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Ghotra SS, Cottier Y, Bruguier C, Dominguez A, Monnin P, Sá Dos Reis C. A pilot study to identify suitable MRI protocols for preoperative planning of total hip arthroplasty. Eur J Radiol 2024; 178:111620. [PMID: 39029238 DOI: 10.1016/j.ejrad.2024.111620] [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: 10/03/2023] [Revised: 06/15/2024] [Accepted: 07/11/2024] [Indexed: 07/21/2024]
Abstract
PURPOSE The purpose of this study is to identify suitable MRI sequences and evaluate the feasibility and performance of MRI for total hip arthroplasty (THA) preoperative planning. METHOD A multicentric pilot study was conducted to evaluate DP TSE and T1 GRE 3D sequences. High-resolution pelvis, hip, knee and ankle images were acquired. Protocols were optimised to enhance image quality (IQ) and reduce acquisition time to fit clinical practice. The final protocol was validated with 19 healthy volunteers with variable BMIs at 1.5 and 3 Tesla. Visual assessment was performed by five radiographers and radiologists using the ViewDEX software. Visual Grading Analysis (VGA), Intraclass Correlation Coefficient (ICC), Prevalence-adjusted and bias-adjusted kappa (PABAK) and Visual Grading Characteristics (VGC) were performed to analyse data. RESULTS VGA scores indicated that the optimised 3D DP TSE and 3D T1 GRE sequences at 3 T, as well as 3D DP TSE sequence at 1.5 T offer adequate IQ and allow a correct visualisation of the anatomy. Overall ICC analysis was moderate to good reliability at 0.749 (95 % CI 0.69-0.79) and increased from good to excellent at 0.846 (95 % CI 0.72-0.91) for DP at 3 T. PABAK shows fair agreement at 0.25 (95 % CI 0.227-0.273). VGC analysis showed that 3D DP TSE sequences performed statistically better than 3D T1 GRE at 1.5 and 3 T (p-value ≤ 0.05). Furthermore, 3 T sequences showed a statistically better performance compared to 1.5 T (p-value ≤ 0.05). CONCLUSIONS According to the results, 3D DP and T1 MRI sequences can be considered for preoperative planning for THA. Further research is required to emphasize the clinical validation of the results.
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Affiliation(s)
- Switinder Singh Ghotra
- School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne 1011, Switzerland; Department of Radiology, Hospital of Yverdon-les-Bains (eHnv), 1400 Yverdon-les-Bains, Switzerland.
| | - Yann Cottier
- Centre d'Imagerie Diagnostique de Lausanne, Lausanne 1011, Switzerland
| | - Christine Bruguier
- Department of Diagnostic & Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne 1011, Switzerland; University Center of Legal Medicine Lausanne - Geneva, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne 1011, Switzerland.
| | - Alejandro Dominguez
- School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne 1011, Switzerland; University Center of Legal Medicine Lausanne - Geneva, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne 1011, Switzerland.
| | - Pascal Monnin
- School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne 1011, Switzerland.
| | - Cláudia Sá Dos Reis
- School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne 1011, Switzerland.
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Lin DJ, Doshi AM, Fritz J, Recht MP. Designing Clinical MRI for Enhanced Workflow and Value. J Magn Reson Imaging 2024; 60:29-39. [PMID: 37795927 DOI: 10.1002/jmri.29038] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/18/2023] [Accepted: 09/18/2023] [Indexed: 10/06/2023] Open
Abstract
MRI is an expensive and traditionally time-intensive modality in imaging. With the paradigm shift toward value-based healthcare, radiology departments must examine the entire MRI process cycle to identify opportunities to optimize efficiency and enhance value for patients. Digital tools such as "frictionless scheduling" prioritize patient preference and convenience, thereby delivering patient-centered care. Recent advances in conventional and deep learning-based accelerated image reconstruction methods have reduced image acquisition time to such a degree that so-called nongradient time now constitutes a major percentage of total room time. For this reason, architectural design strategies that reconfigure patient preparation processes and decrease the turnaround time between scans can substantially impact overall throughput while also improving patient comfort and privacy. Real-time informatics tools that provide an enterprise-wide overview of MRI workflow and Picture Archiving and Communication System (PACS)-integrated instant messaging can complement these efforts by offering transparent, situational data and facilitating communication between radiology team members. Finally, long-term investment in training, recruiting, and retaining a highly skilled technologist workforce is essential for building a pipeline and team of technologists committed to excellence. Here, we highlight various opportunities for optimizing MRI workflow and enhancing value by offering many of our own on-the-ground experiences and conclude by anticipating some of the future directions for process improvement and innovation in clinical MR imaging. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Dana J Lin
- Department of Radiology, NYU Grossman School of Medicine/NYU Langone Health, New York, New York, USA
| | - Ankur M Doshi
- Department of Radiology, NYU Grossman School of Medicine/NYU Langone Health, New York, New York, USA
| | - Jan Fritz
- Department of Radiology, NYU Grossman School of Medicine/NYU Langone Health, New York, New York, USA
| | - Michael P Recht
- Department of Radiology, NYU Grossman School of Medicine/NYU Langone Health, New York, New York, USA
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3
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Vosshenrich J, Fritz J. [Accelerated musculoskeletal magnetic resonance imaging with deep learning-based image reconstruction at 0.55 T-3 T]. RADIOLOGIE (HEIDELBERG, GERMANY) 2024:10.1007/s00117-024-01325-w. [PMID: 38864874 DOI: 10.1007/s00117-024-01325-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/14/2024] [Indexed: 06/13/2024]
Abstract
CLINICAL/METHODICAL ISSUE Magnetic resonance imaging (MRI) is a central component of musculoskeletal imaging. However, long image acquisition times can pose practical barriers in clinical practice. STANDARD RADIOLOGICAL METHODS MRI is the established modality of choice in the diagnostic workup of injuries and diseases of the musculoskeletal system due to its high spatial resolution, excellent signal-to-noise ratio (SNR), and unparalleled soft tissue contrast. METHODOLOGICAL INNOVATIONS Continuous advances in hardware and software technology over the last few decades have enabled four-fold acceleration of 2D turbo-spin-echo (TSE) without compromising image quality or diagnostic performance. The recent clinical introduction of deep learning (DL)-based image reconstruction algorithms helps to minimize further the interdependency between SNR, spatial resolution and image acquisition time and allows the use of higher acceleration factors. PERFORMANCE The combined use of advanced acceleration techniques and DL-based image reconstruction holds enormous potential to maximize efficiency, patient comfort, access, and value of musculoskeletal MRI while maintaining excellent diagnostic accuracy. ACHIEVEMENTS Accelerated MRI with DL-based image reconstruction has rapidly found its way into clinical practice and proven to be of added value. Furthermore, recent investigations suggest that the potential of this technology does not yet appear to be fully harvested. PRACTICAL RECOMMENDATIONS Deep learning-reconstructed fast musculoskeletal MRI examinations can be reliably used for diagnostic work-up and follow-up of musculoskeletal pathologies in clinical practice.
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Affiliation(s)
- Jan Vosshenrich
- Department of Radiology, Grossman School of Medicine, New York University, 660 First Avenue, 10016, New York, NY, USA.
- Klinik für Radiologie und Nuklearmedizin, Universitätsspital Basel, Petersgraben 4, 4031, Basel, Schweiz.
| | - Jan Fritz
- Department of Radiology, Grossman School of Medicine, New York University, 660 First Avenue, 10016, New York, NY, USA
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Vashistha R, Almuqbel MM, Palmer NJ, Keenan RJ, Gilbert K, Wells S, Lynch A, Li A, Kingston-Smith S, Melzer TR, Koerzdoerfer G, O'Brien K. Evaluation of deep-learning TSE images in clinical musculoskeletal imaging. J Med Imaging Radiat Oncol 2024. [PMID: 38837669 DOI: 10.1111/1754-9485.13714] [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: 04/18/2023] [Accepted: 05/15/2024] [Indexed: 06/07/2024]
Abstract
In this study, we compared the fat-saturated (FS) and non-FS turbo spin echo (TSE) magnetic resonance imaging knee sequences reconstructed conventionally (conventional-TSE) against a deep learning-based reconstruction of accelerated TSE (DL-TSE) scans. A total of 232 conventional-TSE and DL-TSE image pairs were acquired for comparison. For each consenting patient, one of the clinically acquired conventional-TSE proton density-weighted sequences in the sagittal or coronal planes (FS and non-FS), or in the axial plane (non-FS), was repeated using a research DL-TSE sequence. The DL-TSE reconstruction resulted in an image resolution that increased by at least 45% and scan times that were up to 52% faster compared to the conventional TSE. All images were acquired on a MAGNETOM Vida 3T scanner (Siemens Healthineers AG, Erlangen, Germany). The reporting radiologists, blinded to the acquisition time, were requested to qualitatively compare the DL-TSE against the conventional-TSE reconstructions. Despite having a faster acquisition time, the DL-TSE was rated to depict smaller structures better for 139/232 (60%) cases, equivalent for 72/232 (31%) cases and worse for 21/232 (9%) cases compared to the conventional-TSE. Overall, the radiologists preferred the DL-TSE reconstruction in 124/232 (53%) cases and stated no preference, implying equivalence, for 65/232 (28%) cases. DL-TSE reconstructions enabled faster acquisition times while enhancing spatial resolution and preserving the image contrast. From these results, the DL-TSE provided added or comparable clinical value and utility in less time. DL-TSE offers the opportunity to further reduce the overall examination time and improve patient comfort with no loss in diagnostic accuracy.
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Affiliation(s)
- Rajat Vashistha
- ARC Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
- Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia
| | - Mustafa M Almuqbel
- Pacific Radiology Group, Christchurch, New Zealand
- New Zealand Brain Research Institute, Christchurch, New Zealand
- Department of Medicine, University of Otago, Christchurch, New Zealand
- South Australia Health and Medical Research Institute, Adelaide, South Australia, Australia
| | | | - Ross J Keenan
- Pacific Radiology Group, Christchurch, New Zealand
- Department of Radiology, Christchurch Hospital, Christchurch, New Zealand
| | | | - Scott Wells
- Pacific Radiology Group, Christchurch, New Zealand
| | - Andrew Lynch
- Pacific Radiology Group, Christchurch, New Zealand
| | - Andrew Li
- Pacific Radiology Group, Christchurch, New Zealand
| | | | - Tracy R Melzer
- New Zealand Brain Research Institute, Christchurch, New Zealand
- Department of Medicine, University of Otago, Christchurch, New Zealand
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | | | - Kieran O'Brien
- ARC Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
- Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia
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Lee S, Jung JY, Chung H, Lee HS, Nickel D, Lee J, Lee SY. Comparative analysis of image quality and interchangeability between standard and deep learning-reconstructed T2-weighted spine MRI. Magn Reson Imaging 2024; 109:211-220. [PMID: 38513791 DOI: 10.1016/j.mri.2024.03.022] [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: 01/03/2024] [Revised: 02/28/2024] [Accepted: 03/16/2024] [Indexed: 03/23/2024]
Abstract
RATIONALE AND OBJECTIVES MRI reconstruction of undersampled data using a deep learning (DL) network has been recently performed as part of accelerated imaging. Herein, we compared DL-reconstructed T2-weighted image (T2-WI) to conventional T2-WI regarding image quality and degenerative lesion detection. MATERIALS AND METHODS Sixty-two patients underwent C-spine (n = 27) or L-spine (n = 35) MRIs, including conventional and DL-reconstructed T2-WI. Image quality was assessed with non-uniformity measurement and 4-scale grading of structural visibility. Three readers (R1, R2, R3) independently assessed the presence and types of degenerative lesions. Student t-test was used to compare non-uniformity measurements. Interprotocol and interobserver agreement of structural visibility was analyzed with Wilcoxon signed-rank test and weighted-κ values, respectively. The diagnostic equivalence of degenerative lesion detection between two protocols was assessed with interchangeability test. RESULTS The acquisition time of DL-reconstructed images was reduced to about 21-58% compared to conventional images. Non-uniformity measurement was insignificantly different between the two images (p-value = 0.17). All readers rated DL-reconstructed images as showing the same or superior structural visibility compared to conventional images. Significantly improved visibility was observed at disk margin of C-spine (R1, p < 0.001; R2, p = 0.04) and dorsal root ganglia (R1, p = 0.03; R3, p = 0.02) and facet joint (R1, p = 0.04; R2, p < 0.001; R3, p = 0.03) of L-spine. Interobserver agreements of image quality were variable in each structure. Clinical interchangeability between two protocols for degenerative lesion detection was verified showing <5% in the upper bounds of 95% confidence intervals of agreement rate differences. CONCLUSIONS DL-reconstructed T2-WI demonstrates comparable image quality and diagnostic performance with conventional T2-WI in spine imaging, with reduced acquisition time.
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Affiliation(s)
- Seungeun Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Joon-Yong Jung
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.
| | - Heeyoung Chung
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Hyun-Soo Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; Siemens Healthineers, Seoul 06620, Republic of Korea.
| | - Dominik Nickel
- Siemens Healthcare GmbH, Allee am Roethelheimpark, Erlangen 91052, Germany.
| | - Jooyeon Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, School of Public Health, Houston, TX 77030, USA.
| | - So-Yeon Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
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Kakigi T, Sakamoto R, Arai R, Yamamoto A, Kuriyama S, Sano Y, Imai R, Numamoto H, Miyake KK, Saga T, Matsuda S, Nakamoto Y. Thin-slice 2D MR Imaging of the Shoulder Joint Using Denoising Deep Learning Reconstruction Provides Higher Image Quality Than 3D MR Imaging. Magn Reson Med Sci 2024:mp.2023-0115. [PMID: 38777762 DOI: 10.2463/mrms.mp.2023-0115] [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: 05/25/2024] Open
Abstract
PURPOSE This study was conducted to evaluate whether thin-slice 2D fat-saturated proton density-weighted images of the shoulder joint in three imaging planes combined with parallel imaging, partial Fourier technique, and denoising approach with deep learning-based reconstruction (dDLR) are more useful than 3D fat-saturated proton density multi-planar voxel images. METHODS Eighteen patients who underwent MRI of the shoulder joint at 3T were enrolled. The denoising effect of dDLR in 2D was evaluated using coefficient of variation (CV). Qualitative evaluation of anatomical structures, noise, and artifacts in 2D after dDLR and 3D was performed by two radiologists using a five-point Likert scale. All were analyzed statistically. Gwet's agreement coefficients were also calculated. RESULTS The CV of 2D after dDLR was significantly lower than that before dDLR (P < 0.05). Both radiologists rated 2D higher than 3D for all anatomical structures and noise (P < 0.05), except for artifacts. Both Gwet's agreement coefficients of anatomical structures, noise, and artifacts in 2D and 3D produced nearly perfect agreement between the two radiologists. The evaluation of 2D tended to be more reproducible than 3D. CONCLUSION 2D with parallel imaging, partial Fourier technique, and dDLR was proved to be superior to 3D for depicting shoulder joint structures with lower noise.
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Affiliation(s)
- Takahide Kakigi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
| | - Ryo Sakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
- Department of Real World Data Research and Development, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
| | - Ryuzo Arai
- Department of Orthopaedic Surgery, Kyoto Katsura Hospital, Kyoto, Kyoto, Japan
| | - Akira Yamamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
- Center for Medical Education, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
| | - Shinichi Kuriyama
- Department of Orthopaedic Surgery, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
| | - Yuichiro Sano
- MRI Systems Division, Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Rimika Imai
- MRI Systems Division, Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Hitomi Numamoto
- Division of Clinical Radiology Service, Kyoto University Hospital, Kyoto, Kyoto, Japan
- Department of Advanced Medical Imaging Research, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
| | - Kanae Kawai Miyake
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
- Department of Advanced Medical Imaging Research, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
| | - Tsuneo Saga
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
- Department of Advanced Medical Imaging Research, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
| | - Shuichi Matsuda
- Department of Orthopaedic Surgery, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
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Zhang Y, Ye Z, Xia C, Tan Y, Zhang M, Lv X, Tang J, Li Z. Clinical Applications and Recent Updates of Simultaneous Multi-slice Technique in Accelerated MRI. Acad Radiol 2024; 31:1976-1988. [PMID: 38220568 DOI: 10.1016/j.acra.2023.12.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 12/15/2023] [Accepted: 12/19/2023] [Indexed: 01/16/2024]
Abstract
Simultaneous multi-slice (SMS) is a magnetic resonance imaging (MRI) acceleration technique that utilizes multi-band radio-frequency pulses to simultaneously excite and encode multiple slices. Currently, SMS has been widely studied and applied in the MRI examination to reduce acquisition time, which can significantly improve the examination efficiency and patient throughput. Moreover, SMS technique can improve spatial resolution, which is of great value in disease diagnosis, treatment response monitoring, and prognosis prediction. This review will briefly introduce the technical principles of SMS, and summarize its current clinical applications. More importantly, we will discuss the recent technical progress and future research direction of SMS, hoping to highlight the clinical value and scientific potential of this technique.
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Affiliation(s)
- Yiteng Zhang
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Zheng Ye
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Chunchao Xia
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Yuqi Tan
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Meng Zhang
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Xinyang Lv
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Jing Tang
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Zhenlin Li
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China.
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Donners R, Vosshenrich J, Gutzeit A, Bach M, Schlicht F, Obmann MM, Harder D, Breit HC. New-Generation 0.55 T MRI of the Knee-Initial Clinical Experience and Comparison With 3 T MRI. Invest Radiol 2024; 59:298-305. [PMID: 37747455 DOI: 10.1097/rli.0000000000001016] [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: 09/26/2023]
Abstract
OBJECTIVES The aim of this study was to compare the detection rate of and reader confidence in 0.55 T knee magnetic resonance imaging (MRI) findings with 3 T knee MRI in patients with acute trauma and knee pain. MATERIALS AND METHODS In this prospective study, 0.55 T and 3 T knee MRI of 25 symptomatic patients (11 women; median age, 38 years) with suspected internal derangement of the knee was obtained in 1 setting. On the 0.55 T system, a commercially available deep learning image reconstruction algorithm was used (Deep Resolve Gain and Deep Resolve Sharp; Siemens Healthineers), which was not available on the 3 T system. Two board-certified radiologists reviewed all images independently and graded image quality parameters, noted MRI findings and their respective reporting confidence level for the presence or absence, as well as graded the bone, cartilage, meniscus, ligament, and tendon lesions. Image quality and reader confidence levels were compared ( P < 0.05 = significant), and clinical findings were correlated between 0.55 T and 3 T MRI by calculation of the intraclass correlation coefficient (ICC). RESULTS Image quality was rated higher at 3 T compared with 0.55 T studies (each P ≤ 0.017). Agreement between 0.55 T and 3 T MRI for the detection and grading of bone marrow edema and fractures, ligament and tendon lesions, high-grade meniscus and cartilage lesions, Baker cysts, and joint effusions was perfect for both readers. Overall identification and grading of cartilage and meniscal lesions showed good agreement between high- and low-field MRI (each ICC > 0.76), with lower agreement for low-grade cartilage (ICC = 0.77) and meniscus lesions (ICC = 0.49). There was no difference in readers' confidence levels for reporting lesions of bone, ligaments, tendons, Baker cysts, and joint effusions between 0.55 T and 3 T (each P > 0.157). Reader reporting confidence was higher for cartilage and meniscal lesions at 3 T (each P < 0.041). CONCLUSIONS New-generation 0.55 T knee MRI, with deep learning-aided image reconstruction, allows for reliable detection and grading of joint lesions in symptomatic patients, but it showed limited accuracy and reader confidence for low-grade cartilage and meniscal lesions in comparison with 3 T MRI.
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Affiliation(s)
- Ricardo Donners
- From the Department of Radiology, University Hospital Basel (R.D., J.V., M.B., F.S., M.O., D.H., H.-C.B.), Basel, Switzerland; and Institute of Radiology and Nuclear Medicine and Breast Center St. Anna (A.G.), Lucerne, Switzerland
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Parikh P, Klanderman M, Teck A, Kunzelman J, Banerjee I, DeYoung D, Hara A, Tan N, Yano M. Effects of Patient Demographics and Examination Factors on Patient Experience in Outpatient MRI Appointments. J Am Coll Radiol 2024; 21:601-608. [PMID: 37247830 DOI: 10.1016/j.jacr.2023.02.032] [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: 11/18/2022] [Revised: 02/08/2023] [Accepted: 02/17/2023] [Indexed: 05/31/2023]
Abstract
OBJECTIVE The objective of this article is to describe the effects of patient demographics and examination factors on patient-reported experience in outpatient MRI examinations. METHODS This institutional review board-waived, HIPPA-compliant quality improvement study evaluated outpatient MRI appointments from March 2021 to January 2022 using a postappointment survey consisting of a 5-point emoji scale and text-based feedback. Patient demographics and examination information were extracted from electronic medical records. Ratings ≤ 3 were categorized as negative, and ratings ≥ 4 were categorized as positive. Continuous variables were analyzed using the Kruskal-Wallis test, and categorical variables were analyzed using the Fisher's exact test. A P value less than .05 was considered significant. A natural language processing algorithm was trained and validated to categorize patient feedback. RESULTS A total of 3,636 patients responded to the survey. Positive ratings had a higher proportion of male respondents compared with negative ratings (47.9% versus 37.0%, P = .004). Examination characteristics were also grouped by positive or negative rating. Patients who endured longer examination time (median 54.0 min versus 44.0 min, P < .001) and longer wait time after check-in (median 61.6 min versus 46.2 min, P < .001) were more likely to give negative ratings. The most common themes of free text feedback included excellent service (84.3%), on-time service (8.4%), and comfortable intravenous line placement (0.4%). Most common negative feedback included long wait times (10.5%), poor communication (8.4%), and physical discomfort during the examination (4.2%). CONCLUSION Male gender, short examination duration, and on-time start were associated with positive patient ratings.
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Affiliation(s)
- Parth Parikh
- Mayo Clinic Alix School of Medicine, Scottsdale, Arizona
| | - Molly Klanderman
- Department of Quantitative Health Sciences, Mayo Clinic Arizona, Scottsdale, Arizona
| | - Alyssa Teck
- Administrative Operations, Mayo Clinic Arizona, Phoenix, Arizona
| | | | - Imon Banerjee
- Department of Radiology, Mayo Clinic Arizona, Phoenix, Arizona
| | - Dyan DeYoung
- Department of Radiology, Mayo Clinic Arizona, Phoenix, Arizona
| | - Amy Hara
- Chair of the Department of Radiology, Mayo Clinic Arizona, Phoenix, Arizona
| | - Nelly Tan
- Associate Professor and Assistant Program Director for the Diagnostic Radiology Residency, Department of Radiology, Mayo Clinic Arizona, Phoenix, Arizona.
| | - Motoyo Yano
- Chair of the Division of Abdominal Radiology, Department of Radiology, Mayo Clinic Arizona, Phoenix, Arizona
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Roemer FW, Jarraya M, Hayashi D, Crema MD, Haugen IK, Hunter DJ, Guermazi A. A perspective on the evolution of semi-quantitative MRI assessment of osteoarthritis: Past, present and future. Osteoarthritis Cartilage 2024; 32:460-472. [PMID: 38211810 DOI: 10.1016/j.joca.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 12/15/2023] [Accepted: 01/04/2024] [Indexed: 01/13/2024]
Abstract
OBJECTIVE This perspective describes the evolution of semi-quantitative (SQ) magnetic resonance imaging (MRI) in characterizing structural tissue pathologies in osteoarthritis (OA) imaging research over the last 30 years. METHODS Authors selected representative articles from a PubMed search to illustrate key steps in SQ MRI development, validation, and application. Topics include main scoring systems, reading techniques, responsiveness, reliability, technical considerations, and potential impact of artificial intelligence (AI). RESULTS Based on original research published between 1993 and 2023, this article introduces available scoring systems, including but not limited to Whole-Organ Magnetic Resonance Imaging Score (WORMS) as the first system for whole-organ assessment of the knee and the now commonly used MRI Osteoarthritis Knee Score (MOAKS) instrument. Specific systems for distinct OA subtypes or applications have been developed as well as MRI scoring instruments for other joints such as the hip, the fingers or thumb base. SQ assessment has proven to be valid, reliable, and responsive, aiding OA investigators in understanding the natural history of the disease and helping to detect response to treatment. AI may aid phenotypic characterization in the future. SQ MRI assessment's role is increasing in eligibility and safety evaluation in knee OA clinical trials. CONCLUSIONS Evidence supports the validity, reliability, and responsiveness of SQ MRI assessment in understanding structural aspects of disease onset and progression. SQ scoring has helped explain associations between structural tissue damage and clinical manifestations, as well as disease progression. While AI may support human readers to more efficiently perform SQ assessment in the future, its current application in clinical trials still requires validation and regulatory approval.
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Affiliation(s)
- Frank W Roemer
- Universitätsklinikum Erlangen & Friedrich Alexander Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA.
| | - Mohamed Jarraya
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Daichi Hayashi
- Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Michel D Crema
- Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA; Institute of Sports Imaging, French National Institute of Sports (INSEP), Paris, France
| | - Ida K Haugen
- Center for Treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, Norway
| | - David J Hunter
- Department of Rheumatology, Royal North Shore Hospital and Sydney Musculoskeletal Health, Kolling Institute, University of Sydney, St. Leonards, NSW, Australia
| | - Ali Guermazi
- Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA; Boston VA Healthcare System, West Roxbury, MA, USA
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11
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Chang EY, Bencardino JT, French CN, Fritz J, Hanrahan CJ, Jibri Z, Kassarjian A, Motamedi K, Ringler MD, Strickland CD, Tiegs-Heiden CA, Walker REA. SSR white paper: guidelines for utilization and performance of direct MR arthrography. Skeletal Radiol 2024; 53:209-244. [PMID: 37566148 PMCID: PMC10730654 DOI: 10.1007/s00256-023-04420-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 07/23/2023] [Accepted: 07/29/2023] [Indexed: 08/12/2023]
Abstract
OBJECTIVE Direct magnetic resonance arthrography (dMRA) is often considered the most accurate imaging modality for the evaluation of intra-articular structures, but utilization and performance vary widely without consensus. The purpose of this white paper is to develop consensus recommendations on behalf of the Society of Skeletal Radiology (SSR) based on published literature and expert opinion. MATERIALS AND METHODS The Standards and Guidelines Committee of the SSR identified guidelines for utilization and performance of dMRA as an important topic for study and invited all SSR members with expertise and interest to volunteer for the white paper panel. This panel was tasked with determining an outline, reviewing the relevant literature, preparing a written document summarizing the issues and controversies, and providing recommendations. RESULTS Twelve SSR members with expertise in dMRA formed the ad hoc white paper authorship committee. The published literature on dMRA was reviewed and summarized, focusing on clinical indications, technical considerations, safety, imaging protocols, complications, controversies, and gaps in knowledge. Recommendations for the utilization and performance of dMRA in the shoulder, elbow, wrist, hip, knee, and ankle/foot regions were developed in group consensus. CONCLUSION Although direct MR arthrography has been previously used for a wide variety of clinical indications, the authorship panel recommends more selective application of this minimally invasive procedure. At present, direct MR arthrography remains an important procedure in the armamentarium of the musculoskeletal radiologist and is especially valuable when conventional MRI is indeterminant or results are discrepant with clinical evaluation.
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Affiliation(s)
- Eric Y Chang
- Radiology Service, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiology, University of California, San Diego Medical Center, San Diego, CA, USA
| | - Jenny T Bencardino
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Cristy N French
- Department of Radiology, Penn State Hershey Medical Center, Hummelstown, PA, USA
| | - Jan Fritz
- Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | | | - Zaid Jibri
- GNMI in Mississauga, Greater Toronto Area, Toronto, ON, Canada
| | - Ara Kassarjian
- Department of Radiology, Division of Musculoskeletal Imaging, Olympia Medical Center, Elite Sports Imaging, Madrid, Spain
| | - Kambiz Motamedi
- Department of Radiology, University of California, Los Angeles Medical Center, Los Angeles, CA, USA
| | | | - Colin D Strickland
- Department of Radiology, University of Colorado School of Medicine, Aurora, CO, USA
| | | | - Richard E A Walker
- McCaig Institute for Bone and Joint Health, Calgary, Canada.
- Cumming School of Medicine, University of Calgary, 3280 Hospital Dr NW, Calgary, AB, T2N 4Z6, Canada.
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12
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Chung CB, Pathria MN, Resnick D. MRI in MSK: is it the ultimate examination? Skeletal Radiol 2024:10.1007/s00256-024-04601-x. [PMID: 38277028 DOI: 10.1007/s00256-024-04601-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 01/17/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024]
Affiliation(s)
- Christine B Chung
- Department of Radiology, University of California, San Diego, CA, USA.
- Department of Radiology, Veterans Affairs Medical Center, San Diego, CA, USA.
| | - Mini N Pathria
- Department of Radiology, University of California, San Diego, CA, USA
| | - Donald Resnick
- Department of Radiology, University of California, San Diego, CA, USA
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13
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Guermazi A, Omoumi P, Tordjman M, Fritz J, Kijowski R, Regnard NE, Carrino J, Kahn CE, Knoll F, Rueckert D, Roemer FW, Hayashi D. How AI May Transform Musculoskeletal Imaging. Radiology 2024; 310:e230764. [PMID: 38165245 PMCID: PMC10831478 DOI: 10.1148/radiol.230764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 06/18/2023] [Accepted: 07/11/2023] [Indexed: 01/03/2024]
Abstract
While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists. Upon successful clinical implementation, a wide variety of AI-based tools can improve the musculoskeletal radiologist's workflow by triaging imaging examinations, helping with image interpretation, and decreasing the reporting time. Additional AI applications may also be helpful for business, education, and research purposes if successfully integrated into the daily practice of musculoskeletal radiology. The question is not whether AI will replace radiologists, but rather how musculoskeletal radiologists can take advantage of AI to enhance their expert capabilities.
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Affiliation(s)
- Ali Guermazi
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Patrick Omoumi
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Mickael Tordjman
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Jan Fritz
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Richard Kijowski
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Nor-Eddine Regnard
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - John Carrino
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Charles E. Kahn
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Florian Knoll
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Daniel Rueckert
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Frank W. Roemer
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Daichi Hayashi
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
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14
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Yoon MA, Gold GE, Chaudhari AS. Accelerated Musculoskeletal Magnetic Resonance Imaging. J Magn Reson Imaging 2023. [PMID: 38156716 DOI: 10.1002/jmri.29205] [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: 10/24/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 01/03/2024] Open
Abstract
With a substantial growth in the use of musculoskeletal MRI, there has been a growing need to improve MRI workflow, and faster imaging has been suggested as one of the solutions for a more efficient examination process. Consequently, there have been considerable advances in accelerated MRI scanning methods. This article aims to review the basic principles and applications of accelerated musculoskeletal MRI techniques including widely used conventional acceleration methods, more advanced deep learning-based techniques, and new approaches to reduce scan time. Specifically, conventional accelerated MRI techniques, including parallel imaging, compressed sensing, and simultaneous multislice imaging, and deep learning-based accelerated MRI techniques, including undersampled MR image reconstruction, super-resolution imaging, artifact correction, and generation of unacquired contrast images, are discussed. Finally, new approaches to reduce scan time, including synthetic MRI, novel sequences, and new coil setups and designs, are also reviewed. We believe that a deep understanding of these fast MRI techniques and proper use of combined acceleration methods will synergistically improve scan time and MRI workflow in daily practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Min A Yoon
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
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15
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Ensle F, Kaniewska M, Tiessen A, Lohezic M, Getzmann JM, Guggenberger R. Diagnostic performance of deep learning-based reconstruction algorithm in 3D MR neurography. Skeletal Radiol 2023; 52:2409-2418. [PMID: 37191931 PMCID: PMC10581934 DOI: 10.1007/s00256-023-04362-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/05/2023] [Accepted: 05/07/2023] [Indexed: 05/17/2023]
Abstract
OBJECTIVE The study aims to evaluate the diagnostic performance of deep learning-based reconstruction method (DLRecon) in 3D MR neurography for assessment of the brachial and lumbosacral plexus. MATERIALS AND METHODS Thirty-five exams (18 brachial and 17 lumbosacral plexus) of 34 patients undergoing routine clinical MR neurography at 1.5 T were retrospectively included (mean age: 49 ± 12 years, 15 female). Coronal 3D T2-weighted short tau inversion recovery fast spin echo with variable flip angle sequences covering plexial nerves on both sides were obtained as part of the standard protocol. In addition to standard-of-care (SOC) reconstruction, k-space was reconstructed with a 3D DLRecon algorithm. Two blinded readers evaluated images for image quality and diagnostic confidence in assessing nerves, muscles, and pathology using a 4-point scale. Additionally, signal-to-noise ratio (SNR) and contrast-to-noise ratios (CNR) between nerve, muscle, and fat were measured. For comparison of visual scoring result non-parametric paired sample Wilcoxon signed-rank testing and for quantitative analysis paired sample Student's t-testing was performed. RESULTS DLRecon scored significantly higher than SOC in all categories of image quality (p < 0.05) and diagnostic confidence (p < 0.05), including conspicuity of nerve branches and pathology. With regard to artifacts there was no significant difference between the reconstruction methods. Quantitatively, DLRecon achieved significantly higher CNR and SNR than SOC (p < 0.05). CONCLUSION DLRecon enhanced overall image quality, leading to improved conspicuity of nerve branches and pathology, and allowing for increased diagnostic confidence in evaluation of the brachial and lumbosacral plexus.
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Affiliation(s)
- Falko Ensle
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), University of Zurich, Raemistrasse 100, CH-8091, Zurich, Switzerland.
- University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland.
| | - Malwina Kaniewska
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), University of Zurich, Raemistrasse 100, CH-8091, Zurich, Switzerland
- University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland
| | - Anja Tiessen
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), University of Zurich, Raemistrasse 100, CH-8091, Zurich, Switzerland
- University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland
| | | | - Jonas M Getzmann
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), University of Zurich, Raemistrasse 100, CH-8091, Zurich, Switzerland
- University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland
| | - Roman Guggenberger
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), University of Zurich, Raemistrasse 100, CH-8091, Zurich, Switzerland
- University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland
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16
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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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17
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Khodarahmi I, Khanuja HS, Stern SE, Carrino JA, Fritz J. Compressed Sensing SEMAC MRI of Hip, Knee, and Ankle Arthroplasty Implants: A 1.5-T and 3-T Intrapatient Performance Comparison for Diagnosing Periprosthetic Abnormalities. AJR Am J Roentgenol 2023; 221:661-672. [PMID: 37255041 DOI: 10.2214/ajr.23.29380] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
BACKGROUND. The utility of 3-T MRI for diagnosing joint disorders is established, but its performance for diagnosing abnormalities around arthroplasty implants is unclear. OBJECTIVE. The purpose of this study was to compare 1.5-T and 3-T compressed sensing slice encoding for metal artifact correction (SEMAC) MRI for diagnosing peri-prosthetic abnormalities around hip, knee, and ankle arthroplasty implants. METHODS. Forty-five participants (26 women, 19 men; mean age ± SD, 71 ± 14 years) with symptomatic lower extremity arthroplasty (hip, knee, and ankle, 15 each) prospectively underwent consecutive 1.5- and 3-T MRI examinations with intermediate-weighted (IW) and STIR compressed sensing SEMAC sequences. Using a Likert scale, three radiologists evaluated the presence or absence of periprosthetic abnormalities, including bone marrow edema-like signal, osteolysis, stress reaction/fracture, synovitis, and tendon abnormalities and collections; image quality; and visibility of anatomic structures. Statistical analysis included nonparametric comparison and interchangeability testing. RESULTS. For diagnosing periprosthetic abnormalities, 1.5-T and 3-T compressed sensing SEMAC MRI were interchangeable. Across all three joints, 3-T MRI had lower noise than 1.5-T MRI (median IW and STIR scores at 3 T vs 1.5 T, 4 and 4 [range, 2-5 and 3-5] vs 3 and 3 [range, 2-5 and 2-4]; p < .01 for both), sharper edges (median IW and STIR scores at 3 T vs 1.5 T, 4 and 4 [both ranges, 2-5] vs 3 and 3 [range, 2-4 and 2-5]; p < .02 and p < .05), and more effective metal artifact reduction (median IW and STIR scores at 3 T vs 1.5 T, 4 and 4 [range, 3-5 and 2-5] vs 4 and 4 [both ranges, 3-5]; p < .02 and p = .72). Agreement was moderate to substantial for image contrast (IW and STIR, 0.66 and 0.54 [95% CI, 0.41-0.91 and 0.29-0.80]; p = .58 and p = .16) and joint capsule visualization (IW and STIR, 0.57 and 0.70 [range, 0.32-0.81 and 0.51-0.89]; p = .16 and p = .19). The bone-implant interface was more visible at 1.5 T (median IW and STIR scores, 4 and 4 [both ranges, 2-5] at 1.5 T vs 3 and 3 [both ranges, 2-5] at 3 T; p = .08 and p = .58), but periprosthetic tissues had superior visibility at 3 T (IW and STIR, 4 and 4 [both ranges, 3-5] at 3 T vs 4 and 4 [ranges, 2-5 and 3-5] at 1.5 T; p = .07 and p = .19). CONCLUSION. Optimized 1.5-T and 3-T compressed sensing SEMAC MRI are interchangeable for diagnosing periprosthetic abnormalities, although metallic artifacts are larger at 3 T. CLINICAL IMPACT. With compressed sensing SEMAC MRI, lower extremity arthroplasty implants can be imaged at 3 T rather than 1.5 T.
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Affiliation(s)
- Iman Khodarahmi
- Department of Radiology, NYU Grossman School of Medicine, 660 1st Ave, 3rd Fl, Rm 313, New York, NY 10016
| | - Harpal S Khanuja
- Department of Orthopaedic Surgery, Johns Hopkins School of Medicine, Baltimore, MD
| | - Steven E Stern
- Centre for Data Analytics, Bond University, Gold Coast, Australia
| | - John A Carrino
- Department of Radiology, Hospital for Special Surgery, New York, NY
| | - Jan Fritz
- Department of Radiology, NYU Grossman School of Medicine, 660 1st Ave, 3rd Fl, Rm 313, New York, NY 10016
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18
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Ryan ME. Commentary: The emerging roll of rapid MR spine imaging in pediatrics. Pediatr Radiol 2023; 53:2490-2491. [PMID: 37735207 DOI: 10.1007/s00247-023-05776-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 09/12/2023] [Accepted: 09/14/2023] [Indexed: 09/23/2023]
Affiliation(s)
- Maura E Ryan
- Department of Medical Imaging, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, 225 East Chicago Ave, Chicago, IL, 60611, USA.
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19
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Herrmann J, Afat S, Gassenmaier S, Koerzdoerfer G, Lingg A, Almansour H, Nickel D, Werner S. Image Quality and Diagnostic Performance of Accelerated 2D Hip MRI with Deep Learning Reconstruction Based on a Deep Iterative Hierarchical Network. Diagnostics (Basel) 2023; 13:3241. [PMID: 37892062 PMCID: PMC10606422 DOI: 10.3390/diagnostics13203241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 10/10/2023] [Accepted: 10/14/2023] [Indexed: 10/29/2023] Open
Abstract
OBJECTIVES Hip MRI using standard multiplanar sequences requires long scan times. Accelerating MRI is accompanied by reduced image quality. This study aimed to compare standard two-dimensional (2D) turbo spin echo (TSE) sequences with accelerated 2D TSE sequences with deep learning (DL) reconstruction (TSEDL) for routine clinical hip MRI at 1.5 and 3 T in terms of feasibility, image quality, and diagnostic performance. MATERIAL AND METHODS In this prospective, monocentric study, TSEDL was implemented clinically and evaluated in 14 prospectively enrolled patients undergoing a clinically indicated hip MRI at 1.5 and 3T between October 2020 and May 2021. Each patient underwent two examinations: For the first exam, we used standard sequences with generalized autocalibrating partial parallel acquisition reconstruction (TSES). For the second exam, we implemented prospectively undersampled TSE sequences with DL reconstruction (TSEDL). Two radiologists assessed the TSEDL and TSES regarding image quality, artifacts, noise, edge sharpness, diagnostic confidence, and delineation of anatomical structures using an ordinal five-point Likert scale (1 = non-diagnostic; 2 = poor; 3 = moderate; 4 = good; 5 = excellent). Both sequences were compared regarding the detection of common pathologies of the hip. Comparative analyses were conducted to assess the differences between TSEDL and TSES. RESULTS Compared with TSES, TSEDL was rated to be significantly superior in terms of image quality (p ≤ 0.020) with significantly reduced noise (p ≤ 0.001) and significantly improved edge sharpness (p = 0.003). No difference was found between TSES and TSEDL concerning the extent of artifacts, diagnostic confidence, or the delineation of anatomical structures (p > 0.05). Example acquisition time reductions for the TSE sequences of 52% at 3 Tesla and 70% at 1.5 Tesla were achieved. CONCLUSION TSEDL of the hip is clinically feasible, showing excellent image quality and equivalent diagnostic performance compared with TSES, reducing the acquisition time significantly.
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Affiliation(s)
- Judith Herrmann
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Gregor Koerzdoerfer
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052 Erlangen, Germany
| | - Andreas Lingg
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Dominik Nickel
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052 Erlangen, Germany
| | - Sebastian Werner
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
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20
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Dalili D, Isaac A, Fritz J. Selective MR neurography-guided lumbosacral plexus perineural injections: techniques, targets, and territories. Skeletal Radiol 2023; 52:1929-1947. [PMID: 37495713 DOI: 10.1007/s00256-023-04384-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/02/2023] [Accepted: 06/02/2023] [Indexed: 07/28/2023]
Abstract
The T12 to S4 spinal nerves form the lumbosacral plexus in the retroperitoneum, providing sensory and motor innervation to the pelvis and lower extremities. The lumbosacral plexus has a wide range of anatomic variations and interchange of fibers between nerve anastomoses. Neuropathies of the lumbosacral plexus cause a broad spectrum of complex pelvic and lower extremity pain syndromes, which can be challenging to diagnose and treat successfully. In their workup, selective nerve blocks are employed to test the hypothesis that a lumbosacral plexus nerve contributes to a suspected pelvic and extremity pain syndrome, whereas therapeutic perineural injections aim to alleviate pain and paresthesia symptoms. While the sciatic and femoral nerves are large in caliber, the iliohypogastric and ilioinguinal, genitofemoral, lateral femoral cutaneous, anterior femoral cutaneous, posterior femoral cutaneous, obturator, and pudendal nerves are small, measuring a few millimeters in diameter and have a wide range of anatomic variants. Due to their minuteness, direct visualization of the smaller lumbosacral plexus branches can be difficult during selective nerve blocks, particularly in deeper pelvic locations or larger patients. In this setting, the high spatial and contrast resolution of interventional MR neurography guidance benefits nerve visualization and targeting, needle placement, and visualization of perineural injectant distribution, providing a highly accurate alternative to more commonly used ultrasonography, fluoroscopy, and computed tomography guidance for perineural injections. This article offers a practical guide for MR neurography-guided lumbosacral plexus perineural injections, including interventional setup, pulse sequence protocols, lumbosacral plexus MR neurography anatomy, anatomic variations, and injection targets.
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Affiliation(s)
- Danoob Dalili
- Academic Surgical Unit, Southwest London Elective Orthopaedic Centre (SWLEOC), Dorking Road, Epsom, KT18 7EG, London, UK
- Department of Radiology, Epsom and St Hellier University Hospitals NHS Trust, Dorking Road, Epsom, London, KT18 7EG, UK
| | - Amanda Isaac
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Jan Fritz
- Department of Radiology, New York University Grossman School of Medicine, NY, USA.
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21
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Fritz B, de Cesar Netto C, Fritz J. Multiaxial 3D MRI of the Ankle: Advanced High-Resolution Visualization of Ligaments, Tendons, and Articular Cartilage. Foot Ankle Clin 2023; 28:529-550. [PMID: 37536817 DOI: 10.1016/j.fcl.2023.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
MRI is a valuable tool for diagnosing a broad spectrum of acute and chronic ankle disorders, including ligament tears, tendinopathy, and osteochondral lesions. Traditional two-dimensional (2D) MRI provides a high image signal and contrast of anatomic structures for accurately characterizing articular cartilage, bone marrow, synovium, ligaments, tendons, and nerves. However, 2D MRI limitations are thick slices and fixed slice orientations. In clinical practice, 2D MRI is limited to 2 to 3 mm slice thickness, which can cause blurred contours of oblique structures due to volume averaging effects within the image slice. In addition, image plane orientations are fixated and cannot be changed after the scan, resulting in 2D MRI lacking multiplanar and multiaxial reformation abilities for individualized image plane orientations along oblique and curved anatomic structures, such as ankle ligaments and tendons. In contrast, three-dimensional (3D) MRI is a newer, clinically available MRI technique capable of acquiring high-resolution ankle MRI data sets with isotropic voxel size. The inherently high spatial resolution of 3D MRI permits up to five times thinner (0.5 mm) image slices. In addition, 3D MRI can be acquired image voxel with the same edge length in all three space dimensions (isotropism), permitting unrestricted multiplanar and multiaxial image reformation and postprocessing after the MRI scan. Clinical 3D MRI of the ankle with 0.5 to 0.7 mm isotropic voxel size resolves the smallest anatomic ankle structures and abnormalities of ligament and tendon fibers, osteochondral lesions, and nerves. After acquiring the images, operators can align image planes individually along any anatomic structure of interest, such as ligaments and tendons segments. In addition, curved multiplanar image reformations can unfold the entire course of multiaxially curved structures, such as perimalleolar tendons, into one image plane. We recommend adding 3D MRI pulse sequences to traditional 2D MRI protocols to visualize small and curved ankle structures to better advantage. This article provides an overview of the clinical application of 3D MRI of the ankle, compares diagnostic performances of 2D and 3D MRI for diagnosing ankle abnormalities, and illustrates clinical 3D ankle MRI applications.
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Affiliation(s)
- Benjamin Fritz
- Department of Radiology, Balgrist University Hospital, Forchstrasse 340, Zurich 8008, Switzerland; Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Cesar de Cesar Netto
- Department of Orthopaedics and Rehabilitation, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
| | - Jan Fritz
- Department of Radiology, Division of Musculoskeletal Radiology, NYU Grossman School of Medicine, 660 1st Avenue, New York, NY 10016, USA.
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22
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Fritz J, Rashidi A, de Cesar Netto C. Magnetic Resonance Imaging of Total Ankle Arthroplasty: State-of-The-Art Assessment of Implant-Related Pain and Dysfunction. Foot Ankle Clin 2023; 28:463-492. [PMID: 37536814 DOI: 10.1016/j.fcl.2023.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
Total ankle arthroplasty (TAA) is an effective alternative for treating patients with end-stage ankle degeneration, improving mobility, and providing pain relief. Implant survivorship is constantly improving; however, complications occur. Many causes of pain and dysfunction after total ankle arthroplasty can be diagnosed accurately with clinical examination, laboratory, radiography, and computer tomography. However, when there are no or inconclusive imaging findings, magnetic resonance imaging (MRI) is highly accurate in identifying and characterizing bone resorption, osteolysis, infection, osseous stress reactions, nondisplaced fractures, polyethylene damage, nerve injuries and neuropathies, as well as tendon and ligament tears. Multiple vendors offer effective, clinically available MRI techniques for metal artifact reduction MRI of total ankle arthroplasty. This article reviews the MRI appearances of common TAA implant systems, clinically available techniques and protocols for metal artifact reduction MRI of TAA implants, and the MRI appearances of a broad spectrum of TAA-related complications.
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Affiliation(s)
- Jan Fritz
- Department of Orthopedic Surgery, Division of Foot and Ankle Surgery, Duke University, Durham, NC, USA.
| | - Ali Rashidi
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, 660 1st Ave, 3rd Floor, Rm 313, New York, NY 10016, USA
| | - Cesar de Cesar Netto
- Department of Radiology, Molecular Imaging Program at StanDepartment of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA
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23
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Sneag DB, Abel F, Potter HG, Fritz J, Koff MF, Chung CB, Pedoia V, Tan ET. MRI Advancements in Musculoskeletal Clinical and Research Practice. Radiology 2023; 308:e230531. [PMID: 37581501 PMCID: PMC10477516 DOI: 10.1148/radiol.230531] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 06/01/2023] [Accepted: 06/07/2023] [Indexed: 08/16/2023]
Abstract
Over the past decades, MRI has become increasingly important for diagnosing and longitudinally monitoring musculoskeletal disorders, with ongoing hardware and software improvements aiming to optimize image quality and speed. However, surging demand for musculoskeletal MRI and increased interest to provide more personalized care will necessitate a stronger emphasis on efficiency and specificity. Ongoing hardware developments include more powerful gradients, improvements in wide-bore magnet designs to maintain field homogeneity, and high-channel phased-array coils. There is also interest in low-field-strength magnets with inherently lower magnetic footprints and operational costs to accommodate global demand in middle- and low-income countries. Previous approaches to decrease acquisition times by means of conventional acceleration techniques (eg, parallel imaging or compressed sensing) are now largely overshadowed by deep learning reconstruction algorithms. It is expected that greater emphasis will be placed on improving synthetic MRI and MR fingerprinting approaches to shorten overall acquisition times while also addressing the demand of personalized care by simultaneously capturing microstructural information to provide greater detail of disease severity. Authors also anticipate increased research emphasis on metal artifact reduction techniques, bone imaging, and MR neurography to meet clinical needs.
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Affiliation(s)
- Darryl B. Sneag
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Frederik Abel
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Hollis G. Potter
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Jan Fritz
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Matthew F. Koff
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Christine B. Chung
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Valentina Pedoia
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Ek T. Tan
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
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24
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Recht MP, White LM, Fritz J, Resnick DL. Advances in Musculoskeletal Imaging: Recent Developments and Predictions for the Future. Radiology 2023; 308:e230615. [PMID: 37642575 DOI: 10.1148/radiol.230615] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Affiliation(s)
- Michael P Recht
- From the Department of Radiology, NYU Grossman School of Medicine, 660 First Ave, 3rd Floor, New York, NY 10016 (M.P.R., J.F.); Department of Medical Imaging, University Health Network, Sinai Health System and Women's College Hospital, Toronto, Canada (L.M.W.); and Department of Radiology, UCSD Teleradiology and Education Center, La Jolla, Calif (D.L.R.)
| | - Lawrence M White
- From the Department of Radiology, NYU Grossman School of Medicine, 660 First Ave, 3rd Floor, New York, NY 10016 (M.P.R., J.F.); Department of Medical Imaging, University Health Network, Sinai Health System and Women's College Hospital, Toronto, Canada (L.M.W.); and Department of Radiology, UCSD Teleradiology and Education Center, La Jolla, Calif (D.L.R.)
| | - Jan Fritz
- From the Department of Radiology, NYU Grossman School of Medicine, 660 First Ave, 3rd Floor, New York, NY 10016 (M.P.R., J.F.); Department of Medical Imaging, University Health Network, Sinai Health System and Women's College Hospital, Toronto, Canada (L.M.W.); and Department of Radiology, UCSD Teleradiology and Education Center, La Jolla, Calif (D.L.R.)
| | - Donald L Resnick
- From the Department of Radiology, NYU Grossman School of Medicine, 660 First Ave, 3rd Floor, New York, NY 10016 (M.P.R., J.F.); Department of Medical Imaging, University Health Network, Sinai Health System and Women's College Hospital, Toronto, Canada (L.M.W.); and Department of Radiology, UCSD Teleradiology and Education Center, La Jolla, Calif (D.L.R.)
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25
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Kaniewska M, Deininger-Czermak E, Lohezic M, Ensle F, Guggenberger R. Deep Learning Convolutional Neural Network Reconstruction and Radial k-Space Acquisition MR Technique for Enhanced Detection of Retropatellar Cartilage Lesions of the Knee Joint. Diagnostics (Basel) 2023; 13:2438. [PMID: 37510182 PMCID: PMC10378433 DOI: 10.3390/diagnostics13142438] [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: 05/16/2023] [Revised: 07/13/2023] [Accepted: 07/15/2023] [Indexed: 07/30/2023] Open
Abstract
OBJECTIVES To assess diagnostic performance of standard radial k-space (PROPELLER) MRI sequences and compare with accelerated acquisitions combined with a deep learning-based convolutional neural network (DL-CNN) reconstruction for evaluation of the knee joint. METHODS Thirty-five patients undergoing MR imaging of the knee at 1.5 T were prospectively included. Two readers evaluated image quality and diagnostic confidence of standard and DL-CNN accelerated PROPELLER MR sequences using a four-point Likert scale. Pathological findings of bone, cartilage, cruciate and collateral ligaments, menisci, and joint space were analyzed. Inter-reader agreement (IRA) for image quality and diagnostic confidence was assessed using intraclass coefficients (ICC). Cohen's Kappa method was used for evaluation of IRA and consensus between sequences in assessing different structures. In addition, image quality was quantitatively evaluated by signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measurements. RESULTS Mean acquisition time of standard vs. DL-CNN sequences was 10 min 3 s vs. 4 min 45 s. DL-CNN sequences showed significantly superior image quality and diagnostic confidence compared to standard MR sequences. There was moderate and good IRA for assessment of image quality in standard and DL-CNN sequences with ICC of 0.524 and 0.830, respectively. Pathological findings of the knee joint could be equally well detected in both sequences (κ-value of 0.8). Retropatellar cartilage could be significantly better assessed on DL-CNN sequences. SNR and CNR was significantly higher for DL-CNN sequences (both p < 0.05). CONCLUSIONS In MR imaging of the knee, DL-CNN sequences showed significantly higher image quality and diagnostic confidence compared to standard PROPELLER sequences, while reducing acquisition time substantially. Both sequences perform comparably in the detection of knee-joint pathologies, while DL-CNN sequences are superior for evaluation of retropatellar cartilage lesions.
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Affiliation(s)
- Malwina Kaniewska
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), Raemistrasse 100, 8091 Zurich, Switzerland
- Institute of Diagnostic and Interventional Radiology, University of Zurich (UZH), Raemistrasse 100, 8091 Zurich, Switzerland
| | - Eva Deininger-Czermak
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), Raemistrasse 100, 8091 Zurich, Switzerland
- Institute of Diagnostic and Interventional Radiology, University of Zurich (UZH), Raemistrasse 100, 8091 Zurich, Switzerland
- Department of Forensic Medicine and Imaging, Institute of Forensic Medicine, University of Zurich, 8152 Zurich, Switzerland
| | - Maelene Lohezic
- Advanced Technology, Science and Technology Organization, GE HealthCare, 8152 Zurich, Switzerland
| | - Falko Ensle
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), Raemistrasse 100, 8091 Zurich, Switzerland
- Institute of Diagnostic and Interventional Radiology, University of Zurich (UZH), Raemistrasse 100, 8091 Zurich, Switzerland
| | - Roman Guggenberger
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), Raemistrasse 100, 8091 Zurich, Switzerland
- Institute of Diagnostic and Interventional Radiology, University of Zurich (UZH), Raemistrasse 100, 8091 Zurich, Switzerland
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26
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Sun B, Liu J, Li S, Lovell JF, Zhang Y. Imaging of Gastrointestinal Tract Ailments. J Imaging 2023; 9:115. [PMID: 37367463 DOI: 10.3390/jimaging9060115] [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: 04/24/2023] [Revised: 05/20/2023] [Accepted: 05/25/2023] [Indexed: 06/28/2023] Open
Abstract
Gastrointestinal (GI) disorders comprise a diverse range of conditions that can significantly reduce the quality of life and can even be life-threatening in serious cases. The development of accurate and rapid detection approaches is of essential importance for early diagnosis and timely management of GI diseases. This review mainly focuses on the imaging of several representative gastrointestinal ailments, such as inflammatory bowel disease, tumors, appendicitis, Meckel's diverticulum, and others. Various imaging modalities commonly used for the gastrointestinal tract, including magnetic resonance imaging (MRI), positron emission tomography (PET) and single photon emission computed tomography (SPECT), and photoacoustic tomography (PAT) and multimodal imaging with mode overlap are summarized. These achievements in single and multimodal imaging provide useful guidance for improved diagnosis, staging, and treatment of the corresponding gastrointestinal diseases. The review evaluates the strengths and weaknesses of different imaging techniques and summarizes the development of imaging techniques used for diagnosing gastrointestinal ailments.
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Affiliation(s)
- Boyang Sun
- Key Laboratory of Systems Bioengineering, School of Chemical Engineering and Technology, Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300350, China
| | - Jingang Liu
- Key Laboratory of Systems Bioengineering, School of Chemical Engineering and Technology, Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300350, China
| | - Silu Li
- Key Laboratory of Systems Bioengineering, School of Chemical Engineering and Technology, Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300350, China
| | - Jonathan F Lovell
- Department of Biomedical Engineering, The State University of New York at Buffalo, Buffalo, NY 14260, USA
| | - Yumiao Zhang
- Key Laboratory of Systems Bioengineering, School of Chemical Engineering and Technology, Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300350, China
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27
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Park EH, Fritz J. The role of imaging in osteoarthritis. Best Pract Res Clin Rheumatol 2023; 37:101866. [PMID: 37659890 DOI: 10.1016/j.berh.2023.101866] [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: 04/24/2023] [Revised: 08/08/2023] [Accepted: 08/13/2023] [Indexed: 09/04/2023]
Abstract
Osteoarthritis is a complex whole-organ disorder that involves molecular, anatomic, and physiologic derangement. Advances in imaging techniques have expanded the role of imaging in evaluating osteoarthritis and functional changes. Radiography, magnetic resonance imaging, computed tomography (CT), and ultrasonography are commonly used imaging modalities, each with advantages and limitations in evaluating osteoarthritis. Radiography comprehensively analyses alignment and osseous features, while MRI provides detailed information about cartilage damage, bone marrow edema, synovitis, and soft tissue abnormalities. Compositional imaging derives quantitative data for detecting cartilage and tendon degeneration before structural damage occurs. Ultrasonography permits real-time scanning and dynamic joint evaluation, whereas CT is useful for assessing final osseous detail. Imaging plays an essential role in the diagnosis, management, and research of osteoarthritis. The use of imaging can help differentiate osteoarthritis from other diseases with similar symptoms, and recent advances in deep learning have made the acquisition, management, and interpretation of imaging data more efficient and accurate. Imaging is useful in monitoring and predicting the prognosis of osteoarthritis, expanding our understanding of its pathophysiology. Ultimately, this enables early detection and personalized medicine for patients with osteoarthritis. This article reviews the current state of imaging in osteoarthritis, focusing on the strengths and limitations of various imaging modalities, and introduces advanced techniques, including deep learning, applied in clinical practice.
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Affiliation(s)
- Eun Hae Park
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, New York, USA; Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Jan Fritz
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, New York, USA.
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28
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Park EH, de Cesar Netto C, Fritz J. MRI in Acute Ankle Sprains: Should We Be More Aggressive with Indications? Foot Ankle Clin 2023; 28:231-264. [PMID: 37137621 DOI: 10.1016/j.fcl.2023.01.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Acute ankle sprains are common sports injuries. MRI is the most accurate test for assessing the integrity and severity of ligament injuries in acute ankle sprains. However, MRI may not detect syndesmotic and hindfoot instability, and many ankle sprains are treated conservatively, questioning the value of MRI. In our practice, MRI adds value in confirming the absence or presence of ankle sprain-associated hindfoot and midfoot injuries, especially when clinical examinations are challenging, radiographs are inconclusive, and subtle instability is suspected. This article reviews and illustrates the MRI appearances of the spectrum of ankle sprains and associated hindfoot and midfoot injuries.
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Affiliation(s)
- Eun Hae Park
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, 660 1St Ave, 3rd Floor, New York, NY 10016, USA; Department of Radiology, Jeonbuk National University Medical School, Jeonju, Republic of Korea
| | - Cesar de Cesar Netto
- Department of Orthopaedics and Rehabilitation, University of Iowa, 200 Hawkins Dr, Iowa City, IA 52242, USA
| | - Jan Fritz
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, 660 1St Ave, 3rd Floor, New York, NY 10016, USA.
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29
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Preisner F, Hayes JC, Charlet T, Carinci F, Hielscher T, Schwarz D, Vollherbst DF, Breckwoldt MO, Jesser J, Heiland S, Bendszus M, Hilgenfeld T. Simultaneous Multislice Accelerated TSE for Improved Spatiotemporal Resolution and Diagnostic Accuracy in Magnetic Resonance Neurography: A Feasibility Study. Invest Radiol 2023; 58:363-371. [PMID: 36729753 DOI: 10.1097/rli.0000000000000940] [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 This study aims to evaluate the utility of simultaneous multislice (SMS) acceleration for routine magnetic resonance neurography (MRN) at 3 T. MATERIALS AND METHODS Patients with multiple sclerosis underwent MRN of the sciatic nerve consisting of a standard fat-saturated T2-weighted turbo spin echo (TSE) sequence using integrated parallel acquisition technique (PAT2) acceleration and 2 T2 TSE sequences using a combination of PAT-SMS acceleration (1) to reduce scan time (PAT2-SMS2; SMS-TSE FAST ) and (2) for time neutral increase of in-plane resolution (PAT1-SMS2; SMS-TSE HR ). Acquisition times were 5:29 minutes for the standard T2 TSE, 3:12 minutes for the SMS-TSE FAST , and 5:24 minutes for the SMS-TSE HR . Six qualitative imaging parameters were analyzed by 2 blinded readers using a 5-point Likert scale and T2 nerve lesions were quantified, respectively. Qualitative and quantitative image parameters were compared, and both interrater and intrarater reproducibility were statistically assessed. In addition, signal-to-noise ratio/contrast-to-noise ratio (CNR) was obtained in healthy controls using the exact same imaging protocol. RESULTS A total of 15 patients with MS (mean age ± standard deviation, 38.1 ± 11 years) and 10 healthy controls (mean age, 29.1 ± 7 years) were enrolled in this study. CNR analysis was highly reliable (intraclass correlation coefficient, 0.755-0.948) and revealed a significant CNR decrease for the sciatic nerve for both SMS protocols compared with standard T2 TSE (SMS-TSE FAST /SMS-TSE HR , -39%/-55%; P ≤ 0.01). Intrarater and interrater reliability of qualitative image review was good to excellent (κ: 0.672-0.971/0.617-0.883). Compared with the standard T2 TSE sequence, both SMS methods were shown to be superior in reducing pulsatile flow artifacts ( P < 0.01). Ratings for muscle border sharpness, detailed muscle structures, nerve border sharpness, and nerve fascicular structure did not differ significantly between the standard T2 TSE and the SMS-TSE FAST ( P > 0.05) and were significantly better for the SMS-TSE HR than for standard T2 TSE ( P < 0.001). Muscle signal homogeneity was mildly inferior for both SMS-TSE FAST ( P > 0.05) and SMS-TSE HR ( P < 0.001). A significantly higher number of T2 nerve lesions were detected by SMS-TSE HR ( P ≤ 0.01) compared with the standard T2 TSE and SMS-TSE FAST , whereas no significant difference was observed between the standard T2 TSE and SMS-TSE FAST . CONCLUSIONS Implementation of SMS offers either to substantially reduce acquisition time by over 40% without significantly impeding image quality compared with the standard T2 TSE or to increase in-plane resolution for a high-resolution approach and improved depiction of T2 nerve lesions while keeping acquisition times constant. This addresses the specific needs of MRN by providing different imaging approaches for 2D clinical MRN.
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Affiliation(s)
- Fabian Preisner
- From the Department of Neuroradiology, Heidelberg University Hospital, Heidelberg
| | - Jennifer C Hayes
- From the Department of Neuroradiology, Heidelberg University Hospital, Heidelberg
| | - Tobias Charlet
- From the Department of Neuroradiology, Heidelberg University Hospital, Heidelberg
| | | | - Thomas Hielscher
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel Schwarz
- From the Department of Neuroradiology, Heidelberg University Hospital, Heidelberg
| | - Dominik F Vollherbst
- From the Department of Neuroradiology, Heidelberg University Hospital, Heidelberg
| | - Michael O Breckwoldt
- From the Department of Neuroradiology, Heidelberg University Hospital, Heidelberg
| | - Jessica Jesser
- From the Department of Neuroradiology, Heidelberg University Hospital, Heidelberg
| | - Sabine Heiland
- From the Department of Neuroradiology, Heidelberg University Hospital, Heidelberg
| | - Martin Bendszus
- From the Department of Neuroradiology, Heidelberg University Hospital, Heidelberg
| | - Tim Hilgenfeld
- From the Department of Neuroradiology, Heidelberg University Hospital, Heidelberg
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30
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Dalili D, Isaac A, Fritz J. MRI-guided sacroiliac joint injections in children and adults: current practice and future developments. Skeletal Radiol 2023; 52:951-965. [PMID: 36006462 DOI: 10.1007/s00256-022-04161-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 08/10/2022] [Accepted: 08/11/2022] [Indexed: 02/02/2023]
Abstract
Common etiologies of low back pain include degenerative arthrosis and inflammatory arthropathy of the sacroiliac joints. The diagnostic workup revolves around identifying and confirming the sacroiliac joints as a pain generator. Diagnostic sacroiliac joint injections often serve as functional additions to the diagnostic workup through eliciting a pain response that tests the hypothesis that the sacroiliac joints do or do not contribute to the patient's pain syndrome. Therapeutic sacroiliac joint injections aim to provide medium- to long-term relief of symptoms and reduce inflammatory activity and, ultimately, irreversible structural damage. Ultrasonography, fluoroscopy, computed tomography, and magnetic resonance imaging (MRI) may be used to guide sacroiliac joint injections. The populations that may benefit most from MRI-guided sacroiliac joint procedures include children, adolescents, adults of childbearing age, and patients receiving serial injections due to the ability of interventional MRI to avoid radiation exposure. Most clinical wide-bore MRI systems can be used for MRI-guided sacroiliac joint injections. Turbo spin echo pulse sequences optimized for interventional needle display visualize the needle tip with an error margin of < 1 mm or less. Published success rates of intra-articular sacroiliac joint drug delivery with MRI guidance range between 87 and 100%. The time required for MR-guided sacroiliac joint injections in adults range between 23-35 min and 40 min in children. In this article, we describe techniques for MRI-guided sacroiliac joint injections, share our practice of incorporating interventional MRI in the care of patients with sacroiliac joint mediated pain, discuss the rationales, benefits, and limitations of interventional MRI, and conclude with future developments.
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Affiliation(s)
- Danoob Dalili
- Academic Surgical Unit, South West London Elective Orthopaedic Centre (SWLEOC), Dorking Road, KT18 7EG, London, UK
| | - Amanda Isaac
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Diagnostic and Interventional Radiology, Guy's and St Thomas' Hospitals NHS Foundation Trust, London, UK
| | - Jan Fritz
- Department of Radiology, New York University Grossman School of Medicine, 660 1st Ave, 3rd Floor, Rm 313, New York, NY, 10016, USA.
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31
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Hahn S, Yi J, Lee HJ, Lee Y, Lee J, Wang X, Fung M. Comparison of deep learning-based reconstruction of PROPELLER Shoulder MRI with conventional reconstruction. Skeletal Radiol 2023:10.1007/s00256-023-04321-8. [PMID: 36943429 DOI: 10.1007/s00256-023-04321-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/08/2023] [Accepted: 03/08/2023] [Indexed: 03/23/2023]
Abstract
OBJECTIVE To compare the image quality and agreement among conventional and accelerated periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI with both conventional reconstruction (CR) and deep learning-based reconstruction (DLR) methods for evaluation of shoulder. MATERIALS AND METHODS We included patients who underwent conventional (acquisition time, 8 min) and accelerated (acquisition time, 4 min and 24 s; 45% reduction) PROPELLER shoulder MRI using both CR and DLR methods between February 2021 and February 2022 on a 3 T MRI system. Quantitative evaluation was performed by calculating the signal-to-noise ratio (SNR). Two musculoskeletal radiologists compared the image quality using conventional sequence with CR as the reference standard. Interobserver agreement between image sets for evaluating shoulder was analyzed using weighted/unweighted kappa statistics. RESULTS Ninety-two patients with 100 shoulder MRI scans were included. Conventional sequence with DLR had the highest SNR (P < .001), followed by accelerated sequence with DLR, conventional sequence with CR, and accelerated sequence with CR. Comparison of image quality by both readers revealed that conventional sequence with DLR (P = .003 and P < .001) and accelerated sequence with DLR (P = .016 and P < .001) had better image quality than the conventional sequence with CR. Interobserver agreement was substantial to almost perfect for detecting shoulder abnormalities (κ = 0.600-0.884). Agreement between the image sets was substantial to almost perfect (κ = 0.691-1). CONCLUSION Accelerated PROPELLER with DLR showed even better image quality than conventional PROPELLER with CR and interobserver agreement for shoulder pathologies comparable to that of conventional PROPELLER with CR, despite the shorter scan time.
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Affiliation(s)
- Seok Hahn
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea, Republic of Korea
| | - Jisook Yi
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea, Republic of Korea.
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea, Republic of Korea
| | - Yedaun Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea, Republic of Korea
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32
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Murthy S, Fritz J. Metal Artifact Reduction MRI in the Diagnosis of Periprosthetic Hip Joint Infection. Radiology 2023; 306:e220134. [PMID: 36318029 DOI: 10.1148/radiol.220134] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
A 54-year-old woman presented with progressive right hip pain after hip arthroplasty 9 years earlier. The emerging role of metal artifact reduction MRI in the noninvasive diagnosis of infectious synovitis as the surrogate marker for periprosthetic hip joint infection and differentiation from other synovitis types is discussed.
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Affiliation(s)
- Sindhoora Murthy
- From the Department of Radiology, New York University Grossman School of Medicine, 660 1st Ave, 3rd Floor, Room 313, New York, NY 10016
| | - Jan Fritz
- From the Department of Radiology, New York University Grossman School of Medicine, 660 1st Ave, 3rd Floor, Room 313, New York, NY 10016
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33
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Daniels SP, Fritz J. Acute and Chronic Elbow Disorders. Magn Reson Imaging Clin N Am 2023; 31:269-284. [PMID: 37019550 DOI: 10.1016/j.mric.2023.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
Elbow pain is very common and can be due to many pathologic conditions. After radiographs are obtained, advanced imaging is often necessary. Both ultrasonography and MR imaging can be used to evaluate the many important soft-tissue structures of the elbow, with each modality having advantages and disadvantages in certain clinical scenarios. Imaging findings between the two modalities often correlate. It is important for musculoskeletal radiologists to understand normal elbow anatomy and how best to use ultrasonography and MR imaging to evaluate elbow pain. In this way, radiologists can provide expert guidance to referring clinicians and best guide patient management.
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Abstract
Acute knee injury ranges among the most common joint injuries in professional and recreational athletes. Radiographs can detect joint effusion, fractures, deformities, and malalignment; however, MR imaging is most accurate for radiographically occult fractures, chondral injury, and soft tissue injuries. Using a structured checklist approach for systematic MR imaging evaluation and reporting, this article reviews the MR imaging appearances of the spectrum of traumatic knee injuries, including osteochondral injuries, cruciate ligament tears, meniscus tears and ramp lesions, anterolateral complex and collateral ligament injuries, patellofemoral translation, extensor mechanism tears, and nerve and vascular injuries.
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35
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Baylosis BL, McQuiston AS, Bayne CO, Szabo RM, Boutin RD. Pre-operative imaging for surgical decision-making and the frequency of wrist arthrodesis and carpectomy procedures: a scoping review. Skeletal Radiol 2023; 52:143-150. [PMID: 35970955 DOI: 10.1007/s00256-022-04157-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/06/2022] [Accepted: 08/08/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVES Our objectives were to (1) analyze the imaging modalities utilized pre-operatively that influence surgical decision-making for wrist arthrodesis and carpectomy procedures and (2) determine the type and frequency of these procedures for the treatment of wrist arthritis. MATERIALS AND METHODS This review was performed according to the guidelines of PRISMA Extension for Scoping Reviews. Using PubMed, Embase, and Scopus, peer-reviewed literature from 2011 to 2022 was searched for use of imaging in pre-operative decision-making for wrist arthrodesis and carpectomy surgical procedures. Data were compiled to determine the type(s) of imaging modalities used pre-operatively and types of surgical techniques reported in the literature. RESULTS Of 307 articles identified, 35 articles satisfied eligibility criteria, with a total of 1377 patients (68% men; age mean, 50.9 years [range, 10-81]) and 1428 wrist surgical interventions. Radiography was reported for pre-operative planning in all articles for all patients. Pre-operative cross-sectional imaging was reported in 2 articles (5.7%), but no articles reported detailed data on how CT or MRI influenced pre-operative wrist arthrodesis and carpectomy procedure decision-making. A dozen different types of surgical techniques were reported. The four most common procedures were four-corner arthrodesis with scaphoid excision (846, 59%), proximal row carpectomy (239, 17%), total wrist arthrodesis (130, 9%), and scaphocapitate arthrodesis (53, 4%). CONCLUSION Radiography is always used in pre-operative decision-making, but the literature lacks data on the influence of CT and MRI for selecting among a dozen different types of wrist arthrodesis and carpectomy procedures.
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Affiliation(s)
- Barry L Baylosis
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305-5105, USA
| | - Alexander S McQuiston
- Stanford University School of Medicine, 3801 Miranda Ave. Bldg. 710, Palo Alto, CA, 94304, USA
| | - Christopher O Bayne
- Department of Orthopaedic Surgery, UC Davis School of Medicine, 4860 Y St, Ste 3800, Sacramento, CA, 95817, USA
| | - Robert M Szabo
- Department of Orthopaedic Surgery, UC Davis School of Medicine, 4860 Y St, Ste 3800, Sacramento, CA, 95817, USA
| | - Robert D Boutin
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305-5105, USA.
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36
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Lang M, Cartmell S, Tabari A, Briggs D, Pianykh O, Kirsch J, Cauley S, Lo WC, Risacher S, Filho AG, Succi MD, Rapalino O, Schaefer P, Conklin J, Huang SY. Evaluation of the Aggregated Time Savings in Adopting Fast Brain MRI Techniques for Outpatient Brain MRI. Acad Radiol 2023; 30:341-348. [PMID: 34635436 PMCID: PMC8989721 DOI: 10.1016/j.acra.2021.07.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 06/29/2021] [Accepted: 07/02/2021] [Indexed: 01/11/2023]
Abstract
INTRODUCTION Clinical validation studies have demonstrated the ability of accelerated MRI sequences to decrease acquisition time and motion artifact while preserving image quality. The operational benefits, however, have been less explored. Here, we report our initial clinical experience in implementing fast MRI techniques for outpatient brain imaging during the COVID-19 pandemic. METHODS Aggregate acquisition times were extracted from the medical record on consecutive imaging examinations performed during matched pre-implementation (7/1/2019-12/31/2019) and post-implementation periods (7/1/2020-12/31/2020). Expected acquisition time reduction for each MRI protocol was calculated through manual collection of acquisition times for the conventional and accelerated sequences performed during the pre- and post-implementation periods. Aggregate and expected acquisition times were compared for the five most frequently performed brain MRI protocols: brain without contrast (BR-), brain with and without contrast (BR+), multiple sclerosis (MS), memory loss (MML), and epilepsy (EPL). RESULTS The expected time reductions for BR-, BR+, MS, MML, and EPL protocols were 6.6 min, 11.9 min, 14 min, 10.8 min, and 14.1 min, respectively. The overall median aggregate acquisition time was 31 [25, 36] min for the pre-implementation period and 18 [15, 22] min for the post-implementation period, with a difference of 13 min (42%). The median acquisition time was reduced by 4 min (25%) for BR-, 14.0 min (44%) for BR+, 14 min (38%) for MS, 11 min (52%) for MML, and 16 min (35%) for EPL. CONCLUSION The implementation of fast brain MRI sequences significantly reduced the acquisition times for the most commonly performed outpatient brain MRI protocols.
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Affiliation(s)
- Min Lang
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts; Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, Massachusetts
| | - Samuel Cartmell
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts
| | - Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts
| | - Daniel Briggs
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts
| | - Oleg Pianykh
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts
| | - John Kirsch
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Stephen Cauley
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Wei-Ching Lo
- Siemens Medical Solutions, Boston, Massachusetts
| | - Seretha Risacher
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts
| | - Augusto Goncalves Filho
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Marc D Succi
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts; Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, Massachusetts
| | - Otto Rapalino
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts
| | - Pamela Schaefer
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts
| | - John Conklin
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts
| | - Susie Y Huang
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.
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Fritz B, Fritz J. MR Imaging–Ultrasonography Correlation of Acute and Chronic Foot and Ankle Conditions. Magn Reson Imaging Clin N Am 2023; 31:321-335. [PMID: 37019553 DOI: 10.1016/j.mric.2023.01.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Foot and ankle injuries are common musculoskeletal disorders. In the acute setting, ligamentous injuries are most common, whereas fractures, osseous avulsion injuries, tendon and retinaculum tears, and osteochondral injuries are less common. The most common chronic and overuse injuries include osteochondral and articular cartilage defects, tendinopathies, stress fractures, impingement syndromes, and neuropathies. Common forefoot conditions include traumatic and stress fractures, metatarsophalangeal and plantar plate injuries and degenerations, intermittent bursitis, and perineural fibrosis. Ultrasonography is well-suited for evaluating superficial tendons, ligaments, and muscles. MR imaging is best for deeper-located soft tissue structures, articular cartilage, and cancellous bone.
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Burke CJ, Fritz J, Samim M. Musculoskeletal Soft-tissue Masses. Magn Reson Imaging Clin N Am 2023; 31:285-308. [PMID: 37019551 DOI: 10.1016/j.mric.2022.10.001] [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: 02/09/2023]
Abstract
Evaluation of soft-tissue masses has become a common clinical practice indication for imaging with both ultrasound and MR imaging. We illustrate the ultrasonography and MR imaging appearances of soft-tissue masses based on the various categories, updates, and reclassifications of the 2020 World Health Organization classification.
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Affiliation(s)
- Christopher J Burke
- NYU Langone Orthopedic Hospital, 301 East 17th Street, New York, NY 10003, USA.
| | - Jan Fritz
- NYU Langone Orthopedic Hospital, 301 East 17th Street, New York, NY 10003, USA
| | - Mohammad Samim
- NYU Langone Orthopedic Hospital, 301 East 17th Street, New York, NY 10003, USA
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Modern Low-Field MRI of the Musculoskeletal System: Practice Considerations, Opportunities, and Challenges. Invest Radiol 2023; 58:76-87. [PMID: 36165841 DOI: 10.1097/rli.0000000000000912] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
ABSTRACT Magnetic resonance imaging (MRI) provides essential information for diagnosing and treating musculoskeletal disorders. Although most musculoskeletal MRI examinations are performed at 1.5 and 3.0 T, modern low-field MRI systems offer new opportunities for affordable MRI worldwide. In 2021, a 0.55 T modern low-field, whole-body MRI system with an 80-cm-wide bore was introduced for clinical use in the United States and Europe. Compared with current higher-field-strength MRI systems, the 0.55 T MRI system has a lower total ownership cost, including purchase price, installation, and maintenance. Although signal-to-noise ratios scale with field strength, modern signal transmission and receiver chains improve signal yield compared with older low-field magnetic resonance scanner generations. Advanced radiofrequency coils permit short echo spacing and overall compacter echo trains than previously possible. Deep learning-based advanced image reconstruction algorithms provide substantial improvements in perceived signal-to-noise ratios, contrast, and spatial resolution. Musculoskeletal tissue contrast evolutions behave differently at 0.55 T, which requires careful consideration when designing pulse sequences. Similar to other field strengths, parallel imaging and simultaneous multislice acquisition techniques are vital for efficient musculoskeletal MRI acquisitions. Pliable receiver coils with a more cost-effective design offer a path to more affordable surface coils and improve image quality. Whereas fat suppression is inherently more challenging at lower field strengths, chemical shift selective fat suppression is reliable and homogeneous with modern low-field MRI technology. Dixon-based gradient echo pulse sequences provide efficient and reliable multicontrast options, including postcontrast MRI. Metal artifact reduction MRI benefits substantially from the lower field strength, including slice encoding for metal artifact correction for effective metal artifact reduction of high-susceptibility metallic implants. Wide-bore scanner designs offer exciting opportunities for interventional MRI. This review provides an overview of the economical aspects, signal and image quality considerations, technological components and coils, musculoskeletal tissue relaxation times, and image contrast of modern low-field MRI and discusses the mainstream and new applications, challenges, and opportunities of musculoskeletal MRI.
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Abstract
ABSTRACT This review summarizes the existing techniques and methods used to generate synthetic contrasts from magnetic resonance imaging data focusing on musculoskeletal magnetic resonance imaging. To that end, the different approaches were categorized into 3 different methodological groups: mathematical image transformation, physics-based, and data-driven approaches. Each group is characterized, followed by examples and a brief overview of their clinical validation, if present. Finally, we will discuss the advantages, disadvantages, and caveats of synthetic contrasts, focusing on the preservation of image information, validation, and aspects of the clinical workflow.
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Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology: An Overview of Novel MRI- and CT-Based Approaches. Invest Radiol 2023; 58:3-13. [PMID: 36070548 DOI: 10.1097/rli.0000000000000907] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
ABSTRACT Radiomics and machine learning-based methods offer exciting opportunities for improving diagnostic performance and efficiency in musculoskeletal radiology for various tasks, including acute injuries, chronic conditions, spinal abnormalities, and neoplasms. While early radiomics-based methods were often limited to a smaller number of higher-order image feature extractions, applying machine learning-based analytic models, multifactorial correlations, and classifiers now permits big data processing and testing thousands of features to identify relevant markers. A growing number of novel deep learning-based methods describe magnetic resonance imaging- and computed tomography-based algorithms for diagnosing anterior cruciate ligament tears, meniscus tears, articular cartilage defects, rotator cuff tears, fractures, metastatic skeletal disease, and soft tissue tumors. Initial radiomics and deep learning techniques have focused on binary detection tasks, such as determining the presence or absence of a single abnormality and differentiation of benign versus malignant. Newer-generation algorithms aim to include practically relevant multiclass characterization of detected abnormalities, such as typing and malignancy grading of neoplasms. So-called delta-radiomics assess tumor features before and after treatment, with temporal changes of radiomics features serving as surrogate markers for tumor responses to treatment. New approaches also predict treatment success rates, surgical resection completeness, and recurrence risk. Practice-relevant goals for the next generation of algorithms include diagnostic whole-organ and advanced classification capabilities. Important research objectives to fill current knowledge gaps include well-designed research studies to understand how diagnostic performances and suggested efficiency gains of isolated research settings translate into routine daily clinical practice. This article summarizes current radiomics- and machine learning-based magnetic resonance imaging and computed tomography approaches for musculoskeletal disease detection and offers a perspective on future goals and objectives.
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Artificial Intelligence-Driven Ultra-Fast Superresolution MRI: 10-Fold Accelerated Musculoskeletal Turbo Spin Echo MRI Within Reach. Invest Radiol 2023; 58:28-42. [PMID: 36355637 DOI: 10.1097/rli.0000000000000928] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
ABSTRACT Magnetic resonance imaging (MRI) is the keystone of modern musculoskeletal imaging; however, long pulse sequence acquisition times may restrict patient tolerability and access. Advances in MRI scanners, coil technology, and innovative pulse sequence acceleration methods enable 4-fold turbo spin echo pulse sequence acceleration in clinical practice; however, at this speed, conventional image reconstruction approaches the signal-to-noise limits of temporal, spatial, and contrast resolution. Novel deep learning image reconstruction methods can minimize signal-to-noise interdependencies to better advantage than conventional image reconstruction, leading to unparalleled gains in image speed and quality when combined with parallel imaging and simultaneous multislice acquisition. The enormous potential of deep learning-based image reconstruction promises to facilitate the 10-fold acceleration of the turbo spin echo pulse sequence, equating to a total acquisition time of 2-3 minutes for entire MRI examinations of joints without sacrificing spatial resolution or image quality. Current investigations aim for a better understanding of stability and failure modes of image reconstruction networks, validation of network reconstruction performance with external data sets, determination of diagnostic performances with independent reference standards, establishing generalizability to other centers, scanners, field strengths, coils, and anatomy, and building publicly available benchmark data sets to compare methods and foster innovation and collaboration between the clinical and image processing community. In this article, we review basic concepts of deep learning-based acquisition and image reconstruction techniques for accelerating and improving the quality of musculoskeletal MRI, commercially available and developing deep learning-based MRI solutions, superresolution, denoising, generative adversarial networks, and combined strategies for deep learning-driven ultra-fast superresolution musculoskeletal MRI. This article aims to equip radiologists and imaging scientists with the necessary practical knowledge and enthusiasm to meet this exciting new era of musculoskeletal MRI.
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Advances in Bone Joint Imaging-Metal Artifact Reduction. Diagnostics (Basel) 2022; 12:diagnostics12123079. [PMID: 36553086 PMCID: PMC9776622 DOI: 10.3390/diagnostics12123079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/01/2022] [Accepted: 12/04/2022] [Indexed: 12/12/2022] Open
Abstract
Numerous types of metal implants have been introduced in orthopedic surgery and are used in everyday practice. To precisely evaluate the postoperative condition of arthroplasty or trauma surgery, periprosthetic infection, and the loosening of implants, it is important to reduce artifacts induced by metal implants. In this review, we focused on technical advances in metal artifact reduction using digital tomosynthesis, computed tomography, and magnetic resonance imaging. We discussed new developments in diagnostic imaging methods and the continuous introduction of novel technologies to reduce metal artifacts; however, these innovations have not yet completely removed metal artifacts. Different algorithms need to be selected depending on the size, shape, material and implanted body parts of an implant. Future advances in metal artifact reduction algorithms and techniques and the development of new sequences may enable further reductions in metal artifacts even on original images taken previously. Moreover, the combination of different imaging modalities may contribute to further reductions in metal artifacts. Clinicians must constantly update their knowledge and work closely with radiologists to select the best diagnostic imaging method for each metal implant.
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Deep Learning-Enhanced Parallel Imaging and Simultaneous Multislice Acceleration Reconstruction in Knee MRI. Invest Radiol 2022; 57:826-833. [PMID: 35776434 DOI: 10.1097/rli.0000000000000900] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVES This study aimed to examine various combinations of parallel imaging (PI) and simultaneous multislice (SMS) acceleration imaging using deep learning (DL)-enhanced and conventional reconstruction. The study also aimed at comparing the diagnostic performance of the various combinations in internal knee derangement and provided a quantitative evaluation of image sharpness and noise using edge rise distance (ERD) and noise power (NP), respectively. MATERIALS AND METHODS The data from adult patients who underwent knee magnetic resonance imaging using various DL-enhanced acquisitions between June 2021 and January 2022 were retrospectively analyzed. The participants underwent conventional 2-fold PI and DL protocols with 4- to 8-fold acceleration imaging (P2S2 [2-fold PI with 2-fold SMS], P3S2, and P4S2). Three readers evaluated the internal knee derangement and the overall image quality. The diagnostic performance was calculated using consensus reading as a standard reference, and we conducted comparative evaluations. We calculated the ERD and NP for quantitative evaluations of image sharpness and noise, respectively. Interreader and intermethod agreements were calculated using Fleiss κ. RESULTS A total of 33 patients (mean age, 49 ± 19 years; 20 women) were included in this study. The diagnostic performance for internal knee derangement and the overall image quality were similar among the evaluated protocols. The NP values were significantly lower using the DL protocols than with conventional imaging ( P < 0.001), whereas the ERD values were similar among these methods ( P > 0.12). Interreader and intermethod agreements were moderate-to-excellent (κ = 0.574-0.838) and good-to-excellent (κ = 0.755-1.000), respectively. In addition, the mean acquisition time was reduced by 47% when using DL with P2S2, by 62% with P3S2, and by 71% with P4S2, compared with conventional P2 imaging (2 minutes and 55 seconds). CONCLUSIONS The combined use of DL-enhanced 8-fold acceleration imaging (4-fold PI with 2-fold SMS) showed comparable performance with conventional 2-fold PI for the evaluation of internal knee derangement, with a 71% reduction in acquisition time.
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Postoperative MRI of the Ankle and Foot. Magn Reson Imaging Clin N Am 2022; 30:733-755. [DOI: 10.1016/j.mric.2022.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Wang B, Siddiq SS, Walczyk J, Bruno M, Khodarahmi I, Brinkmann IM, Rehner R, Lakshmanan K, Fritz J, Brown R. A flexible MRI coil based on a cable conductor and applied to knee imaging. Sci Rep 2022; 12:15010. [PMID: 36056131 PMCID: PMC9440226 DOI: 10.1038/s41598-022-19282-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 08/26/2022] [Indexed: 11/08/2022] Open
Abstract
Flexible radiofrequency coils for magnetic resonance imaging (MRI) have garnered attention in research and industrial communities because they provide improved accessibility and performance and can accommodate a range of anatomic postures. Most recent flexible coil developments involve customized conductors or substrate materials and/or target applications at 3 T or above. In contrast, we set out to design a flexible coil based on an off-the-shelf conductor that is suitable for operation at 0.55 T (23.55 MHz). Signal-to-noise ratio (SNR) degradation can occur in such an environment because the resistance of the coil conductor can be significant with respect to the sample. We found that resonating a commercially available RG-223 coaxial cable shield with a lumped capacitor while the inner conductor remained electrically floating gave rise to a highly effective "cable coil." A 10-cm diameter cable coil was flexible enough to wrap around the knee, an application that can benefit from flexible coils, and had similar conductor loss and SNR as a standard-of-reference rigid copper coil. A two-channel cable coil array also provided good SNR robustness against geometric variability, outperforming a two-channel coaxial coil array by 26 and 16% when the elements were overlapped by 20-40% or gapped by 30-50%, respectively. A 6-channel cable coil array was constructed for 0.55 T knee imaging. Incidental cartilage and bone pathologies were clearly delineated in T1- and T2-weighted turbo spin echo images acquired in 3-4 min with the proposed coil, suggesting that clinical quality knee imaging is feasible in an acceptable examination timeframe. Correcting for T1, the SNR measured with the cable coil was approximately threefold lower than that measured with a 1.5 T state-of-the-art 18-channel coil, which is expected given the threefold difference in main magnetic field strength. This result suggests that the 0.55 T cable coil conductor loss does not deleteriously impact SNR, which might be anticipated at low field.
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Affiliation(s)
- Bili Wang
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, 660 First Ave, New York, NY, USA
| | - Syed S Siddiq
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, 660 First Ave, New York, NY, USA
- Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jerzy Walczyk
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, 660 First Ave, New York, NY, USA
| | - Mary Bruno
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, 660 First Ave, New York, NY, USA
| | - Iman Khodarahmi
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, 660 First Ave, New York, NY, USA
- Division of Musculoskeletal Radiology, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | | | | | - Karthik Lakshmanan
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, 660 First Ave, New York, NY, USA
| | - Jan Fritz
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, 660 First Ave, New York, NY, USA
- Division of Musculoskeletal Radiology, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Ryan Brown
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, 660 First Ave, New York, NY, USA.
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Burke CJ, Khodarahmi I, Fritz J. Postoperative MR Imaging of Joints. Magn Reson Imaging Clin N Am 2022; 30:583-600. [DOI: 10.1016/j.mric.2022.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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Khodarahmi I, Brinkmann IM, Lin DJ, Bruno M, Johnson PM, Knoll F, Keerthivasan MB, Chandarana H, Fritz J. New-Generation Low-Field Magnetic Resonance Imaging of Hip Arthroplasty Implants Using Slice Encoding for Metal Artifact Correction: First In Vitro Experience at 0.55 T and Comparison With 1.5 T. Invest Radiol 2022; 57:517-526. [PMID: 35239614 PMCID: PMC9363001 DOI: 10.1097/rli.0000000000000866] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES Despite significant progress, artifact-free visualization of the bone and soft tissues around hip arthroplasty implants remains an unmet clinical need. New-generation low-field magnetic resonance imaging (MRI) systems now include slice encoding for metal artifact correction (SEMAC), which may result in smaller metallic artifacts and better image quality than standard-of-care 1.5 T MRI. This study aims to assess the feasibility of SEMAC on a new-generation 0.55 T system, optimize the pulse protocol parameters, and compare the results with those of a standard-of-care 1.5 T MRI. MATERIALS AND METHODS Titanium (Ti) and cobalt-chromium total hip arthroplasty implants embedded in a tissue-mimicking American Society for Testing and Materials gel phantom were evaluated using turbo spin echo, view angle tilting (VAT), and combined VAT and SEMAC (VAT + SEMAC) pulse sequences. To refine an MRI protocol at 0.55 T, the type of metal artifact reduction techniques and the effect of various pulse sequence parameters on metal artifacts were assessed through qualitative ranking of the images by 3 expert readers while taking measured spatial resolution, signal-to-noise ratios, and acquisition times into consideration. Signal-to-noise ratio efficiency and artifact size of the optimized 0.55 T protocols were compared with the 1.5 T standard and compressed-sensing SEMAC sequences. RESULTS Overall, the VAT + SEMAC sequence with at least 6 SEMAC encoding steps for Ti and 9 for cobalt-chromium implants was ranked higher than other sequences for metal reduction ( P < 0.05). Additional SEMAC encoding partitions did not result in further metal artifact reductions. Permitting minimal residual artifacts, low magnetic susceptibility Ti constructs may be sufficiently imaged with optimized turbo spin echo sequences obviating the need for SEMAC. In cross-platform comparison, 0.55 T acquisitions using the optimized protocols are associated with 45% to 64% smaller artifacts than 1.5 T VAT + SEMAC and VAT + compressed-sensing/SEMAC protocols at the expense of a 17% to 28% reduction in signal-to-noise ratio efficiency. B 1 -related artifacts are invariably smaller at 0.55 T than 1.5 T; however, artifacts related to B 0 distortion, although frequently smaller, may appear as signal pileups at 0.55 T. CONCLUSIONS Our results suggest that new-generation low-field SEMAC MRI reduces metal artifacts around hip arthroplasty implants to better advantage than current 1.5 T MRI standard of care. While the appearance of B 0 -related artifacts changes, reduction in B 1 -related artifacts plays a major role in the overall benefit of 0.55 T.
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Affiliation(s)
- Iman Khodarahmi
- Department of Radiology, New York University Grossman School of Medicine
| | | | - Dana J. Lin
- Department of Radiology, New York University Grossman School of Medicine
| | - Mary Bruno
- Department of Radiology, New York University Grossman School of Medicine
| | | | - Florian Knoll
- Department of Radiology, New York University Grossman School of Medicine
| | | | - Hersh Chandarana
- Department of Radiology, New York University Grossman School of Medicine
| | - Jan Fritz
- Department of Radiology, New York University Grossman School of Medicine
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Samim M, Khodarahmi I, Burke C, Fritz J. Postoperative Musculoskeletal Imaging and Interventions Following Hip Preservation Surgery, Deformity Correction, and Hip Arthroplasty. Semin Musculoskelet Radiol 2022; 26:242-257. [PMID: 35654093 DOI: 10.1055/s-0041-1740996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Total hip arthroplasty and hip preservation surgeries have substantially increased over the past few decades. Musculoskeletal imaging and interventions are cornerstones of comprehensive postoperative care and surveillance in patients undergoing established and more recently introduced hip surgeries. Hence the radiologist's role continues to evolve and expand. A strong understanding of hip joint anatomy and biomechanics, surgical procedures, expected normal postoperative imaging appearances, and postoperative complications ensures accurate imaging interpretation, intervention, and optimal patient care. This article presents surgical principles and procedural details pertinent to postoperative imaging evaluation strategies after common hip surgeries, such as radiography, ultrasonography, computed tomography, and magnetic resonance imaging. We review and illustrate the expected postoperative imaging appearances and complications following chondrolabral repair, acetabuloplasty, osteochondroplasty, periacetabular osteotomy, realigning and derotational femoral osteotomies, and hip arthroplasty.
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Affiliation(s)
- Mohammad Samim
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, New York, New York
| | - Iman Khodarahmi
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, New York, New York
| | - Christopher Burke
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, New York, New York
| | - Jan Fritz
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, New York, New York
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Shafiei M, Chalian M, Luna R, Ahlawat S, Fayad LM. Imaging in Musculoskeletal Oncology. Radiol Clin North Am 2022; 60:657-668. [DOI: 10.1016/j.rcl.2022.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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