1
|
Kim M, Lee HJ, Lee S, Lee J, Kang Y. Three-dimensional heavily T2-weighted FLAIR in the detection of blood-labyrinthine barrier leakage in patients with sudden sensorineural hearing loss: comparison with T1 sequences and application of deep learning-based reconstruction. Eur Radiol 2024; 34:5379-5388. [PMID: 38231393 DOI: 10.1007/s00330-023-10580-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 11/22/2023] [Accepted: 12/11/2023] [Indexed: 01/18/2024]
Abstract
OBJECTIVE Blood-labyrinthine barrier leakage has been reported in sudden sensorineural hearing loss (SSNHL). We compared immediate post-contrast 3D heavily T2-weighted fluid-attenuated inversion recovery (FLAIR), T1 spin echo (SE), and 3D T1 gradient echo (GRE) sequences, and heavily T2-weighted FLAIR (hvT2F) with and without deep learning-based reconstruction (DLR) in detecting perilymphatic enhancement. METHODS Fifty-four patients with unilateral SSNHL who underwent ear MRI with three sequences were included. We compared asymmetry scores, confidence scores, and detection rates of perilymphatic enhancement among the three sequences and obtained 3D hvT2F with DLR from 35 patients. The above parameters and subjective image quality between 3D hvT2F with and without DLR were compared. RESULTS Asymmetry scores and detection rate of 3D hvT2F were significantly higher than 3D GRE T1 and SE T1 (respectively, 1.37, 0.11, 0.19; p < 0.001). Asymmetry scores significantly increased with DLR compared to 3D hvT2F for experienced and inexperienced readers (respectively, 1.77 vs. 1.40, p = 0.036; 1.49 vs. 1.03, p = 0.012). The detection rate significantly increased only for the latter (57.1% vs. 31.4%, p = 0.022). Patients with perilymphatic enhancement had significantly higher air conduction thresholds on initial (77.96 vs. 57.79, p = 0.002) and 5 days after presentation (63.38 vs. 41.85, p = 0.019). CONCLUSION 3D hvT2F significantly increased the detectability of perilymphatic enhancement compared to 3D GRE T1 and SE T1. DLR further improved the conspicuity of perilymphatic enhancement in 3D hvT2F. 3D hvT2F and DLR are useful for evaluating blood-labyrinthine barrier leakage; furthermore, they might provide prognostic value in the early post-treatment period. CLINICAL RELEVANCE STATEMENT Ten-minute post-contrast 3D heavily T2-weighed FLAIR imaging is a potentially efficacious sequence in demonstrating perilymphatic enhancement in patients with sudden sensorineural hearing loss and may be further improved by deep learning-based reconstruction. KEY POINTS • 3D heavily T2-weighted FLAIR (3D hvT2F) is a sequence sensitive in detecting low concentrations of contrast in the perilymphatic space. • 3D hvT2F sequences properly demonstrated perilymphatic enhancement in sudden sensorineural hearing loss compared to T1 sequences and were further improved by deep learning-based reconstruction (DLR). • 3D hvT2F and DLR are efficacious sequences in detecting blood-labyrinthine barrier leakage and with potential prognostic information.
Collapse
Affiliation(s)
- Mingyu Kim
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Seokhwan Lee
- Department of Otorhinolaryngology-Head and Neck Surgery, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | | | - Yeonah Kang
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
| |
Collapse
|
2
|
Nagaraj UD, Dillman JR, Tkach JA, Greer JS, Leach JL. Evaluation of 3D T1-weighted spoiled gradient echo MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain. Neuroradiology 2024:10.1007/s00234-024-03417-9. [PMID: 38967815 DOI: 10.1007/s00234-024-03417-9] [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: 04/30/2024] [Accepted: 06/25/2024] [Indexed: 07/06/2024]
Abstract
PURPOSE To assess image quality and diagnostic confidence of 3D T1-weighted spoiled gradient echo (SPGR) MRI using artificial intelligence (AI) reconstruction. MATERIALS AND METHODS This prospective, IRB-approved study enrolled 50 pediatric patients (mean age = 11.8 ± 3.1 years) undergoing clinical brain MRI. In addition to standard of care (SOC) compressed SENSE (CS = 2.5), 3D T1-weighted SPGR images were obtained with higher CS acceleration factors (5 and 8) to evaluate the ability of AI reconstruction to improve image quality and reduce scan time. Images were reviewed independently on dedicated research PACS workstations by two neuroradiologists. Quantitative analysis of signal intensities to calculate apparent grey and white matter signal to noise (aSNR) and grey-white matter apparent contrast to noise ratios (aCNR) was performed. RESULTS AI improved overall image quality compared to standard CS reconstruction in 35% (35/100) of evaluations in CS = 2.5 (average scan time = 221 ± 6.9 s), 100% (46/46) of CS = 5 (average scan time = 113.3 ± 4.6 s) and 94% (47/50) of CS = 8 (average scan time = 74.1 ± 0.01 s). Quantitative analysis revealed significantly higher grey matter aSNR, white matter aSNR and grey-white matter aCNR with AI reconstruction compared to standard reconstruction for CS 5 and 8 (all p-values < 0.001), however not for CS 2.5. CONCLUSIONS AI reconstruction improved overall image quality and gray-white matter qualitative and quantitative aSNR and aCNR in highly accelerated (CS = 5 and 8) 3D T1W SPGR images in the majority of pediatric patients.
Collapse
Affiliation(s)
- Usha D Nagaraj
- Department of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3026, USA.
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
| | - Jonathan R Dillman
- Department of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3026, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Jean A Tkach
- Department of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3026, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Joshua S Greer
- Department of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3026, USA
- Philips Healthcare, Cincinnati, OH, USA
| | - James L Leach
- Department of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3026, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Levin JM, Lorentz SG, Hurley ET, Lee J, Throckmorton TW, Garrigues GE, MacDonald P, Anakwenze O, Schoch BS, Klifto C. Artificial intelligence in shoulder and elbow surgery: overview of current and future applications. J Shoulder Elbow Surg 2024; 33:1633-1641. [PMID: 38430978 DOI: 10.1016/j.jse.2024.01.033] [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: 11/22/2023] [Revised: 01/10/2024] [Accepted: 01/14/2024] [Indexed: 03/05/2024]
Abstract
Artificial intelligence (AI) is amongst the most rapidly growing technologies in orthopedic surgery. With the exponential growth in healthcare data, computing power, and complex predictive algorithms, this technology is poised to aid providers in data processing and clinical decision support throughout the continuum of orthopedic care. Understanding the utility and limitations of this technology is vital to practicing orthopedic surgeons, as these applications will become more common place in everyday practice. AI has already demonstrated its utility in shoulder and elbow surgery for imaging-based diagnosis, predictive modeling of clinical outcomes, implant identification, and automated image segmentation. The future integration of AI and robotic surgery represents the largest potential application of AI in shoulder and elbow surgery with the potential for significant clinical and financial impact. This editorial's purpose is to summarize common AI terms, provide a framework to understand and interpret AI model results, and discuss current applications and future directions within shoulder and elbow surgery.
Collapse
Affiliation(s)
- Jay M Levin
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA.
| | - Samuel G Lorentz
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA
| | - Eoghan T Hurley
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA
| | - Julia Lee
- Department of Orthopedic Surgery, Sierra Pacific Orthopedics, Fresno, CA, USA
| | - Thomas W Throckmorton
- Department of Orthopaedic Surgery, University of Tennessee-Campbell Clinic, Germantown, TN, USA
| | | | - Peter MacDonald
- Section of Orthopaedic Surgery & The Pan Am Clinic, University of Manitoba, Winnipeg, MB, Canada
| | - Oke Anakwenze
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA
| | - Bradley S Schoch
- Department of Orthopedic Surgery, Mayo Clinic, Jacksonville, FL, USA
| | - Christopher Klifto
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA
| |
Collapse
|
5
|
Nagaraj UD, Dillman JR, Tkach JA, Greer JS, Leach JL. Evaluation of T2W FLAIR MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain. Pediatr Radiol 2024; 54:1337-1343. [PMID: 38890153 PMCID: PMC11254965 DOI: 10.1007/s00247-024-05968-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND Artificial intelligence (AI) reconstruction techniques have the potential to improve image quality and decrease imaging time. However, these techniques must be assessed for safe and effective use in clinical practice. OBJECTIVE To assess image quality and diagnostic confidence of AI reconstruction in the pediatric brain on fluid-attenuated inversion recovery (FLAIR) imaging. MATERIALS AND METHODS This prospective, institutional review board (IRB)-approved study enrolled 50 pediatric patients (median age=12 years, Q1=10 years, Q3=14 years) undergoing clinical brain MRI. T2-weighted (T2W) FLAIR images were reconstructed by both standard clinical and AI reconstruction algorithms (strong denoising). Images were independently rated by two neuroradiologists on a dedicated research picture archiving and communication system (PACS) to indicate whether AI increased, decreased, or had no effect on image quality compared to standard reconstruction. Quantitative analysis of signal intensities was also performed to calculate apparent signal to noise (aSNR) and apparent contrast to noise (aCNR) ratios. RESULTS AI reconstruction was better than standard in 99% (reader 1, 49/50; reader 2, 50/50) for overall image quality, 99% (reader 1, 49/50; reader 2, 50/50) for subjective SNR, and 98% (reader 1, 49/50; reader 2, 49/50) for diagnostic preference. Quantitative analysis revealed significantly higher gray matter aSNR (30.6±6.5), white matter aSNR (21.4±5.6), and gray-white matter aCNR (7.1±1.6) in AI-reconstructed images compared to standard reconstruction (18±2.7, 14.2±2.8, 4.4±0.8, p<0.001) respectively. CONCLUSION We conclude that AI reconstruction improved T2W FLAIR image quality in most patients when compared with standard reconstruction in pediatric patients.
Collapse
Affiliation(s)
- Usha D Nagaraj
- Department of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3026, USA.
- Department of Radiology, University of Cincinnati, Cincinnati, OH, USA.
| | - Jonathan R Dillman
- Department of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3026, USA
- Department of Radiology, University of Cincinnati, Cincinnati, OH, USA
| | - Jean A Tkach
- Department of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3026, USA
- Department of Radiology, University of Cincinnati, Cincinnati, OH, USA
| | - Joshua S Greer
- Department of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3026, USA
- Philips Healthcare, Cincinnati, OH, USA
| | - James L Leach
- Department of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3026, USA
- Department of Radiology, University of Cincinnati, Cincinnati, OH, USA
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Yun SY, Heo YJ. Clinical feasibility of post-contrast accelerated 3D T1-Sampling Perfection with Application-optimized Contrasts using different flip angle Evolutions (SPACE) with iterative denoising for intracranial enhancing lesions: a retrospective study. Acta Radiol 2024; 65:654-662. [PMID: 38623647 DOI: 10.1177/02841851241245104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
BACKGROUND Post-contrast T1-Sampling Perfection with Application-optimized Contrasts using different flip angle Evolutions (SPACE) is the preferred 3D T1 spin-echo sequence for evaluating brain metastases, regardless of the prolonged scan time. PURPOSE To evaluate the application of accelerated post-contrast T1-SPACE with iterative denoising (ID) for intracranial enhancing lesions in oncologic patients. MATERIAL AND METHODS For evaluation of intracranial lesions, 108 patients underwent standard and accelerated T1-SPACE during the same imaging session. Two neuroradiologists evaluated the overall image quality, artifacts, degree of enhancement, mean contrast-to-noise ratiolesion/parenchyma, and number of enhancing lesions for standard and accelerated T1-SPACE without ID. RESULTS Although there was a significant difference in the overall image quality and mean contrast-to-noise ratiolesion/parenchyma between standard and accelerated T1-SPACE without ID and accelerated SPACE with and without ID, there was no significant difference between standard and accelerated T1-SPACE with ID. Accelerated T1-SPACE showed more artifacts than standard T1-SPACE; however, accelerated T1-SPACE with ID showed significantly fewer artifacts than accelerated T1-SPACE without ID. Accelerated T1-SPACE without ID showed a significantly lower number of enhancing lesions than standard- and accelerated T1-SPACE with ID; however, there was no significant difference between standard and accelerated T1-SPACE with ID, regardless of lesion size. CONCLUSION Although accelerated T1-SPACE markedly decreased the scan time, it showed lower overall image quality and lesion detectability than the standard T1-SPACE. Application of ID to accelerated T1-SPACE resulted in comparable overall image quality and detection of enhancing lesions in brain parenchyma as standard T1-SPACE. Accelerated T1-SPACE with ID may be a promising replacement for standard T1-SPACE.
Collapse
Affiliation(s)
- Su Young Yun
- Department of Radiology, Inje University Busan Paik Hospital, Busan, Republic of Korea
| | - Young Jin Heo
- Department of Radiology, Inje University Busan Paik Hospital, Busan, Republic of Korea
| |
Collapse
|
8
|
Herrmann J, Feng YS, Gassenmaier S, Grunz JP, Koerzdoerfer G, Lingg A, Almansour H, Nickel D, Othman AE, Afat S. Fast 5-minute shoulder MRI protocol with accelerated TSE-sequences and deep learning image reconstruction for the assessment of shoulder pain at 1.5 and 3 Tesla. Eur J Radiol Open 2024; 12:100557. [PMID: 38495213 PMCID: PMC10943294 DOI: 10.1016/j.ejro.2024.100557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 02/13/2024] [Accepted: 02/18/2024] [Indexed: 03/19/2024] Open
Abstract
Purpose The objective of this study was to implement a 5-minute MRI protocol for the shoulder in routine clinical practice consisting of accelerated 2D turbo spin echo (TSE) sequences with deep learning (DL) reconstruction at 1.5 and 3 Tesla, and to compare the image quality and diagnostic performance to that of a standard 2D TSE protocol. Methods Patients undergoing shoulder MRI between October 2020 and June 2021 were prospectively enrolled. Each patient underwent two MRI examinations: first a standard, fully sampled TSE (TSES) protocol reconstructed with a standard reconstruction followed by a second fast, prospectively undersampled TSE protocol with a conventional parallel imaging undersampling pattern reconstructed with a DL reconstruction (TSEDL). Image quality and visualization of anatomic structures as well as diagnostic performance with respect to shoulder lesions were assessed using a 5-point Likert-scale (5 = best). Interchangeability analysis, Wilcoxon signed-rank test and kappa statistics were performed to compare the two protocols. Results A total of 30 participants was included (mean age 50±15 years; 15 men). Overall image quality was evaluated to be superior in TSEDL versus TSES (p<0.001). Noise and edge sharpness were evaluated to be significantly superior in TSEDL versus TSES (noise: p<0.001, edge sharpness: p<0.05). No difference was found concerning qualitative diagnostic confidence, assessability of anatomical structures (p>0.05), and quantitative diagnostic performance for shoulder lesions when comparing the two sequences. Conclusions A fast 5-minute TSEDL MRI protocol of the shoulder is feasible in routine clinical practice at 1.5 and 3 T, with interchangeable results concerning the diagnostic performance, allowing a reduction in scan time of more than 50% compared to the standard TSES protocol.
Collapse
Affiliation(s)
- Judith Herrmann
- Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Eberhard Karls University, Tuebingen, Germany
| | - You-Shan Feng
- Institute for Clinical Epidemiology and Applied Biometrics, University Hospital Tuebingen, Eberhard Karls University, Tuebingen, Germany
| | - Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Eberhard Karls University, Tuebingen, Germany
| | - Jan-Peter Grunz
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | | | - Andreas Lingg
- Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Eberhard Karls University, Tuebingen, Germany
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Eberhard Karls University, Tuebingen, Germany
| | - Dominik Nickel
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Ahmed E. Othman
- Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Eberhard Karls University, Tuebingen, Germany
- Department of Neuroradiology, University Medical Center Mainz, Mainz, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Eberhard Karls University, Tuebingen, Germany
| |
Collapse
|
9
|
Cheng C, Liang X, Guo D, Xie D. Application of Artificial Intelligence in Shoulder Pathology. Diagnostics (Basel) 2024; 14:1091. [PMID: 38893618 PMCID: PMC11171621 DOI: 10.3390/diagnostics14111091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 05/16/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
Artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopedic-specific problems. One particular area of investigation focuses on shoulder pathology, which is a range of disorders or abnormalities of the shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate current AI applications in shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI technology are also discussed.
Collapse
Affiliation(s)
- Cong Cheng
- Department of Orthopaedics, People’s Hospital of Longhua, Shenzhen 518000, China;
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Xinzhi Liang
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Dong Guo
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Denghui Xie
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
- Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
| |
Collapse
|
10
|
Zhang X, Wang Y, Xu X, Zhang J, Sun Y, Hu M, Wang S, Li Y, Chen Y, Zhao X. Bladder MRI with deep learning-based reconstruction: a prospective evaluation of muscle invasiveness using VI-RADS. Abdom Radiol (NY) 2024; 49:1615-1625. [PMID: 38652125 DOI: 10.1007/s00261-024-04280-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/03/2024] [Accepted: 03/05/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE To investigate the influence of deep learning reconstruction (DLR) on bladder MRI, specifically examination time, image quality, and diagnostic performance of vesical imaging reporting and data system (VI-RADS) within a prospective clinical cohort. METHODS Seventy participants with bladder cancer who underwent MRI between August 2022 and February 2023 with a protocol containing standard T2-weighted imaging (T2WIS), standard diffusion-weighted imaging (DWIS), fast T2WI with DLR (T2WIDL), and fast DWI with DLR (DWIDL) were enrolled in this prospective study. Imaging quality was evaluated by measuring signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and qualitative image quality scoring. Additionally, the apparent diffusion coefficient (ADC) of bladder lesions derived from DWIS and DWIDL was measured and VI-RADS scoring was performed. Paired t-test or paired Wilcoxon signed-rank test were performed to compare image quality score, SNR, CNR, and ADC between standard sequences and fast sequences with DLR. The diagnostic performance for VI-RADS was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS Compared to T2WIS and DWIS, T2WIDL and DWIDL reduced the acquisition time from 5:57 min to 3:13 min and showed significantly higher SNR, CNR, qualitative image quality score of overall image quality, image sharpness, and lesion conspicuity. There were no significant differences in ADC and AUC of VI-RADS between standard sequences and fast sequences with DLR. CONCLUSIONS The application of DLR to T2WI and DWI reduced examination time and significantly improved image quality, maintaining ADC and the diagnostic performance of VI-RADS for evaluating muscle invasion in bladder cancer.
Collapse
Affiliation(s)
- Xinxin Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yichen Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Xiaojuan Xu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Jie Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yuying Sun
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Mancang Hu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Sicong Wang
- GE Healthcare, MR Research China, Tongji South Road No1, Beijing, 100176, China
| | - Yi Li
- School of Statistics and Mathematics, Nanjing Audit University, Nanjing, 211815, China
| | - Yan Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| |
Collapse
|
11
|
Ensle F, Kaniewska M, Lohezic M, Guggenberger R. Enhanced bone assessment of the shoulder using zero-echo time MRI with deep-learning image reconstruction. Skeletal Radiol 2024:10.1007/s00256-024-04690-8. [PMID: 38658419 DOI: 10.1007/s00256-024-04690-8] [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] [Received: 01/22/2024] [Revised: 04/07/2024] [Accepted: 04/18/2024] [Indexed: 04/26/2024]
Abstract
OBJECTIVES To assess a deep learning-based reconstruction algorithm (DLRecon) in zero echo-time (ZTE) MRI of the shoulder at 1.5 Tesla for improved delineation of osseous findings. METHODS In this retrospective study, 63 consecutive exams of 52 patients (28 female) undergoing shoulder MRI at 1.5 Tesla in clinical routine were included. Coronal 3D isotropic radial ZTE pulse sequences were acquired in the standard MR shoulder protocol. In addition to standard-of-care (SOC) image reconstruction, the same raw data was reconstructed with a vendor-supplied prototype DLRecon algorithm. Exams were classified into three subgroups: no pathological findings, degenerative changes, and posttraumatic changes, respectively. Two blinded readers performed bone assessment on a 4-point scale (0-poor, 3-perfect) by qualitatively grading image quality features and delineation of osseous pathologies including diagnostic confidence in the respective subgroups. Quantitatively, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of bone were measured. Qualitative variables were compared using the Wilcoxon signed-rank test for ordinal data and the McNemar test for dichotomous variables; quantitative measures were compared with Student's t-testing. RESULTS DLRecon scored significantly higher than SOC in all visual metrics of image quality (all, p < 0.03), except in the artifact category (p = 0.37). DLRecon also received superior qualitative scores for delineation of osseous pathologies and diagnostic confidence (p ≤ 0.03). Quantitatively, DLRecon achieved superior CNR (95 CI [1.4-3.1]) and SNR (95 CI [15.3-21.5]) of bone than SOC (p < 0.001). CONCLUSION DLRecon enhanced image quality in ZTE MRI and improved delineation of osseous pathologies, allowing for increased diagnostic confidence in bone assessment.
Collapse
Affiliation(s)
- Falko Ensle
- Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland.
- University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland.
| | - Malwina Kaniewska
- Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland
- University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland
| | | | - Roman Guggenberger
- Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland
- University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland
| |
Collapse
|
12
|
Yi J, Hahn S, Lee HJ, Lee Y, Bang JY, Kim Y, Lee J. Thin-slice elbow MRI with deep learning reconstruction: Superior diagnostic performance of elbow ligament pathologies. Eur J Radiol 2024; 175:111471. [PMID: 38636411 DOI: 10.1016/j.ejrad.2024.111471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 03/31/2024] [Accepted: 04/15/2024] [Indexed: 04/20/2024]
Abstract
PURPOSE With the slice thickness routinely used in elbow MRI, small or subtle lesions may be overlooked or misinterpreted as insignificant. To compare 1 mm slice thickness MRI (1 mm MRI) with deep learning reconstruction (DLR) to 3 mm slice thickness MRI (3 mm MRI) without/with DLR, and 1 mm MRI without DLR regarding image quality and diagnostic performance for elbow tendons and ligaments. METHODS This retrospective study included 53 patients between February 2021 and January 2022, who underwent 3 T elbow MRI, including T2-weighted fat-saturated coronal 3 mm and 1 mm MRI without/with DLR. Two radiologists independently assessed four MRI scans for image quality and artefacts, and identified the pathologies of the five elbow tendons and ligaments. In 19 patients underwent elbow surgery after elbow MRI, diagnostic performance was evaluated using surgical records as a reference standard. RESULTS For both readers, 3 mm MRI with DLR had significant higher image quality scores than 3 mm MRI without DLR and 1 mm MRI with DLR (all P < 0.01). For common extensor tendon and elbow ligament pathologies, 1 mm MRI with DLR showed the highest number of pathologies for both readers. The 1 mm MRI with DLR had the highest kappa values for all tendons and ligaments. For reader 1, 1 mm MRI with DLR showed superior diagnostic performance than 3 mm MRI without/with DLR. For reader 2, 1 mm MRI with DLR showed the highest diagnostic performance; however, there was no significant difference. CONCLUSIONS One mm MRI with DLR showed the highest diagnostic performance for evaluating elbow tendon and ligament pathologies, with similar subjective image qualities and artefacts.
Collapse
Affiliation(s)
- Jisook Yi
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, 875 Haeundae-ro, Haeundae-gu, Busan 48108, Republic of Korea
| | - Seok Hahn
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, 875 Haeundae-ro, Haeundae-gu, Busan 48108, Republic of Korea.
| | - Ho-Joon Lee
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, 875 Haeundae-ro, Haeundae-gu, Busan 48108, Republic of Korea
| | - Yedaun Lee
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, 875 Haeundae-ro, Haeundae-gu, Busan 48108, Republic of Korea
| | - Jin-Young Bang
- Department of Orthopaedic Surgery, Inje University College of Medicine, Haeundae Paik Hospital, 875 Haeundae-ro, Haeundae-gu, Busan 48108, Republic of Korea
| | - Youngbok Kim
- Department of Orthopaedic Surgery, Inje University College of Medicine, Haeundae Paik Hospital, 875 Haeundae-ro, Haeundae-gu, Busan 48108, Republic of Korea
| | | |
Collapse
|
13
|
Chen W, Lim LJR, Lim RQR, Yi Z, Huang J, He J, Yang G, Liu B. Artificial intelligence powered advancements in upper extremity joint MRI: A review. Heliyon 2024; 10:e28731. [PMID: 38596104 PMCID: PMC11002577 DOI: 10.1016/j.heliyon.2024.e28731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/11/2024] Open
Abstract
Magnetic resonance imaging (MRI) is an indispensable medical imaging examination technique in musculoskeletal medicine. Modern MRI techniques achieve superior high-quality multiplanar imaging of soft tissue and skeletal pathologies without the harmful effects of ionizing radiation. Some current limitations of MRI include long acquisition times, artifacts, and noise. In addition, it is often challenging to distinguish abutting or closely applied soft tissue structures with similar signal characteristics. In the past decade, Artificial Intelligence (AI) has been widely employed in musculoskeletal MRI to help reduce the image acquisition time and improve image quality. Apart from being able to reduce medical costs, AI can assist clinicians in diagnosing diseases more accurately. This will effectively help formulate appropriate treatment plans and ultimately improve patient care. This review article intends to summarize AI's current research and application in musculoskeletal MRI, particularly the advancement of DL in identifying the structure and lesions of upper extremity joints in MRI images.
Collapse
Affiliation(s)
- Wei Chen
- Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| | - Lincoln Jian Rong Lim
- Department of Medical Imaging, Western Health, Footscray Hospital, Victoria, Australia
- Department of Surgery, The University of Melbourne, Victoria, Australia
| | - Rebecca Qian Ru Lim
- Department of Hand & Reconstructive Microsurgery, Singapore General Hospital, Singapore
| | - Zhe Yi
- Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| | - Jiaxing Huang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jia He
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Ge Yang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Bo Liu
- Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
14
|
Kim JH, Yoon JH, Kim SW, Park J, Bae SH, Lee JM. Application of a deep learning algorithm for three-dimensional T1-weighted gradient-echo imaging of gadoxetic acid-enhanced MRI in patients at a high risk of hepatocellular carcinoma. Abdom Radiol (NY) 2024; 49:738-747. [PMID: 38095685 DOI: 10.1007/s00261-023-04124-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/30/2023] [Accepted: 11/03/2023] [Indexed: 03/05/2024]
Abstract
PURPOSE To evaluate the efficacy of a vendor-specific deep learning reconstruction algorithm (DLRA) in enhancing image quality and focal lesion detection using three-dimensional T1-weighted gradient-echo images in gadoxetic acid-enhanced liver magnetic resonance imaging (MRI) in patients at a high risk of hepatocellular carcinoma. MATERIALS AND METHODS In this retrospective analysis, 83 high-risk patients with hepatocellular carcinoma underwent gadoxetic acid-enhanced liver MRI using a 3-T scanner. Triple arterial phase, high-resolution portal venous phase, and high-resolution hepatobiliary phase images were reconstructed using conventional reconstruction techniques and DLRA (AIRTM Recon DL; GE Healthcare) for subsequent comparison. Image quality and solid focal lesion detection were assessed by three abdominal radiologists and compared between conventional and DL methods. Focal liver lesion detection was evaluated using figures of merit (FOMs) from a jackknife alternative free-response receiver operating characteristic analysis on a per-lesion basis. RESULTS DLRA-reconstructed images exhibited significantly improved overall image quality, image contrast, lesion conspicuity, vessel conspicuity, and liver edge sharpness and reduced subjective image noise, ringing artifacts, and motion artifacts compared to conventionally reconstructed images (all P < 0.05). Although there was no significant difference in the FOMs of non-cystic focal liver lesions between the conventional and DL methods, DLRA-reconstructed images showed notably higher pooled sensitivity than conventionally reconstructed images (P < 0.05) in all phases and higher detection rates for viable post-treatment HCCs in the arterial and hepatobiliary phases (all P < 0.05). CONCLUSIONS Implementing DLRA can enhance the image quality in 3D T1-weighted gradient-echo sequences of gadoxetic acid-enhanced liver MRI examinations, leading to improved detection of viable post-treatment HCCs.
Collapse
Affiliation(s)
- Jae Hyun Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, 28, Yongon-dong, Chongno-gu, Seoul, 110-744, Republic of Korea
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, 28, Yongon-dong, Chongno-gu, Seoul, 110-744, Republic of Korea
| | - Se Woo Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, 28, Yongon-dong, Chongno-gu, Seoul, 110-744, Republic of Korea
| | - Junghoan Park
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, 28, Yongon-dong, Chongno-gu, Seoul, 110-744, Republic of Korea
| | - Seong Hwan Bae
- Department of Radiology, Soonchunhyang University Seoul Hospital, Seoul, Republic of Korea
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Radiology, Seoul National University College of Medicine, 28, Yongon-dong, Chongno-gu, Seoul, 110-744, Republic of Korea.
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
| |
Collapse
|
15
|
Zhan H, Teng F, Liu Z, Yi Z, He J, Chen Y, Geng B, Xia Y, Wu M, Jiang J. Artificial Intelligence Aids Detection of Rotator Cuff Pathology: A Systematic Review. Arthroscopy 2024; 40:567-578. [PMID: 37355191 DOI: 10.1016/j.arthro.2023.06.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 05/28/2023] [Accepted: 06/01/2023] [Indexed: 06/26/2023]
Abstract
PURPOSE To determine the model performance of artificial intelligence (AI) in detecting rotator cuff pathology using different imaging modalities and to compare capability with physicians in clinical scenarios. METHODS The review followed the PRISMA guidelines and was registered on PROSPERO. The criteria were as follows: 1) studies on the application of AI in detecting rotator cuff pathology using medical images, and 2) studies on smart devices for assisting in diagnosis were excluded. The following data were extracted and recorded: statistical characteristics, input features, AI algorithms used, sample sizes of training and testing sets, and model performance. The data extracted from the included studies were narratively reviewed. RESULTS A total of 14 articles, comprising 23,119 patients, met the inclusion and exclusion criteria. The pooled mean age of the patients was 56.7 years, and the female rate was 56.1%. The area under the curve (AUC) of the algorithmic model to detect rotator cuff pathology from ultrasound images, MRI images, and radiographic series ranged from 0.789 to 0.950, 0.844 to 0.943, and 0.820 to 0.830, respectively. Notably, 1 of the studies reported that AI models based on ultrasound images demonstrated a diagnostic performance similar to that of radiologists. Another comparative study demonstrated that AI models using MRI images exhibited greater accuracy and specificity compared to orthopedic surgeons in the diagnosis of rotator cuff pathology, albeit not in sensitivity. CONCLUSIONS The detection of rotator cuff pathology has been significantly aided by the exceptional performance of AI models. In particular, these models are equally adept as musculoskeletal radiologists in using ultrasound to diagnose rotator cuff pathology. Furthermore, AI models exhibit statistically superior levels of accuracy and specificity when using MRI to diagnose rotator cuff pathology, albeit with no marked difference in sensitivity, in comparison to orthopaedic surgeons. LEVEL OF EVIDENCE Level III, systematic review of Level III studies.
Collapse
Affiliation(s)
- Hongwei Zhan
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Fei Teng
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Zhongcheng Liu
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Zhi Yi
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Jinwen He
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Yi Chen
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Bin Geng
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Yayi Xia
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China.
| | - Meng Wu
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Jin Jiang
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| |
Collapse
|
16
|
Erber B, Hesse N, Goller S, Reidler P. [Pathologies of the shoulder joint : Anatomy and examination techniques]. RADIOLOGIE (HEIDELBERG, GERMANY) 2024; 64:101-109. [PMID: 38085326 DOI: 10.1007/s00117-023-01246-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/17/2023] [Indexed: 01/27/2024]
Abstract
CLINICAL ISSUE The movements and stability of the human shoulder are a complex dynamic interaction between several joints, muscles and ligaments, which on the one hand enable extensive mobility and on the other hand must provide the necessary stability. Furthermore, the complexity of the shoulder is increased by a large number of normal variants. This article aims to explain the relevant anatomical structures and the radiological examination techniques necessary to visualize them. STANDARD RADIOLOGICAL PROCEDURES Various modalities contribute to the examination of the shoulder. These include X‑rays, computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound. METHODOLOGICAL INNOVATION It is important to use the various procedures appropriately. Especially with MRI arthrography, it is necessary to pay attention to suitable sequences and possibly additional examination positions. PRACTICAL RECOMMENDATION The multimodal examination of the shoulder can contribute to the diagnosis of numerous clinical pictures. Anatomical and methodological foundations are essential for this.
Collapse
Affiliation(s)
- B Erber
- Klinik und Poliklinik für Radiologie, LMU Klinikum, LMU München, Marchioninistr. 15, 81377, München, Deutschland.
| | - N Hesse
- Klinik und Poliklinik für Radiologie, LMU Klinikum, LMU München, Marchioninistr. 15, 81377, München, Deutschland
| | - S Goller
- Klinik und Poliklinik für Radiologie, LMU Klinikum, LMU München, Marchioninistr. 15, 81377, München, Deutschland
- Radiologie, Universitätsklinik Balgrist, Forchstr. 340, 8008, Zürich, Schweiz
| | - P Reidler
- Klinik und Poliklinik für Radiologie, LMU Klinikum, LMU München, Marchioninistr. 15, 81377, München, Deutschland
| |
Collapse
|
17
|
Kim M, Kim SH, Hong S, Kim YJ, Kim HR, Kim JY. Evaluation of Extra-Prostatic Extension on Deep Learning-Reconstructed High-Resolution Thin-Slice T2-Weighted Images in Patients with Prostate Cancer. Cancers (Basel) 2024; 16:413. [PMID: 38254901 PMCID: PMC10814256 DOI: 10.3390/cancers16020413] [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: 12/15/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/24/2024] Open
Abstract
The aim of this study was to compare diagnostic performance for extra-prostatic extension (EPE) and image quality among three image datasets: conventional T2-weighted images (T2WIconv, slice thickness, 3 mm) and high-resolution thin-slice T2WI (T2WIHR, 2 mm), with and without deep learning reconstruction (DLR) in patients with prostatic cancer (PCa). A total of 88 consecutive patients (28 EPE-positive and 60 negative) diagnosed with PCa via radical prostatectomy who had undergone 3T-MRI were included. Two independent reviewers performed a crossover review in three sessions, in which each reviewer recorded five-point confidence scores for the presence of EPE and image quality using a five-point Likert scale. Pathologic topographic maps served as the reference standard. For both reviewers, T2WIconv showed better diagnostic performance than T2WIHR with and without DLR (AUCs, in order, for reviewer 1, 0.883, 0.806, and 0.772, p = 0.0006; for reviewer 2, 0.803, 0.762, and 0.745, p = 0.022). The image quality was also the best in T2WIconv, followed by T2WIHR with DLR and T2WIHR without DLR for both reviewers (median, in order, 3, 4, and 5, p < 0.0001). In conclusion, T2WIconv was optimal in regard to image quality and diagnostic performance for the evaluation of EPE in patients with PCa.
Collapse
Affiliation(s)
- Mingyu Kim
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Republic of Korea
| | - Seung Ho Kim
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Republic of Korea
| | - Sujin Hong
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Republic of Korea
| | - Yeon Jung Kim
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Republic of Korea
| | - Hye Ri Kim
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Republic of Korea
| | - Joo Yeon Kim
- Department of Pathology, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Republic of Korea
| |
Collapse
|
18
|
Xie Y, Tao H, Li X, Hu Y, Liu C, Zhou B, Cai J, Nickel D, Fu C, Xiong B, Chen S. Prospective Comparison of Standard and Deep Learning-reconstructed Turbo Spin-Echo MRI of the Shoulder. Radiology 2024; 310:e231405. [PMID: 38193842 DOI: 10.1148/radiol.231405] [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: 01/10/2024]
Abstract
Background Deep learning (DL)-based MRI reconstructions can reduce imaging times for turbo spin-echo (TSE) examinations. However, studies that prospectively use DL-based reconstructions of rapidly acquired, undersampled MRI in the shoulder are lacking. Purpose To compare the acquisition time, image quality, and diagnostic confidence of DL-reconstructed TSE (TSEDL) with standard TSE in patients indicated for shoulder MRI. Materials and Methods This prospective single-center study included consecutive adult patients with various shoulder abnormalities who were clinically referred for shoulder MRI between February and March 2023. Each participant underwent standard TSE MRI (proton density- and T1-weighted imaging; conventional TSE sequence was used as reference for comparison), followed by a prospectively undersampled accelerated TSEDL examination. Six musculoskeletal radiologists evaluated images using a four-point Likert scale (1, poor; 4, excellent) for overall image quality, perceived signal-to-noise ratio, sharpness, artifacts, and diagnostic confidence. The frequency of major pathologic features and acquisition times were also compared between the acquisition protocols. The intergroup comparisons were performed using the Wilcoxon signed rank test. Results Overall, 135 shoulders in 133 participants were evaluated (mean age, 47.9 years ± 17.1 [SD]; 73 female participants). The median acquisition time of the TSEDL protocol was lower than that of the standard TSE protocol (288 seconds [IQR, 288-288 seconds] vs 926 seconds [IQR, 926-950 seconds], respectively; P < .001), achieving a 69% lower acquisition time. TSEDL images were given higher scores for overall image quality, perceived signal-to-noise ratio, and artifacts (all P < .001). Similar frequency of pathologic features (P = .48 to > .99), sharpness (P = .06), or diagnostic confidence (P = .05) were noted between images from the two protocols. Conclusion In a clinical setting, TSEDL led to reduced examination time and higher image quality with similar diagnostic confidence compared with standard TSE MRI in the shoulder. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Chang and Chow in this issue.
Collapse
Affiliation(s)
- Yuxue Xie
- From the Department of Radiology & Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Rd, Shanghai 200040, China (Y.X., H.T., X.L., Y.H., C.L., B.Z., J.C., S.C.); MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany (D.N.); MR Collaboration, Siemens (Shenzhen) Magnetic Resonance, Shenzhen, China (C.F.); and MR Application, Siemens Healthineers, Shanghai, China (B.X.)
| | - Hongyue Tao
- From the Department of Radiology & Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Rd, Shanghai 200040, China (Y.X., H.T., X.L., Y.H., C.L., B.Z., J.C., S.C.); MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany (D.N.); MR Collaboration, Siemens (Shenzhen) Magnetic Resonance, Shenzhen, China (C.F.); and MR Application, Siemens Healthineers, Shanghai, China (B.X.)
| | - Xiangwen Li
- From the Department of Radiology & Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Rd, Shanghai 200040, China (Y.X., H.T., X.L., Y.H., C.L., B.Z., J.C., S.C.); MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany (D.N.); MR Collaboration, Siemens (Shenzhen) Magnetic Resonance, Shenzhen, China (C.F.); and MR Application, Siemens Healthineers, Shanghai, China (B.X.)
| | - Yiwen Hu
- From the Department of Radiology & Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Rd, Shanghai 200040, China (Y.X., H.T., X.L., Y.H., C.L., B.Z., J.C., S.C.); MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany (D.N.); MR Collaboration, Siemens (Shenzhen) Magnetic Resonance, Shenzhen, China (C.F.); and MR Application, Siemens Healthineers, Shanghai, China (B.X.)
| | - Changyan Liu
- From the Department of Radiology & Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Rd, Shanghai 200040, China (Y.X., H.T., X.L., Y.H., C.L., B.Z., J.C., S.C.); MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany (D.N.); MR Collaboration, Siemens (Shenzhen) Magnetic Resonance, Shenzhen, China (C.F.); and MR Application, Siemens Healthineers, Shanghai, China (B.X.)
| | - Bijing Zhou
- From the Department of Radiology & Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Rd, Shanghai 200040, China (Y.X., H.T., X.L., Y.H., C.L., B.Z., J.C., S.C.); MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany (D.N.); MR Collaboration, Siemens (Shenzhen) Magnetic Resonance, Shenzhen, China (C.F.); and MR Application, Siemens Healthineers, Shanghai, China (B.X.)
| | - Jiajie Cai
- From the Department of Radiology & Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Rd, Shanghai 200040, China (Y.X., H.T., X.L., Y.H., C.L., B.Z., J.C., S.C.); MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany (D.N.); MR Collaboration, Siemens (Shenzhen) Magnetic Resonance, Shenzhen, China (C.F.); and MR Application, Siemens Healthineers, Shanghai, China (B.X.)
| | - Dominik Nickel
- From the Department of Radiology & Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Rd, Shanghai 200040, China (Y.X., H.T., X.L., Y.H., C.L., B.Z., J.C., S.C.); MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany (D.N.); MR Collaboration, Siemens (Shenzhen) Magnetic Resonance, Shenzhen, China (C.F.); and MR Application, Siemens Healthineers, Shanghai, China (B.X.)
| | - Caixia Fu
- From the Department of Radiology & Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Rd, Shanghai 200040, China (Y.X., H.T., X.L., Y.H., C.L., B.Z., J.C., S.C.); MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany (D.N.); MR Collaboration, Siemens (Shenzhen) Magnetic Resonance, Shenzhen, China (C.F.); and MR Application, Siemens Healthineers, Shanghai, China (B.X.)
| | - Bo Xiong
- From the Department of Radiology & Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Rd, Shanghai 200040, China (Y.X., H.T., X.L., Y.H., C.L., B.Z., J.C., S.C.); MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany (D.N.); MR Collaboration, Siemens (Shenzhen) Magnetic Resonance, Shenzhen, China (C.F.); and MR Application, Siemens Healthineers, Shanghai, China (B.X.)
| | - Shuang Chen
- From the Department of Radiology & Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Rd, Shanghai 200040, China (Y.X., H.T., X.L., Y.H., C.L., B.Z., J.C., S.C.); MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany (D.N.); MR Collaboration, Siemens (Shenzhen) Magnetic Resonance, Shenzhen, China (C.F.); and MR Application, Siemens Healthineers, Shanghai, China (B.X.)
| |
Collapse
|
19
|
Chang PD, Chow DS. Revolutionizing Shoulder MRI: Accelerated Imaging with Deep Learning Reconstruction. Radiology 2024; 310:e233301. [PMID: 38193840 DOI: 10.1148/radiol.233301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Affiliation(s)
- Peter D Chang
- From the Department of Radiological Sciences, University of California Irvine, 1 Medical Plaza Dr, Irvine, CA 92697
| | - Daniel S Chow
- From the Department of Radiological Sciences, University of California Irvine, 1 Medical Plaza Dr, Irvine, CA 92697
| |
Collapse
|
20
|
Velasquez Garcia A, Hsu KL, Marinakis K. Advancements in the diagnosis and management of rotator cuff tears. The role of artificial intelligence. J Orthop 2024; 47:87-93. [PMID: 38059047 PMCID: PMC10696306 DOI: 10.1016/j.jor.2023.11.011] [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] [Received: 10/25/2023] [Accepted: 11/03/2023] [Indexed: 12/08/2023] Open
Abstract
Background This review examined the role of artificial intelligence (AI) in the diagnosis and management of rotator cuff tears (RCTs). Methods A literature search was conducted in October 2023 using PubMed (MEDLINE), SCOPUS, and EMBASE databases, included only peer-reviewed studies. Relevant articles on AI technology in RCTs. A critical analysis of the relevant literature was conducted. Results AI is transforming RCTs management through faster and more precise identification and assessment using algorithms that facilitate segmentation, quantification, and classification of the RCTs across various imaging modalities. Precise algorithms focusing on preoperative factors to assess RCTs reparability have been developed for personalized treatment planning and outcome prediction. AI also aids in exercise classification and promotes patient adherence during at-home physiotherapy. Despite promising advancements, challenges in data quality and symptom integration persist. Future research should include refining AI algorithms, expanding their integration into various imaging techniques, and exploring their roles in postoperative care and surgical decision-making. Conclusions AI-driven solutions improve diagnostic accuracy and have the potential to influence treatment planning and postoperative outcomes through the automated RCTs analysis of medical imaging. Integration of high-quality datasets and clinical symptoms into AI models can enhance their reliability. Current AI algorithms can also be refined, integrated into other imaging techniques, and explored further in surgical decision-making and postoperative care.
Collapse
Affiliation(s)
- Ausberto Velasquez Garcia
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
- Clínica Universidad de los Andes, Department of Orthopedic Surgery, Santiago, Chile
| | - Kai-Lan Hsu
- Department of Orthopaedic Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan
| | | |
Collapse
|
21
|
Seo G, Lee SJ, Park DH, Paeng SH, Koerzdoerfer G, Nickel MD, Sung J. Image quality and lesion detectability of deep learning-accelerated T2-weighted Dixon imaging of the cervical spine. Skeletal Radiol 2023; 52:2451-2459. [PMID: 37233758 DOI: 10.1007/s00256-023-04364-x] [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: 01/16/2023] [Revised: 05/08/2023] [Accepted: 05/09/2023] [Indexed: 05/27/2023]
Abstract
OBJECTIVES To validate the subjective image quality and lesion detectability of deep learning-accelerated Dixon (DL-Dixon) imaging of the cervical spine compared with routine Dixon imaging. MATERIALS AND METHODS A total of 50 patients underwent sagittal routine Dixon and DL-Dixon imaging of the cervical spine. Acquisition parameters were compared and non-uniformity (NU) values were calculated. Two radiologists independently assessed the two imaging methods for subjective image quality and lesion detectability. Interreader and intermethod agreements were estimated with weighted kappa values. RESULTS Compared with the routine Dixon imaging, the DL-Dixon imaging reduced the acquisition time by 23.76%. The NU value is slightly higher in DL-Dixon imaging (p value: 0.015). DL-Dixon imaging showed superior visibility of all four anatomical structures (spinal cord, disc margin, dorsal root ganglion, and facet joint) for both readers (p value: < 0.001 ~ 0.002). The motion artifact scores were slightly higher in the DL-Dixon images than in routine Dixon images (p value = 0.785). Intermethod agreements were almost perfect for disc herniation, facet osteoarthritis, uncovertebral arthritis, central canal stenosis (κ range: 0.830 ~ 0.980, all p values < 0.001) and substantial to almost perfect for foraminal stenosis (κ = 0.955, 0.705 for each reader). There was an improvement in the interreader agreement of foraminal stenosis by DL-Dixon images, from moderate to substantial agreement. CONCLUSION The DLR sequence can substantially decrease the acquisition time of the Dixon sequence with subjective image quality at least as good as the conventional sequence. And no significant differences in lesion detectability were observed between the two sequence types.
Collapse
Affiliation(s)
- Geojeong Seo
- Department of Radiology, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Sun Joo Lee
- Department of Radiology, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
| | - Dae Hyun Park
- Department of Orthopaedic Surgery, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Sung Hwa Paeng
- Department of Neurosurgery, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | | | | | | |
Collapse
|
22
|
Yang J, Wang F, Wang Z, Zhang W, Xie L, Wang L. Evaluation of late gadolinium enhancement cardiac MRI using deep learning reconstruction. Acta Radiol 2023; 64:2714-2721. [PMID: 37700572 DOI: 10.1177/02841851231192786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
BACKGROUND Deep learning (DL)-based methods have been used to improve the imaging quality of magnetic resonance imaging (MRI) by denoising. PURPOSE To assess the effects of DL-based MR reconstruction (DLR) method on late gadolinium enhancement (LGE) image quality. MATERIAL AND METHODS A total of 85 patients who underwent cardiovascular magnetic resonance (CMR) examination, including LGE imaging using conventional construction and DLR with varying levels of noise reduction (NR) levels, were included. Both magnitude LGE (MLGE) and phase-sensitive LGE (PSLGE) images were reviewed independently by double-blinded observers who used a 5-point Likert scale for multiple measures regarding image quality. Meanwhile, the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness of images were calculated and compared between conventional LGE imaging and DLR LGE imaging. RESULTS Both MLGE and PSLGE with DLR at 50% and 75% noise reduction levels received significantly higher scores than conventional imaging for overall imaging quality (all P < 0.01). In addition, the SNR, CNR, and edge sharpness of all DLR LGE imaging are higher than conventional imaging (all P < 0.01). The highest subjective score and best image quality is obtained when the DLR noise reduction level is at 75%. CONCLUSION DLR reduced image noise while improving image contrast and sharpness in the cardiovascular LGE imaging.
Collapse
Affiliation(s)
- Jing Yang
- Hebei University of Chinese Medicine, Shijiazhuang, PR China
- Department of Cardiovascular Disease, Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine, Cangzhou, PR China
| | - Feng Wang
- Department of Cardiovascular Disease, Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine, Cangzhou, PR China
| | - Zhirong Wang
- Department of Cardiovascular Disease, Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine, Cangzhou, PR China
| | - Wei Zhang
- Department of Radiology, Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine, Cangzhou, PR China
| | - Lizhi Xie
- GE Healthcare, MR Research China, Beijing, PR China
| | - LiXin Wang
- Hebei University of Chinese Medicine, Shijiazhuang, PR China
- Department of Cardiovascular Disease, Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine, Cangzhou, PR China
| |
Collapse
|
23
|
Rodriguez HC, Rust B, Hansen PY, Maffulli N, Gupta M, Potty AG, Gupta A. Artificial Intelligence and Machine Learning in Rotator Cuff Tears. Sports Med Arthrosc Rev 2023; 31:67-72. [PMID: 37976127 DOI: 10.1097/jsa.0000000000000371] [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: 11/19/2023]
Abstract
Rotator cuff tears (RCTs) negatively impacts patient well-being. Artificial intelligence (AI) is emerging as a promising tool in medical decision-making. Within AI, deep learning allows to autonomously solve complex tasks. This review assesses the current and potential applications of AI in the management of RCT, focusing on diagnostic utility, challenges, and future perspectives. AI demonstrates promise in RCT diagnosis, aiding clinicians in interpreting complex imaging data. Deep learning frameworks, particularly convoluted neural networks architectures, exhibit remarkable diagnostic accuracy in detecting RCTs on magnetic resonance imaging. Advanced segmentation algorithms improve anatomic visualization and surgical planning. AI-assisted radiograph interpretation proves effective in ruling out full-thickness tears. Machine learning models predict RCT diagnosis and postoperative outcomes, enhancing personalized patient care. Challenges include small data sets and classification complexities, especially for partial thickness tears. Current applications of AI in RCT management are promising yet experimental. The potential of AI to revolutionize personalized, efficient, and accurate care for RCT patients is evident. The integration of AI with clinical expertise holds potential to redefine treatment strategies and optimize patient outcomes. Further research, larger data sets, and collaborative efforts are essential to unlock the transformative impact of AI in orthopedic surgery and RCT management.
Collapse
Affiliation(s)
- Hugo C Rodriguez
- Department of Orthopaedic Surgery, Larkin Community Hospital, South Miami
- Department of Orthopaedic Surgery, Hospital for Special Surgery Florida, West Palm Beach
| | - Brandon Rust
- Nova Southeastern University, Dr. Kiran Patel College of Osteopathic Medicine, Fort Lauderdale
| | - Payton Yerke Hansen
- Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL
| | - Nicola Maffulli
- Department of Musculoskeletal Disorders, School of Medicine and Surgery, University of Salerno, Fisciano
- San Giovanni di Dio e Ruggi D'Aragona Hospital "Clinica Ortopedica" Department, Hospital of Salerno, Salerno, Italy
- Barts and the London School of Medicine and Dentistry, Centre for Sports and Exercise Medicine, Queen Mary University of London, London
- School of Pharmacy and Bioengineering, Keele University School of Medicine, Stoke on Trent, UK
| | - Manu Gupta
- Polar Aesthetics Dental & Cosmetic Centre, Noida, Uttar Pradesh
| | - Anish G Potty
- South Texas Orthopaedic Research Institute (STORI Inc.), Laredo, TX
| | - Ashim Gupta
- Regenerative Orthopaedics, Noida, India
- South Texas Orthopaedic Research Institute (STORI Inc.), Laredo, TX
- Future Biologics
- BioIntegrate, Lawrenceville, GA
| |
Collapse
|
24
|
Puel U, Lombard C, Hossu G, Louis M, Blum A, Teixeira PAG, Gillet R. Zero echo time MRI in shoulder MRI protocols for the diagnosis of rotator cuff calcific tendinopathy improves identification of calcific deposits compared to conventional MR sequences but remains sub-optimal compared to radiographs. Eur Radiol 2023; 33:6381-6391. [PMID: 37014406 DOI: 10.1007/s00330-023-09602-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 02/02/2023] [Accepted: 02/22/2023] [Indexed: 04/05/2023]
Abstract
OBJECTIVE To compare the diagnostic performance of standard MRI and standard MRI + ZTE images for the detection of rotator cuff calcific tendinopathy (RCCT) and to describe the artifacts encountered with ZTE images, using computed radiography (CR) as a reference. METHODS In a retrospective study, patients with suspicion of rotator cuff tendinopathy who underwent standard MRI + ZTE images after radiography were enrolled between June 2021 and June 2022. Images were independently analyzed for calcific deposit presence and ZTE images artifacts, by two radiologists. Diagnostic performance was calculated individually with MRI + CR as the reference standard. RESULTS A total of 46 RCCT subjects (27 women; mean age, 55.3 years ± 12.4) and 51 control subjects (27 men; mean age, 45.5 ± 12.9) were evaluated. For both readers, there was an increase in the sensitivity for the identification of calcific deposits of MRI + ZTE compared to MRI (77% (95% CI: 64.5-86.8) and 75.4% (95% CI: 62.7-85.5) versus 57.4% (95% IC: 44.1-70) and 47.5% (95% IC: 34.6-60.7), for R1 and R2, respectively). Specificity was quite similar for both readers and both imaging techniques and ranged from 96.6% (95% IC: 93.3-98.5) to 98.7% (95% IC: 96.3-99.7). Hyperintense joint fluid (62.8% of patients), long head of the biceps tendon (in 60.8%), and subacromial bursa (in 27.8%) on ZTE were considered artifactual. CONCLUSION The addition of ZTE images to a standard MRI protocol improved MRI diagnostic performance of RCCT, but with a suboptimal detection rate and a relatively high frequency of artifactual soft tissue signal hyperintensity. KEY POINTS • Adding ZTE images to standard shoulder MRI improves the MR-based detection of rotator cuff calcific tendinopathy, but half of the calcification unseen with standard MRI remained unseen with ZTE MRI. On ZTE images, joint fluid and long head biceps tendon were hyperintense in about 60% of the shoulders, as well as the subacromial bursa in about 30%, without calcific deposit on conventional radiographs. • The detection rate of calcific deposits using ZTE images was dependent on the disease phase. In the calcific stage, it reached 100% in this study but remained at a maximum of 80.7% in the resorptive phase.
Collapse
Affiliation(s)
- Ulysse Puel
- Guilloz Imaging Department, Central Hospital, University Hospital Center of Nancy, 29 Avenue du Marechal de Lattre de Tassigny, 54000, Nancy, France
- Guilloz Imaging Department, Saint-Charles Hospital, University Hospital Center of Nancy, Toul, France
| | - Charles Lombard
- Guilloz Imaging Department, Central Hospital, University Hospital Center of Nancy, 29 Avenue du Marechal de Lattre de Tassigny, 54000, Nancy, France
- Guilloz Imaging Department, Saint-Charles Hospital, University Hospital Center of Nancy, Toul, France
| | - Gabriela Hossu
- Université de Lorraine, INSERM, IADI, Nancy, France
- CIC, Innovation Technologique, Université de Lorraine, University Hospital Center of Nancy, Nancy, France
| | - Mathias Louis
- Guilloz Imaging Department, Central Hospital, University Hospital Center of Nancy, 29 Avenue du Marechal de Lattre de Tassigny, 54000, Nancy, France
- Guilloz Imaging Department, Saint-Charles Hospital, University Hospital Center of Nancy, Toul, France
| | - Alain Blum
- Guilloz Imaging Department, Central Hospital, University Hospital Center of Nancy, 29 Avenue du Marechal de Lattre de Tassigny, 54000, Nancy, France
- Guilloz Imaging Department, Saint-Charles Hospital, University Hospital Center of Nancy, Toul, France
- Université de Lorraine, INSERM, IADI, Nancy, France
| | - Pedro Augusto Gondim Teixeira
- Guilloz Imaging Department, Central Hospital, University Hospital Center of Nancy, 29 Avenue du Marechal de Lattre de Tassigny, 54000, Nancy, France
- Guilloz Imaging Department, Saint-Charles Hospital, University Hospital Center of Nancy, Toul, France
- Université de Lorraine, INSERM, IADI, Nancy, France
| | - Romain Gillet
- Guilloz Imaging Department, Central Hospital, University Hospital Center of Nancy, 29 Avenue du Marechal de Lattre de Tassigny, 54000, Nancy, France.
- Guilloz Imaging Department, Saint-Charles Hospital, University Hospital Center of Nancy, Toul, France.
- Université de Lorraine, INSERM, IADI, Nancy, France.
| |
Collapse
|
25
|
Choi H, Lee SK, Choi H, Lee Y, Lee K. Deep learning-based reconstruction for canine brain magnetic resonance imaging could improve image quality while reducing scan time. Vet Radiol Ultrasound 2023; 64:873-880. [PMID: 37582510 DOI: 10.1111/vru.13279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/26/2023] [Accepted: 06/15/2023] [Indexed: 08/17/2023] Open
Abstract
Optimal magnetic resonance imaging (MRI) quality and shorter scan time are challenging to achieve in veterinary practices. Recently, deep learning-based reconstruction (DLR) has been proposed for ideal image quality. We hypothesized that DLR-based MRI will improve brain imaging quality and reduce scan time. This prospective, methods comparison study compared the MR image denoising performances of DLR and conventional methods, with the aim of reducing scan time and improving canine brain image quality. Transverse T2-weighted and fluid-attenuated inversion recovery (FLAIR) sequences of the brain were performed in 12 clinically healthy beagle dogs. Different numbers of excitations (NEX) were used to obtain the image groups NEX4, NEX2, and NEX1. DLR was applied to NEX2 and NEX1 to obtain NEX2DL and NEX1DL . The scan times were recorded, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated for quantitative analysis. Five blinded veterinarians assessed the overall quality, contrast, and perceived SNR on four-point Likert scales. Quantitative and qualitative values were compared among the five groups. Compared with NEX4, NEX2 and NEX1 reduced scan time by 50% and 75%, respectively. The mean SNR and CNR of NEX2DL and NEX1DL were significantly superior to those of NEX4, NEX2, and NEX1 (P < 0.05). In all image quality indices, DLR-applied images for both T2-weighted and FLAIR images were significantly higher than NEX4 and NEX2DL had significantly better quality than NEX1DL for FLAIR (P < 0.05). Findings indicated that DLR reduced scan time and improved image quality compared with conventional MRI images in a sample of clinically healthy beagles.
Collapse
Affiliation(s)
- Hyejoon Choi
- College of Veterinary Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Sang-Kwon Lee
- College of Veterinary Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Hojung Choi
- College of Veterinary Medicine, College of Veterinary Medicine, Chungnam National University, Daejeon, Republic of Korea
| | - Youngwon Lee
- College of Veterinary Medicine, College of Veterinary Medicine, Chungnam National University, Daejeon, Republic of Korea
| | - Kija Lee
- College of Veterinary Medicine, Kyungpook National University, Daegu, Republic of Korea
| |
Collapse
|
26
|
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.
Collapse
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
| |
Collapse
|
27
|
Johnson PM, Lin DJ, Zbontar J, Zitnick CL, Sriram A, Muckley M, Babb JS, Kline M, Ciavarra G, Alaia E, Samim M, Walter WR, Calderon L, Pock T, Sodickson DK, Recht MP, Knoll F. Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI. Radiology 2023; 307:e220425. [PMID: 36648347 PMCID: PMC10102623 DOI: 10.1148/radiol.220425] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 10/20/2022] [Accepted: 11/15/2022] [Indexed: 01/18/2023]
Abstract
Background MRI is a powerful diagnostic tool with a long acquisition time. Recently, deep learning (DL) methods have provided accelerated high-quality image reconstructions from undersampled data, but it is unclear if DL image reconstruction can be reliably translated to everyday clinical practice. Purpose To determine the diagnostic equivalence of prospectively accelerated DL-reconstructed knee MRI compared with conventional accelerated MRI for evaluating internal derangement of the knee in a clinical setting. Materials and Methods A DL reconstruction model was trained with images from 298 clinical 3-T knee examinations. In a prospective analysis, patients clinically referred for knee MRI underwent a conventional accelerated knee MRI protocol at 3 T followed by an accelerated DL protocol between January 2020 and February 2021. The equivalence of the DL reconstruction of the images relative to the conventional images for the detection of an abnormality was assessed in terms of interchangeability. Each examination was reviewed by six musculoskeletal radiologists. Analyses pertaining to the detection of meniscal or ligament tears and bone marrow or cartilage abnormalities were based on four-point ordinal scores for the likelihood of an abnormality. Additionally, the protocols were compared with use of four-point ordinal scores for each aspect of image quality: overall image quality, presence of artifacts, sharpness, and signal-to-noise ratio. Results A total of 170 participants (mean age ± SD, 45 years ± 16; 76 men) were evaluated. The DL-reconstructed images were determined to be of diagnostic equivalence with the conventional images for detection of abnormalities. The overall image quality score, averaged over six readers, was significantly better (P < .001) for the DL than for the conventional images. Conclusion In a clinical setting, deep learning reconstruction enabled a nearly twofold reduction in scan time for a knee MRI and was diagnostically equivalent with the conventional protocol. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Roemer in this issue.
Collapse
Affiliation(s)
- Patricia M. Johnson
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| | - Dana J. Lin
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| | - Jure Zbontar
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| | - C. Lawrence Zitnick
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| | - Anuroop Sriram
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| | - Matthew Muckley
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| | - James S. Babb
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| | - Mitchell Kline
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| | - Gina Ciavarra
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| | - Erin Alaia
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| | - Mohammad Samim
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| | - William R. Walter
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| | - Liz Calderon
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| | - Thomas Pock
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| | - Daniel K. Sodickson
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| | - Michael P. Recht
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| | - Florian Knoll
- From the Department of Radiology, New York University Grossman School of Medicine, 650 1st Ave, New York, NY 10016 (P.M.J., D.J.L., J.S.B., M.K., G.C., E.A., M.S., W.R.W., L.C., D.K.S., M.P.R., F.K.); Meta AI Research (FAIR), Menlo Park, Calif (J.Z., C.L.Z., A.S.); Meta AI Research, New York, NY (M.M.); Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (T.P.); and Faculty of Engineering, Friedrich Alexander University Erlangen-Nurnberg (FAU), Erlangen, Germany (F.K.)
| |
Collapse
|
28
|
Liu J, Li W, Li Z, Yang J, Wang K, Cao X, Qin N, Xue K, Dai Y, Wu P, Qiu J. Magnetic resonance shoulder imaging using deep learning-based algorithm. Eur Radiol 2023:10.1007/s00330-023-09470-x. [PMID: 36826500 DOI: 10.1007/s00330-023-09470-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/03/2023] [Accepted: 01/22/2023] [Indexed: 02/25/2023]
Abstract
OBJECTIVE To investigate the feasibility of deep learning-based MRI (DL-MRI) in its application in shoulder imaging and compare its performance with conventional MR imaging (non-DL-MRI). METHODS This retrospective study was approved by the local ethics committee. Seventy consecutive patients who had been examined with both DL-MRI and non-DL-MRI were enrolled for the image quality and lesion diagnosis comparison. Another 400 patients had been examined only with DL-MRI. Their images' quality was assessed by 20 radiologists using a satisfaction survey. The Kendall W test was performed to assess interobserver agreement. The Wilcoxon test was performed to compare the image quality. For lesion diagnosis, the interobserver and interstudy agreement were evaluated by kappa analysis. RESULTS The scan time of DL-MRI (6 min 1 s) was nearly 50% decreased compared with that of non-DL-MRI (11 min 25 s). The image quality was higher in both PDWI (4.85 ± 0.31 for DL, and 4.73 ± 0.29 for non-DL) and T2WI (4.95 ± 0.2 for DL, and 4.74 ± 0.41 for non-DL) of DL-MRI. Good interobserver agreement was found for the image quality of all the MR sequences on both DL-MRI (Kendall W: 0.588~0.902) and non-DL-MRI (Kendall W: 0751~0.865). Both the SNRs and |CNR| were significantly higher in PDWI and T2WI of DL-MRI. High interobserver and interstudy agreements for the lesions in non-DL-MRI and DL-MRI (kappa value = 0.913 to 1.000) were observed. The results of the image quality satisfaction survey in 400 patients receiving DL-MRI in the shoulder obtained 5 scores among all the radiologists. CONCLUSION Shoulder DL-MRI can greatly reduce the scan time, while improve imaging quality of PDWI and T2WI compared to non-DL-MRI. KEY POINTS • Shoulder 2D DL-MRI can greatly reduce the whole scan time and improve imaging quality of both PDWI and T2WI compared to conventional parallel MRI. • Shoulder 2D DL-MRI could be a clinical routine with greatly improved work efficiency in the future.
Collapse
Affiliation(s)
- Jing Liu
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Wei Li
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Ziyuan Li
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Junzhe Yang
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Ke Wang
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Xinming Cao
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Naishan Qin
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Ke Xue
- Central Research Institute, United Imaging Healthcare, 2258 Chengbei Rd., Jiading District, Shanghai, 201807, China
| | - Yongming Dai
- Central Research Institute, United Imaging Healthcare, 2258 Chengbei Rd., Jiading District, Shanghai, 201807, China
| | - Peng Wu
- Central Research Institute, United Imaging Healthcare, 2258 Chengbei Rd., Jiading District, Shanghai, 201807, China
| | - Jianxing Qiu
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China.
| |
Collapse
|
29
|
Kim B, Lee CM, Jang JK, Kim J, Lim SB, Kim AY. Deep learning-based imaging reconstruction for MRI after neoadjuvant chemoradiotherapy for rectal cancer: effects on image quality and assessment of treatment response. ABDOMINAL RADIOLOGY (NEW YORK) 2023; 48:201-210. [PMID: 36261505 DOI: 10.1007/s00261-022-03701-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 10/01/2022] [Accepted: 10/03/2022] [Indexed: 01/25/2023]
Abstract
PURPOSE To investigate the effects of deep learning-based imaging reconstruction (DLR) on the image quality of MRI of rectal cancer after chemoradiotherapy (CRT), and its accuracy in diagnosing pathological complete responses (pCR). METHODS We included 39 patients (men: women, 21:18; mean age ± standard deviation, 59.1 ± 9.7 years) with mid-to-lower rectal cancer who underwent a long-course of CRT and high-resolution rectal MRIs between January 2020 and April 2021. Axial T2WI was reconstructed using the conventional method (MRIconv) and DLR with two different noise reduction factors (MRIDLR30 and MRIDLR50). The signal-to-noise ratio (SNR) of the tumor was measured. Two experienced radiologists independently made a blind assessment of the complete response on MRI. The sensitivity and specificity for pCR were analyzed using a multivariable logistic regression analysis with generalized estimating equations. RESULTS Thirty-four patients did not have a pCR whereas five (12.8%) had pCR. Compared with the SNR of MRIconv (mean ± SD, 7.94 ± 1.92), MRIDLR30 and MRIDLR50 showed higher SNR (9.44 ± 2.31 and 11.83 ± 3.07, respectively) (p < 0.001). Compared to MRIconv, MRIDLR30 and MRIDLR50 showed significantly higher specificity values (p < 0.036) while the sensitivity values were not significantly different (p > 0.301). The sensitivity and specificity for pCR were 48.9% and 80.8% for MRIconv; 48.9% and 88.2% for MRIDLR30; and 38.8% and 86.7% for MRIDLR50, respectively. CONCLUSION DLR produced MR images with higher resolution and SNR. The specificity of MRI for identification of pCR was significantly higher with DLR than with conventional MRI.
Collapse
Affiliation(s)
- Bona Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Chul-Min Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.,Department of Radiology, Hanyang University Medical Center, 222-1, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Jong Keon Jang
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
| | - Jihun Kim
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Seok-Byung Lim
- Division of Colon and Rectal Surgery, Department of Surgery, University of Ulsan College of Medicine, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Ah Young Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| |
Collapse
|
30
|
Kojima S. [[MRI] 3. Current Status of AI Image Reconstruction in Clinical MRI Systems]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2023; 79:1200-1209. [PMID: 37866905 DOI: 10.6009/jjrt.2023-2260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Affiliation(s)
- Shinya Kojima
- Department of Medical Radiology, Faculty of Medical Technology, Teikyo University
| |
Collapse
|
31
|
Deep learning reconstruction in pediatric brain MRI: comparison of image quality with conventional T2-weighted MRI. Neuroradiology 2023; 65:207-214. [PMID: 36156109 DOI: 10.1007/s00234-022-03053-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 09/09/2022] [Indexed: 01/10/2023]
Abstract
INTRODUCTION Deep learning-based MRI reconstruction has recently been introduced to improve image quality. This study aimed to evaluate the performance of deep learning reconstruction in pediatric brain MRI. METHODS A total of 107 consecutive children who underwent 3.0 T brain MRI were included in this study. T2-weighted brain MRI was reconstructed using the three different reconstruction modes: deep learning reconstruction, conventional reconstruction with an intensity filter, and original T2 image without a filter. Two pediatric radiologists independently evaluated the following image quality parameters of three reconstructed images on a 5-point scale: overall image quality, image noisiness, sharpness of gray-white matter differentiation, truncation artifact, motion artifact, cerebrospinal fluid and vascular pulsation artifacts, and lesion conspicuity. The subjective image quality parameters were compared among the three reconstruction modes. Quantitative analysis of the signal uniformity using the coefficient of variation was performed for each reconstruction. RESULTS The overall image quality, noisiness, and gray-white matter sharpness were significantly better with deep learning reconstruction than with conventional or original reconstruction (all P < 0.001). Deep learning reconstruction had significantly fewer truncation artifacts than the other two reconstructions (all P < 0.001). Motion and pulsation artifacts showed no significant differences among the three reconstruction modes. For 36 lesions in 107 patients, lesion conspicuity was better with deep learning reconstruction than original reconstruction. Deep learning reconstruction showed lower signal variation compared to conventional and original reconstructions. CONCLUSION Deep learning reconstruction can reduce noise and truncation artifacts and improve lesion conspicuity and overall image quality in pediatric T2-weighted brain MRI.
Collapse
|
32
|
Kaniewska M, Deininger-Czermak E, Getzmann JM, Wang X, Lohezic M, Guggenberger R. Application of deep learning-based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time. Eur Radiol 2023; 33:1513-1525. [PMID: 36166084 PMCID: PMC9935676 DOI: 10.1007/s00330-022-09151-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 08/11/2022] [Accepted: 09/07/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To compare the image quality and diagnostic performance of conventional motion-corrected periodically rotated overlapping parallel line with enhanced reconstruction (PROPELLER) MRI sequences with post-processed PROPELLER MRI sequences using deep learning-based (DL) reconstructions. METHODS In this prospective study of 30 patients, conventional (19 min 18 s) and accelerated MRI sequences (7 min 16 s) using the PROPELLER technique were acquired. Accelerated sequences were post-processed using DL. The image quality and diagnostic confidence were qualitatively assessed by 2 readers using a 5-point Likert scale. Analysis of the pathological findings of cartilage, rotator cuff tendons and muscles, glenoid labrum and subacromial bursa was performed. Inter-reader agreement was calculated using Cohen's kappa statistic. Quantitative evaluation of image quality was measured using the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). RESULTS Mean image quality and diagnostic confidence in evaluation of all shoulder structures were higher in DL sequences (p value = 0.01). Inter-reader agreement ranged between kappa values of 0.155 (assessment of the bursa) and 0.947 (assessment of the rotator cuff muscles). In 17 cases, thickening of the subacromial bursa of more than 2 mm was only visible in DL sequences. The pathologies of the other structures could be properly evaluated by conventional and DL sequences. Mean SNR (p value = 0.01) and CNR (p value = 0.02) were significantly higher for DL sequences. CONCLUSIONS The accelerated PROPELLER sequences with DL post-processing showed superior image quality and higher diagnostic confidence compared to the conventional PROPELLER sequences. Subacromial bursa can be thoroughly assessed in DL sequences, while the other structures of the shoulder joint can be assessed in conventional and DL sequences with a good agreement between sequences. KEY POINTS • MRI of the shoulder requires long scan times and can be hampered by motion artifacts. • Deep learning-based convolutional neural networks are used to reduce image noise and scan time while maintaining optimal image quality. The radial k-space acquisition technique (PROPELLER) can reduce the scan time and has potential to reduce motion artifacts. • DL sequences show a higher diagnostic confidence than conventional sequences and therefore are preferred for assessment of the subacromial bursa, while conventional and DL sequences show comparable performance in the evaluation of the shoulder joint.
Collapse
Affiliation(s)
- Malwina Kaniewska
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), Raemistrasse 100, CH-8091, Zurich, Switzerland. .,University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland.
| | - Eva Deininger-Czermak
- grid.412004.30000 0004 0478 9977Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), Raemistrasse 100, CH-8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich (UZH), Raemistrasse 100, CH-8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650Department of Forensic Medicine and Imaging, Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland
| | - Jonas M. Getzmann
- grid.412004.30000 0004 0478 9977Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), Raemistrasse 100, CH-8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich (UZH), Raemistrasse 100, CH-8091 Zurich, Switzerland
| | - Xinzeng Wang
- grid.418143.b0000 0001 0943 0267Global MR Applications & Workflow, GE Healthcare, Houston, TX USA
| | - Maelene Lohezic
- grid.420685.d0000 0001 1940 6527Applications & Workflow, GE Healthcare, Manchester, UK
| | - Roman Guggenberger
- grid.412004.30000 0004 0478 9977Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), Raemistrasse 100, CH-8091 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich (UZH), Raemistrasse 100, CH-8091 Zurich, Switzerland
| |
Collapse
|
33
|
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.
Collapse
|
34
|
Familiari F, Galasso O, Massazza F, Mercurio M, Fox H, Srikumaran U, Gasparini G. Artificial Intelligence in the Management of Rotator Cuff Tears. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16779. [PMID: 36554660 PMCID: PMC9779744 DOI: 10.3390/ijerph192416779] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/08/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
Technological innovation is a key component of orthopedic surgery. Artificial intelligence (AI), which describes the ability of computers to process massive data and "learn" from it to produce outputs that mirror human cognition and problem solving, may become an important tool for orthopedic surgeons in the future. AI may be able to improve decision making, both clinically and surgically, via integrating additional data-driven problem solving into practice. The aim of this article will be to review the current applications of AI in the management of rotator cuff tears. The article will discuss various stages of the clinical course: predictive models and prognosis, diagnosis, intraoperative applications, and postoperative care and rehabilitation. Throughout the article, which is a review in terms of study design, we will introduce the concept of AI in rotator cuff tears and provide examples of how these tools can impact clinical practice and patient care. Though many advancements in AI have been made regarding evaluating rotator cuff tears-particularly in the realm of diagnostic imaging-further advancements are required before they become a regular facet of daily clinical practice.
Collapse
Affiliation(s)
- Filippo Familiari
- Department of Orthopaedic and Trauma Surgery, “Mater Domini” University Hospital, “Magna Græcia” University, 88100 Catanzaro, Italy
| | - Olimpio Galasso
- Department of Orthopaedic and Trauma Surgery, “Mater Domini” University Hospital, “Magna Græcia” University, 88100 Catanzaro, Italy
| | - Federica Massazza
- Department of Orthopaedic and Trauma Surgery, “Mater Domini” University Hospital, “Magna Græcia” University, 88100 Catanzaro, Italy
| | - Michele Mercurio
- Department of Orthopaedic and Trauma Surgery, “Mater Domini” University Hospital, “Magna Græcia” University, 88100 Catanzaro, Italy
| | - Henry Fox
- Department of Orthopaedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Uma Srikumaran
- Department of Orthopaedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Giorgio Gasparini
- Department of Orthopaedic and Trauma Surgery, “Mater Domini” University Hospital, “Magna Græcia” University, 88100 Catanzaro, Italy
| |
Collapse
|
35
|
Kim E, Cho HH, Cho SH, Park B, Hong J, Shin KM, Hwang MJ, You SK, Lee SM. Accelerated Synthetic MRI with Deep Learning-Based Reconstruction for Pediatric Neuroimaging. AJNR Am J Neuroradiol 2022; 43:1653-1659. [PMID: 36175085 PMCID: PMC9731246 DOI: 10.3174/ajnr.a7664] [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: 05/11/2022] [Accepted: 08/31/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND AND PURPOSE Synthetic MR imaging is a time-efficient technique. However, its rather long scan time can be challenging for children. This study aimed to evaluate the clinical feasibility of accelerated synthetic MR imaging with deep learning-based reconstruction in pediatric neuroimaging and to investigate the impact of deep learning-based reconstruction on image quality and quantitative values in synthetic MR imaging. MATERIALS AND METHODS This study included 47 children 2.3-14.7 years of age who underwent both standard and accelerated synthetic MR imaging at 3T. The accelerated synthetic MR imaging was reconstructed using a deep learning pipeline. The image quality, lesion detectability, tissue values, and brain volumetry were compared among accelerated deep learning and accelerated and standard synthetic data sets. RESULTS The use of deep learning-based reconstruction in the accelerated synthetic scans significantly improved image quality for all contrast weightings (P < .001), resulting in image quality comparable with or superior to that of standard scans. There was no significant difference in lesion detectability between the accelerated deep learning and standard scans (P > .05). The tissue values and brain tissue volumes obtained with accelerated deep learning and the other 2 scans showed excellent agreement and a strong linear relationship (all, R 2 > 0.9). The difference in quantitative values of accelerated scans versus accelerated deep learning scans was very small (tissue values, <0.5%; volumetry, -1.46%-0.83%). CONCLUSIONS The use of deep learning-based reconstruction in synthetic MR imaging can reduce scan time by 42% while maintaining image quality and lesion detectability and providing consistent quantitative values. The accelerated deep learning synthetic MR imaging can replace standard synthetic MR imaging in both contrast-weighted and quantitative imaging.
Collapse
Affiliation(s)
- E Kim
- From the Departments of Medical and Biological Engineering (E.K.)
- Korea Radioisotope Center for Pharmaceuticals (E.K.), Korea Institute of Radiological and Medical Sciences, Seoul, South Korea
| | - H-H Cho
- Department of Radiology and Medical Research Institute (H.-H.C.), College of Medicine, Ewha Womans University, Seoul, South Korea
| | - S H Cho
- Radiology (S.H.C., B.P., J.H., K.M.S., S.M.L.), School of Medicine, Kyungpook National University, Daegu, South Korea
- Department of Radiology (S.H.C., B.P., J.H., K.M.S., S.M.L.), Kyungpook National University Chilgok Hospital, Daegu, South Korea
| | - B Park
- Radiology (S.H.C., B.P., J.H., K.M.S., S.M.L.), School of Medicine, Kyungpook National University, Daegu, South Korea
- Department of Radiology (S.H.C., B.P., J.H., K.M.S., S.M.L.), Kyungpook National University Chilgok Hospital, Daegu, South Korea
| | - J Hong
- Radiology (S.H.C., B.P., J.H., K.M.S., S.M.L.), School of Medicine, Kyungpook National University, Daegu, South Korea
- Department of Radiology (S.H.C., B.P., J.H., K.M.S., S.M.L.), Kyungpook National University Chilgok Hospital, Daegu, South Korea
| | - K M Shin
- Radiology (S.H.C., B.P., J.H., K.M.S., S.M.L.), School of Medicine, Kyungpook National University, Daegu, South Korea
- Department of Radiology (S.H.C., B.P., J.H., K.M.S., S.M.L.), Kyungpook National University Chilgok Hospital, Daegu, South Korea
| | - M J Hwang
- GE Healthcare Korea (M.J.H.), Seoul, South Korea
| | - S K You
- Department of Radiology (S.K.Y.), Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon, South Korea
| | - S M Lee
- Radiology (S.H.C., B.P., J.H., K.M.S., S.M.L.), School of Medicine, Kyungpook National University, Daegu, South Korea
- Department of Radiology (S.H.C., B.P., J.H., K.M.S., S.M.L.), Kyungpook National University Chilgok Hospital, Daegu, South Korea
| |
Collapse
|
36
|
Foreman SC, Neumann J, Han J, Harrasser N, Weiss K, Peeters JM, Karampinos DC, Makowski MR, Gersing AS, Woertler K. Deep learning-based acceleration of Compressed Sense MR imaging of the ankle. Eur Radiol 2022; 32:8376-8385. [PMID: 35751695 DOI: 10.1007/s00330-022-08919-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 05/13/2022] [Accepted: 05/30/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES To evaluate a compressed sensing artificial intelligence framework (CSAI) to accelerate MRI acquisition of the ankle. METHODS Thirty patients were scanned at 3T. Axial T2-w, coronal T1-w, and coronal/sagittal intermediate-w scans with fat saturation were acquired using compressed sensing only (12:44 min, CS), CSAI with an acceleration factor of 4.6-5.3 (6:45 min, CSAI2x), and CSAI with an acceleration factor of 6.9-7.7 (4:46 min, CSAI3x). Moreover, a high-resolution axial T2-w scan was obtained using CSAI with a similar scan duration compared to CS. Depiction and presence of abnormalities were graded. Signal-to-noise and contrast-to-noise were calculated. Wilcoxon signed-rank test and Cohen's kappa were used to compare CSAI with CS sequences. RESULTS The correlation was perfect between CS and CSAI2x (κ = 1.0) and excellent for CS and CSAI3x (κ = 0.86-1.0). No significant differences were found for the depiction of structures between CS and CSAI2x and the same abnormalities were detected in both protocols. For CSAI3x the depiction was graded lower (p ≤ 0.001), though most abnormalities were also detected. For CSAI2x contrast-to-noise fluid/muscle was higher compared to CS (p ≤ 0.05), while no differences were found for other tissues. Signal-to-noise and contrast-to-noise were higher for CSAI3x compared to CS (p ≤ 0.05). The high - resolution axial T2-w sequence specifically improved the depiction of tendons and the tibial nerve (p ≤ 0.005). CONCLUSIONS Acquisition times can be reduced by 47% using CSAI compared to CS without decreasing diagnostic image quality. Reducing acquisition times by 63% is feasible but should be reserved for specific patients. The depiction of specific structures is improved using a high-resolution axial T2-w CSAI scan. KEY POINTS • Prospective study showed that CSAI enables reduction in acquisition times by 47% without decreasing diagnostic image quality. • Reducing acquisition times by 63% still produces images with an acceptable diagnostic accuracy but should be reserved for specific patients. • CSAI may be implemented to scan at a higher resolution compared to standard CS images without increasing acquisition times.
Collapse
Affiliation(s)
- Sarah C Foreman
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany.
| | - Jan Neumann
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - Jessie Han
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - Norbert Harrasser
- Department of Orthopaedic Surgery, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - Kilian Weiss
- Philips GmbH, Röntgenstrasse 22, 22335, Hamburg, Germany
| | - Johannes M Peeters
- Philips Healthcare, Veenpluis 4-6, Building QR-0.113, 5684, Best, PC, Netherlands
| | - Dimitrios C Karampinos
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - Marcus R Makowski
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - Alexandra S Gersing
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany.,Department of Neuroradiology, University Hospital Munich (LMU), Marchioninistrasse 15, 81377, Munich, Germany
| | - Klaus Woertler
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| |
Collapse
|
37
|
Editor's Notebook: March 2022. AJR Am J Roentgenol 2022; 218:393-395. [PMID: 35192375 DOI: 10.2214/ajr.21.27183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|