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Ni M, He M, Yang Y, Wen X, Zhao Y, Gao L, Yan R, Xu J, Zhang Y, Chen W, Jiang C, Li Y, Zhao Q, Wu P, Li C, Qu J, Yuan H. Application research of AI-assisted compressed sensing technology in MRI scanning of the knee joint: 3D-MRI perspective. Eur Radiol 2024; 34:3046-3058. [PMID: 37932390 DOI: 10.1007/s00330-023-10368-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/29/2023] [Accepted: 09/04/2023] [Indexed: 11/08/2023]
Abstract
OBJECTIVE To investigate the potential applicability of AI-assisted compressed sensing (ACS) in knee MRI to enhance and optimize the scanning process. METHODS Volunteers and patients with sports-related injuries underwent prospective MRI scans with a range of acceleration techniques. The volunteers were subjected to varied ACS acceleration levels to ascertain the most effective level. Patients underwent scans at the determined optimal 3D-ACS acceleration level, and 3D compressed sensing (CS) and 2D parallel acquisition technology (PAT) scans were performed. The resultant 3D-ACS images underwent 3.5 mm/2.0 mm multiplanar reconstruction (MPR). Experienced radiologists evaluated and compared the quality of images obtained by 3D-ACS-MRI and 3D-CS-MRI, 3.5 mm/2.0 mm MPR and 2D-PAT-MRI, diagnosed diseases, and compared the results with the arthroscopic findings. The diagnostic agreement was evaluated using Cohen's kappa correlation coefficient, and both absolute and relative evaluation methods were utilized for objective assessment. RESULTS The study involved 15 volunteers and 53 patients. An acceleration factor of 10.69 × was identified as optimal. The quality evaluation showed that 3D-ACS provided poorer bone structure visualization, and improved cartilage visualization and less satisfactory axial images with 3.5 mm/2.0 mm MPR than 2D-PAT. In terms of objective evaluation, the relative evaluation yielded satisfactory results across different groups, while the absolute evaluation revealed significant variances in most features. Nevertheless, high levels of diagnostic agreement (κ: 0.81-0.94) and accuracy (0.83-0.98) were observed across all diagnoses. CONCLUSION ACS technology presents significant potential as a replacement for traditional CS in 3D-MRI knee scans, allowing thinner MPRs and markedly faster scans without sacrificing diagnostic accuracy. CLINICAL RELEVANCE STATEMENT 3D-ACS-MRI of the knee can be completed in the 160 s with good diagnostic consistency and image quality. 3D-MRI-MPR can replace 2D-MRI and reconstruct images with thinner slices, which helps to optimize the current MRI examination process and shorten scanning time. KEY POINTS • AI-assisted compressed sensing technology can reduce knee MRI scan time by over 50%. • 3D AI-assisted compressed sensing MRI and related multiplanar reconstruction can replace traditional accelerated MRI and yield thinner 2D multiplanar reconstructions. • Successful application of 3D AI-assisted compressed sensing MRI can help optimize the current knee MRI process.
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Affiliation(s)
- Ming Ni
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Miao He
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People's Republic of China
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, People's Republic of China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, People's Republic of China
| | - Yuxin Yang
- United Imaging Research Institute of Intelligent Imaging, Beijing, People's Republic of China
| | - Xiaoyi Wen
- Institute of Statistics and Big Data, Renmin University of China, Beijing, People's Republic of China
| | - Yuqing Zhao
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Lixiang Gao
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Ruixin Yan
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Jiajia Xu
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Yarui Zhang
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Wen Chen
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Chenyu Jiang
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Yali Li
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Qiang Zhao
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Peng Wu
- United Imaging Healthcare Co, Shanghai, People's Republic of China
| | - Chunlin Li
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People's Republic of China
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, People's Republic of China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, People's Republic of China
| | - Junda Qu
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People's Republic of China.
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, People's Republic of China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, People's Republic of China.
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China.
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Ghasemi A, Ahlawat S, Fayad LM. Magnetic Resonance Imaging Biomarkers of Bone and Soft Tissue Tumors. Semin Musculoskelet Radiol 2024; 28:39-48. [PMID: 38330969 DOI: 10.1055/s-0043-1776433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Magnetic resonance imaging (MRI) is essential in the management of musculoskeletal (MSK) tumors. This review delves into the diverse MRI modalities, focusing on anatomical, functional, and metabolic sequences that provide essential biomarkers for tumor detection, characterization, disease extent determination, and assessment of treatment response. MRI's multimodal capabilities offer a range of biomarkers that enhance MSK tumor evaluation, aiding in better patient management.
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Affiliation(s)
- Ali Ghasemi
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Shivani Ahlawat
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Laura Marie Fayad
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, Maryland
- Department of Orthopaedic Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
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Lacroix M, Aouad T, Feydy J, Biau D, Larousserie F, Fournier L, Feydy A. Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications. Diagn Interv Imaging 2023; 104:18-23. [PMID: 36270953 DOI: 10.1016/j.diii.2022.10.004] [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: 10/02/2022] [Accepted: 10/05/2022] [Indexed: 01/10/2023]
Abstract
Artificial intelligence (AI) is increasingly being studied in musculoskeletal oncology imaging. AI has been applied to both primary and secondary bone tumors and assessed for various predictive tasks that include detection, segmentation, classification, and prognosis. Still, in the field of clinical research, further efforts are needed to improve AI reproducibility and reach an acceptable level of evidence in musculoskeletal oncology. This review describes the basic principles of the most common AI techniques, including machine learning, deep learning and radiomics. Then, recent developments and current results of AI in the field of musculoskeletal oncology are presented. Finally, limitations and future perspectives of AI in this field are discussed.
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Affiliation(s)
- Maxime Lacroix
- Department of Radiology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, 75015, France; Université Paris Cité, Faculté de Médecine, Paris, 75006, France; PARCC UMRS 970, INSERM, Paris 75015, France
| | - Theodore Aouad
- Université Paris-Saclay, CentraleSupélec, Inria, Centre for Visual Computing, 91190, Gif-sur-Yvette, France
| | - Jean Feydy
- Université Paris Cité, HeKA team, Inria Paris, Inserm, 75006, Paris, France
| | - David Biau
- Université Paris Cité, Faculté de Médecine, Paris, 75006, France; Department of Orthopedic Surgery, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
| | - Frédérique Larousserie
- Université Paris Cité, Faculté de Médecine, Paris, 75006, France; Department of Pathology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
| | - Laure Fournier
- Department of Radiology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, 75015, France; Université Paris Cité, Faculté de Médecine, Paris, 75006, France; PARCC UMRS 970, INSERM, Paris 75015, France
| | - Antoine Feydy
- Université Paris Cité, Faculté de Médecine, Paris, 75006, France; Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
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