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Shen L, Xu H, Liao Q, Yuan Y, Yu D, Wei J, Yang Z, Wang L. A Feasibility Study of AI-Assisted Compressed Sensing in Prostate T2-Weighted Imaging. Acad Radiol 2024:S1076-6332(24)00434-3. [PMID: 39068095 DOI: 10.1016/j.acra.2024.06.048] [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: 04/21/2024] [Revised: 06/15/2024] [Accepted: 06/28/2024] [Indexed: 07/30/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate the image quality and PI-RADS scoring performance of prostate T2-weighted imaging (T2WI) based on AI-assisted compressed sensing (ACS). MATERIALS AND METHODS In this prospective study, adult male urological outpatients or inpatients underwent prostate MRI, including T2WI, diffusion-weighted imaging and apparent diffusion coefficient maps. Three accelerated scanning protocols using parallel imaging (PI) and ACS: T2WIPI, T2WIACS1 and T2WIACS2 were evaluated through comparative analysis. Quantitative analysis included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), slope profile, and edge rise distance (ERD). Image quality was qualitatively assessed using a five-point Likert scale (ranging from 1 = non-diagnostic to 5 = excellent). PI-RADS scores were determined for the largest or most suspicious lesions in each patient. The Friedman test and one-way ANOVA with post hoc tests were utilized for group comparisons, with statistical significance set at P < 0.05. RESULTS This study included 40 participants. Compared to PI, ACS reduced acquisition time by over 50%, significantly enhancing the CNR of sagittal and axial T2WI (P < 0.05), significantly improving the image quality of sagittal and axial T2WI (P < 0.05). No significant differences were observed in slope profile, ERD, and PI-RADS scores between groups (P > 0.05). CONCLUSION ACS reduced prostate T2WI acquisition time by half while improving image quality without affecting PI-RADS scores.
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Affiliation(s)
- Liting Shen
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China (L.S., H.X., Q.L., Y.Y., Z.Y., L.W.)
| | - Hui Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China (L.S., H.X., Q.L., Y.Y., Z.Y., L.W.)
| | - Qian Liao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China (L.S., H.X., Q.L., Y.Y., Z.Y., L.W.)
| | - Ying Yuan
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China (L.S., H.X., Q.L., Y.Y., Z.Y., L.W.)
| | - Dan Yu
- United Imaging Research Institute of Intelligent Imaging, Beijing 100050, China (D.Y.)
| | - Jie Wei
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200000, China (J.W.)
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China (L.S., H.X., Q.L., Y.Y., Z.Y., L.W.)
| | - Liang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China (L.S., H.X., Q.L., Y.Y., Z.Y., L.W.).
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Shimada R, Sofue K, Ueno Y, Wakayama T, Yamaguchi T, Ueshima E, Kusaka A, Hori M, Murakami T. Utility of Thin-slice Fat-suppressed Single-shot T2-weighted MR Imaging with Deep Learning Image Reconstruction as a Protocol for Evaluating the Pancreas. Magn Reson Med Sci 2024:mp.2024-0017. [PMID: 38910138 DOI: 10.2463/mrms.mp.2024-0017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024] Open
Abstract
PURPOSE To compare the utility of thin-slice fat-suppressed single-shot T2-weighted imaging (T2WI) with deep learning image reconstruction (DLIR) and conventional fast spin-echo T2WI with DLIR for evaluating pancreatic protocol. METHODS This retrospective study included 42 patients (mean age, 70.2 years) with pancreatic cancer who underwent gadoxetic acid-enhanced MRI. Three fat-suppressed T2WI, including conventional fast-spin echo with 6 mm thickness (FSE 6 mm), single-shot fast-spin echo with 6 mm and 3 mm thickness (SSFSE 6 mm and SSFSE 3 mm), were acquired for each patient. For quantitative analysis, the SNRs of the upper abdominal organs were calculated between images with and without DLIR. The pancreas-to-lesion contrast on DLIR images was also calculated. For qualitative analysis, two abdominal radiologists independently scored the image quality on a 5-point scale in the FSE 6 mm, SSFSE 6 mm, and SSFSE 3 mm with DLIR. RESULTS The SNRs significantly improved among the three T2-weighted images with DLIR compared to those without DLIR in all patients (P < 0.001). The pancreas-to-lesion contrast of SSFSE 3 mm was higher than those of the FSE 6 mm (P < 0.001) and tended to be higher than SSFSE 6 mm (P = 0.07). SSFSE 3 mm had the highest image qualities regarding pancreas edge sharpness, pancreatic duct clarity, and overall image quality, followed by SSFSE 6 mm and FSE 6 mm (P < 0.0001). CONCLUSION SSFSE 3 mm with DLIR demonstrated significant improvements in SNRs of the pancreas, pancreas-to-lesion contrast, and image quality more efficiently than did SSFSE 6 mm and FSE 6 mm. Thin-slice fat-suppressed single-shot T2WI with DLIR can be easily implemented for pancreatic MR protocol.
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Affiliation(s)
- Ryuji Shimada
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
- Center for Radiology and Radiation Oncology, Kobe University Hospital, Kobe, Hyogo, Japan
| | - Keitaro Sofue
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Yoshiko Ueno
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Tetsuya Wakayama
- MR Collaborations and Development, GE Healthcare, Hino, Tokyo, Japan
| | - Takeru Yamaguchi
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Eisuke Ueshima
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Akiko Kusaka
- Center for Radiology and Radiation Oncology, Kobe University Hospital, Kobe, Hyogo, Japan
| | - Masatoshi Hori
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Takamichi Murakami
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
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Liu K, Sun H, Wang X, Wen X, Yang J, Zhang X, Chen C, Zeng M. Feasibility of the application of deep learning-reconstructed ultra-fast respiratory-triggered T2-weighted imaging at 3 T in liver imaging. Magn Reson Imaging 2024; 109:27-33. [PMID: 38438094 DOI: 10.1016/j.mri.2024.03.001] [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: 01/29/2024] [Revised: 02/25/2024] [Accepted: 03/01/2024] [Indexed: 03/06/2024]
Abstract
OBJECTIVE The evaluate the feasibility of a novel deep learning-reconstructed ultra-fast respiratory-triggered T2WI sequence (DL-RT-T2WI) In liver imaging, compared with respiratory-triggered Arms-T2WI (Arms-RT-T2WI) and respiratory-triggered FSE-T2WI (FSE-RT-T2WI) sequences. METHODS 71 patients with liver lesions underwent 3-T MRI and were prospectively enrolled. Two readers independently analyzed images acquired with DL-RT-T2WI, Arms-RT-T2WI, and FSE-RT-T2WI. The qualitative evaluation indicators, including overall image quality (OIQ), sharpness, noise, artifacts, lesion detectability (LC), lesion characterization (LD), cardiacmotion-related signal loss (CSL), and diagnostic confidence (DC), were evaluated in two readers, and further statistically compared using paired Wilcoxon rank-sum test among three sequences. RESULTS 176 lesions were detected in DL-RT-T2W and Arms-RT-T2WI, and 175 were detected in FSE-RT-T2WI. The acquisition time of DL-RT-T2WI was improved by 4.8-7.9 folds compared to the other two sequences. The OIQ was scored highest for DL-RT-T2WI (R1, 4.61 ± 0.52 and R2, 4.62 ± 0.49), was significantly superior to Arms-RT-T2WI (R1, 4.30 ± 0.66 and R2, 4.34 ± 0.69) and FSE-RT-T2WI (R1, 3.65 ± 1.08 and R2, 3.75 ± 1.01). Artifacts and sharpness scored highest for DL-RT-T2WI, followed by Arms-RT-T2WI, and were lowest for FSE-RT-T2WI in both two readers. Noise and CSL for DL-RT-T2WI scored similar to Arms-RT-T2WI (P > 0.05) and were significantly superior to FSE-RT-T2WI (P < 0.001). Both LD and LC for DL-RT-T2WI were significantly superior to Arms-RT-T2WI and FSE-RT-T2WI in two readers (P < 0.001). DC for DL-RT-T2WI scored best, significantly superior to Arms-RT-T2WI (P < 0.010) and FSE-RT-T2WI (P < 0.001). CONCLUSIONS The novel ultra-fast DL-RT-T2WI is feasible for liver imaging and lesion characterization and diagnosis, not only offers a significant improvement in acquisition time but also outperforms Arms-RT-T2WI and FSE-RT-T2WI concerning image quality and DC.
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Affiliation(s)
- Kai Liu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai 200032, China
| | - Haitao Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai 200032, China
| | - Xingxing Wang
- Department of Pathology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai 200032, China
| | - Xixi Wen
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201807, China
| | - Jun Yang
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201807, China
| | - Xingjian Zhang
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201807, China
| | - Caizhong Chen
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai 200032, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai 200032, China.
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Mio M, Tabata N, Toyofuku T, Nakamura H. [Reduction of Motion Artifacts in Liver MRI Using Deep Learning with High-pass Filtering]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2024; 80:510-518. [PMID: 38462509 DOI: 10.6009/jjrt.2024-1408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
PURPOSE To investigate whether deep learning with high-pass filtering can be used to effectively reduce motion artifacts in magnetic resonance (MR) images of the liver. METHODS The subjects were 69 patients who underwent liver MR examination at our hospital. Simulated motion artifact images (SMAIs) were created from non-artifact images (NAIs) and used for deep learning. Structural similarity index measure (SSIM) and contrast ratio (CR) were used to verify the effect of reducing motion artifacts in motion artifact reduction image (MARI) output from the obtained deep learning model. In the visual assessment, reduction of motion artifacts and image sharpness were evaluated between motion artifact images (MAIs) and MARIs. RESULTS The SSIM values were 0.882 on the MARIs and 0.869 on the SMAIs. There was no statistically significant difference in CR between NAIs and MARIs. The visual assessment showed that MARIs had reduced motion artifacts and improved sharpness compared to MAIs. CONCLUSION The learning model in this study is indicated to be reduced motion artifacts without decreasing the sharpness of liver MR images.
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Affiliation(s)
- Motohira Mio
- Department of Radiology, Fukuoka University Chikushi Hospital
| | - Nariaki Tabata
- Department of Radiology, Fukuoka University Chikushi Hospital
| | - Tatsuo Toyofuku
- Department of Radiology, Fukuoka University Chikushi Hospital
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Ni M, He M, Yang Y, Wen X, Zhao Y, Gao L, Yan R, Xu J, Zhang Y, Chen W, Jiang C, Li Y, Zhao Q, Wu P, Li C, Qu J, Yuan H. Application research of AI-assisted compressed sensing technology in MRI scanning of the knee joint: 3D-MRI perspective. Eur Radiol 2024; 34:3046-3058. [PMID: 37932390 DOI: 10.1007/s00330-023-10368-x] [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: 07/13/2023] [Revised: 08/29/2023] [Accepted: 09/04/2023] [Indexed: 11/08/2023]
Abstract
OBJECTIVE To investigate the potential applicability of AI-assisted compressed sensing (ACS) in knee MRI to enhance and optimize the scanning process. METHODS Volunteers and patients with sports-related injuries underwent prospective MRI scans with a range of acceleration techniques. The volunteers were subjected to varied ACS acceleration levels to ascertain the most effective level. Patients underwent scans at the determined optimal 3D-ACS acceleration level, and 3D compressed sensing (CS) and 2D parallel acquisition technology (PAT) scans were performed. The resultant 3D-ACS images underwent 3.5 mm/2.0 mm multiplanar reconstruction (MPR). Experienced radiologists evaluated and compared the quality of images obtained by 3D-ACS-MRI and 3D-CS-MRI, 3.5 mm/2.0 mm MPR and 2D-PAT-MRI, diagnosed diseases, and compared the results with the arthroscopic findings. The diagnostic agreement was evaluated using Cohen's kappa correlation coefficient, and both absolute and relative evaluation methods were utilized for objective assessment. RESULTS The study involved 15 volunteers and 53 patients. An acceleration factor of 10.69 × was identified as optimal. The quality evaluation showed that 3D-ACS provided poorer bone structure visualization, and improved cartilage visualization and less satisfactory axial images with 3.5 mm/2.0 mm MPR than 2D-PAT. In terms of objective evaluation, the relative evaluation yielded satisfactory results across different groups, while the absolute evaluation revealed significant variances in most features. Nevertheless, high levels of diagnostic agreement (κ: 0.81-0.94) and accuracy (0.83-0.98) were observed across all diagnoses. CONCLUSION ACS technology presents significant potential as a replacement for traditional CS in 3D-MRI knee scans, allowing thinner MPRs and markedly faster scans without sacrificing diagnostic accuracy. CLINICAL RELEVANCE STATEMENT 3D-ACS-MRI of the knee can be completed in the 160 s with good diagnostic consistency and image quality. 3D-MRI-MPR can replace 2D-MRI and reconstruct images with thinner slices, which helps to optimize the current MRI examination process and shorten scanning time. KEY POINTS • AI-assisted compressed sensing technology can reduce knee MRI scan time by over 50%. • 3D AI-assisted compressed sensing MRI and related multiplanar reconstruction can replace traditional accelerated MRI and yield thinner 2D multiplanar reconstructions. • Successful application of 3D AI-assisted compressed sensing MRI can help optimize the current knee MRI process.
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Affiliation(s)
- Ming Ni
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Miao He
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People's Republic of China
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, People's Republic of China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, People's Republic of China
| | - Yuxin Yang
- United Imaging Research Institute of Intelligent Imaging, Beijing, People's Republic of China
| | - Xiaoyi Wen
- Institute of Statistics and Big Data, Renmin University of China, Beijing, People's Republic of China
| | - Yuqing Zhao
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Lixiang Gao
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Ruixin Yan
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Jiajia Xu
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Yarui Zhang
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Wen Chen
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Chenyu Jiang
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Yali Li
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Qiang Zhao
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Peng Wu
- United Imaging Healthcare Co, Shanghai, People's Republic of China
| | - Chunlin Li
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People's Republic of China
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, People's Republic of China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, People's Republic of China
| | - Junda Qu
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People's Republic of China.
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, People's Republic of China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, People's Republic of China.
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China.
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Ikeda H, Ohno Y, Yamamoto K, Murayama K, Ikedo M, Yui M, Kumazawa Y, Shimamura Y, Takagi Y, Nakagaki Y, Hanamatsu S, Obama Y, Ueda T, Nagata H, Ozawa Y, Iwase A, Toyama H. Deep Learning Reconstruction for DWIs by EPI and FASE Sequences for Head and Neck Tumors. Cancers (Basel) 2024; 16:1714. [PMID: 38730665 PMCID: PMC11083776 DOI: 10.3390/cancers16091714] [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: 04/09/2024] [Revised: 04/25/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND Diffusion-weighted images (DWI) obtained by echo-planar imaging (EPI) are frequently degraded by susceptibility artifacts. It has been suggested that DWI obtained by fast advanced spin-echo (FASE) or reconstructed with deep learning reconstruction (DLR) could be useful for image quality improvements. The purpose of this investigation using in vitro and in vivo studies was to determine the influence of sequence difference and of DLR for DWI on image quality, apparent diffusion coefficient (ADC) evaluation, and differentiation of malignant from benign head and neck tumors. METHODS For the in vitro study, a DWI phantom was scanned by FASE and EPI sequences and reconstructed with and without DLR. Each ADC within the phantom for each DWI was then assessed and correlated for each measured ADC and standard value by Spearman's rank correlation analysis. For the in vivo study, DWIs obtained by EPI and FASE sequences were also obtained for head and neck tumor patients. Signal-to-noise ratio (SNR) and ADC were then determined based on ROI measurements, while SNR of tumors and ADC were compared between all DWI data sets by means of Tukey's Honest Significant Difference test. RESULTS For the in vitro study, all correlations between measured ADC and standard reference were significant and excellent (0.92 ≤ ρ ≤ 0.99, p < 0.0001). For the in vivo study, the SNR of FASE with DLR was significantly higher than that of FASE without DLR (p = 0.02), while ADC values for benign and malignant tumors showed significant differences between each sequence with and without DLR (p < 0.05). CONCLUSION In comparison with EPI sequence, FASE sequence and DLR can improve image quality and distortion of DWIs without significantly influencing ADC measurements or differentiation capability of malignant from benign head and neck tumors.
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Affiliation(s)
- Hirotaka Ikeda
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yoshiharu Ohno
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Kaori Yamamoto
- Canon Medical Systems Corporation, Otawara 324-8550, Tochigi, Japan
| | - Kazuhiro Murayama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Masato Ikedo
- Canon Medical Systems Corporation, Otawara 324-8550, Tochigi, Japan
| | - Masao Yui
- Canon Medical Systems Corporation, Otawara 324-8550, Tochigi, Japan
| | - Yunosuke Kumazawa
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yurika Shimamura
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yui Takagi
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yuhei Nakagaki
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Satomu Hanamatsu
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yuki Obama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Takahiro Ueda
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Hiroyuki Nagata
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yoshiyuki Ozawa
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Akiyoshi Iwase
- Department of Radiology, Fujita Health University Hospital, Toyoake 470-1192, Aichi, Japan
| | - Hiroshi Toyama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
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Wu D, Kang L, Li H, Ba R, Cao Z, Liu Q, Tan Y, Zhang Q, Li B, Yuan J. Developing an AI-empowered head-only ultra-high-performance gradient MRI system for high spatiotemporal neuroimaging. Neuroimage 2024; 290:120553. [PMID: 38403092 DOI: 10.1016/j.neuroimage.2024.120553] [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: 07/03/2023] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 02/27/2024] Open
Abstract
Recent advances in neuroscience requires high-resolution MRI to decipher the structural and functional details of the brain. Developing a high-performance gradient system is an ongoing effort in the field to facilitate high spatial and temporal encoding. Here, we proposed a head-only gradient system NeuroFrontier, dedicated for neuroimaging with an ultra-high gradient strength of 650 mT/m and 600 T/m/s. The proposed system features in 1) ultra-high power of 7MW achieved by running two gradient power amplifiers using a novel paralleling method; 2) a force/torque balanced gradient coil design with a two-step mechanical structure that allows high-efficiency and flexible optimization of the peripheral nerve stimulation; 3) a high-density integrated RF system that is miniaturized and customized for the head-only system; 4) an AI-empowered compressed sensing technique that enables ultra-fast acquisition of high-resolution images and AI-based acceleration in q-t space for diffusion MRI (dMRI); and 5) a prospective head motion correction technique that effectively corrects motion artifacts in real-time with 3D optical tracking. We demonstrated the potential advantages of the proposed system in imaging resolution, speed, and signal-to-noise ratio for 3D structural MRI (sMRI), functional MRI (fMRI) and dMRI in neuroscience applications of submillimeter layer-specific fMRI and dMRI. We also illustrated the unique strength of this system for dMRI-based microstructural mapping, e.g., enhanced lesion contrast at short diffusion-times or high b-values, and improved estimation accuracy for cellular microstructures using diffusion-time-dependent dMRI or for neurite microstructures using q-space approaches.
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Affiliation(s)
- Dan Wu
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China; Innovation Center for Smart Medical Technologies & Devices, Binjiang Institute of Zhejiang University, Hangzhou, China.
| | - Liyi Kang
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China; Innovation Center for Smart Medical Technologies & Devices, Binjiang Institute of Zhejiang University, Hangzhou, China
| | - Haotian Li
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Ruicheng Ba
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Zuozhen Cao
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Qian Liu
- United Imaging Healthcare Co., Ltd, Shanghai, China
| | - Yingchao Tan
- United Imaging Healthcare Co., Ltd, Shanghai, China
| | - Qinwei Zhang
- Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Bo Li
- United Imaging Healthcare Co., Ltd, Shanghai, China
| | - Jianmin Yuan
- United Imaging Healthcare Co., Ltd, Shanghai, China
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Wang M, Ma Y, Li L, Pan X, Wen Y, Qiu Y, Guo D, Zhu Y, Lian J, Tong D. Compressed Sensitivity Encoding Artificial Intelligence Accelerates Brain Metastasis Imaging by Optimizing Image Quality and Reducing Scan Time. AJNR Am J Neuroradiol 2024; 45:444-452. [PMID: 38485196 PMCID: PMC11288577 DOI: 10.3174/ajnr.a8161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 12/25/2023] [Indexed: 04/10/2024]
Abstract
BACKGROUND AND PURPOSE Accelerating the image acquisition speed of MR imaging without compromising the image quality is challenging. This study aimed to evaluate the feasibility of contrast-enhanced (CE) 3D T1WI and CE 3D-FLAIR sequences reconstructed with compressed sensitivity encoding artificial intelligence (CS-AI) for detecting brain metastases (BM) and explore the optimal acceleration factor (AF) for clinical BM imaging. MATERIALS AND METHODS Fifty-one patients with cancer with suspected BM were included. Fifty participants underwent different customized CE 3D-T1WI or CE 3D-FLAIR sequence scans. Compressed SENSE encoding acceleration 6 (CS6), a commercially available standard sequence, was used as the reference standard. Quantitative and qualitative methods were used to evaluate image quality. The SNR and contrast-to-noise ratio (CNR) were calculated, and qualitative evaluations were independently conducted by 2 neuroradiologists. After exploring the optimal AF, sample images were obtained from 1 patient by using both optimized sequences. RESULTS Quantitatively, the CNR of the CS-AI protocol for CE 3D-T1WI and CE 3D-FLAIR sequences was superior to that of the CS protocol under the same AF (P < .05). Compared with reference CS6, the CS-AI groups had higher CNR values (all P < .05), with the CS-AI10 scan having the highest value. The SNR of the CS-AI group was better than that of the reference for both CE 3D-T1WI and CE 3D-FLAIR sequences (all P < .05). Qualitatively, the CS-AI protocol produced higher image quality scores than did the CS protocol with the same AF (all P < .05). In contrast to the reference CS6, the CS-AI group showed good image quality scores until an AF of up to 10 (all P < .05). The CS-AI10 scan provided the optimal images, improving the delineation of normal gray-white matter boundaries and lesion areas (P < .05). Compared with the reference, CS-AI10 showed reductions in scan time of 39.25% and 39.93% for CE 3D-T1WI and CE 3D-FLAIR sequences, respectively. CONCLUSIONS CE 3D-T1WI and CE 3D-FLAIR sequences reconstructed with CS-AI for the detection of BM may provide a more effective alternative reconstruction approach than CS. CS-AI10 is suitable for clinical applications, providing optimal image quality and a shortened scan time.
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Affiliation(s)
- Mengmeng Wang
- From the Department of Radiology (M.W., Y.M., L.L., X.P., Y.W., Y.Q., D.G., D.T.), The First Hospital of Jilin University, Changchun, China
| | - Yue Ma
- From the Department of Radiology (M.W., Y.M., L.L., X.P., Y.W., Y.Q., D.G., D.T.), The First Hospital of Jilin University, Changchun, China
| | - Linna Li
- From the Department of Radiology (M.W., Y.M., L.L., X.P., Y.W., Y.Q., D.G., D.T.), The First Hospital of Jilin University, Changchun, China
| | - Xingchen Pan
- From the Department of Radiology (M.W., Y.M., L.L., X.P., Y.W., Y.Q., D.G., D.T.), The First Hospital of Jilin University, Changchun, China
| | - Yafei Wen
- From the Department of Radiology (M.W., Y.M., L.L., X.P., Y.W., Y.Q., D.G., D.T.), The First Hospital of Jilin University, Changchun, China
| | - Ying Qiu
- From the Department of Radiology (M.W., Y.M., L.L., X.P., Y.W., Y.Q., D.G., D.T.), The First Hospital of Jilin University, Changchun, China
| | - Dandan Guo
- From the Department of Radiology (M.W., Y.M., L.L., X.P., Y.W., Y.Q., D.G., D.T.), The First Hospital of Jilin University, Changchun, China
| | - Yi Zhu
- Philips Healthcare (Y.Z., J.L., D.T.), Beijing, China
| | - Jianxiu Lian
- Philips Healthcare (Y.Z., J.L., D.T.), Beijing, China
| | - Dan Tong
- From the Department of Radiology (M.W., Y.M., L.L., X.P., Y.W., Y.Q., D.G., D.T.), The First Hospital of Jilin University, Changchun, China
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9
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Kiso K, Tsuboyama T, Onishi H, Ogawa K, Nakamoto A, Tatsumi M, Ota T, Fukui H, Yano K, Honda T, Kakemoto S, Koyama Y, Tarewaki H, Tomiyama N. Effect of Deep Learning Reconstruction on Respiratory-triggered T2-weighted MR Imaging of the Liver: A Comparison between the Single-shot Fast Spin-echo and Fast Spin-echo Sequences. Magn Reson Med Sci 2024; 23:214-224. [PMID: 36990740 PMCID: PMC11024712 DOI: 10.2463/mrms.mp.2022-0111] [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: 09/07/2022] [Accepted: 03/07/2023] [Indexed: 03/30/2023] Open
Abstract
PURPOSE To compare the effects of deep learning reconstruction (DLR) on respiratory-triggered T2-weighted MRI of the liver between single-shot fast spin-echo (SSFSE) and fast spin-echo (FSE) sequences. METHODS Respiratory-triggered fat-suppressed liver T2-weighted MRI was obtained with the FSE and SSFSE sequences at the same spatial resolution in 55 patients. Conventional reconstruction (CR) and DLR were applied to each sequence, and the SNR and liver-to-lesion contrast were measured on FSE-CR, FSE-DLR, SSFSE-CR, and SSFSE-DLR images. Image quality was independently assessed by three radiologists. The results of the qualitative and quantitative analyses were compared among the four types of images using repeated-measures analysis of variance or Friedman's test for normally and non-normally distributed data, respectively, and a visual grading characteristics (VGC) analysis was performed to evaluate the image quality improvement by DLR on the FSE and SSFSE sequences. RESULTS The liver SNR was lowest on SSFSE-CR and highest on FSE-DLR and SSFSE-DLR (P < 0.01). The liver-to-lesion contrast did not differ significantly among the four types of images. Qualitatively, noise scores were worst on SSFSE-CR but best on SSFSE-DLR because DLR significantly reduced noise (P < 0.01). In contrast, artifact scores were worst both on FSE-CR and FSE-DLR (P < 0.01) because DLR did not reduce the artifacts. Lesion conspicuity was significantly improved by DLR compared with CR in the SSFSE (P < 0.01) but not in FSE sequences for all readers. Overall image quality was significantly improved by DLR compared with CR for all readers in the SSFSE (P < 0.01) but only one reader in the FSE (P < 0.01). The mean area under the VGC curve values for the FSE-DLR and SSFSE-DLR sequences were 0.65 and 0.94, respectively. CONCLUSION In liver T2-weighted MRI, DLR produced more marked improvements in image quality in SSFSE than in FSE.
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Affiliation(s)
- Kengo Kiso
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Hiromitsu Onishi
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Kazuya Ogawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Atsushi Nakamoto
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Mitsuaki Tatsumi
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Takashi Ota
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Hideyuki Fukui
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Keigo Yano
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Toru Honda
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Shinji Kakemoto
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Yoshihiro Koyama
- Department of Radiology, Osaka University Hospital, Suita, Osaka, Japan
| | - Hiroyuki Tarewaki
- Department of Radiology, Osaka University Hospital, Suita, Osaka, Japan
| | - Noriyuki Tomiyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
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10
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Lønning K, Caan MWA, Nowee ME, Sonke JJ. Dynamic recurrent inference machines for accelerated MRI-guided radiotherapy of the liver. Comput Med Imaging Graph 2024; 113:102348. [PMID: 38368665 DOI: 10.1016/j.compmedimag.2024.102348] [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: 08/04/2023] [Revised: 01/10/2024] [Accepted: 02/01/2024] [Indexed: 02/20/2024]
Abstract
Recurrent inference machines (RIM), a deep learning model that learns an iterative scheme for reconstructing sparsely sampled MRI, has been shown able to perform well on accelerated 2D and 3D MRI scans, learn from small datasets and generalize well to unseen types of data. Here we propose the dynamic recurrent inference machine (DRIM) for reconstructing sparsely sampled 4D MRI by exploiting correlations between respiratory states. The DRIM was applied to a 4D protocol for MR-guided radiotherapy of liver lesions based on repetitive interleaved coronal 2D multi-slice T2-weighted acquisitions. We demonstrate with an ablation study that the DRIM outperforms the RIM, increasing the SSIM score from about 0.89 to 0.95. The DRIM allowed for an approximately 2.7 times faster scan time than the current clinical protocol with only a slight loss in image sharpness. Correlations between slice locations can also be used, but were found to be of less importance, as were a majority of tested variations in network architecture, as long as the respiratory states are processed by the network. Through cross-validation, the DRIM is also shown to be robust in terms of training data. We further demonstrate a good performance across a large range of subsampling factors, and conclude through an evaluation by a radiation oncologist that reconstructed images of the liver contour and inner structures are of a clinically acceptable standard at acceleration factors 10x and 8x, respectively. Finally, we show that binning the data with respect to respiratory states prior to reconstruction comes at a slight cost to reconstruction quality, but at greater speed of the overall protocol.
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Affiliation(s)
- Kai Lønning
- Netherlands Cancer Institute, Department of Radiotherapy, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands; Spinoza Centre for Neuroimaging, Meibergdreef 75, 1105 BK Amsterdam, The Netherlands
| | - Matthan W A Caan
- Amsterdam UMC location University of Amsterdam, Department of Biomedical Engineering and Physics, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Marlies E Nowee
- Netherlands Cancer Institute, Department of Radiotherapy, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Jan-Jakob Sonke
- Netherlands Cancer Institute, Department of Radiotherapy, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.
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11
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Liu K, Li Q, Wang X, Fu C, Sun H, Chen C, Zeng M. Feasibility of deep learning-reconstructed thin-slice single-breath-hold HASTE for detecting pancreatic lesions: A comparison with two conventional T2-weighted imaging sequences. RESEARCH IN DIAGNOSTIC AND INTERVENTIONAL IMAGING 2024; 9:100038. [PMID: 39076579 PMCID: PMC11265199 DOI: 10.1016/j.redii.2023.100038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 12/26/2023] [Indexed: 07/31/2024]
Abstract
Objective The objective of this study was to evaluate the clinical feasibility of deep learning reconstruction-accelerated thin-slice single-breath-hold half-Fourier single-shot turbo spin echo imaging (HASTEDL) for detecting pancreatic lesions, in comparison with two conventional T2-weighted imaging sequences: compressed-sensing HASTE (HASTECS) and BLADE. Methods From March 2022 to January 2023, a total of 63 patients with suspected pancreatic-related disease underwent the HASTEDL, HASTECS, and BLADE sequences were enrolled in this retrospectively study. The acquisition time, the pancreatic lesion conspicuity (LCP), respiratory motion artifact (RMA), main pancreatic duct conspicuity (MPDC), overall image quality (OIQ), signal-to-noise ratio (SNR), and contrast-noise-ratio (CNR) of the pancreatic lesions were compared among the three sequences by two readers. Results The acquisition time of both HASTEDL and HASTECS was 16 s, which was significantly shorter than that of 102 s for BLADE. In terms of qualitative parameters, Reader 1 and Reader 2 assigned significantly higher scores to the LCP, RMA, MPDC, and OIQ for HASTEDL compared to HASTECS and BLADE sequences; As for the quantitative parameters, the SNR values of the pancreatic head, body, tail, and lesions, the CNR of the pancreatic lesion measured by the two readers were also significantly higher for HASTEDL than for HASTECS and BLADE sequences. Conclusions Compared to conventional T2WI sequences (HASTECS and BLADE), deep-learning reconstructed HASTE enables thin slice and single-breath-hold acquisition with clinical acceptable image quality for detection of pancreatic lesions.
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Affiliation(s)
- Kai Liu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai 200032, China
| | - Qing Li
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai 200032, China
| | - Xingxing Wang
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Caixia Fu
- Siemens (Shenzhen) Magnetic Resonance Ltd., Shenzhen, China
| | - Haitao Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai 200032, China
| | - Caizhong Chen
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai 200032, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai 200032, China
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Childs DD, Lalwani N, Craven T, Arif H, Morgan M, Anderson M, Fulcher A. A meta-analysis of the performance of ultrasound, hepatobiliary scintigraphy, CT and MRI in the diagnosis of acute cholecystitis. Abdom Radiol (NY) 2024; 49:384-398. [PMID: 37982832 DOI: 10.1007/s00261-023-04059-w] [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: 06/02/2023] [Revised: 09/09/2023] [Accepted: 09/11/2023] [Indexed: 11/21/2023]
Abstract
PURPOSE To evaluate the recently reported relative diagnostic accuracy of US, CT, MRI, and cholescintigraphy for diagnosing acute cholecystitis. METHODS 2 radiologists independently performed systematic electronic searches for articles published between 2000 and 2021 and applied inclusion/exclusion criteria. 2 different radiologists extracted data from the articles and scored each with a methodological quality tool. Pooled estimates of sensitivity and specificity were calculated with a bivariate linear mixed model. A second analysis made head-to-head comparisons (US vs. CT, US vs. cholescintigraphy). Factors were also analyzed for potential confounding effects on diagnostic accuracy. RESULTS Of 6121 initial titles, 22 were included. The prevalence of cholecystitis varied widely across studies (9.4-98%). Pooled sensitivity and specificity estimates were 69% (confidence limit [CL] 62-76%) and 79% (CL 71-86%) for US, 91% (CL 86-94%) and 63% (CL 51-74%) for cholescintigraphy, 78% (CL 69-84%) and 81% (CL 71-88%) for CT, and 91% (CL 78-97%) and 93% (CL 70-99%) for MRI. Regarding head-to-head comparisons, the sensitivity of CT (87.6%, CL 70-96%) was significantly higher than US (66.8%, CL 43-84%), while specificities (81.7% with CL 54-95% for US, 91.9% with CL 67-99% for CT) were similar. The sensitivity of cholescintigraphy (87.4%, CL 76-94%) was significantly greater than US (61.6%, CL 44-77%), while the specificity of US (82%, CL 65-92%) was significantly higher than cholescintigraphy (68%, CL 47-84%). CONCLUSION Recent data suggests that CT may have a higher sensitivity than US for diagnosing acute cholecystitis, with similar specificity. Cholescintigraphy remains a highly sensitive modality with lower specificity than previously reported. MRI remains under studied, but with promising results.
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Affiliation(s)
- David D Childs
- Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
| | - Neeraj Lalwani
- Department of Radiology, Virginia Commonwealth University Health, Richmond, VA, USA
| | - Timothy Craven
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Hina Arif
- Department of Medical Imaging, College of Medicine, The University of Arizona, Tucson, AZ, USA
| | - Mathew Morgan
- Department of Radiology, University of Pennsylvania Health System, Philadelphia, PA, USA
| | - Mark Anderson
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ann Fulcher
- Department of Radiology, Virginia Commonwealth University Health, Richmond, VA, USA
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Tang H, Peng C, Zhao Y, Hu C, Dai Y, Lin C, Cai L, Wang Q, Wang S. An applicability study of rapid artificial intelligence-assisted compressed sensing (ACS) in anal fistula magnetic resonance imaging. Heliyon 2024; 10:e22817. [PMID: 38169794 PMCID: PMC10758725 DOI: 10.1016/j.heliyon.2023.e22817] [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: 12/21/2022] [Revised: 11/15/2023] [Accepted: 11/20/2023] [Indexed: 01/05/2024] Open
Abstract
Objective To evaluate the applicability of artificial intelligence-assisted compressed sensing (ACS) to anal fistula magnetic resonance imaging (MRI). Methods 51 patients were included in this study and underwent T2-weighted sequence of MRI examinations both with ACS and without ACS technology in a 3.0 T MR scanner. Subjective image quality scores, and objective image quality-related metrics including scanning time, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), were evaluated and statistically compared between the images collected with and without ACS. Results No significant difference in the subjective image quality of lesion conspicuity was observed between the two groups. However, ACS MRI decreased the acquisition time with regard to control group (74.00 s vs. 156.00 s). Besides, SNR of perianal and muscle in the ACS group was significantly higher than that of the control group (164.07 ± 33.35 vs 130.81 ± 29.10, p < 0.001; 109.87 ± 22.01 vs 87.61 ± 17.95, p < 0.001; respectively). The CNR was significantly higher in the ACS group than in the control group (54.02 ± 23.98 vs 43.20 ± 21.00; p < 0.001). Moreover, the accuracy rate of the ACS groups in evaluating the direction and internal opening of the fistula was 88.89 %, exactly the same as that of the control group. Conclusion We demonstrated the applicability of using ACS to accelerate MR of anal fistulas with improved SNR and CNR. Meanwhile, the accuracy rates of the ACS group and the control were equivalent in evaluating the direction and internal opening of the fistula, based on the results of surgical exploration.
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Affiliation(s)
- Hao Tang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jie Fang Road, Han Kou District, Wu Han, 430030, Hu Bei Province, China
| | - Chengdong Peng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jie Fang Road, Han Kou District, Wu Han, 430030, Hu Bei Province, China
| | - Yanjie Zhao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jie Fang Road, Han Kou District, Wu Han, 430030, Hu Bei Province, China
| | - Chenglin Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jie Fang Road, Han Kou District, Wu Han, 430030, Hu Bei Province, China
| | - Yongming Dai
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, 201800, China
| | - Chen Lin
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, 201800, China
| | - Lingli Cai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jie Fang Road, Han Kou District, Wu Han, 430030, Hu Bei Province, China
| | - Qiuxia Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jie Fang Road, Han Kou District, Wu Han, 430030, Hu Bei Province, China
| | - Shaofang Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jie Fang Road, Han Kou District, Wu Han, 430030, Hu Bei Province, China
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Wang Q, Zhao W, Xing X, Wang Y, Xin P, Chen Y, Zhu Y, Xu J, Zhao Q, Yuan H, Lang N. Feasibility of AI-assisted compressed sensing protocols in knee MR imaging: a prospective multi-reader study. Eur Radiol 2023; 33:8585-8596. [PMID: 37382615 PMCID: PMC10667384 DOI: 10.1007/s00330-023-09823-6] [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: 12/02/2022] [Revised: 03/02/2023] [Accepted: 03/22/2023] [Indexed: 06/30/2023]
Abstract
OBJECTIVES To evaluate the image quality and diagnostic performance of AI-assisted compressed sensing (ACS) accelerated two-dimensional fast spin-echo MRI compared with standard parallel imaging (PI) in clinical 3.0T rapid knee scans. METHODS This prospective study enrolled 130 consecutive participants between March and September 2022. The MRI scan procedure included one 8.0-min PI protocol and two ACS protocols (3.5 min and 2.0 min). Quantitative image quality assessments were performed by evaluating edge rise distance (ERD) and signal-to-noise ratio (SNR). Shapiro-Wilk tests were performed and investigated by the Friedman test and post hoc analyses. Three radiologists independently evaluated structural disorders for each participant. Fleiss κ analysis was used to compare inter-reader and inter-protocol agreements. The diagnostic performance of each protocol was investigated and compared by DeLong's test. The threshold for statistical significance was set at p < 0.05. RESULTS A total of 150 knee MRI examinations constituted the study cohort. For the quantitative assessment of four conventional sequences with ACS protocols, SNR improved significantly (p < 0.001), and ERD was significantly reduced or equivalent to the PI protocol. For the abnormality evaluated, the intraclass correlation coefficient ranged from moderate to substantial between readers (κ = 0.75-0.98) and between protocols (κ = 0.73-0.98). For meniscal tears, cruciate ligament tears, and cartilage defects, the diagnostic performance of ACS protocols was considered equivalent to PI protocol (Delong test, p > 0.05). CONCLUSIONS Compared with the conventional PI acquisition, the novel ACS protocol demonstrated superior image quality and was feasible for achieving equivalent detection of structural abnormalities while reducing acquisition time by half. CLINICAL RELEVANCE STATEMENT Artificial intelligence-assisted compressed sensing (ACS) providing excellent quality and a 75% reduction in scanning time presents significant clinical advantages in improving the efficiency and accessibility of knee MRI for more patients. KEY POINTS • The prospective multi-reader study showed no difference in diagnostic performance between parallel imaging and AI-assisted compression sensing (ACS) was found. • Reduced scan time, sharper delineation, and less noise with ACS reconstruction. • Improved efficiency of the clinical knee MRI examination by the ACS acceleration.
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Affiliation(s)
- Qizheng Wang
- Department of Radiology, Peking University Third Hospital, Haidian District, 49 North Garden Road, Beijing, 100191, People's Republic of China
| | - Weili Zhao
- Department of Radiology, Peking University Third Hospital, Haidian District, 49 North Garden Road, Beijing, 100191, People's Republic of China
| | - Xiaoying Xing
- Department of Radiology, Peking University Third Hospital, Haidian District, 49 North Garden Road, Beijing, 100191, People's Republic of China
| | - Ying Wang
- Department of Radiology, Peking University Third Hospital, Haidian District, 49 North Garden Road, Beijing, 100191, People's Republic of China
| | - Peijin Xin
- Department of Radiology, Peking University Third Hospital, Haidian District, 49 North Garden Road, Beijing, 100191, People's Republic of China
| | - Yongye Chen
- Department of Radiology, Peking University Third Hospital, Haidian District, 49 North Garden Road, Beijing, 100191, People's Republic of China
| | - Yupeng Zhu
- Department of Radiology, Peking University Third Hospital, Haidian District, 49 North Garden Road, Beijing, 100191, People's Republic of China
| | - Jiajia Xu
- Department of Radiology, Peking University Third Hospital, Haidian District, 49 North Garden Road, Beijing, 100191, People's Republic of China
| | - Qiang Zhao
- Department of Radiology, Peking University Third Hospital, Haidian District, 49 North Garden Road, Beijing, 100191, People's Republic of China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Haidian District, 49 North Garden Road, Beijing, 100191, People's Republic of China
| | - Ning Lang
- Department of Radiology, Peking University Third Hospital, Haidian District, 49 North Garden Road, Beijing, 100191, People's Republic of China.
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Liu H, Deng D, Zeng W, Huang Y, Zheng C, Li X, Li H, Xie C, He H, Xu G. AI-assisted compressed sensing and parallel imaging sequences for MRI of patients with nasopharyngeal carcinoma: comparison of their capabilities in terms of examination time and image quality. Eur Radiol 2023; 33:7686-7696. [PMID: 37219618 PMCID: PMC10598173 DOI: 10.1007/s00330-023-09742-6] [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: 09/23/2022] [Revised: 03/21/2023] [Accepted: 04/14/2023] [Indexed: 05/24/2023]
Abstract
OBJECTIVE To compare examination time and image quality between artificial intelligence (AI)-assisted compressed sensing (ACS) technique and parallel imaging (PI) technique in MRI of patients with nasopharyngeal carcinoma (NPC). METHODS Sixty-six patients with pathologically confirmed NPC underwent nasopharynx and neck examination using a 3.0-T MRI system. Transverse T2-weighted fast spin-echo (FSE) sequence, transverse T1-weighted FSE sequence, post-contrast transverse T1-weighted FSE sequence, and post-contrast coronal T1-weighted FSE were obtained by both ACS and PI techniques, respectively. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and duration of scanning of both sets of images analyzed by ACS and PI techniques were compared. The images from the ACS and PI techniques were scored for lesion detection, margin sharpness of lesions, artifacts, and overall image quality using the 5-point Likert scale. RESULTS The examination time with ACS technique was significantly shorter than that with PI technique (p < 0.0001). The comparison of SNR and CNR showed that ACS technique was significantly superior with PI technique (p < 0.005). Qualitative image analysis showed that the scores of lesion detection, margin sharpness of lesions, artifacts, and overall image quality were higher in the ACS sequences than those in the PI sequences (p < 0.0001). Inter-observer agreement was evaluated for all qualitative indicators for each method, in which the results showed satisfactory-to-excellent agreement (p < 0.0001). CONCLUSION Compared with the PI technique, the ACS technique for MR examination of NPC can not only shorten scanning time but also improve image quality. CLINICAL RELEVANCE STATEMENT The artificial intelligence (AI)-assisted compressed sensing (ACS) technique shortens examination time for patients with nasopharyngeal carcinoma, while improving the image quality and examination success rate, which will benefit more patients. KEY POINTS • Compared with the parallel imaging (PI) technique, the artificial intelligence (AI)-assisted compressed sensing (ACS) technique not only reduced examination time, but also improved image quality. • Artificial intelligence (AI)-assisted compressed sensing (ACS) pulls the state-of-the-art deep learning technique into the reconstruction procedure and helps find an optimal balance of imaging speed and image quality.
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Affiliation(s)
- Haibin Liu
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Dele Deng
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Weilong Zeng
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Yingyi Huang
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Chunling Zheng
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Xinyang Li
- United Imaging Healthcare, Shanghai, People's Republic of China
| | - Hui Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Chuanmiao Xie
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Haoqiang He
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.
| | - Guixiao Xu
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.
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Park EJ, Lee Y, Lee J. Impact of Deep-Learning Based Reconstruction on Single-Breath-Hold, Single-Shot Fast Spin-Echo in MR Enterography for Crohn's Disease. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2023; 84:1309-1323. [PMID: 38107694 PMCID: PMC10721413 DOI: 10.3348/jksr.2023.0008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/18/2023] [Accepted: 05/06/2023] [Indexed: 12/19/2023]
Abstract
Purpose To assess the quality of four images obtained using single-breath-hold (SBH), single-shot fast spin-echo (SSFSE) and multiple-breath-hold (MBH) SSFSE with and without deep-learning based reconstruction (DLR) in patients with Crohn's disease. Materials and Methods This study included 61 patients who underwent MR enterography (MRE) for Crohn's disease. The following images were compared: SBH-SSFSE with (SBH-DLR) and without (SBH-conventional reconstruction [CR]) DLR and MBH-SSFSE with (MBH-DLR) and without (MBH-CR) DLR. Two radiologists independently reviewed the overall image quality, artifacts, sharpness, and motion-related signal loss using a 5-point scale. Three inflammatory parameters were evaluated in the ileum, the terminal ileum, and the colon. Moreover, the presence of a spatial misalignment was evaluated. Signal-to-noise ratio (SNR) was calculated at two locations for each sequence. Results DLR significantly improved the image quality, artifacts, and sharpness of the SBH images. No significant differences in scores between MBH-CR and SBH-DLR were detected. SBH-DLR had the highest SNR (p < 0.001). The inter-reader agreement for inflammatory parameters was good to excellent (κ = 0.76-0.95) and the inter-sequence agreement was nearly perfect (κ = 0.92-0.94). Misalignment artifacts were observed more frequently in the MBH images than in the SBH images (p < 0.001). Conclusion SBH-DLR demonstrated equivalent quality and performance compared to MBH-CR. Furthermore, it can be acquired in less than half the time, without multiple BHs and reduce slice misalignments.
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Zhao Q, Xu J, Yang YX, Yu D, Zhao Y, Wang Q, Yuan H. AI-assisted accelerated MRI of the ankle: clinical practice assessment. Eur Radiol Exp 2023; 7:62. [PMID: 37857868 PMCID: PMC10587051 DOI: 10.1186/s41747-023-00374-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 08/04/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND High-spatial resolution magnetic resonance imaging (MRI) is essential for imaging ankle joints. However, the clinical application of fast spin-echo sequences remains limited by their lengthy acquisition time. Artificial intelligence-assisted compressed sensing (ACS) technology has been recently introduced as an integrative acceleration solution. We compared ACS-accelerated 3-T ankle MRI to conventional methods of compressed sensing (CS) and parallel imaging (PI) . METHODS We prospectively included 2 healthy volunteers and 105 patients with ankle pain. ACS acceleration factors for ankle protocol of T1-, T2-, and proton density (PD)-weighted sequences were optimized in a pilot study on healthy volunteers (acceleration factor 3.2-3.3×). Images of patients acquired using ACS and conventional acceleration methods were compared in terms of acquisition times, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), subjective image quality, and diagnostic agreement. Shapiro-Wilk test, Cohen κ, intraclass correlation coefficient, and one-way ANOVA with post hoc tests (Tukey or Dunn) were used. RESULTS ACS acceleration reduced the acquisition times of T1-, T2-, and PD-weighted sequences by 32-43%, compared with conventional CS and PI, while maintaining image quality (mostly higher SNR with p < 0.004 and higher CNR with p < 0.047). The diagnostic agreement between ACS and conventional sequences was rated excellent (κ = 1.00). CONCLUSIONS The optimum ACS acceleration factors for ankle MRI were found to be 3.2-3.3× protocol. The ACS allows faster imaging, yielding similar image quality and diagnostic performance. RELEVANCE STATEMENT AI-assisted compressed sensing significantly accelerates ankle MRI times while preserving image quality and diagnostic precision, potentially expediting patient diagnoses and improving clinical workflows. KEY POINTS • AI-assisted compressed sensing (ACS) significantly reduced scan duration for ankle MRI. • Similar image quality achieved by ACS compared to conventional acceleration methods. • A high agreement by three acceleration methods in the diagnosis of ankle lesions was observed.
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Affiliation(s)
- Qiang Zhao
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Jiajia Xu
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Yu Xin Yang
- United Imaging Research Institute of Intelligent Imaging, Beijing, People's Republic of China
| | - Dan Yu
- United Imaging Research Institute of Intelligent Imaging, Beijing, People's Republic of China
| | - Yuqing Zhao
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Qizheng Wang
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
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Ichinohe F, Oyama K, Yamada A, Hayashihara H, Adachi Y, Kitoh Y, Kanki Y, Maruyama K, Nickel MD, Fujinaga Y. Usefulness of Breath-Hold Fat-Suppressed T2-Weighted Images With Deep Learning-Based Reconstruction of the Liver: Comparison to Conventional Free-Breathing Turbo Spin Echo. Invest Radiol 2023; 58:373-379. [PMID: 36728880 DOI: 10.1097/rli.0000000000000943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVES The aim of this study was to evaluate the usefulness of breath-hold turbo spin echo with deep learning-based reconstruction (BH-DL-TSE) in acquiring fat-suppressed T2-weighted images (FS-T2WI) of the liver by comparing this method with conventional free-breathing turbo spin echo (FB-TSE) and breath-hold half Fourier single-shot turbo spin echo with deep learning-based reconstruction (BH-DL-HASTE). MATERIALS AND METHODS The study cohort comprised 111 patients with suspected liver disease who underwent 3 T magnetic resonance imaging. Fifty-eight focal solid liver lesions ≥10 mm were also evaluated. Three sets of FS-T2WI were acquired using FB-TSE, prototypical BH-DL-TSE, and prototypical BH-DL-HASTE, respectively. In the qualitative analysis, 2 radiologists evaluated the image quality using a 5-point scale. In the quantitative analysis, we calculated the lesion-to-liver signal intensity ratio (LEL-SIR). Friedman test and Dunn multiple comparison test were performed to assess differences among 3 types of FS-T2WI with respect to image quality and LEL-SIR. RESULTS The mean acquisition time was 4 minutes and 43 seconds ± 1 minute and 21 seconds (95% confidence interval, 4 minutes and 28 seconds to 4 minutes and 58 seconds) for FB-TSE, 40 seconds for BH-DL-TSE, and 20 seconds for BH-DL-HASTE. In the qualitative analysis, BH-DL-HASTE resulted in the fewest respiratory motion artifacts ( P < 0.0001). BH-DL-TSE and FB-TSE exhibited significantly less motion-related signal loss and clearer intrahepatic vessels than BH-DL-HASTE ( P < 0.0001). Regarding the edge sharpness of the left lobe, BH-DL-HASTE scored the highest ( P < 0.0001), and BH-DL-TSE scored higher than FB-TSE ( P = 0.0290). There were no significant differences among 3 types of FS-T2WI with respect to the edge sharpness of the right lobe ( P = 0.1290), lesion conspicuity ( P = 0.5292), and LEL-SIR ( P = 0.6026). CONCLUSIONS BH-DL-TSE provides a shorter acquisition time and comparable or better image quality than FB-TSE, and could replace FB-TSE in acquiring FS-T2WI of the liver. BH-DL-TSE and BH-DL-HASTE have their own advantages and may be used complementarily.
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Affiliation(s)
- Fumihito Ichinohe
- From the Department of Radiology, Shinshu University School of Medicine
| | - Kazuki Oyama
- From the Department of Radiology, Shinshu University School of Medicine
| | - Akira Yamada
- From the Department of Radiology, Shinshu University School of Medicine
| | | | - Yasuo Adachi
- Radiology Division, Shinshu University Hospital, Matsumoto
| | | | | | - Katsuya Maruyama
- MR Research and Collaboration Department, Siemens Healthcare K.K., Tokyo, Japan
| | | | - Yasunari Fujinaga
- From the Department of Radiology, Shinshu University School of Medicine
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Liu J, Li W, Li Z, Yang J, Wang K, Cao X, Qin N, Xue K, Dai Y, Wu P, Qiu J. Magnetic resonance shoulder imaging using deep learning-based algorithm. Eur Radiol 2023:10.1007/s00330-023-09470-x. [PMID: 36826500 DOI: 10.1007/s00330-023-09470-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/03/2023] [Accepted: 01/22/2023] [Indexed: 02/25/2023]
Abstract
OBJECTIVE To investigate the feasibility of deep learning-based MRI (DL-MRI) in its application in shoulder imaging and compare its performance with conventional MR imaging (non-DL-MRI). METHODS This retrospective study was approved by the local ethics committee. Seventy consecutive patients who had been examined with both DL-MRI and non-DL-MRI were enrolled for the image quality and lesion diagnosis comparison. Another 400 patients had been examined only with DL-MRI. Their images' quality was assessed by 20 radiologists using a satisfaction survey. The Kendall W test was performed to assess interobserver agreement. The Wilcoxon test was performed to compare the image quality. For lesion diagnosis, the interobserver and interstudy agreement were evaluated by kappa analysis. RESULTS The scan time of DL-MRI (6 min 1 s) was nearly 50% decreased compared with that of non-DL-MRI (11 min 25 s). The image quality was higher in both PDWI (4.85 ± 0.31 for DL, and 4.73 ± 0.29 for non-DL) and T2WI (4.95 ± 0.2 for DL, and 4.74 ± 0.41 for non-DL) of DL-MRI. Good interobserver agreement was found for the image quality of all the MR sequences on both DL-MRI (Kendall W: 0.588~0.902) and non-DL-MRI (Kendall W: 0751~0.865). Both the SNRs and |CNR| were significantly higher in PDWI and T2WI of DL-MRI. High interobserver and interstudy agreements for the lesions in non-DL-MRI and DL-MRI (kappa value = 0.913 to 1.000) were observed. The results of the image quality satisfaction survey in 400 patients receiving DL-MRI in the shoulder obtained 5 scores among all the radiologists. CONCLUSION Shoulder DL-MRI can greatly reduce the scan time, while improve imaging quality of PDWI and T2WI compared to non-DL-MRI. KEY POINTS • Shoulder 2D DL-MRI can greatly reduce the whole scan time and improve imaging quality of both PDWI and T2WI compared to conventional parallel MRI. • Shoulder 2D DL-MRI could be a clinical routine with greatly improved work efficiency in the future.
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Affiliation(s)
- Jing Liu
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Wei Li
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Ziyuan Li
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Junzhe Yang
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Ke Wang
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Xinming Cao
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Naishan Qin
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Ke Xue
- Central Research Institute, United Imaging Healthcare, 2258 Chengbei Rd., Jiading District, Shanghai, 201807, China
| | - Yongming Dai
- Central Research Institute, United Imaging Healthcare, 2258 Chengbei Rd., Jiading District, Shanghai, 201807, China
| | - Peng Wu
- Central Research Institute, United Imaging Healthcare, 2258 Chengbei Rd., Jiading District, Shanghai, 201807, China
| | - Jianxing Qiu
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China.
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Sui H, Gong Y, Liu L, Lv Z, Zhang Y, Dai Y, Mo Z. Comparison of Artificial Intelligence-Assisted Compressed Sensing (ACS) and Routine Two-Dimensional Sequences on Lumbar Spine Imaging. J Pain Res 2023; 16:257-267. [PMID: 36744117 PMCID: PMC9891076 DOI: 10.2147/jpr.s388219] [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: 09/06/2022] [Accepted: 12/20/2022] [Indexed: 01/29/2023] Open
Abstract
Purpose To evaluate and compare the image quality and diagnostic accuracy of Artificial Intelligence-assisted Compressed Sensing (ACS) sequences for lumbar disease, as an acceleration method for MRI combining parallel imaging, half-Fourier, compressed sensing and neural network and routine 2D sequences for lumbar spine. Methods We collected data from 82 healthy subjects and 213 patients who used 2D ACS accelerated sequences to examine the lumbar spine while 95 healthy subjects and 234 patients used routine 2D sequences. Acquisitions included axial T2WI, sagittal T2WI, T1WI, and T2-fs sequences. All obtained images of these subjects were analyzed in the light of calculating image quality factors such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for selected regions of interest. The lumbar image quality, artifacts and visibility of lesion structure were assessed by two radiologists independently. Differences between the evaluation values above were tested for statistical significance by the Wilcoxon signed-ranks test. Inter-observer agreements of image quality between two radiologists were measured using Cohen's kappa correlation coefficient. Results The ACS accelerated sequences not only reduced the scanning time by 18.9%, but also retained basically the same image quality as the routine 2D sequences in both healthy subjects and patients. Artifacts are less produced on ACS accelerated sequences compared with routine 2D sequences (p < 0.05). Apart from this, there were no significant differences in quantitative SNR, CNR measurements and qualitative scores within reviewing radiologists for each group (p > 0.05). Moreover, inter-observer agreement between two radiologists in scoring image quality was substantial consistently for ACS accelerated sequences and routine sequences (kappa = 0.622-0.986). Conclusion Compared with routine 2D sequences, ACS accelerated sequences allow for faster lumbar spine imaging with similar imaging quality and present reliable diagnostic accuracy, which can potentially improve workflow and patient comfort in musculoskeletal examinations.
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Affiliation(s)
- He Sui
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Yu Gong
- Medical Imaging Department, Linyi People’s Hospital, Linyi, People’s Republic of China
| | - Lin Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Zhongwen Lv
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Yunfei Zhang
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, People’s Republic of China
| | - Yongming Dai
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, People’s Republic of China
| | - Zhanhao Mo
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China,Correspondence: Zhanhao Mo, Department of Radiology, China-Japan Union Hospital of Jilin University, No. 126 Xiantai St., Erdao Dist., Changchun, People’s Republic of China, Email
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Wang K, Li X, Liu J, Guo X, Li W, Cao X, Yang J, Xue K, Dai Y, Wang X, Qiu J, Qin N. Predicting the image quality of respiratory-gated and breath-hold 3D MRCP from the breathing curve: a prospective study. Eur Radiol 2022; 33:4333-4343. [PMID: 36543903 DOI: 10.1007/s00330-022-09293-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 10/20/2022] [Accepted: 10/27/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To compare the image quality of breath-hold magnetic resonance cholangiopancreatography (BH-MRCP) and respiratory-gating MRCP (RG-MRCP), and to explore breathing curve-based factors and patient-related data affecting image quality. METHODS A total of 126 participants who underwent RG-MRCP and BH-MRCP on a 3-T magnetic resonance (MR) scanner were enrolled from May to December 2021. The images were evaluated by three radiologists on a 5-point scale. Respiratory parameters were extracted from the breathing curves. The Wilcoxon test was used to compare the image quality between the two MRCPs. Logistic regression analyzes were performed to identify age, sex, abdominal pain, and breathing predictor variables of better image quality. RESULTS BH-MRCP performed better in visualizing intrahepatic bile ducts and overall image quality than RG-MRCP (p < 0.01). Factors predicting relatively good image quality included lower standard deviation of the respiratory amplitude (SDamp)-minimum-peak (odds ratio = 0.16, p < 0.01) for RG-MRCP and lower SDamp (OR = 0.69, p < 0.01) for BH-MRCP. CONCLUSIONS BH-MRCP had significantly better overall image quality than RG-MRCP. Respiratory conditions exerted a significant impact on MRCP image quality, and parameters derived from the breathing curve could help predict the image quality of both sequences. KEY POINTS • Both breath-hold (BH) and respiratory-gating (RG) MRCP demonstrate satisfying image quality. • BH-GRASE-MRCP is significantly better than RG-MRCP at the group level, but not for every individual. • Respiratory conditions exert a significant impact on the image quality, and the breathing curve can help predict the image quality.
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Affiliation(s)
- Ke Wang
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Xinying Li
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Jing Liu
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Xiaochao Guo
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Wei Li
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Xinming Cao
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Junzhe Yang
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Ke Xue
- Central Research Institute, United Imaging Healthcare, 2258 Chengbei Rd., Jiading District, Shanghai, 201807, China
| | - Yongming Dai
- Central Research Institute, United Imaging Healthcare, 2258 Chengbei Rd., Jiading District, Shanghai, 201807, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Jianxing Qiu
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China.
| | - Naishan Qin
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China.
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Liu K, Xi B, Sun H, Wang J, Chen C, Wen X, Zhang Y, Zeng M. The clinical feasibility of artificial intelligence-assisted compressed sensing single-shot fluid-attenuated inversion recovery (ACS-SS-FLAIR) for evaluation of uncooperative patients with brain diseases: comparison with the conventional T2-FLAIR with parallel imaging. Acta Radiol 2022; 64:1943-1949. [PMID: 36423247 DOI: 10.1177/02841851221139125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Background Satisfactory magnetic resonance imaging (MRI) of those patients with involuntary head motion due to brain diseases is essential in avoiding missed diagnosis and guiding treatment. Purpose To investigate the clinical feasibility of artificial intelligence-assisted compressed sensing single-shot fluid-attenuated inversion recovery (ACS-SS-FLAIR) in evaluating patients with involuntary head motion due to brain diseases, compared with the conventional T2-FLAIR with parallel imaging (PI-FLAIR). Material and Methods A total of 33 uncooperative patients with brain disease were prospectively enrolled. Two readers independently reviewed images acquired with ACS-SS-FLAIR and PI-FLAIR at a 3.0-T MR scanner. In the aspects of qualitative evaluation of image quality, overall image quality and lesion conspicuity of ACS-SS-FLAIR and PI-FLAIR were assessed and then statistically compared by paired Wilcoxon rank-sum test. For quantitative evaluation, the relative contrast of lesion-to-cerebral parenchyma were calculated and compared. Results Overall image quality scores of ACS-SS-FLAIR evaluated by two readers were 2.94 ± 0.24 and 2.91 ± 0.29, respectively, both of which were significantly higher than that of PI-FLAIR, respectively ( P < 0.001 and <0.001). Lesion conspicuity scores of were 2.74 ± 0.47 and 2.79 ± 0.44, both of which were significantly higher than that of PI-FLAIR, respectively ( P < 0.001 and <0.001). In the quantitative evaluation for image quality, the relative contrast of lesion-to-cerebral parenchyma was 0.34 ± 0.09 in the ACS-SS-FLAIR sequence, significantly larger than that in the PI-FLAIR sequence ( P = 0.001). Conclusion The ACS-SS-FLAIR sequence is clinically feasible in the MRI workup of those patients with involuntary head motion due to brain diseases, showing shorter image acquisition time and better image quality compared with conventional PI-FLAIR.
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Affiliation(s)
- Kai Liu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, PR China
| | - Bin Xi
- Wuxi 9th People's Hospital Affiliated to Soochow University, Wuxi, PR China
| | - Haitao Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, PR China
| | - Jian Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, PR China
| | - Caizhong Chen
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, PR China
| | - Xixi Wen
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, PR China
| | - Yunfei Zhang
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, PR China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, PR China
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Single-breath-hold T2WI MRI with artificial intelligence-assisted technique in liver imaging: As compared with conventional respiratory-triggered T2WI. Magn Reson Imaging 2022; 93:175-180. [PMID: 35987419 DOI: 10.1016/j.mri.2022.08.012] [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: 05/09/2022] [Revised: 08/14/2022] [Accepted: 08/14/2022] [Indexed: 11/23/2022]
Abstract
OBJECTIVE To investigate the clinical feasibility of single-breath-hold T2-weighted (SBH-T2WI) liver MRI using Artificial Intelligence-assisted Compressed Sensing (ACS) technique in liver imaging as compared with conventional respiratory-triggered T2WI (RT-T2WI). METHODS From January 2021 to October 2021, 81 patients suspected of liver lesions were enrolled in this prospective study. The liver MRI was performed, including both RT-T2WI and ACS SBH-T2WI. Two experienced radiologists reviewed all images of each studied sequence, and recorded the lesion location and the largest diameter of the lesions. The image quality was quantitatively and qualitatively analyzed regarding signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), contrast ratio (CR), motion artifact, lesion conspicuity, liver boundary sharpness, and overall image quality. The lesion detection and image quality were compared between two sequences using the Chi-square test or Wilcoxon signed-rank test. RESULTS For lesion detection, 64 lesions were identified in 53 enrolled patients as the reference standard. The average size was 12.09 ± 7.4 mm for the benign lesions and 45.89 ± 22.01 mm for the malignant lesions. Of 64 liver lesions, ACS SBH-T2WI detected 60 lesions (93.8%), and RT-T2WI detected 58 lesions (90.6%). For image quality analysis, the motion artifact of ACS SBH-T2WI sequence was significantly reduced compared with the conventional RT-T2WI sequence (p < 0.05). The SNR, liver boundary sharpness, and overall image quality showed no statistical differences between the two sequences. While the CNR, CR, and lesion conspicuity of ACS SBH-T2WI were significantly better than RT-T2WI (all p < 0.05). CONCLUSIONS The SBH-T2WI with ACS technique showed promising performance as it provided significantly better image quality and lesion detectability with a considerable decrease in scanning time as compared with the conventional RT-T2WI.
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Zhao Y, Peng C, Wang S, Liang X, Meng X. The feasibility investigation of AI -assisted compressed sensing in kidney MR imaging: an ultra-fast T2WI imaging technology. BMC Med Imaging 2022; 22:119. [PMID: 35787673 PMCID: PMC9254529 DOI: 10.1186/s12880-022-00842-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 05/31/2022] [Indexed: 11/27/2022] Open
Abstract
Object To explore the feasibility and clinical application of AI -assisted compressed sensing (ACS) technology in kidney MR imaging.
Methods 33 patients were enrolled in this study, affiliated to our hospital from September 2020 to April 2021. The patients underwent T2-weighed sequences of both the ACS scan and the conventional respiratory navigator (NAVI) scan. We evaluated the subjective image quality scores, including the sharpness of image edge, artifact and the overall image quality, and compared the objective image quality indicators such as scanning time, signal-to-noise ratio (SNR), and contrast signal-to-noise ratio (CNR). The Wilcoxon’s rank sum test and the paired t test were used to compare the image quality between ACS and NAVI groups. The p-value less than 0.05 indicated a statistically significant difference. Results The edge sharpness of the ACS group was significant lower than that of the NAVI group (p < 0.01), however, there were no significant differences in the artifact and the overall rating of image quality between the two groups (p > 0.05). In terms of the objective image quality scores, the scanning time of the ACS group is significantly lower than that of control group. The SNR and CNR of ACS group were significantly higher than those of NAVI group (SNR:3.63 ± 0.76 vs 3.04 ± 0.44, p < 0.001; CNR: 14.44 ± 4.53 vs 12.05 ± 3.32, p < 0.001). In addition, the subjective and objective measurement results of the two radiologists were in good agreement (ICC = 0.61–0.88). Conclusion ACS technology has obvious advantages when applied to kidney MR imaging, which can realize ultra-fast MR imaging. The images can be acquired with a single breath-hold (17 s), which greatly shortens the scanning time. Moreover, the image quality is equal to or better than the conventional technology, which can meet the diagnostic requirements. Thus, it has obvious advantages in diagnosis for kidney disease patients with different tolerance levels for the clinical promotion. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00842-1.
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Affiliation(s)
- Yanjie Zhao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Chengdong Peng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Shaofang Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | | | - Xiaoyan Meng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
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Shang Y, Liu J, Zhang L, Wu X, Zhang P, Yin L, Hui H, Tian J. Deep learning for improving the spatial resolution of magnetic particle imaging. Phys Med Biol 2022; 67. [PMID: 35533677 DOI: 10.1088/1361-6560/ac6e24] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 05/09/2022] [Indexed: 11/11/2022]
Abstract
Objective.Magnetic particle imaging (MPI) is a new medical, non-destructive, imaging method for visualizing the spatial distribution of superparamagnetic iron oxide nanoparticles. In MPI, spatial resolution is an important indicator of efficiency; traditional techniques for improving the spatial resolution may result in higher costs, lower sensitivity, or reduced contrast.Approach.Therefore, we propose a deep-learning approach to improve the spatial resolution of MPI by fusing a dual-sampling convolutional neural network (FDS-MPI). An end-to-end model is established to generate high-spatial-resolution images from low-spatial-resolution images, avoiding the aforementioned shortcomings.Main results.We evaluate the performance of the proposed FDS-MPI model through simulation and phantom experiments. The results demonstrate that the FDS-MPI model can improve the spatial resolution by a factor of two.Significance.This significant improvement in MPI could facilitate the preclinical application of medical imaging modalities in the future.
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Affiliation(s)
- Yaxin Shang
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100069, People's Republic of China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, People's Republic of China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, People's Republic of China
| | - Jie Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100069, People's Republic of China
| | - Liwen Zhang
- Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, People's Republic of China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, People's Republic of China
| | - Xiangjun Wu
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100083, People's Republic of China
| | - Peng Zhang
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100069, People's Republic of China
| | - Lin Yin
- Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, People's Republic of China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, People's Republic of China
| | - Hui Hui
- Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, People's Republic of China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, People's Republic of China.,University of Chinese Academy of Sciences, Beijing, 100080, People's Republic of China
| | - Jie Tian
- Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, People's Republic of China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, People's Republic of China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100083, People's Republic of China.,Zhuhai Precision Medical Center, Zhuhai People's Hospital affiliated with Jinan University, Zhuhai, 519000, People's Republic of China
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Mulé S, Kharrat R, Zerbib P, Massire A, Nickel MD, Ambarki K, Reizine E, Baranes L, Zegai B, Pigneur F, Kobeiter H, Luciani A. Fast T2-weighted liver MRI: Image quality and solid focal lesions conspicuity using a deep learning accelerated single breath-hold HASTE fat-suppressed sequence. Diagn Interv Imaging 2022; 103:479-485. [PMID: 35597761 DOI: 10.1016/j.diii.2022.05.001] [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: 04/13/2022] [Revised: 04/27/2022] [Accepted: 05/02/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE Acceleration of MRI acquisitions and especially of T2-weighted sequences is essential to reduce the duration of MRI examinations but also kinetic artifacts in liver imaging. The purpose of this study was to compare the acquisition time and the image quality of a single-shot fat-suppressed turbo spin-echo (TSE) T2-weighted sequence with deep learning reconstruction (HASTEDL) with that of a fat-suppressed T2-weighted BLADE TSE sequence in patients with focal liver lesions. MATERIALS AND METHODS Ninety-five patients (52 men, 43 women; mean age: 61 ± 14 [SD]; age range: 28-87 years) with 42 focal liver lesions (17 hepatocellular carcinomas, 10 sarcoidosis lesions, 9 myeloma lesions, 3 liver metastases and 3 focal nodular hyperplasias) who underwent liver MRI at 1.5 T including HASTEDL and BLADE sequences were retrospectively included. Overall image quality, noise level in the liver, lesion conspicuity and sharpness of liver lesion contours were assessed by two independent readers. Liver signal-to-noise ratio (SNR) and lesion contrast-to-noise ratio (CNR) were measured and compared between the two sequences, as well as the mean duration of the sequences (Student t-test or Wilcoxon test for paired data). RESULTS Median overall quality on HASTEDL images (3; IQR: 3, 3) was significantly greater than that on BLADE images (2; IQR: 1, 3) (P < 0.001). Median noise level in the liver on HASTEDL images (0; IQR: 0, 0.5) was significantly lower than that on BLADE images (1; IQR: 1, 2) (P < 0.001). On HASTEDL images, mean liver SNR (107.3 ± 39.7 [SD]) and mean focal liver lesion CNR (87.0 ± 76.6 [SD]) were significantly greater than those on BLADE images (67.1 ± 23.8 [SD], P < 0.001 and 48.6 ± 43.9 [SD], P = 0.027, respectively). Acquisition time was significantly shorter with the HASTEDL sequence (18 ± [0] s; range: 18-18 s) compared to BLADE sequence (152 ± 47 [SD] s; range: 87-263 s) (P < 0.001). CONCLUSION By comparison with the BLADE sequence, HASTEDL sequence significantly reduces acquisition time while improving image quality, liver SNR and focal liver lesions CNR.
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Affiliation(s)
- Sébastien Mulé
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France; Faculté de Santé, Université Paris Est Créteil, Créteil 94000, France; INSERM IMRB, U 955, Equipe 18, Créteil 94000, France.
| | - Rym Kharrat
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France
| | - Pierre Zerbib
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France
| | | | | | | | - Edouard Reizine
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France; Faculté de Santé, Université Paris Est Créteil, Créteil 94000, France; INSERM IMRB, U 955, Equipe 18, Créteil 94000, France
| | - Laurence Baranes
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France
| | - Benhalima Zegai
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France
| | - Frederic Pigneur
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France
| | - Hicham Kobeiter
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France; Faculté de Santé, Université Paris Est Créteil, Créteil 94000, France
| | - Alain Luciani
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France; Faculté de Santé, Université Paris Est Créteil, Créteil 94000, France; INSERM IMRB, U 955, Equipe 18, Créteil 94000, France
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Exploring Multiple Application Scenarios of Visual Communication Course Using Deep Learning Under the Digital Twins. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5844290. [PMID: 35211166 PMCID: PMC8863483 DOI: 10.1155/2022/5844290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/06/2022] [Accepted: 01/10/2022] [Indexed: 11/18/2022]
Abstract
The emergence of intelligent technology has brought a particular impact and allows for virtuality-reality interaction in the educational field. In particular, digital twins (DTs) feature virtuality-reality symbiosis, solid virtual simulation, and high real-time interaction. It has also seen extended applications to the field of education. This study optimizes the design of the visual communication (Viscom) course based on the deep learning (DL) algorithm. Firstly, the theory of DL is analyzed following the relevant literature, and the typical DL networks, network structures, and related algorithms are introduced. Secondly, Viscom technology is expounded, and DL technology is applied to the Viscom course. Then, the applicability and feasibility of DL in the Viscom course are analyzed through a questionnaire survey (QS) design by collecting students’ attitudes towards Viscom courses before and after the experiment. After introducing DL into the Viscom course, the results show that students’ learning interest and satisfaction with the practical knowledge mastery have increased. However, the satisfaction with theoretical knowledge mastery before practical courses has decreased; overall, the teaching effect of the Viscom course has been improved. Therefore, the introduction of DL into the DT-enabled Viscom can provide a reference for developing the Viscom course. The research content offers technical support (TS) for integrating DT technology and modern education.
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Deep learning-accelerated T2-weighted imaging of the prostate: Impact of further acceleration with lower spatial resolution on image quality. Eur J Radiol 2021; 145:110012. [PMID: 34753082 DOI: 10.1016/j.ejrad.2021.110012] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/19/2021] [Accepted: 10/26/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE To compare image quality in prostate MRI among standard T2-weighted imaging (T2-std), accelerated T2-weighted imaging (T2WI) with high resolution (T2-HR) and more accelerated T2WI with lower resolution (T2-LR) using both conventional reconstruction (C) and deep learning reconstruction (DL). MATERIALS AND METHODS In 46 consecutive patients, T2-std, T2-HR and T2-LR were acquired in 3:32 min, 1:06 min and 0.52 min, respectively. Both reconstruction techniques (C and DL) were applied to T2-HR and T2-LR. Five sets of images (T2-std, T2-HRC, T2-LRC, T2-HRDL, and T2-LRDL) for each patient were independently evaluated by two radiologists. Quantitative analysis including the signal-to-noise ratio (SNR) and contrast ratio (CR) and qualitative analysis with a 5-point scale for the sharpness of structures, ghosting or other artifacts, noise and overall image quality were performed. RESULTS The SNR was not different in either the peripheral zone (PZ) or transition zone (TZ) between T2-LRDL and T2-std with the median value of 21.7 versus 22.6 in PZ and 16.5 versus 17.3 in TZ, respectively. The CR between the prostate gland and muscle was significantly lower on T2-HRC and T2-LRC than on T2-std. Most of the evaluated factors showed significantly lower scores on T2-HRC and T2-LRC than on T2-std. Although noise and overall image quality on T2-HRDL and other artifacts on T2-LRDL were rated significantly lower than on T2-std (median value 4.0 versus 4.5, P < 0.001; 4.5 versus 5.0, P = 0.001; 4.5 versus 5.0, P = 0.006, respectively), other factors did not differ between T2-std and T2-HRDL or T2-LRDL. CONCLUSION DL is useful to improve image quality in accelerated T2WI of the prostate gland. Using DL, accelerated T2WI with lower spatial resolution than T2-std can be achieved with similar image quality in much shorter scan time (75.5% reduction in the acquisition time).
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