventional methods.
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Affiliation(s)
- Yangsean Choi
- From the Department of Radiology and Research Institute of Radiology, Asan Medical Center, Seoul, Republic of Korea (Y.C., J.S.K., J.E.P.); AIRS Medical LLC, Seoul, Republic of Korea (G.J.); Department of Radiology, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea (M.S.); Department of Radiology, Harvard Medical School, Boston, MA (Y.J., S.F., B.B.); Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA (Y.J., S.F., B.B.); and Harvard/MIT Health Sciences and Technology, Cambridge, MA (B.B.)
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Ishimoto Y, Ide S, Watanabe K, Oyu K, Kasai S, Umemura Y, Sasaki M, Nagaya H, Tatsuo S, Nozaki A, Ikushima Y, Wakayama T, Asano K, Saito A, Tomiyama M, Kakeda S. Usefulness of pituitary high-resolution 3D MRI with deep-learning-based reconstruction for perioperative evaluation of pituitary adenomas. Neuroradiology 2024; 66:937-945. [PMID: 38374411 DOI: 10.1007/s00234-024-03315-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 02/12/2024] [Indexed: 02/21/2024]
Abstract
PURPOSE To evaluate the diagnostic value of T1-weighted 3D fast spin-echo sequence (CUBE) with deep learning-based reconstruction (DLR) for depiction of pituitary adenoma and parasellar regions on contrast-enhanced MRI. METHODS We evaluated 24 patients with pituitary adenoma or residual tumor using CUBE with and without DLR, 1-mm slice thickness 2D T1WI (1-mm 2D T1WI) with DLR, and 3D spoiled gradient echo sequence (SPGR) as contrast-enhanced MRI. Depiction scores of pituitary adenoma and parasellar regions were assigned by two neuroradiologists, and contrast-to-noise ratio (CNR) was calculated. RESULTS CUBE with DLR showed significantly higher scores for depicting pituitary adenoma or residual tumor compared to CUBE without DLR, 1-mm 2D T1WI with DLR, and SPGR (p < 0.01). The depiction score for delineation of the boundary between adenoma and the cavernous sinus was higher for CUBE with DLR than for 1-mm 2D T1WI with DLR (p = 0.01), but the difference was not significant when compared to SPGR (p = 0.20). CUBE with DLR had better interobserver agreement for evaluating adenomas than 1-mm 2D T1WI with DLR (Kappa values, 0.75 vs. 0.41). The CNR of the adenoma to the brain parenchyma increased to a ratio of 3.6 (obtained by dividing 13.7, CNR of CUBE with DLR, by 3.8, that without DLR, p < 0.01). CUBE with DLR had a significantly higher CNR than SPGR, but not 1-mm 2D T1WI with DLR. CONCLUSION On the contrast-enhanced MRI, compared to CUBE without DLR, 1-mm 2D T1WI with DLR and SPGR, CUBE with DLR improves the depiction of pituitary adenoma and parasellar regions.
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Affiliation(s)
- Yuka Ishimoto
- Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan
| | - Satoru Ide
- Department of Radiology, University of Occupational and Environmental Health, School of Medicine, Kitakyushu, Japan.
| | - Keita Watanabe
- Open Innovation Institute, Kyoto University, Kyoto, Japan
| | - Kazuhiko Oyu
- Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan
| | - Sera Kasai
- Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan
| | - Yoshihito Umemura
- Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan
| | - Miho Sasaki
- Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan
| | - Haruka Nagaya
- Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan
| | - Soichiro Tatsuo
- Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan
| | | | | | | | - Kenichiro Asano
- Department of Neurosurgery, Hirosaki University School of Medicine, Hirosaki, Aomori, Japan
| | - Atsushi Saito
- Department of Neurosurgery, Hirosaki University School of Medicine, Hirosaki, Aomori, Japan
| | - Masahiko Tomiyama
- Department of Neurology, Hirosaki University School of Medicine, Hirosaki, Aomori, Japan
| | - Shingo Kakeda
- Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan
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Yi J, Hahn S, Lee HJ, Lee Y, Bang JY, Kim Y, Lee J. Thin-slice elbow MRI with deep learning reconstruction: Superior diagnostic performance of elbow ligament pathologies. Eur J Radiol 2024; 175:111471. [PMID: 38636411 DOI: 10.1016/j.ejrad.2024.111471] [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: 02/21/2024] [Revised: 03/31/2024] [Accepted: 04/15/2024] [Indexed: 04/20/2024]
Abstract
PURPOSE With the slice thickness routinely used in elbow MRI, small or subtle lesions may be overlooked or misinterpreted as insignificant. To compare 1 mm slice thickness MRI (1 mm MRI) with deep learning reconstruction (DLR) to 3 mm slice thickness MRI (3 mm MRI) without/with DLR, and 1 mm MRI without DLR regarding image quality and diagnostic performance for elbow tendons and ligaments. METHODS This retrospective study included 53 patients between February 2021 and January 2022, who underwent 3 T elbow MRI, including T2-weighted fat-saturated coronal 3 mm and 1 mm MRI without/with DLR. Two radiologists independently assessed four MRI scans for image quality and artefacts, and identified the pathologies of the five elbow tendons and ligaments. In 19 patients underwent elbow surgery after elbow MRI, diagnostic performance was evaluated using surgical records as a reference standard. RESULTS For both readers, 3 mm MRI with DLR had significant higher image quality scores than 3 mm MRI without DLR and 1 mm MRI with DLR (all P < 0.01). For common extensor tendon and elbow ligament pathologies, 1 mm MRI with DLR showed the highest number of pathologies for both readers. The 1 mm MRI with DLR had the highest kappa values for all tendons and ligaments. For reader 1, 1 mm MRI with DLR showed superior diagnostic performance than 3 mm MRI without/with DLR. For reader 2, 1 mm MRI with DLR showed the highest diagnostic performance; however, there was no significant difference. CONCLUSIONS One mm MRI with DLR showed the highest diagnostic performance for evaluating elbow tendon and ligament pathologies, with similar subjective image qualities and artefacts.
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Affiliation(s)
- Jisook Yi
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, 875 Haeundae-ro, Haeundae-gu, Busan 48108, Republic of Korea
| | - Seok Hahn
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, 875 Haeundae-ro, Haeundae-gu, Busan 48108, Republic of Korea.
| | - Ho-Joon Lee
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, 875 Haeundae-ro, Haeundae-gu, Busan 48108, Republic of Korea
| | - Yedaun Lee
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, 875 Haeundae-ro, Haeundae-gu, Busan 48108, Republic of Korea
| | - Jin-Young Bang
- Department of Orthopaedic Surgery, Inje University College of Medicine, Haeundae Paik Hospital, 875 Haeundae-ro, Haeundae-gu, Busan 48108, Republic of Korea
| | - Youngbok Kim
- Department of Orthopaedic Surgery, Inje University College of Medicine, Haeundae Paik Hospital, 875 Haeundae-ro, Haeundae-gu, Busan 48108, Republic of Korea
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Liu Z, Wen B, Wang Z, Wang K, Xie L, Kang Y, Tao Q, Wang W, Zhang Y, Cheng J, Zhang Y. Deep learning-based reconstruction enhances image quality and improves diagnosis in magnetic resonance imaging of the shoulder joint. Quant Imaging Med Surg 2024; 14:2840-2856. [PMID: 38617178 PMCID: PMC11007508 DOI: 10.21037/qims-23-1412] [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: 10/09/2023] [Accepted: 02/13/2024] [Indexed: 04/16/2024]
Abstract
Background Accelerated magnetic resonance imaging sequences reconstructed using the vendor-provided Recon deep learning algorithm (DL-MRI) were found to be more likely than conventional magnetic resonance imaging (MRI) sequences to detect subacromial (SbA) bursal thickening. However, the extent of this thickening was not extensively explored. This study aimed to compare the image quality of DL-MRI with conventional MRI sequences reconstructed via conventional pipelines (Conventional-MRI) for shoulder examinations and evaluate the effectiveness of DL-MRI in accurately demonstrating the degree of SbA bursal and subcoracoid (SC) bursal thickening. Methods This prospective cross-sectional study enrolled 41 patients with chronic shoulder pain who underwent 3-T MRI (including both Conventional-MRI and accelerated MRI sequences) of the shoulder between December 2022 and April 2023. Each protocol consisted of oblique axial, coronal, and sagittal images, including proton density-weighted imaging (PDWI) with fat suppression (FS) and oblique coronal T1-weighted imaging (T1WI) with FS. The image quality and degree of artifacts were assessed using a 5-point Likert scale for both Conventional-MRI and DL-MRI. Additionally, the degree of SbA and SC bursal thickening, as well as the relative signal-to-noise ratio (rSNR) and relative contrast-to-noise ratio (rCNR) were analyzed using the paired Wilcoxon test. Statistical significance was defined as P<0.05. Results The utilization of accelerated sequences resulted in a remarkable 54.7% reduction in total scan time. Overall, DL-MRI exhibited superior image quality scores and fewer artifacts compared to Conventional-MRI. Specifically, DL-MRI demonstrated significantly higher measurements of SC bursal thickenings in the oblique sagittal PDWI sequence compared to Conventional-MRI [3.92 (2.83, 5.82) vs. 3.74 (2.92, 5.96) mm, P=0.028]. Moreover, DL-MRI exhibited higher detection of SbA bursal thickenings in the oblique coronal PDWI sequence [2.61 (1.85, 3.46) vs. 2.48 (1.84, 3.25) mm], with a notable trend towards significant differences (P=0.071). Furthermore, the rSNRs of the muscle in DL-MRI images were significantly higher than those in Conventional-MRI images across most sequences (P<0.001). However, the rSNRs of bone on Conventional-MRI of oblique axial and oblique coronal PDWI sequences showed adverse results [oblique axial: 1.000 (1.000, 1.000) vs. 0.444 (0.367, 0.523); and oblique coronal: 1.000 (1.000, 1.000) vs. 0.460 (0.387, 0.631); all P<0.001]. Additionally, all DL-MRI images exhibited significantly greater rSNRs and rCNRs compared to accelerated MRI sequences reconstructed using traditional pipelines (P<0.001). Conclusions In conclusion, the utilization of DL-MRI enhances image quality and improves diagnostic capabilities, making it a promising alternative to Conventional-MRI for shoulder imaging.
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Affiliation(s)
- Zijun Liu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Baohong Wen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ziyu Wang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kaiyu Wang
- MR Research China, GE Healthcare, Beijing, China
| | - Lizhi Xie
- MR Research China, GE Healthcare, Beijing, China
| | - Yimeng Kang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qiuying Tao
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yan Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Iwamura M, Ide S, Sato K, Kakuta A, Tatsuo S, Nozaki A, Wakayama T, Ueno T, Haga R, Kakizaki M, Yokoyama Y, Yamauchi R, Tsushima F, Shibutani K, Tomiyama M, Kakeda S. Thin-slice Two-dimensional T2-weighted Imaging with Deep Learning-based Reconstruction: Improved Lesion Detection in the Brain of Patients with Multiple Sclerosis. Magn Reson Med Sci 2024; 23:184-192. [PMID: 36927877 PMCID: PMC11024714 DOI: 10.2463/mrms.mp.2022-0112] [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: 09/08/2022] [Accepted: 02/10/2023] [Indexed: 03/18/2023] Open
Abstract
PURPOSE Brain MRI with high spatial resolution allows for a more detailed delineation of multiple sclerosis (MS) lesions. The recently developed deep learning-based reconstruction (DLR) technique enables image denoising with sharp edges and reduced artifacts, which improves the image quality of thin-slice 2D MRI. We, therefore, assessed the diagnostic value of 1 mm-slice-thickness 2D T2-weighted imaging (T2WI) with DLR (1 mm T2WI with DLR) compared with conventional MRI for identifying MS lesions. METHODS Conventional MRI (5 mm T2WI, 2D and 3D fluid-attenuated inversion recovery) and 1 mm T2WI with DLR (imaging time: 7 minutes) were performed in 42 MS patients. For lesion detection, two neuroradiologists counted the MS lesions in two reading sessions (conventional MRI interpretation with 5 mm T2WI and MRI interpretations with 1 mm T2WI with DLR). The numbers of lesions per region category (cerebral hemisphere, basal ganglia, brain stem, cerebellar hemisphere) were then compared between the two reading sessions. RESULTS For the detection of MS lesions by 2 neuroradiologists, the total number of detected MS lesions was significantly higher for MRI interpretation with 1 mm T2WI with DLR than for conventional MRI interpretation with 5 mm T2WI (765 lesions vs. 870 lesions at radiologist A, < 0.05). In particular, of the 33 lesions in the brain stem, radiologist A detected 21 (63.6%) additional lesions by 1 mm T2WI with DLR. CONCLUSION Using the DLR technique, whole-brain 1 mm T2WI can be performed in about 7 minutes, which is feasible for routine clinical practice. MRI with 1 mm T2WI with DLR enabled increased MS lesion detection, particularly in the brain stem.
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Affiliation(s)
- Masatoshi Iwamura
- Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan
- Department of Radiology, Aomori Prefectural Central Hospital, Aomori, Aomori, Japan
| | - Satoru Ide
- Department of Radiology, University of Occupational and Environmental Health, School of Medicine, Kitakyushu, Fukuoka, Japan
| | - Kenya Sato
- Department of Radiology, Aomori Prefectural Central Hospital, Aomori, Aomori, Japan
| | - Akihisa Kakuta
- Department of Radiology, Aomori Prefectural Central Hospital, Aomori, Aomori, Japan
| | - Soichiro Tatsuo
- Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan
| | - Atsushi Nozaki
- MR Application and Workflow, GE Healthcare, Tokyo, Japan
| | | | - Tatsuya Ueno
- Department of Neurology, Aomori Prefectural Central Hospital, Aomori, Aomori, Japan
| | - Rie Haga
- Department of Neurology, Aomori Prefectural Central Hospital, Aomori, Aomori, Japan
| | - Misako Kakizaki
- Department of Radiology, Aomori Prefectural Central Hospital, Aomori, Aomori, Japan
| | - Yoko Yokoyama
- Department of Radiology, Aomori Prefectural Central Hospital, Aomori, Aomori, Japan
| | - Ryoichi Yamauchi
- Department of Radiology, Aomori Prefectural Central Hospital, Aomori, Aomori, Japan
| | - Fumiyasu Tsushima
- Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan
| | - Koichi Shibutani
- Department of Radiology, Aomori Prefectural Central Hospital, Aomori, Aomori, Japan
| | - Masahiko Tomiyama
- Department of Neurology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan
| | - Shingo Kakeda
- Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan
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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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Li H, Liu Z, Li F, Shi F, Xia Y, Zhou Q, Zeng Q. Preoperatively Predicting Ki67 Expression in Pituitary Adenomas Using Deep Segmentation Network and Radiomics Analysis Based on Multiparameter MRI. Acad Radiol 2024; 31:617-627. [PMID: 37330356 DOI: 10.1016/j.acra.2023.05.023] [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: 04/01/2023] [Revised: 05/19/2023] [Accepted: 05/21/2023] [Indexed: 06/19/2023]
Abstract
RATIONALE AND OBJECTIVES Ki67 proliferation index is associated with more aggressive tumor behavior and recurrence of pituitary adenomas (PAs). Recently, radiomics and deep learning have been introduced into the study of pituitary tumors. The present study aimed to investigate the feasibility of predicting the Ki67 proliferation index of PAs using the deep segmentation network and radiomics analysis based on multiparameter MRI. MATERIALS AND METHODS First, the cfVB-Net autosegmentation model was trained; subsequently, its performance was evaluated in terms of the dice similarity coefficient (DSC). In the present study, 1214 patients were classified into the high Ki67 expression group (HG) and the low Ki67 expression group (LG). Analyses of three classification models based on radiomics features were performed to distinguish HG from LG. Clinical factors, imaging features, and Radscores were collectively used to create a nomogram in order to effectively predict Ki67 expression. RESULTS The cfVB-Net segmentation model demonstrated good performance (DSC: 0.723-0.930). Overall, 18, 15, and 11 optimal features in contrast-enhanced (CE) T1WI, T1WI, and T2WI were obtained for differentiating between HG and LG, respectively. Notably, the best results were presented in the bagging decision tree when CE T1WI and T1WI were combined (area under the receiver operating characteristic curve: training set, 0.927; validation set, 0.831; and independent testing set, 0.825). In the nomogram, age, Hardy' grade, and Radscores were identified as risk predictors of high Ki67 expression. CONCLUSION The deep segmentation network and radiomics analysis based on multiparameter MRI exhibited good performance and clinical application value in predicting the expression of Ki67 in PAs.
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Affiliation(s)
- Hongxia Li
- Department of Radiology, The Second Hospital of Shandong University, Jinan 250033, China (H.L.)
| | - Zhiling Liu
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan 250098, China (Z.L.)
| | - Fuyan Li
- Department of Radiology, Shandong Medical Imaging Research Institute, Jinan 250021, China (F.L.)
| | - Feng Shi
- Shanghai United Imaging Intelligence, Co., Ltd., 701 Yunjin Road, Xuhui District, Shanghai 200030, China (F.S., Y.X., Q.Z.)
| | - Yuwei Xia
- Shanghai United Imaging Intelligence, Co., Ltd., 701 Yunjin Road, Xuhui District, Shanghai 200030, China (F.S., Y.X., Q.Z.)
| | - Qing Zhou
- Shanghai United Imaging Intelligence, Co., Ltd., 701 Yunjin Road, Xuhui District, Shanghai 200030, China (F.S., Y.X., Q.Z.)
| | - Qingshi Zeng
- Department of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, No.16766 Jingshi Road, Jinan 250013, China (Q.Z.).
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Kang H, Noh D, Lee SK, Choi S, Lee K. Deep learning-based reconstruction can improve canine thoracolumbar magnetic resonance image quality and reduce slice thickness. Vet Radiol Ultrasound 2023; 64:1063-1070. [PMID: 37667979 DOI: 10.1111/vru.13298] [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: 02/24/2023] [Revised: 08/14/2023] [Accepted: 08/15/2023] [Indexed: 09/06/2023] Open
Abstract
In veterinary practice, thin-sliced thoracolumbar MRI is useful in detecting small lesions, especially in small-breed dogs. However, it is challenging due to the partial volume averaging effect and increase in scan time. Currently, deep learning-based reconstruction (DLR), a part of artificial intelligence, has been applied in diagnostic imaging. We hypothesized that the diagnostic performance of thin-slice thoracolumbar MRI with DLR would be superior to conventional MRI. This prospective, method comparison study aimed to determine the adequate slice thickness of a deep learning model for thin-slice thoracolumbar MRI. Sagittal and transverse T2-weighted MRI at the thoracolumbar region were performed on 12 clinically healthy beagle dogs; the images obtained were categorized into five groups according to slice thickness: conventional thickness of 3 mm (3 CON) and thicknesses of 3, 2, 1.5, and 1 mm with DLR (3 DLR, 2 DLR, 1.5 DLR, and 1 DLR, respectively). Quantitative analysis was performed using signal-to-noise ratio (SNR) and contrast-to-noise ratio. Qualitative analysis involved the evaluation of perceived SNR, structural visibility, and overall image quality using a four-point scale. Moreover, nerve root visibility was evaluated using transverse images. Quantitative and qualitative values were compared among the five groups. Compared with the 3 CON group, the 3 DLR, 2 DLR, and 1.5 DLR groups exhibited significantly higher quantitative and qualitative values. Nerve root visibility was significantly higher in 2 DLR, 1.5 DLR, and 1 DLR images than in 3 DLR and 3 CON images. Compared with conventional MRI, DLR reduced the slice thickness by up to one-half and improved image quality in this sample of clinically healthy beagles.
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Affiliation(s)
- Hyesun Kang
- College of Veterinary Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Daji Noh
- College of Veterinary Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Sang-Kwon Lee
- College of Veterinary Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Sooyoung Choi
- College of Veterinary Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Kija Lee
- College of Veterinary Medicine, Kyungpook National University, Daegu, Republic of Korea
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Choi H, Lee SK, Choi H, Lee Y, Lee K. Deep learning-based reconstruction for canine brain magnetic resonance imaging could improve image quality while reducing scan time. Vet Radiol Ultrasound 2023; 64:873-880. [PMID: 37582510 DOI: 10.1111/vru.13279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/26/2023] [Accepted: 06/15/2023] [Indexed: 08/17/2023] Open
Abstract
Optimal magnetic resonance imaging (MRI) quality and shorter scan time are challenging to achieve in veterinary practices. Recently, deep learning-based reconstruction (DLR) has been proposed for ideal image quality. We hypothesized that DLR-based MRI will improve brain imaging quality and reduce scan time. This prospective, methods comparison study compared the MR image denoising performances of DLR and conventional methods, with the aim of reducing scan time and improving canine brain image quality. Transverse T2-weighted and fluid-attenuated inversion recovery (FLAIR) sequences of the brain were performed in 12 clinically healthy beagle dogs. Different numbers of excitations (NEX) were used to obtain the image groups NEX4, NEX2, and NEX1. DLR was applied to NEX2 and NEX1 to obtain NEX2DL and NEX1DL . The scan times were recorded, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated for quantitative analysis. Five blinded veterinarians assessed the overall quality, contrast, and perceived SNR on four-point Likert scales. Quantitative and qualitative values were compared among the five groups. Compared with NEX4, NEX2 and NEX1 reduced scan time by 50% and 75%, respectively. The mean SNR and CNR of NEX2DL and NEX1DL were significantly superior to those of NEX4, NEX2, and NEX1 (P < 0.05). In all image quality indices, DLR-applied images for both T2-weighted and FLAIR images were significantly higher than NEX4 and NEX2DL had significantly better quality than NEX1DL for FLAIR (P < 0.05). Findings indicated that DLR reduced scan time and improved image quality compared with conventional MRI images in a sample of clinically healthy beagles.
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Affiliation(s)
- Hyejoon Choi
- College of Veterinary Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Sang-Kwon Lee
- College of Veterinary Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Hojung Choi
- College of Veterinary Medicine, College of Veterinary Medicine, Chungnam National University, Daejeon, Republic of Korea
| | - Youngwon Lee
- College of Veterinary Medicine, College of Veterinary Medicine, Chungnam National University, Daejeon, Republic of Korea
| | - Kija Lee
- College of Veterinary Medicine, Kyungpook National University, Daegu, Republic of Korea
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Koechli C, Zwahlen DR, Schucht P, Windisch P. Radiomics and machine learning for predicting the consistency of benign tumors of the central nervous system: A systematic review. Eur J Radiol 2023; 164:110866. [PMID: 37207398 DOI: 10.1016/j.ejrad.2023.110866] [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: 03/14/2023] [Revised: 04/28/2023] [Accepted: 05/03/2023] [Indexed: 05/21/2023]
Abstract
PURPOSE Predicting the consistency of benign central nervous system (CNS) tumors prior to surgery helps to improve surgical outcomes. This review summarizes and analyzes the literature on using radiomics and/or machine learning (ML) for consistency prediction. METHOD The Medical Literature Analysis and Retrieval System Online (MEDLINE) database was screened for studies published in English from January 1st 2000. Data was extracted according to the PRISMA guidelines and quality of the studies was assessed in compliance with the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). RESULTS Eight publications were included focusing on pituitary macroadenomas (n = 5), pituitary adenomas (n = 1), and meningiomas (n = 2) using a retrospective (n = 6), prospective (n = 1), and unknown (n = 1) study design with a total of 763 patients for the consistency prediction. The studies reported an area under the curve (AUC) of 0.71-0.99 for their respective best performing model regarding the consistency prediction. Of all studies, four articles validated their models internally whereas none validated their models externally. Two articles stated making data available on request with the remaining publications lacking information with regard to data availability. CONCLUSIONS The research on consistency prediction of CNS tumors is still at an early stage regarding the use of radiomics and different ML techniques. Best-practice procedures regarding radiomics and ML need to be followed more rigorously to facilitate the comparison between publications and, accordingly, the possible implementation into clinical practice in the future.
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Affiliation(s)
- Carole Koechli
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland; Universitätsklinik für Neurochirurgie, Bern University Hospital, 3010 Bern, Switzerland.
| | - Daniel R Zwahlen
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland
| | - Philippe Schucht
- Universitätsklinik für Neurochirurgie, Bern University Hospital, 3010 Bern, Switzerland
| | - Paul Windisch
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland
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