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Reis EP, Blankemeier L, Zambrano Chaves JM, Jensen MEK, Yao S, Truyts CAM, Willis MH, Adams S, Amaro E, Boutin RD, Chaudhari AS. Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm. Eur Radiol 2024:10.1007/s00330-024-10769-6. [PMID: 38683384 DOI: 10.1007/s00330-024-10769-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 03/11/2024] [Accepted: 03/20/2024] [Indexed: 05/01/2024]
Abstract
OBJECTIVES To develop and validate an open-source artificial intelligence (AI) algorithm to accurately detect contrast phases in abdominal CT scans. MATERIALS AND METHODS Retrospective study aimed to develop an AI algorithm trained on 739 abdominal CT exams from 2016 to 2021, from 200 unique patients, covering 1545 axial series. We performed segmentation of five key anatomic structures-aorta, portal vein, inferior vena cava, renal parenchyma, and renal pelvis-using TotalSegmentator, a deep learning-based tool for multi-organ segmentation, and a rule-based approach to extract the renal pelvis. Radiomics features were extracted from the anatomical structures for use in a gradient-boosting classifier to identify four contrast phases: non-contrast, arterial, venous, and delayed. Internal and external validation was performed using the F1 score and other classification metrics, on the external dataset "VinDr-Multiphase CT". RESULTS The training dataset consisted of 172 patients (mean age, 70 years ± 8, 22% women), and the internal test set included 28 patients (mean age, 68 years ± 8, 14% women). In internal validation, the classifier achieved an accuracy of 92.3%, with an average F1 score of 90.7%. During external validation, the algorithm maintained an accuracy of 90.1%, with an average F1 score of 82.6%. Shapley feature attribution analysis indicated that renal and vascular radiodensity values were the most important for phase classification. CONCLUSION An open-source and interpretable AI algorithm accurately detects contrast phases in abdominal CT scans, with high accuracy and F1 scores in internal and external validation, confirming its generalization capability. CLINICAL RELEVANCE STATEMENT Contrast phase detection in abdominal CT scans is a critical step for downstream AI applications, deploying algorithms in the clinical setting, and for quantifying imaging biomarkers, ultimately allowing for better diagnostics and increased access to diagnostic imaging. KEY POINTS Digital Imaging and Communications in Medicine labels are inaccurate for determining the abdominal CT scan phase. AI provides great help in accurately discriminating the contrast phase. Accurate contrast phase determination aids downstream AI applications and biomarker quantification.
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Affiliation(s)
- Eduardo Pontes Reis
- Department of Radiology, Stanford University, Stanford, CA, USA.
- Center for Artificial Intelligence in Medicine & Imaging (AIMI), Stanford University, Stanford, CA, USA.
- Hospital Israelita Albert Einstein, Sao Paulo, Brazil.
| | - Louis Blankemeier
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Juan Manuel Zambrano Chaves
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | | | - Sally Yao
- Department of Radiology, Stanford University, Stanford, CA, USA
| | | | - Marc H Willis
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Scott Adams
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Edson Amaro
- Hospital Israelita Albert Einstein, Sao Paulo, Brazil
| | - Robert D Boutin
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Akshay S Chaudhari
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
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Zambrano Chaves JM, Lenchik L, Gallegos IO, Blankemeier L, Rubin DL, Willis MH, Chaudhari AS, Boutin RD. Abdominal CT metrics in 17,646 patients reveal associations between myopenia, myosteatosis, and medical phenotypes: a phenome-wide association study. EBioMedicine 2024; 103:105116. [PMID: 38636199 PMCID: PMC11031722 DOI: 10.1016/j.ebiom.2024.105116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 03/29/2024] [Accepted: 03/30/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Deep learning facilitates large-scale automated imaging evaluation of body composition. However, associations of body composition biomarkers with medical phenotypes have been underexplored. Phenome-wide association study (PheWAS) techniques search for medical phenotypes associated with biomarkers. A PheWAS integrating large-scale analysis of imaging biomarkers and electronic health record (EHR) data could discover previously unreported associations and validate expected associations. Here we use PheWAS methodology to determine the association of abdominal CT-based skeletal muscle metrics with medical phenotypes in a large North American cohort. METHODS An automated deep learning pipeline was used to measure skeletal muscle index (SMI; biomarker of myopenia) and skeletal muscle density (SMD; biomarker of myosteatosis) from abdominal CT scans of adults between 2012 and 2018. A PheWAS was performed with logistic regression using patient sex and age as covariates to assess for associations between CT-derived muscle metrics and 611 common EHR-derived medical phenotypes. PheWAS P values were considered significant at a Bonferroni corrected threshold (α = 0.05/1222). FINDINGS 17,646 adults (mean age, 56 years ± 19 [SD]; 57.5% women) were included. CT-derived SMI was significantly associated with 268 medical phenotypes; SMD with 340 medical phenotypes. Previously unreported associations with the highest magnitude of significance included higher SMI with decreased cardiac dysrhythmias (OR [95% CI], 0.59 [0.55-0.64]; P < 0.0001), decreased epilepsy (OR, 0.59 [0.50-0.70]; P < 0.0001), and increased elevated prostate-specific antigen (OR, 1.84 [1.47-2.31]; P < 0.0001), and higher SMD with decreased decubitus ulcers (OR, 0.36 [0.31-0.42]; P < 0.0001), sleep disorders (OR, 0.39 [0.32-0.47]; P < 0.0001), and osteomyelitis (OR, 0.43 [0.36-0.52]; P < 0.0001). INTERPRETATION PheWAS methodology reveals previously unreported associations between CT-derived biomarkers of myopenia and myosteatosis and EHR medical phenotypes. The high-throughput PheWAS technique applied on a population scale can generate research hypotheses related to myopenia and myosteatosis and can be adapted to research possible associations of other imaging biomarkers with hundreds of EHR medical phenotypes. FUNDING National Institutes of Health, Stanford AIMI-HAI pilot grant, Stanford Precision Health and Integrated Diagnostics, Stanford Cardiovascular Institute, Stanford Center for Digital Health, and Stanford Knight-Hennessy Scholars.
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Affiliation(s)
- Juan M Zambrano Chaves
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA; Department of Radiology, Stanford University, Stanford, CA, USA
| | - Leon Lenchik
- Department of Diagnostic Radiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Isabel O Gallegos
- Department of Computer Science, (IOG), Stanford University, Stanford, CA, USA
| | - Louis Blankemeier
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Daniel L Rubin
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA; Department of Radiology, Stanford University, Stanford, CA, USA
| | - Marc H Willis
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Akshay S Chaudhari
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA; Department of Radiology, Stanford University, Stanford, CA, USA
| | - Robert D Boutin
- Department of Radiology, Stanford University, Stanford, CA, USA.
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Van Veen D, Van Uden C, Blankemeier L, Delbrouck JB, Aali A, Bluethgen C, Pareek A, Polacin M, Reis EP, Seehofnerová A, Rohatgi N, Hosamani P, Collins W, Ahuja N, Langlotz CP, Hom J, Gatidis S, Pauly J, Chaudhari AS. Adapted large language models can outperform medical experts in clinical text summarization. Nat Med 2024; 30:1134-1142. [PMID: 38413730 DOI: 10.1038/s41591-024-02855-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/02/2024] [Indexed: 02/29/2024]
Abstract
Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP) tasks, their effectiveness on a diverse range of clinical summarization tasks remains unproven. Here we applied adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks: radiology reports, patient questions, progress notes and doctor-patient dialogue. Quantitative assessments with syntactic, semantic and conceptual NLP metrics reveal trade-offs between models and adaptation methods. A clinical reader study with 10 physicians evaluated summary completeness, correctness and conciseness; in most cases, summaries from our best-adapted LLMs were deemed either equivalent (45%) or superior (36%) compared with summaries from medical experts. The ensuing safety analysis highlights challenges faced by both LLMs and medical experts, as we connect errors to potential medical harm and categorize types of fabricated information. Our research provides evidence of LLMs outperforming medical experts in clinical text summarization across multiple tasks. This suggests that integrating LLMs into clinical workflows could alleviate documentation burden, allowing clinicians to focus more on patient care.
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Affiliation(s)
- Dave Van Veen
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA.
| | - Cara Van Uden
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Louis Blankemeier
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
| | - Jean-Benoit Delbrouck
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
| | - Asad Aali
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Christian Bluethgen
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Anuj Pareek
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
- Copenhagen University Hospital, Copenhagen, Denmark
| | - Malgorzata Polacin
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Eduardo Pontes Reis
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
- Albert Einstein Israelite Hospital, São Paulo, Brazil
| | - Anna Seehofnerová
- Department of Medicine, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Nidhi Rohatgi
- Department of Medicine, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Poonam Hosamani
- Department of Medicine, Stanford University, Stanford, CA, USA
| | - William Collins
- Department of Medicine, Stanford University, Stanford, CA, USA
| | - Neera Ahuja
- Department of Medicine, Stanford University, Stanford, CA, USA
| | - Curtis P Langlotz
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
- Department of Medicine, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Jason Hom
- Department of Medicine, Stanford University, Stanford, CA, USA
| | - Sergios Gatidis
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - John Pauly
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Akshay S Chaudhari
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford, CA, USA
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Chaudhari AS. AI in osteoarthritis: Illuminating the meandering path forward. Osteoarthritis Cartilage 2024; 32:227-228. [PMID: 38013138 DOI: 10.1016/j.joca.2023.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/13/2023] [Accepted: 11/21/2023] [Indexed: 11/29/2023]
Affiliation(s)
- Akshay S Chaudhari
- Department of Radiology, Stanford University, 1201 Welch Road P269, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
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Tan T, Shull PB, Hicks JL, Uhlrich SD, Chaudhari AS. Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation. IEEE Trans Biomed Eng 2024; PP:1-10. [PMID: 38315597 DOI: 10.1109/tbme.2024.3361888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
OBJECTIVE Recent deep learning techniques hold promise to enable IMU-driven kinetic assessment; however, they require large extents of ground reaction force (GRF) data to serve as labels for supervised model training. We thus propose using existing self-supervised learning (SSL) techniques to leverage large IMU datasets to pre-train deep learning models, which can improve the accuracy and data efficiency of IMU-based GRF estimation. METHODS We performed SSL by masking a random portion of the input IMU data and training a transformer model to reconstruct the masked portion. We systematically compared a series of masking ratios across three pre-training datasets that included real IMU data, synthetic IMU data, or a combination of the two. Finally, we built models that used pre-training and labeled data to estimate GRF during three prediction tasks: overground walking, treadmill walking, and drop landing. RESULTS When using the same amount of labeled data, SSL pre-training significantly improved the accuracy of 3-axis GRF estimation during walking compared to baseline models trained by conventional supervised learning. Fine-tuning SSL model with 1-10% of walking data yielded comparable accuracy to training baseline model with 100% of walking data. The optimal masking ratio for SSL is 6.25-12.5%. CONCLUSION SSL leveraged large real and synthetic IMU datasets to increase the accuracy and data efficiency of deep-learning-based GRF estimation, reducing the need for labeled data. SIGNIFICANCE This work, with its open-source code and models, may unlock broader use cases of IMU-driven kinetic assessment by mitigating the scarcity of GRF measurements in practical applications.
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Chang CY, Lenchik L, Blankemeier L, Chaudhari AS, Boutin RD. Biomarkers of Body Composition. Semin Musculoskelet Radiol 2024; 28:78-91. [PMID: 38330972 DOI: 10.1055/s-0043-1776430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
The importance and impact of imaging biomarkers has been increasing over the past few decades. We review the relevant clinical and imaging terminology needed to understand the clinical and research applications of body composition. Imaging biomarkers of bone, muscle, and fat tissues obtained with dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging, and ultrasonography are described.
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Affiliation(s)
- Connie Y Chang
- Division of Musculoskeletal Imaging and Intervention, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Leon Lenchik
- Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Louis Blankemeier
- Department of Electrical Engineering, Stanford University, Stanford, California
| | - Akshay S Chaudhari
- Department of Radiology and of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
| | - Robert D Boutin
- Department of Radiology, Stanford University School of Medicine, Stanford, California
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Tan T, Shull PB, Hicks JL, Uhlrich SD, Chaudhari AS. Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation. bioRxiv 2024:2023.10.25.564057. [PMID: 38328126 PMCID: PMC10849467 DOI: 10.1101/2023.10.25.564057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Objective Recent deep learning techniques hold promise to enable IMU-driven kinetic assessment; however, they require large extents of ground reaction force (GRF) data to serve as labels for supervised model training. We thus propose using existing self-supervised learning (SSL) techniques to leverage large IMU datasets to pre-train deep learning models, which can improve the accuracy and data efficiency of IMU-based GRF estimation. Methods We performed SSL by masking a random portion of the input IMU data and training a transformer model to reconstruct the masked portion. We systematically compared a series of masking ratios across three pre-training datasets that included real IMU data, synthetic IMU data, or a combination of the two. Finally, we built models that used pre-training and labeled data to estimate GRF during three prediction tasks: overground walking, treadmill walking, and drop landing. Results When using the same amount of labeled data, SSL pre-training significantly improved the accuracy of 3-axis GRF estimation during walking compared to baseline models trained by conventional supervised learning. Fine-tuning SSL model with 1-10% of walking data yielded comparable accuracy to training baseline model with 100% of walking data. The optimal masking ratio for SSL is 6.25-12.5%. Conclusion SSL leveraged large real and synthetic IMU datasets to increase the accuracy and data efficiency of deep-learning-based GRF estimation, reducing the need for labeled data. Significance This work, with its open-source code and models, may unlock broader use cases of IMU-driven kinetic assessment by mitigating the scarcity of GRF measurements in practical applications.
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Affiliation(s)
- Tian Tan
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Peter B Shull
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jenifer L Hicks
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Scott D Uhlrich
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Akshay S Chaudhari
- Department of Radiology and Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
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Stevens KJ, Chaudhari AS, Kuhn KJ. Differences in Anatomic Adaptation and Injury Patterns Related to Valgus Extension Overload in Overhead Throwing Athletes. Diagnostics (Basel) 2024; 14:217. [PMID: 38275464 PMCID: PMC10814069 DOI: 10.3390/diagnostics14020217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/10/2024] [Accepted: 01/15/2024] [Indexed: 01/27/2024] Open
Abstract
The purpose of our study was to determine differences in adaptative and injury patterns in the elbow related to valgus extension overload (VEO) in overhead throwing athletes by age. A total of 86 overhead throwing athletes and 23 controls underwent MRI or MR arthrography (MRA) of the elbow. Throwing athletes were divided by age into three groups: ≤16 years (26 subjects), 17-19 years (25 subjects), and ≥20 years (35 subjects). Consensus interpretation of each MRI was performed, with measurements of ulnar collateral ligament (UCL) thickness and subchondral sclerosis at the radial head, humeral trochlea, and olecranon process. A higher frequency of apophyseal and stress injuries was seen in adolescent athletes and increased incidence of soft tissue injuries was observed in older athletes. Early adaptive and degenerative changes were observed with high frequency independent of age. Significant differences were observed between athletes and controls for UCL thickness (p < 0.001) and subchondral sclerosis at the radial head (p < 0.001), humeral trochlea (p < 0.001), and olecranon process (p < 0.001). Significant differences based on athlete age were observed for UCL thickness (p < 0.001) and subchondral sclerosis at the olecranon process (p = 0.002). Our study highlights differences in anatomic adaptations related to VEO at the elbow between overhead throwing athletes and control subjects, as well as across age in throwing athletes.
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Affiliation(s)
- Kathryn J. Stevens
- Department of Radiology, Stanford University Medical Center, Palo Alto, CA 94304, USA;
| | - Akshay S. Chaudhari
- Department of Radiology, Stanford University Medical Center, Palo Alto, CA 94304, USA;
| | - Karin J. Kuhn
- MAPMG: Mid-Atlantic Permanente Medical Group, Rockville, MD 20852, USA;
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Blankemeier L, Yao L, Long J, Reis EP, Lenchik L, Chaudhari AS, Boutin RD. Skeletal Muscle Area on CT: Determination of an Optimal Height Scaling Power and Testing for Mortality Risk Prediction. AJR Am J Roentgenol 2024; 222:e2329889. [PMID: 37877596 DOI: 10.2214/ajr.23.29889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
BACKGROUND. Sarcopenia is commonly assessed on CT by use of the skeletal muscle index (SMI), which is calculated as the skeletal muscle area (SMA) at L3 divided by patient height squared (i.e., a height scaling power of 2). OBJECTIVE. The purpose of this study was to determine the optimal height scaling power for SMA measurements on CT and to test the influence of the derived optimal scaling power on the utility of SMI in predicting all-cause mortality. METHODS. This retrospective study included 16,575 patients (6985 men, 9590 women; mean age, 56.4 years) who underwent abdominal CT from December 2012 through October 2018. The SMA at L3 was determined using automated software. The sample was stratified into two groups: 5459 patients without major medical conditions (based on ICD-9 and ICD-10 codes) who were included in the analysis for determining the optimal height scaling power and 11,116 patients with major medical conditions who were included for the purpose of testing this power. The optimal scaling power was determined by allometric analysis (whereby regression coefficients were fitted to log-linear sex-specific models relating height to SMA) and by analysis of statistical independence of SMI from height across scaling powers. Cox proportional hazards models were used to test the influence of the derived optimal scaling power on the utility of SMI in predicting all-cause mortality. RESULTS. In allometric analysis, the regression coefficient of log(height) in patients 40 years old and younger was 1.02 in men and 1.08 in women, and in patients older than 40 years old, it was 1.07 in men and 1.10 in women (all p < .05 vs regression coefficient of 2). In analyses for statistical independence of SMI from height, the optimal height scaling power (i.e., those yielding correlations closest to 0) was, in patients 40 years old and younger, 0.97 in men and 1.08 in women, whereas in patients older than 40 years old, it was 1.03 in men and 1.09 in women. In the Cox model used for testing, SMI predicted all-cause mortality with a higher concordance index using of a height scaling power of 1 rather than 2 in men (0.675 vs 0.663, p < .001) and in women (0.664 vs 0.653, p < .001). CONCLUSION. The findings support a height scaling power of 1, rather than a conventional power of 2, for SMI computation. CLINICAL IMPACT. A revised height scaling power for SMI could impact the utility of CT-based sarcopenia diagnoses in risk assessment.
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Affiliation(s)
- Louis Blankemeier
- Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Lawrence Yao
- Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, MD
| | - Jin Long
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Palo Alto, CA
| | - Eduardo P Reis
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, MC-5105, Stanford, CA 94305
| | - Leon Lenchik
- Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Akshay S Chaudhari
- Department of Radiology and of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA
| | - Robert D Boutin
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, MC-5105, Stanford, CA 94305
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Yoon MA, Gold GE, Chaudhari AS. Accelerated Musculoskeletal Magnetic Resonance Imaging. J Magn Reson Imaging 2023. [PMID: 38156716 DOI: 10.1002/jmri.29205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 01/03/2024] Open
Abstract
With a substantial growth in the use of musculoskeletal MRI, there has been a growing need to improve MRI workflow, and faster imaging has been suggested as one of the solutions for a more efficient examination process. Consequently, there have been considerable advances in accelerated MRI scanning methods. This article aims to review the basic principles and applications of accelerated musculoskeletal MRI techniques including widely used conventional acceleration methods, more advanced deep learning-based techniques, and new approaches to reduce scan time. Specifically, conventional accelerated MRI techniques, including parallel imaging, compressed sensing, and simultaneous multislice imaging, and deep learning-based accelerated MRI techniques, including undersampled MR image reconstruction, super-resolution imaging, artifact correction, and generation of unacquired contrast images, are discussed. Finally, new approaches to reduce scan time, including synthetic MRI, novel sequences, and new coil setups and designs, are also reviewed. We believe that a deep understanding of these fast MRI techniques and proper use of combined acceleration methods will synergistically improve scan time and MRI workflow in daily practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Min A Yoon
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
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Zambrano Chaves JM, Wentland AL, Desai AD, Banerjee I, Kaur G, Correa R, Boutin RD, Maron DJ, Rodriguez F, Sandhu AT, Rubin D, Chaudhari AS, Patel BN. Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach. Sci Rep 2023; 13:21034. [PMID: 38030716 PMCID: PMC10687235 DOI: 10.1038/s41598-023-47895-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 11/20/2023] [Indexed: 12/01/2023] Open
Abstract
Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD) events-the leading cause of global mortality-have known limitations and may be improved by imaging biomarkers. While body composition (BC) imaging biomarkers derived from abdominopelvic computed tomography (CT) correlate with IHD risk, they are impractical to measure manually. Here, in a retrospective cohort of 8139 contrast-enhanced abdominopelvic CT examinations undergoing up to 5 years of follow-up, we developed multimodal opportunistic risk assessment models for IHD by automatically extracting BC features from abdominal CT images and integrating these with features from each patient's electronic medical record (EMR). Our predictive methods match and, in some cases, outperform clinical risk scores currently used in IHD risk assessment. We provide clinical interpretability of our model using a new method of determining tissue-level contributions from CT along with weightings of EMR features contributing to IHD risk. We conclude that such a multimodal approach, which automatically integrates BC biomarkers and EMR data, can enhance IHD risk assessment and aid primary prevention efforts for IHD. To further promote research, we release the Opportunistic L3 Ischemic heart disease (OL3I) dataset, the first public multimodal dataset for opportunistic CT prediction of IHD.
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Affiliation(s)
- Juan M Zambrano Chaves
- Department of Biomedical Data Science, Stanford University, 1265 Welch Road, MSOB West Wing, Third Floor, Stanford, CA, 94305, USA
| | - Andrew L Wentland
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792, USA
| | - Arjun D Desai
- Department of Radiology, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
- Department of Electrical Engineering, Stanford University, 350 Jane Stanford Way, Stanford, CA, 94305, USA
| | - Imon Banerjee
- Department of Radiology, Mayo Clinic, 13400 East Shea Blvd, Scottsdale, AZ, 85259, USA
| | - Gurkiran Kaur
- Department of Radiology, Mayo Clinic, 13400 East Shea Blvd, Scottsdale, AZ, 85259, USA
| | - Ramon Correa
- Department of Radiology, Mayo Clinic, 13400 East Shea Blvd, Scottsdale, AZ, 85259, USA
| | - Robert D Boutin
- Department of Radiology, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - David J Maron
- Division of Cardiovascular Medicine, Department of Medicine, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
- Department of Medicine, Stanford Prevention Research Center, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine, Department of Medicine, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Alexander T Sandhu
- Division of Cardiovascular Medicine, Department of Medicine, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Daniel Rubin
- Department of Biomedical Data Science, Stanford University, 1265 Welch Road, MSOB West Wing, Third Floor, Stanford, CA, 94305, USA
- Department of Radiology, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Akshay S Chaudhari
- Department of Biomedical Data Science, Stanford University, 1265 Welch Road, MSOB West Wing, Third Floor, Stanford, CA, 94305, USA
- Department of Radiology, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
- Cardiovascular Institute, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Bhavik N Patel
- Department of Radiology, Mayo Clinic, 13400 East Shea Blvd, Scottsdale, AZ, 85259, USA.
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12
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Desai AD, Ozturkler BM, Sandino CM, Boutin R, Willis M, Vasanawala S, Hargreaves BA, Ré C, Pauly JM, Chaudhari AS. Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning. Magn Reson Med 2023; 90:2052-2070. [PMID: 37427449 DOI: 10.1002/mrm.29759] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 07/11/2023]
Abstract
PURPOSE To develop a method for building MRI reconstruction neural networks robust to changes in signal-to-noise ratio (SNR) and trainable with a limited number of fully sampled scans. METHODS We propose Noise2Recon, a consistency training method for SNR-robust accelerated MRI reconstruction that can use both fully sampled (labeled) and undersampled (unlabeled) scans. Noise2Recon uses unlabeled data by enforcing consistency between model reconstructions of undersampled scans and their noise-augmented counterparts. Noise2Recon was compared to compressed sensing and both supervised and self-supervised deep learning baselines. Experiments were conducted using retrospectively accelerated data from the mridata three-dimensional fast-spin-echo knee and two-dimensional fastMRI brain datasets. All methods were evaluated in label-limited settings and among out-of-distribution (OOD) shifts, including changes in SNR, acceleration factors, and datasets. An extensive ablation study was conducted to characterize the sensitivity of Noise2Recon to hyperparameter choices. RESULTS In label-limited settings, Noise2Recon achieved better structural similarity, peak signal-to-noise ratio, and normalized-RMS error than all baselines and matched performance of supervised models, which were trained with14 × $$ 14\times $$ more fully sampled scans. Noise2Recon outperformed all baselines, including state-of-the-art fine-tuning and augmentation techniques, among low-SNR scans and when generalizing to OOD acceleration factors. Augmentation extent and loss weighting hyperparameters had negligible impact on Noise2Recon compared to supervised methods, which may indicate increased training stability. CONCLUSION Noise2Recon is a label-efficient reconstruction method that is robust to distribution shifts, such as changes in SNR, acceleration factors, and others, with limited or no fully sampled training data.
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Affiliation(s)
- Arjun D Desai
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Batu M Ozturkler
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Christopher M Sandino
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Robert Boutin
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Marc Willis
- Department of Radiology, Stanford University, Stanford, California, USA
| | | | - Brian A Hargreaves
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Christopher Ré
- Department of Computer Science, Stanford University, Stanford, California, USA
| | - John M Pauly
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Akshay S Chaudhari
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
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13
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Van Veen D, Van Uden C, Blankemeier L, Delbrouck JB, Aali A, Bluethgen C, Pareek A, Polacin M, Reis EP, Seehofnerová A, Rohatgi N, Hosamani P, Collins W, Ahuja N, Langlotz CP, Hom J, Gatidis S, Pauly J, Chaudhari AS. Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts. Res Sq 2023:rs.3.rs-3483777. [PMID: 37961377 PMCID: PMC10635391 DOI: 10.21203/rs.3.rs-3483777/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Sifting through vast textual data and summarizing key information from electronic health records (EHR) imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural language processing (NLP) tasks, their efficacy on a diverse range of clinical summarization tasks has not yet been rigorously demonstrated. In this work, we apply domain adaptation methods to eight LLMs, spanning six datasets and four distinct clinical summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Our thorough quantitative assessment reveals trade-offs between models and adaptation methods in addition to instances where recent advances in LLMs may not improve results. Further, in a clinical reader study with ten physicians, we show that summaries from our best-adapted LLMs are preferable to human summaries in terms of completeness and correctness. Our ensuing qualitative analysis highlights challenges faced by both LLMs and human experts. Lastly, we correlate traditional quantitative NLP metrics with reader study scores to enhance our understanding of how these metrics align with physician preferences. Our research marks the first evidence of LLMs outperforming human experts in clinical text summarization across multiple tasks. This implies that integrating LLMs into clinical workflows could alleviate documentation burden, empowering clinicians to focus more on personalized patient care and the inherently human aspects of medicine.
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Affiliation(s)
- Dave Van Veen
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
| | - Cara Van Uden
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Louis Blankemeier
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
| | - Jean-Benoit Delbrouck
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
| | - Asad Aali
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Christian Bluethgen
- Department of Medicine, Stanford, CA, USA
- University Hospital Zurich, Zurich, Switzerland
| | - Anuj Pareek
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
- Copenhagen University Hospital, Copenhagen, Denmark
| | - Malgorzata Polacin
- Department of Medicine, Stanford, CA, USA
- University Hospital Zurich, Zurich, Switzerland
| | - Eduardo Pontes Reis
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
- Albert Einstein Israelite Hospital, São Paulo, Brazil
| | - Anna Seehofnerová
- Department of Medicine, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Nidhi Rohatgi
- Department of Medicine, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | | | | | | | - Curtis P. Langlotz
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
- Department of Medicine, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford, CA, USA
| | - Jason Hom
- Department of Medicine, Stanford, CA, USA
| | - Sergios Gatidis
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - John Pauly
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Akshay S. Chaudhari
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford, CA, USA
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14
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Uhlrich SD, Falisse A, Kidziński Ł, Muccini J, Ko M, Chaudhari AS, Hicks JL, Delp SL. OpenCap: Human movement dynamics from smartphone videos. PLoS Comput Biol 2023; 19:e1011462. [PMID: 37856442 PMCID: PMC10586693 DOI: 10.1371/journal.pcbi.1011462] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 08/24/2023] [Indexed: 10/21/2023] Open
Abstract
Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in large-scale research studies or clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing both the kinematics (i.e., motion) and dynamics (i.e., forces) of human movement using videos captured from two or more smartphones. OpenCap leverages pose estimation algorithms to identify body landmarks from videos; deep learning and biomechanical models to estimate three-dimensional kinematics; and physics-based simulations to estimate muscle activations and musculoskeletal dynamics. OpenCap's web application enables users to collect synchronous videos and visualize movement data that is automatically processed in the cloud, thereby eliminating the need for specialized hardware, software, and expertise. We show that OpenCap accurately predicts dynamic measures, like muscle activations, joint loads, and joint moments, which can be used to screen for disease risk, evaluate intervention efficacy, assess between-group movement differences, and inform rehabilitation decisions. Additionally, we demonstrate OpenCap's practical utility through a 100-subject field study, where a clinician using OpenCap estimated musculoskeletal dynamics 25 times faster than a laboratory-based approach at less than 1% of the cost. By democratizing access to human movement analysis, OpenCap can accelerate the incorporation of biomechanical metrics into large-scale research studies, clinical trials, and clinical practice.
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Affiliation(s)
- Scott D. Uhlrich
- Departments of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Antoine Falisse
- Departments of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Łukasz Kidziński
- Departments of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Julie Muccini
- Radiology, Stanford University, Stanford, California, United States of America
| | - Michael Ko
- Radiology, Stanford University, Stanford, California, United States of America
| | - Akshay S. Chaudhari
- Radiology, Stanford University, Stanford, California, United States of America
- Biomedical Data Science, Stanford University, Stanford, California, United States of America
| | - Jennifer L. Hicks
- Departments of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Scott L. Delp
- Departments of Bioengineering, Stanford University, Stanford, California, United States of America
- Mechanical Engineering, Stanford University, Stanford, California, United States of America
- Orthopaedic Surgery, Stanford University, Stanford, California, United States of America
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15
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Peng AW, Dudum R, Jain SS, Maron DJ, Patel BN, Khandwala N, Eng D, Chaudhari AS, Sandhu AT, Rodriguez F. Association of Coronary Artery Calcium Detected by Routine Ungated CT Imaging With Cardiovascular Outcomes. J Am Coll Cardiol 2023; 82:1192-1202. [PMID: 37704309 PMCID: PMC11009374 DOI: 10.1016/j.jacc.2023.06.040] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/30/2023] [Accepted: 06/20/2023] [Indexed: 09/15/2023]
Abstract
BACKGROUND Coronary artery calcium (CAC) is a strong predictor of cardiovascular events across all racial and ethnic groups. CAC can be quantified on nonelectrocardiography (ECG)-gated computed tomography (CT) performed for other reasons, allowing for opportunistic screening for subclinical atherosclerosis. OBJECTIVES The authors investigated whether incidental CAC quantified on routine non-ECG-gated CTs using a deep-learning (DL) algorithm provided cardiovascular risk stratification beyond traditional risk prediction methods. METHODS Incidental CAC was quantified using a DL algorithm (DL-CAC) on non-ECG-gated chest CTs performed for routine care in all settings at a large academic medical center from 2014 to 2019. We measured the association between DL-CAC (0, 1-99, or ≥100) with all-cause death (primary outcome), and the secondary composite outcomes of death/myocardial infarction (MI)/stroke and death/MI/stroke/revascularization using Cox regression. We adjusted for age, sex, race, ethnicity, comorbidities, systolic blood pressure, lipid levels, smoking status, and antihypertensive use. Ten-year atherosclerotic cardiovascular disease risk was calculated using the pooled cohort equations. RESULTS Of 5,678 adults without ASCVD (51% women, 18% Asian, 13% Hispanic/Latinx), 52% had DL-CAC >0. Those with DL-CAC ≥100 had an average 10-year ASCVD risk of 24%; yet, only 26% were on statins. After adjustment, patients with DL-CAC ≥100 had increased risk of death (HR: 1.51; 95% CI: 1.28-1.79), death/MI/stroke (HR: 1.57; 95% CI: 1.33-1.84), and death/MI/stroke/revascularization (HR: 1.69; 95% CI: 1.45-1.98) compared with DL-CAC = 0. CONCLUSIONS Incidental CAC ≥100 was associated with an increased risk of all-cause death and adverse cardiovascular outcomes, beyond traditional risk factors. DL-CAC from routine non-ECG-gated CTs identifies patients at increased cardiovascular risk and holds promise as a tool for opportunistic screening to facilitate earlier intervention.
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Affiliation(s)
- Allison W Peng
- Department of Medicine, Stanford University, Stanford, California, USA; Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA. https://twitter.com/AllisonWPeng
| | - Ramzi Dudum
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA
| | - Sneha S Jain
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA
| | - David J Maron
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA; Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, California, USA
| | - Bhavik N Patel
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA
| | | | - David Eng
- Bunkerhill Health, Palo Alto, California, USA
| | - Akshay S Chaudhari
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Department of Radiology, Stanford University, Stanford, California, USA; Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Alexander T Sandhu
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA; Veteran's Affairs Palo Alto Healthcare System, Palo Alto, California, USA. https://twitter.com/ATSandhu
| | - Fatima Rodriguez
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA.
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16
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Watts RE, Gorbachova T, Fritz RC, Saad SS, Lutz AM, Kim J, Chaudhari AS, Shea KG, Sherman SL, Boutin RD. Patellar Tracking: An Old Problem with New Insights. Radiographics 2023; 43:e220177. [PMID: 37261964 DOI: 10.1148/rg.220177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Patellofemoral pain and instability are common indications for imaging that are encountered in everyday practice. The authors comprehensively review key aspects of patellofemoral instability pertinent to radiologists that can be seen before the onset of osteoarthritis, highlighting the anatomy, clinical evaluation, diagnostic imaging, and treatment. Regarding the anatomy, the medial patellofemoral ligament (MPFL) is the primary static soft-tissue restraint to lateral patellar displacement and is commonly reconstructed surgically in patients with MPFL dysfunction and patellar instability. Osteoarticular abnormalities that predispose individuals to patellar instability include patellar malalignment, trochlear dysplasia, and tibial tubercle lateralization. Clinically, patients with patellar instability may be divided into two broad groups with imaging findings that sometimes overlap: patients with a history of overt patellar instability after a traumatic event (eg, dislocation, subluxation) and patients without such a history. In terms of imaging, radiography is generally the initial examination of choice, and MRI is the most common cross-sectional examination performed preoperatively. For all imaging techniques, there has been a proliferation of published radiologic measurement methods. The authors summarize the most common validated measurements for patellar malalignment, trochlear dysplasia, and tibial tubercle lateralization. Given that static imaging is inherently limited in the evaluation of patellar motion, dynamic imaging with US, CT, or MRI may be requested by some surgeons. The primary treatment strategy for patellofemoral pain is conservative. Surgical treatment options include MPFL reconstruction with or without osseous corrections such as trochleoplasty and tibial tubercle osteotomy. Postoperative complications evaluated at imaging include patellar fracture, graft failure, graft malposition, and medial patellar subluxation. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.
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Affiliation(s)
- Robert E Watts
- From the Departments of Radiology (R.E.W., A.M.L., R.D.B.) and Orthopaedic Surgery (S.L.S.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5101; Department of Radiology, Einstein Healthcare Network and Jefferson Health, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA (T.G.); Department of Musculoskeletal Radiology, National Orthopedic Imaging Associates, Greenbrae, CA (R.C.F.); Department of Musculoskeletal Radiology, Atlantic Medical Imaging, Galloway, NJ (S.S.S.); Department of Radiology, Benning Martin Army Community Hospital, Fort Benning, GA (J.K.); Departments of Radiology and Biomedical Data Science, Stanford University, Stanford, CA (A.S.C.); and Department of Orthopaedic Surgery, Lucile Packard Children's Hospital at Stanford, Palo Alto, CA (K.G.S.)
| | - Tetyana Gorbachova
- From the Departments of Radiology (R.E.W., A.M.L., R.D.B.) and Orthopaedic Surgery (S.L.S.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5101; Department of Radiology, Einstein Healthcare Network and Jefferson Health, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA (T.G.); Department of Musculoskeletal Radiology, National Orthopedic Imaging Associates, Greenbrae, CA (R.C.F.); Department of Musculoskeletal Radiology, Atlantic Medical Imaging, Galloway, NJ (S.S.S.); Department of Radiology, Benning Martin Army Community Hospital, Fort Benning, GA (J.K.); Departments of Radiology and Biomedical Data Science, Stanford University, Stanford, CA (A.S.C.); and Department of Orthopaedic Surgery, Lucile Packard Children's Hospital at Stanford, Palo Alto, CA (K.G.S.)
| | - Russell C Fritz
- From the Departments of Radiology (R.E.W., A.M.L., R.D.B.) and Orthopaedic Surgery (S.L.S.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5101; Department of Radiology, Einstein Healthcare Network and Jefferson Health, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA (T.G.); Department of Musculoskeletal Radiology, National Orthopedic Imaging Associates, Greenbrae, CA (R.C.F.); Department of Musculoskeletal Radiology, Atlantic Medical Imaging, Galloway, NJ (S.S.S.); Department of Radiology, Benning Martin Army Community Hospital, Fort Benning, GA (J.K.); Departments of Radiology and Biomedical Data Science, Stanford University, Stanford, CA (A.S.C.); and Department of Orthopaedic Surgery, Lucile Packard Children's Hospital at Stanford, Palo Alto, CA (K.G.S.)
| | - Sherif S Saad
- From the Departments of Radiology (R.E.W., A.M.L., R.D.B.) and Orthopaedic Surgery (S.L.S.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5101; Department of Radiology, Einstein Healthcare Network and Jefferson Health, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA (T.G.); Department of Musculoskeletal Radiology, National Orthopedic Imaging Associates, Greenbrae, CA (R.C.F.); Department of Musculoskeletal Radiology, Atlantic Medical Imaging, Galloway, NJ (S.S.S.); Department of Radiology, Benning Martin Army Community Hospital, Fort Benning, GA (J.K.); Departments of Radiology and Biomedical Data Science, Stanford University, Stanford, CA (A.S.C.); and Department of Orthopaedic Surgery, Lucile Packard Children's Hospital at Stanford, Palo Alto, CA (K.G.S.)
| | - Amelie M Lutz
- From the Departments of Radiology (R.E.W., A.M.L., R.D.B.) and Orthopaedic Surgery (S.L.S.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5101; Department of Radiology, Einstein Healthcare Network and Jefferson Health, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA (T.G.); Department of Musculoskeletal Radiology, National Orthopedic Imaging Associates, Greenbrae, CA (R.C.F.); Department of Musculoskeletal Radiology, Atlantic Medical Imaging, Galloway, NJ (S.S.S.); Department of Radiology, Benning Martin Army Community Hospital, Fort Benning, GA (J.K.); Departments of Radiology and Biomedical Data Science, Stanford University, Stanford, CA (A.S.C.); and Department of Orthopaedic Surgery, Lucile Packard Children's Hospital at Stanford, Palo Alto, CA (K.G.S.)
| | - Jiyoon Kim
- From the Departments of Radiology (R.E.W., A.M.L., R.D.B.) and Orthopaedic Surgery (S.L.S.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5101; Department of Radiology, Einstein Healthcare Network and Jefferson Health, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA (T.G.); Department of Musculoskeletal Radiology, National Orthopedic Imaging Associates, Greenbrae, CA (R.C.F.); Department of Musculoskeletal Radiology, Atlantic Medical Imaging, Galloway, NJ (S.S.S.); Department of Radiology, Benning Martin Army Community Hospital, Fort Benning, GA (J.K.); Departments of Radiology and Biomedical Data Science, Stanford University, Stanford, CA (A.S.C.); and Department of Orthopaedic Surgery, Lucile Packard Children's Hospital at Stanford, Palo Alto, CA (K.G.S.)
| | - Akshay S Chaudhari
- From the Departments of Radiology (R.E.W., A.M.L., R.D.B.) and Orthopaedic Surgery (S.L.S.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5101; Department of Radiology, Einstein Healthcare Network and Jefferson Health, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA (T.G.); Department of Musculoskeletal Radiology, National Orthopedic Imaging Associates, Greenbrae, CA (R.C.F.); Department of Musculoskeletal Radiology, Atlantic Medical Imaging, Galloway, NJ (S.S.S.); Department of Radiology, Benning Martin Army Community Hospital, Fort Benning, GA (J.K.); Departments of Radiology and Biomedical Data Science, Stanford University, Stanford, CA (A.S.C.); and Department of Orthopaedic Surgery, Lucile Packard Children's Hospital at Stanford, Palo Alto, CA (K.G.S.)
| | - Kevin G Shea
- From the Departments of Radiology (R.E.W., A.M.L., R.D.B.) and Orthopaedic Surgery (S.L.S.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5101; Department of Radiology, Einstein Healthcare Network and Jefferson Health, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA (T.G.); Department of Musculoskeletal Radiology, National Orthopedic Imaging Associates, Greenbrae, CA (R.C.F.); Department of Musculoskeletal Radiology, Atlantic Medical Imaging, Galloway, NJ (S.S.S.); Department of Radiology, Benning Martin Army Community Hospital, Fort Benning, GA (J.K.); Departments of Radiology and Biomedical Data Science, Stanford University, Stanford, CA (A.S.C.); and Department of Orthopaedic Surgery, Lucile Packard Children's Hospital at Stanford, Palo Alto, CA (K.G.S.)
| | - Seth L Sherman
- From the Departments of Radiology (R.E.W., A.M.L., R.D.B.) and Orthopaedic Surgery (S.L.S.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5101; Department of Radiology, Einstein Healthcare Network and Jefferson Health, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA (T.G.); Department of Musculoskeletal Radiology, National Orthopedic Imaging Associates, Greenbrae, CA (R.C.F.); Department of Musculoskeletal Radiology, Atlantic Medical Imaging, Galloway, NJ (S.S.S.); Department of Radiology, Benning Martin Army Community Hospital, Fort Benning, GA (J.K.); Departments of Radiology and Biomedical Data Science, Stanford University, Stanford, CA (A.S.C.); and Department of Orthopaedic Surgery, Lucile Packard Children's Hospital at Stanford, Palo Alto, CA (K.G.S.)
| | - Robert D Boutin
- From the Departments of Radiology (R.E.W., A.M.L., R.D.B.) and Orthopaedic Surgery (S.L.S.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5101; Department of Radiology, Einstein Healthcare Network and Jefferson Health, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA (T.G.); Department of Musculoskeletal Radiology, National Orthopedic Imaging Associates, Greenbrae, CA (R.C.F.); Department of Musculoskeletal Radiology, Atlantic Medical Imaging, Galloway, NJ (S.S.S.); Department of Radiology, Benning Martin Army Community Hospital, Fort Benning, GA (J.K.); Departments of Radiology and Biomedical Data Science, Stanford University, Stanford, CA (A.S.C.); and Department of Orthopaedic Surgery, Lucile Packard Children's Hospital at Stanford, Palo Alto, CA (K.G.S.)
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17
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Huang SC, Pareek A, Jensen M, Lungren MP, Yeung S, Chaudhari AS. Self-supervised learning for medical image classification: a systematic review and implementation guidelines. NPJ Digit Med 2023; 6:74. [PMID: 37100953 PMCID: PMC10131505 DOI: 10.1038/s41746-023-00811-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 03/30/2023] [Indexed: 04/28/2023] Open
Abstract
Advancements in deep learning and computer vision provide promising solutions for medical image analysis, potentially improving healthcare and patient outcomes. However, the prevailing paradigm of training deep learning models requires large quantities of labeled training data, which is both time-consuming and cost-prohibitive to curate for medical images. Self-supervised learning has the potential to make significant contributions to the development of robust medical imaging models through its ability to learn useful insights from copious medical datasets without labels. In this review, we provide consistent descriptions of different self-supervised learning strategies and compose a systematic review of papers published between 2012 and 2022 on PubMed, Scopus, and ArXiv that applied self-supervised learning to medical imaging classification. We screened a total of 412 relevant studies and included 79 papers for data extraction and analysis. With this comprehensive effort, we synthesize the collective knowledge of prior work and provide implementation guidelines for future researchers interested in applying self-supervised learning to their development of medical imaging classification models.
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Affiliation(s)
- Shih-Cheng Huang
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA.
| | - Anuj Pareek
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA
| | - Malte Jensen
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Matthew P Lungren
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Serena Yeung
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Akshay S Chaudhari
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
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18
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Schmidt AM, Desai AD, Watkins LE, Crowder HA, Black MS, Mazzoli V, Rubin EB, Lu Q, MacKay JW, Boutin RD, Kogan F, Gold GE, Hargreaves BA, Chaudhari AS. Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry. J Magn Reson Imaging 2023; 57:1029-1039. [PMID: 35852498 PMCID: PMC9849481 DOI: 10.1002/jmri.28365] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Deep learning (DL)-based automatic segmentation models can expedite manual segmentation yet require resource-intensive fine-tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine-tuning is not well characterized. PURPOSE Evaluate the generalizability of DL-based models by deploying pretrained models on independent datasets varying by MR scanner, acquisition parameters, and subject population. STUDY TYPE Retrospective based on prospectively acquired data. POPULATION Overall test dataset: 59 subjects (26 females); Study 1: 5 healthy subjects (zero females), Study 2: 8 healthy subjects (eight females), Study 3: 10 subjects with osteoarthritis (eight females), Study 4: 36 subjects with various knee pathology (10 females). FIELD STRENGTH/SEQUENCE A 3-T, quantitative double-echo steady state (qDESS). ASSESSMENT Four annotators manually segmented knee cartilage. Each reader segmented one of four qDESS datasets in the test dataset. Two DL models, one trained on qDESS data and another on Osteoarthritis Initiative (OAI)-DESS data, were assessed. Manual and automatic segmentations were compared by quantifying variations in segmentation accuracy, volume, and T2 relaxation times for superficial and deep cartilage. STATISTICAL TESTS Dice similarity coefficient (DSC) for segmentation accuracy. Lin's concordance correlation coefficient (CCC), Wilcoxon rank-sum tests, root-mean-squared error-coefficient-of-variation to quantify manual vs. automatic T2 and volume variations. Bland-Altman plots for manual vs. automatic T2 agreement. A P value < 0.05 was considered statistically significant. RESULTS DSCs for the qDESS-trained model, 0.79-0.93, were higher than those for the OAI-DESS-trained model, 0.59-0.79. T2 and volume CCCs for the qDESS-trained model, 0.75-0.98 and 0.47-0.95, were higher than respective CCCs for the OAI-DESS-trained model, 0.35-0.90 and 0.13-0.84. Bland-Altman 95% limits of agreement for superficial and deep cartilage T2 were lower for the qDESS-trained model, ±2.4 msec and ±4.0 msec, than the OAI-DESS-trained model, ±4.4 msec and ±5.2 msec. DATA CONCLUSION The qDESS-trained model may generalize well to independent qDESS datasets regardless of MR scanner, acquisition parameters, and subject population. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Andrew M Schmidt
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Arjun D Desai
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Electrical Engineering, Stanford University, Palo Alto, California, USA
| | - Lauren E Watkins
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Bioengineering, Stanford University, Palo Alto, California, USA
| | - Hollis A Crowder
- Mechanical Engineering, Stanford University, Palo Alto, California, USA
| | - Marianne S Black
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Mechanical Engineering, Stanford University, Palo Alto, California, USA
| | - Valentina Mazzoli
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Elka B Rubin
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Quin Lu
- Philips Healthcare North America, Gainesville, Florida, USA
| | - James W MacKay
- Department of Radiology, University of Cambridge, Cambridge, UK
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Robert D Boutin
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Feliks Kogan
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Bioengineering, Stanford University, Palo Alto, California, USA
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Electrical Engineering, Stanford University, Palo Alto, California, USA
- Bioengineering, Stanford University, Palo Alto, California, USA
| | - Akshay S Chaudhari
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Biomedical Data Science, Stanford University, Palo Alto, California, USA
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19
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Tan T, Gatti AA, Fan B, Shea KG, Sherman SL, Uhlrich SD, Hicks JL, Delp SL, Shull PB, Chaudhari AS. A scoping review of portable sensing for out-of-lab anterior cruciate ligament injury prevention and rehabilitation. NPJ Digit Med 2023; 6:46. [PMID: 36934194 PMCID: PMC10024704 DOI: 10.1038/s41746-023-00782-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 02/17/2023] [Indexed: 03/20/2023] Open
Abstract
Anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) surgery are common. Laboratory-based biomechanical assessment can evaluate ACL injury risk and rehabilitation progress after ACLR; however, lab-based measurements are expensive and inaccessible to most people. Portable sensors such as wearables and cameras can be deployed during sporting activities, in clinics, and in patient homes. Although many portable sensing approaches have demonstrated promising results during various assessments related to ACL injury, they have not yet been widely adopted as tools for out-of-lab assessment. The purpose of this review is to summarize research on out-of-lab portable sensing applied to ACL and ACLR and offer our perspectives on new opportunities for future research and development. We identified 49 original research articles on out-of-lab ACL-related assessment; the most common sensing modalities were inertial measurement units, depth cameras, and RGB cameras. The studies combined portable sensors with direct feature extraction, physics-based modeling, or machine learning to estimate a range of biomechanical parameters (e.g., knee kinematics and kinetics) during jump-landing tasks, cutting, squats, and gait. Many of the reviewed studies depict proof-of-concept methods for potential future clinical applications including ACL injury risk screening, injury prevention training, and rehabilitation assessment. By synthesizing these results, we describe important opportunities that exist for clinical validation of existing approaches, using sophisticated modeling techniques, standardization of data collection, and creation of large benchmark datasets. If successful, these advances will enable widespread use of portable-sensing approaches to identify ACL injury risk factors, mitigate high-risk movements prior to injury, and optimize rehabilitation paradigms.
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Affiliation(s)
- Tian Tan
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Anthony A Gatti
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Bingfei Fan
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Kevin G Shea
- Department of Orthopaedic Surgery, Stanford University, Stanford, CA, USA
| | - Seth L Sherman
- Department of Orthopaedic Surgery, Stanford University, Stanford, CA, USA
| | - Scott D Uhlrich
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Jennifer L Hicks
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Scott L Delp
- Department of Orthopaedic Surgery, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Mechanical Engineering, Stanford University, Stanford, CA, USA
| | - Peter B Shull
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, Shanghai, China.
| | - Akshay S Chaudhari
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
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20
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Barbieri M, Chaudhari AS, Moran CJ, Gold GE, Hargreaves BA, Kogan F. A method for measuring B 0 field inhomogeneity using quantitative double-echo in steady-state. Magn Reson Med 2023; 89:577-593. [PMID: 36161727 PMCID: PMC9712261 DOI: 10.1002/mrm.29465] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 08/31/2022] [Accepted: 09/01/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE To develop and validate a method forB 0 $$ {B}_0 $$ mapping for knee imaging using the quantitative Double-Echo in Steady-State (qDESS) exploiting the phase difference (Δ θ $$ \Delta \theta $$ ) between the two echoes acquired. Contrary to a two-gradient-echo (2-GRE) method,Δ θ $$ \Delta \theta $$ depends only on the first echo time. METHODS Bloch simulations were applied to investigate robustness to noise of the proposed methodology and all imaging studies were validated with phantoms and in vivo simultaneous bilateral knee acquisitions. Two phantoms and five healthy subjects were scanned using qDESS, water saturation shift referencing (WASSR), and multi-GRE sequences.Δ B 0 $$ \Delta {B}_0 $$ maps were calculated with the qDESS and the 2-GRE methods and compared against those obtained with WASSR. The comparison was quantitatively assessed exploiting pixel-wise difference maps, Bland-Altman (BA) analysis, and Lin's concordance coefficient (ρ c $$ {\rho}_c $$ ). For in vivo subjects, the comparison was assessed in cartilage using average values in six subregions. RESULTS The proposed method for measuringΔ B 0 $$ \Delta {B}_0 $$ inhomogeneities from a qDESS acquisition providedΔ B 0 $$ \Delta {B}_0 $$ maps that were in good agreement with those obtained using WASSR.Δ B 0 $$ \Delta {B}_0 $$ ρ c $$ {\rho}_c $$ values were≥ $$ \ge $$ 0.98 and 0.90 in phantoms and in vivo, respectively. The agreement between qDESS and WASSR was comparable to that of a 2-GRE method. CONCLUSION The proposed method may allow B0 correction for qDESST 2 $$ {T}_2 $$ mapping using an inherently co-registeredΔ B 0 $$ \Delta {B}_0 $$ map without requiring an additional B0 measurement sequence. More generally, the method may help shorten knee imaging protocols that require an auxiliaryΔ B 0 $$ \Delta {B}_0 $$ map by exploiting a qDESS acquisition that also providesT 2 $$ {T}_2 $$ measurements and high-quality morphological imaging.
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Affiliation(s)
- Marco Barbieri
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
| | - Akshay S. Chaudhari
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
- Department of Biomedical Data Science, Stanford University, Stanford, CA, U.S.A
| | | | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
- Department of Bioengineering, Stanford University, Stanford, CA, U.S.A
| | - Brian A. Hargreaves
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
- Department of Bioengineering, Stanford University, Stanford, CA, U.S.A
- Department of Electrical Engineering, Stanford University, Stanford, CA, U.S.A
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
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21
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Hirvasniemi J, Runhaar J, van der Heijden RA, Zokaeinikoo M, Yang M, Li X, Tan J, Rajamohan HR, Zhou Y, Deniz CM, Caliva F, Iriondo C, Lee JJ, Liu F, Martinez AM, Namiri N, Pedoia V, Panfilov E, Bayramoglu N, Nguyen HH, Nieminen MT, Saarakkala S, Tiulpin A, Lin E, Li A, Li V, Dam EB, Chaudhari AS, Kijowski R, Bierma-Zeinstra S, Oei EHG, Klein S. The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images. Osteoarthritis Cartilage 2023; 31:115-125. [PMID: 36243308 DOI: 10.1016/j.joca.2022.10.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 09/02/2022] [Accepted: 10/03/2022] [Indexed: 11/05/2022]
Abstract
OBJECTIVES The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. DESIGN The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). RESULTS Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57-0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52-0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. CONCLUSION The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.
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Affiliation(s)
- J Hirvasniemi
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
| | - J Runhaar
- Department of General Practice, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - R A van der Heijden
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - M Zokaeinikoo
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, USA
| | - M Yang
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, USA
| | - X Li
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, USA
| | - J Tan
- Department of Radiology, New York University Langone Health, New York, USA
| | - H R Rajamohan
- Department of Radiology, New York University Langone Health, New York, USA
| | - Y Zhou
- Department of Radiology, New York University Langone Health, New York, USA
| | - C M Deniz
- Department of Radiology, New York University Langone Health, New York, USA
| | - F Caliva
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - C Iriondo
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - J J Lee
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - F Liu
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - A M Martinez
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - N Namiri
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - V Pedoia
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - E Panfilov
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - N Bayramoglu
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - H H Nguyen
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - M T Nieminen
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - S Saarakkala
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - A Tiulpin
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - E Lin
- Akousist Co., Ltd., Taoyuan City, Taiwan
| | - A Li
- Akousist Co., Ltd., Taoyuan City, Taiwan
| | - V Li
- Akousist Co., Ltd., Taoyuan City, Taiwan
| | - E B Dam
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - A S Chaudhari
- Department of Radiology, Stanford University, Stanford, USA
| | - R Kijowski
- Department of Radiology, New York University Langone Health, New York, USA
| | - S Bierma-Zeinstra
- Department of General Practice, Erasmus MC University Medical Center, Rotterdam, the Netherlands; Department of Orthopedics & Sport Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - E H G Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - S Klein
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
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22
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Boutin RD, Houston DK, Chaudhari AS, Willis MH, Fausett CL, Lenchik L. Imaging of Sarcopenia. Radiol Clin North Am 2022; 60:575-582. [PMID: 35672090 DOI: 10.1016/j.rcl.2022.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Sarcopenia is currently underdiagnosed and undertreated, but this is expected to change because sarcopenia is now recognized with a specific diagnosis code that can be used for billing in some countries, as well as an expanding body of research on prevention, diagnosis, and management. This article focuses on practical issues of increasing interest by highlighting 3 hot topics fundamental to understanding sarcopenia in older adults: definitions and terminology, current diagnostic imaging techniques, and the emerging role of opportunistic computed tomography.
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Affiliation(s)
- Robert D Boutin
- Department of Radiology, Stanford University School of Medicine, 453 Quarry Road, MC 5659, Palo Alto, CA 94304-5659, USA.
| | - Denise K Houston
- Gerontology and Geriatric Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA
| | - Akshay S Chaudhari
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305-5372, USA; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305-5372, USA
| | - Marc H Willis
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Room H1330A, Stanford, CA 94305-5642, USA
| | - Cameron L Fausett
- Department of Orthopaedic Surgery, Stanford University School of Medicine, 430 Broadway Street, Redwood City, CA 94063-6342, USA
| | - Leon Lenchik
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA
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23
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Keenan KE, Delfino JG, Jordanova KV, Poorman ME, Chirra P, Chaudhari AS, Baessler B, Winfield J, Viswanath SE, deSouza NM. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med Phys 2022; 49:2820-2835. [PMID: 34455593 PMCID: PMC8882689 DOI: 10.1002/mp.15195] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/17/2021] [Accepted: 08/17/2021] [Indexed: 01/31/2023] Open
Abstract
Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance. In this article, we review the challenges to ensuring repeatability and reproducibility of image quantitation methods as well as present strategies to minimize their variance to enable wider clinical implementation. We present possible solutions for achieving clinically acceptable performance of image quantitation methods and briefly discuss the impact of minimizing variance and achieving generalizability towards clinical implementation and adoption.
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Affiliation(s)
- Kathryn E. Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Jana G. Delfino
- Center for Devices and Radiological Health, US Food and Drug Administration, 10993 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Kalina V. Jordanova
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Megan E. Poorman
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Prathyush Chirra
- Dept of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA
| | - Akshay S. Chaudhari
- Department of Radiology, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA
| | - Bettina Baessler
- University Hospital of Zurich and University of Zurich, Raemistrasse 100, 8091 Zurich, Switzerland
| | - Jessica Winfield
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK
| | - Satish E. Viswanath
- Dept of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA
| | - Nandita M. deSouza
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK
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24
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Manzano W, Lenchik L, Chaudhari AS, Yao L, Gupta S, Boutin RD. Sarcopenia in rheumatic disorders: what the radiologist and rheumatologist should know. Skeletal Radiol 2022; 51:513-524. [PMID: 34268590 DOI: 10.1007/s00256-021-03863-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 07/03/2021] [Accepted: 07/04/2021] [Indexed: 02/02/2023]
Abstract
Sarcopenia is defined as the loss of muscle mass, strength, and function. Increasing evidence shows that sarcopenia is common in patients with rheumatic disorders. Although sarcopenia can be diagnosed using bioelectrical impedance analysis or DXA, increasingly it is diagnosed using CT, MRI, and ultrasound. In rheumatic patients, CT and MRI allow "opportunistic" measurement of body composition, including surrogate markers of sarcopenia, from studies obtained during routine patient care. Recognition of sarcopenia is important in rheumatic patients because sarcopenia can be associated with disease progression and poor outcomes. This article reviews how opportunistic evaluation of sarcopenia in rheumatic patients can be accomplished and potentially contribute to improved patient care.
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Affiliation(s)
- Wilfred Manzano
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305-5105, USA.
| | - Leon Lenchik
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Akshay S Chaudhari
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305-5105, USA
| | - Lawrence Yao
- Department of Radiology, National Institute of Health, Bethesda, MD, 20892, USA
| | - Sarthak Gupta
- Department of Medicine, National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), National Institutes of Health, Bethesda, MD, 20892, USA
| | - Robert D Boutin
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305-5105, USA
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25
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Abstract
Machine learning (ML) and Artificial intelligence (AI) has the potential to dramatically improve radiology practice at multiple stages of the imaging pipeline. Most of the attention has been garnered by applications focused on improving the end of the pipeline: image interpretation. However, this article reviews how AI/ML can be applied to improve upstream components of the imaging pipeline, including exam modality selection, hardware design, exam protocol selection, data acquisition, image reconstruction, and image processing. A breadth of applications and their potential for impact is shown across multiple imaging modalities, including ultrasound, computed tomography, and MRI.
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Affiliation(s)
- Christopher M Sandino
- Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA 94305, USA
| | - Elizabeth K Cole
- Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA 94305, USA
| | - Cagan Alkan
- Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA 94305, USA
| | - Akshay S Chaudhari
- Department of Biomedical Data Science, 1201 Welch Road, Stanford, CA 94305, USA; Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA
| | - Andreas M Loening
- Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA
| | - Dongwoon Hyun
- Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA
| | - Jeremy Dahl
- Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA
| | | | - Adam S Wang
- Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA
| | - Shreyas S Vasanawala
- Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA.
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26
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Sveinsson B, Chaudhari AS, Zhu B, Koonjoo N, Torriani M, Gold GE, Rosen MS. Synthesizing Quantitative T2 Maps in Right Lateral Knee Femoral Condyles from Multicontrast Anatomic Data with a Conditional Generative Adversarial Network. Radiol Artif Intell 2021; 3:e200122. [PMID: 34617020 PMCID: PMC8489449 DOI: 10.1148/ryai.2021200122] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 04/11/2021] [Accepted: 05/03/2021] [Indexed: 04/09/2023]
Abstract
PURPOSE To develop a proof-of-concept convolutional neural network (CNN) to synthesize T2 maps in right lateral femoral condyle articular cartilage from anatomic MR images by using a conditional generative adversarial network (cGAN). MATERIALS AND METHODS In this retrospective study, anatomic images (from turbo spin-echo and double-echo in steady-state scans) of the right knee of 4621 patients included in the 2004-2006 Osteoarthritis Initiative were used as input to a cGAN-based CNN, and a predicted CNN T2 was generated as output. These patients included men and women of all ethnicities, aged 45-79 years, with or at high risk for knee osteoarthritis incidence or progression who were recruited at four separate centers in the United States. These data were split into 3703 (80%) for training, 462 (10%) for validation, and 456 (10%) for testing. Linear regression analysis was performed between the multiecho spin-echo (MESE) and CNN T2 in the test dataset. A more detailed analysis was performed in 30 randomly selected patients by means of evaluation by two musculoskeletal radiologists and quantification of cartilage subregions. Radiologist assessments were compared by using two-sided t tests. RESULTS The readers were moderately accurate in distinguishing CNN T2 from MESE T2, with one reader having random-chance categorization. CNN T2 values were correlated to the MESE values in the subregions of 30 patients and in the bulk analysis of all patients, with best-fit line slopes between 0.55 and 0.83. CONCLUSION With use of a neural network-based cGAN approach, it is feasible to synthesize T2 maps in femoral cartilage from anatomic MRI sequences, giving good agreement with MESE scans.See also commentary by Yi and Fritz in this issue.Keywords: Cartilage Imaging, Knee, Experimental Investigations, Quantification, Vision, Application Domain, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms© RSNA, 2021.
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Chaudhari AS, Mittra E, Davidzon GA, Gulaka P, Gandhi H, Brown A, Zhang T, Srinivas S, Gong E, Zaharchuk G, Jadvar H. Low-count whole-body PET with deep learning in a multicenter and externally validated study. NPJ Digit Med 2021; 4:127. [PMID: 34426629 PMCID: PMC8382711 DOI: 10.1038/s41746-021-00497-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 08/03/2021] [Indexed: 02/08/2023] Open
Abstract
More widespread use of positron emission tomography (PET) imaging is limited by its high cost and radiation dose. Reductions in PET scan time or radiotracer dosage typically degrade diagnostic image quality (DIQ). Deep-learning-based reconstruction may improve DIQ, but such methods have not been clinically evaluated in a realistic multicenter, multivendor environment. In this study, we evaluated the performance and generalizability of a deep-learning-based image-quality enhancement algorithm applied to fourfold reduced-count whole-body PET in a realistic clinical oncologic imaging environment with multiple blinded readers, institutions, and scanner types. We demonstrate that the low-count-enhanced scans were noninferior to the standard scans in DIQ (p < 0.05) and overall diagnostic confidence (p < 0.001) independent of the underlying PET scanner used. Lesion detection for the low-count-enhanced scans had a high patient-level sensitivity of 0.94 (0.83-0.99) and specificity of 0.98 (0.95-0.99). Interscan kappa agreement of 0.85 was comparable to intrareader (0.88) and pairwise inter-reader agreements (maximum of 0.72). SUV quantification was comparable in the reference regions and lesions (lowest p-value=0.59) and had high correlation (lowest CCC = 0.94). Thus, we demonstrated that deep learning can be used to restore diagnostic image quality and maintain SUV accuracy for fourfold reduced-count PET scans, with interscan variations in lesion depiction, lower than intra- and interreader variations. This method generalized to an external validation set of clinical patients from multiple institutions and scanner types. Overall, this method may enable either dose or exam-duration reduction, increasing safety and lowering the cost of PET imaging.
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Affiliation(s)
- Akshay S Chaudhari
- Department of Radiology, Stanford University, Palo Alto, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Subtle Medical, Menlo Park, CA, USA.
| | - Erik Mittra
- Division of Diagnostic Radiology, Oregon Health & Science University, Portland, OR, USA
| | - Guido A Davidzon
- Department of Radiology, Stanford University, Palo Alto, CA, USA
| | | | | | - Adam Brown
- Division of Diagnostic Radiology, Oregon Health & Science University, Portland, OR, USA
| | | | - Shyam Srinivas
- Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | | | - Greg Zaharchuk
- Department of Radiology, Stanford University, Palo Alto, CA, USA
- Subtle Medical, Menlo Park, CA, USA
| | - Hossein Jadvar
- Department of Radiology, University of Southern California, Los Angeles, CA, USA
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Chaudhari AS, Sandino CM, Cole EK, Larson DB, Gold GE, Vasanawala SS, Lungren MP, Hargreaves BA, Langlotz CP. Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices. J Magn Reson Imaging 2021; 54:357-371. [PMID: 32830874 PMCID: PMC8639049 DOI: 10.1002/jmri.27331] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/27/2020] [Accepted: 07/31/2020] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence algorithms based on principles of deep learning (DL) have made a large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the large number of retrospective studies using DL, there are fewer applications of DL in the clinic on a routine basis. To address this large translational gap, we review the recent publications to determine three major use cases that DL can have in MRI, namely, that of model-free image synthesis, model-based image reconstruction, and image or pixel-level classification. For each of these three areas, we provide a framework for important considerations that consist of appropriate model training paradigms, evaluation of model robustness, downstream clinical utility, opportunities for future advances, as well recommendations for best current practices. We draw inspiration for this framework from advances in computer vision in natural imaging as well as additional healthcare fields. We further emphasize the need for reproducibility of research studies through the sharing of datasets and software. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
| | - Christopher M Sandino
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Elizabeth K Cole
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - David B Larson
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | | | - Matthew P Lungren
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Biomedical Informatics, Stanford University, Stanford, California, USA
| | - Curtis P Langlotz
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Biomedical Informatics, Stanford University, Stanford, California, USA
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Chaudhari AS, Grissom MJ, Fang Z, Sveinsson B, Lee JH, Gold GE, Hargreaves BA, Stevens KJ. Diagnostic Accuracy of Quantitative Multicontrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement. AJR Am J Roentgenol 2021; 216:1614-1625. [PMID: 32755384 PMCID: PMC8862596 DOI: 10.2214/ajr.20.24172] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
BACKGROUND. Potential approaches for abbreviated knee MRI, including prospective acceleration with deep learning, have achieved limited clinical implementation. OBJECTIVE. The objective of this study was to evaluate the interreader agreement between conventional knee MRI and a 5-minute 3D quantitative double-echo steady-state (qDESS) sequence with automatic T2 mapping and deep learning super-resolutionaugmentation and to compare the diagnostic performance of the two methods regarding findings from arthroscopic surgery. METHODS. Fifty-one patients with knee pain underwent knee MRI that included an additional 3D qDESS sequence with automatic T2 mapping. Fourier interpolation was followed by prospective deep learning super resolution to enhance qDESS slice resolution twofold. A musculoskeletal radiologist and a radiology resident performed independent retrospective evaluations of articular cartilage, menisci, ligaments, bones, extensor mechanism, and synovium using conventional MRI. Following a 2-month washout period, readers reviewed qDESS images alone followed by qDESS with the automatic T2 maps. Interreader agreement between conventional MRI and qDESS was computed using percentage agreement and Cohen kappa. The sensitivity and specificity of conventional MRI, qDESS alone, and qDESS plus T2 mapping were compared with arthroscopic findings using exact McNemar tests. RESULTS. Conventional MRI and qDESS showed 92% agreement in evaluating all tissues. Kappa was 0.79 (95% CI, 0.76-0.81) across all imaging findings. In 43 patients who underwent arthroscopy, sensitivity and specificity were not significantly different (p = .23 to > .99) between conventional MRI (sensitivity, 58-93%; specificity, 27-87%) and qDESS alone (sensitivity, 54-90%; specificity, 23-91%) for cartilage, menisci, ligaments, and synovium. For grade 1 cartilage lesions, sensitivity and specificity were 33% and 56%, respectively, for conventional MRI; 23% and 53% for qDESS (p = .81); and 46% and 39% for qDESS with T2 mapping (p = .80). For grade 2A lesions, values were 27% and 53% for conventional MRI, 26% and 52% for qDESS (p = .02), and 58% and 40% for qDESS with T2 mapping (p < .001). CONCLUSION. The qDESS method prospectively augmented with deep learning showed strong interreader agreement with conventional knee MRI and near-equivalent diagnostic performance regarding arthroscopy. The ability of qDESS to automatically generate T2 maps increases sensitivity for cartilage abnormalities. CLINICAL IMPACT. Using prospective artificial intelligence to enhance qDESS image quality may facilitate an abbreviated knee MRI protocol while generating quantitative T2 maps.
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Affiliation(s)
- Akshay S Chaudhari
- Department of Radiology, Lucas Center for Imaging, Stanford University, 1201 Welch Rd, PS 055B, Stanford, CA 94305
| | | | | | - Bragi Sveinsson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
- Department of Radiology, Harvard Medical School, Boston, MA
| | - Jin Hyung Lee
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA
- Department of Bioengineering, Stanford University, Stanford, CA
- Department of Neurosurgery, Stanford University, Stanford, CA
- Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Garry E Gold
- Department of Radiology, Lucas Center for Imaging, Stanford University, 1201 Welch Rd, PS 055B, Stanford, CA 94305
- Department of Bioengineering, Stanford University, Stanford, CA
- Department of Orthopaedic Surgery, Stanford University, Redwood City, CA
| | - Brian A Hargreaves
- Department of Radiology, Lucas Center for Imaging, Stanford University, 1201 Welch Rd, PS 055B, Stanford, CA 94305
- Department of Bioengineering, Stanford University, Stanford, CA
- Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Kathryn J Stevens
- Department of Radiology, Lucas Center for Imaging, Stanford University, 1201 Welch Rd, PS 055B, Stanford, CA 94305
- Department of Orthopaedic Surgery, Stanford University, Redwood City, CA
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Desai AD, Caliva F, Iriondo C, Mortazi A, Jambawalikar S, Bagci U, Perslev M, Igel C, Dam EB, Gaj S, Yang M, Li X, Deniz CM, Juras V, Regatte R, Gold GE, Hargreaves BA, Pedoia V, Chaudhari AS. The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset. Radiol Artif Intell 2021; 3:e200078. [PMID: 34235438 PMCID: PMC8231759 DOI: 10.1148/ryai.2021200078] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 01/08/2021] [Accepted: 01/25/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE To organize a multi-institute knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. MATERIALS AND METHODS A dataset partition consisting of three-dimensional knee MRI from 88 retrospective patients at two time points (baseline and 1-year follow-up) with ground truth articular (femoral, tibial, and patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated against ground truth segmentations using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a holdout test set. Similarities in automated segmentations were measured using pairwise Dice coefficient correlations. Articular cartilage thickness was computed longitudinally and with scans. Correlation between thickness error and segmentation metrics was measured using the Pearson correlation coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives. RESULTS Six teams (T 1-T 6) submitted entries for the challenge. No differences were observed across any segmentation metrics for any tissues (P = .99) among the four top-performing networks (T 2, T 3, T 4, T 6). Dice coefficient correlations between network pairs were high (> 0.85). Per-scan thickness errors were negligible among networks T 1-T 4 (P = .99), and longitudinal changes showed minimal bias (< 0.03 mm). Low correlations (ρ < 0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top-performing networks (P = .99). Empirical upper-bound performances were similar for both combinations (P = .99). CONCLUSION Diverse networks learned to segment the knee similarly, where high segmentation accuracy did not correlate with cartilage thickness accuracy and voting ensembles did not exceed individual network performance.See also the commentary by Elhalawani and Mak in this issue.Keywords: Cartilage, Knee, MR-Imaging, Segmentation © RSNA, 2020Supplemental material is available for this article.
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Affiliation(s)
- Arjun D. Desai
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Francesco Caliva
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Claudia Iriondo
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Aliasghar Mortazi
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Sachin Jambawalikar
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Ulas Bagci
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Mathias Perslev
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Christian Igel
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Erik B. Dam
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Sibaji Gaj
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Mingrui Yang
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Xiaojuan Li
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Cem M. Deniz
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Vladimir Juras
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Ravinder Regatte
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Garry E. Gold
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Brian A. Hargreaves
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Valentina Pedoia
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Akshay S. Chaudhari
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - on behalf of the IWOAI Segmentation Challenge Writing Group
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
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31
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Tian Q, Bilgic B, Fan Q, Ngamsombat C, Zaretskaya N, Fultz NE, Ohringer NA, Chaudhari AS, Hu Y, Witzel T, Setsompop K, Polimeni JR, Huang SY. Improving in vivo human cerebral cortical surface reconstruction using data-driven super-resolution. Cereb Cortex 2021; 31:463-482. [PMID: 32887984 PMCID: PMC7727379 DOI: 10.1093/cercor/bhaa237] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 07/30/2020] [Accepted: 07/30/2020] [Indexed: 11/14/2022] Open
Abstract
Accurate and automated reconstruction of the in vivo human cerebral cortical surface from anatomical magnetic resonance (MR) images facilitates the quantitative analysis of cortical structure. Anatomical MR images with sub-millimeter isotropic spatial resolution improve the accuracy of cortical surface and thickness estimation compared to the standard 1-millimeter isotropic resolution. Nonetheless, sub-millimeter resolution acquisitions require averaging multiple repetitions to achieve sufficient signal-to-noise ratio and are therefore long and potentially vulnerable to subject motion. We address this challenge by synthesizing sub-millimeter resolution images from standard 1-millimeter isotropic resolution images using a data-driven supervised machine learning-based super-resolution approach achieved via a deep convolutional neural network. We systematically characterize our approach using a large-scale simulated dataset and demonstrate its efficacy in empirical data. The super-resolution data provide improved cortical surfaces similar to those obtained from native sub-millimeter resolution data. The whole-brain mean absolute discrepancy in cortical surface positioning and thickness estimation is below 100 μm at the single-subject level and below 50 μm at the group level for the simulated data, and below 200 μm at the single-subject level and below 100 μm at the group level for the empirical data, making the accuracy of cortical surfaces derived from super-resolution sufficient for most applications.
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Affiliation(s)
- Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - Natalia Zaretskaya
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
- Department of Experimental Psychology and Cognitive Neuroscience, Institute of Psychology, University of Graz, Graz, Austria
- BioTechMed-Graz, Austria
| | - Nina E Fultz
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - Ned A Ohringer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - Akshay S Chaudhari
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, CA, United States
| | - Yuxin Hu
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, CA, United States
| | - Thomas Witzel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
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32
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Eckstein F, Chaudhari AS, Fuerst D, Gaisberger M, Kemnitz J, Baumgartner CF, Konukoglu E, Hunter DJ, Wirth W. A Deep Learning Automated Segmentation Algorithm Accurately Detects Differences in Longitudinal Cartilage Thickness Loss - Data from the FNIH Biomarkers Study of the Osteoarthritis Initiative. Arthritis Care Res (Hoboken) 2020; 74:929-936. [PMID: 33337584 PMCID: PMC9321555 DOI: 10.1002/acr.24539] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 11/11/2020] [Accepted: 12/15/2020] [Indexed: 11/18/2022]
Abstract
Objective To study the longitudinal performance of fully automated cartilage segmentation in knees with radiographic osteoarthritis (OA), we evaluated the sensitivity to change in progressor knees from the Foundation for the National Institutes of Health OA Biomarkers Consortium between the automated and previously reported manual expert segmentation, and we determined whether differences in progression rates between predefined cohorts can be detected by the fully automated approach. Methods The OA Initiative Biomarker Consortium was a nested case–control study. Progressor knees had both medial tibiofemoral radiographic joint space width loss (≥0.7 mm) and a persistent increase in Western Ontario and McMaster Universities Osteoarthritis Index pain scores (≥9 on a 0–100 scale) after 2 years from baseline (n = 194), whereas non‐progressor knees did not have either of both (n = 200). Deep‐learning automated algorithms trained on radiographic OA knees or knees of a healthy reference cohort (HRC) were used to automatically segment medial femorotibial compartment (MFTC) and lateral femorotibial cartilage on baseline and 2‐year follow‐up magnetic resonance imaging. Findings were compared with previously published manual expert segmentation. Results The mean ± SD MFTC cartilage loss in the progressor cohort was –181 ± 245 μm by manual segmentation (standardized response mean [SRM] –0.74), –144 ± 200 μm by the radiographic OA–based model (SRM –0.72), and –69 ± 231 μm by HRC‐based model segmentation (SRM –0.30). Cohen's d for rates of progression between progressor versus the non‐progressor cohort was –0.84 (P < 0.001) for manual, –0.68 (P < 0.001) for the automated radiographic OA model, and –0.14 (P = 0.18) for automated HRC model segmentation. Conclusion A fully automated deep‐learning segmentation approach not only displays similar sensitivity to change of longitudinal cartilage thickness loss in knee OA as did manual expert segmentation but also effectively differentiates longitudinal rates of loss of cartilage thickness between cohorts with different progression profiles.
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Affiliation(s)
- Felix Eckstein
- Department of Imaging and Functional Musculoskeletal Research, Institute of Anatomy and Cell Biology, Paracelsus Medical University Salzburg and Nuremberg, Salzburg, Austria.,Ludwig Boltzmann Institute for Arthritis and Rehabilitation, Paracelsus Medical University, Salzburg, Austria.,Chondrometrics GmbH, Ainring, Germany
| | | | - David Fuerst
- Department of Imaging and Functional Musculoskeletal Research, Institute of Anatomy and Cell Biology, Paracelsus Medical University Salzburg and Nuremberg, Salzburg, Austria.,Ludwig Boltzmann Institute for Arthritis and Rehabilitation, Paracelsus Medical University, Salzburg, Austria.,Chondrometrics GmbH, Ainring, Germany
| | - Martin Gaisberger
- Ludwig Boltzmann Institute for Arthritis and Rehabilitation, Paracelsus Medical University, Salzburg, Austria.,Institute of Physiology and Pathophysiology, Paracelsus Medical University Salzburg and Nuremberg, Salzburg, Austria.,Gastein Research Institute, Paracelsus Medical University, Salzburg, Austria
| | - Jana Kemnitz
- Department of Imaging and Functional Musculoskeletal Research, Institute of Anatomy and Cell Biology, Paracelsus Medical University Salzburg and Nuremberg, Salzburg, Austria
| | | | | | - David J Hunter
- Rheumatology Department, Royal North Shore Hospital and Institute of Bone and Joint Research, Kolling Institute, University of Sydney, Sydney, NSW, Australia
| | - Wolfgang Wirth
- Department of Imaging and Functional Musculoskeletal Research, Institute of Anatomy and Cell Biology, Paracelsus Medical University Salzburg and Nuremberg, Salzburg, Austria.,Ludwig Boltzmann Institute for Arthritis and Rehabilitation, Paracelsus Medical University, Salzburg, Austria.,Chondrometrics GmbH, Ainring, Germany
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33
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Chaudhari AS, Kogan F, Pedoia V, Majumdar S, Gold GE, Hargreaves BA. Rapid Knee MRI Acquisition and Analysis Techniques for Imaging Osteoarthritis. J Magn Reson Imaging 2020; 52:1321-1339. [PMID: 31755191 PMCID: PMC7925938 DOI: 10.1002/jmri.26991] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 10/22/2019] [Accepted: 10/22/2019] [Indexed: 12/16/2022] Open
Abstract
Osteoarthritis (OA) of the knee is a major source of disability that has no known treatment or cure. Morphological and compositional MRI is commonly used for assessing the bone and soft tissues in the knee to enhance the understanding of OA pathophysiology. However, it is challenging to extend these imaging methods and their subsequent analysis techniques to study large population cohorts due to slow and inefficient imaging acquisition and postprocessing tools. This can create a bottleneck in assessing early OA changes and evaluating the responses of novel therapeutics. The purpose of this review article is to highlight recent developments in tools for enhancing the efficiency of knee MRI methods useful to study OA. Advances in efficient MRI data acquisition and reconstruction tools for morphological and compositional imaging, efficient automated image analysis tools, and hardware improvements to further drive efficient imaging are discussed in this review. For each topic, we discuss the current challenges as well as potential future opportunities to alleviate these challenges. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3.
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Affiliation(s)
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
- Center of Digital Health Innovation (CDHI), University of California San Francisco, San Francisco, California, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
- Center of Digital Health Innovation (CDHI), University of California San Francisco, San Francisco, California, USA
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Brian A. Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
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34
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Chaudhari AS, Stevens KJ, Wood JP, Chakraborty AK, Gibbons EK, Fang Z, Desai AD, Lee JH, Gold GE, Hargreaves BA. Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers. J Magn Reson Imaging 2020; 51:768-779. [PMID: 31313397 PMCID: PMC6962563 DOI: 10.1002/jmri.26872] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 07/02/2019] [Accepted: 07/03/2019] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Super-resolution is an emerging method for enhancing MRI resolution; however, its impact on image quality is still unknown. PURPOSE To evaluate MRI super-resolution using quantitative and qualitative metrics of cartilage morphometry, osteophyte detection, and global image blurring. STUDY TYPE Retrospective. POPULATION In all, 176 MRI studies of subjects at varying stages of osteoarthritis. FIELD STRENGTH/SEQUENCE Original-resolution 3D double-echo steady-state (DESS) and DESS with 3× thicker slices retrospectively enhanced using super-resolution and tricubic interpolation (TCI) at 3T. ASSESSMENT A quantitative comparison of femoral cartilage morphometry was performed for the original-resolution DESS, the super-resolution, and the TCI scans in 17 subjects. A reader study by three musculoskeletal radiologists assessed cartilage image quality, overall image sharpness, and osteophytes incidence in all three sets of scans. A referenceless blurring metric evaluated blurring in all three image dimensions for the three sets of scans. STATISTICAL TESTS Mann-Whitney U-tests compared Dice coefficients (DC) of segmentation accuracy for the DESS, super-resolution, and TCI images, along with the image quality readings and blurring metrics. Sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence intervals compared osteophyte detection for the super-resolution and TCI images, with the original-resolution as a reference. RESULTS DC for the original-resolution (90.2 ± 1.7%) and super-resolution (89.6 ± 2.0%) were significantly higher (P < 0.001) than TCI (86.3 ± 5.6%). Segmentation overlap of super-resolution with the original-resolution (DC = 97.6 ± 0.7%) was significantly higher (P < 0.0001) than TCI overlap (DC = 95.0 ± 1.1%). Cartilage image quality for sharpness and contrast levels, and the through-plane quantitative blur factor for super-resolution images, was significantly (P < 0.001) better than TCI. Super-resolution osteophyte detection sensitivity of 80% (76-82%), specificity of 93% (92-94%), and DOR of 32 (22-46) was significantly higher (P < 0.001) than TCI sensitivity of 73% (69-76%), specificity of 90% (89-91%), and DOR of 17 (13-22). DATA CONCLUSION Super-resolution appears to consistently outperform naïve interpolation and may improve image quality without biasing quantitative biomarkers. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:768-779.
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Affiliation(s)
| | - Kathryn J Stevens
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Jeff P Wood
- Austin Radiological Association, Austin, Texas, USA
| | | | - Eric K Gibbons
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
| | | | - Arjun D Desai
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jin Hyung Lee
- Department of Neurology & Neurological Sciences, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Neurosurgery, Stanford University, Stanford, California, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
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35
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Eijgenraam SM, Chaudhari AS, Reijman M, Bierma-Zeinstra SMA, Hargreaves BA, Runhaar J, Heijboer FWJ, Gold GE, Oei EHG. Time-saving opportunities in knee osteoarthritis: T 2 mapping and structural imaging of the knee using a single 5-min MRI scan. Eur Radiol 2019; 30:2231-2240. [PMID: 31844957 PMCID: PMC7062657 DOI: 10.1007/s00330-019-06542-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 10/09/2019] [Accepted: 10/23/2019] [Indexed: 12/22/2022]
Abstract
Objectives To assess the discriminative power of a 5-min quantitative double-echo steady-state (qDESS) sequence for simultaneous T2 measurements of cartilage and meniscus, and structural knee osteoarthritis (OA) assessment, in a clinical OA population, using radiographic knee OA as reference standard. Methods Fifty-three subjects were included and divided over three groups based on radiographic and clinical knee OA: 20 subjects with no OA (Kellgren-Lawrence grade (KLG) 0), 18 with mild OA (KLG2), and 15 with moderate OA (KLG3). All patients underwent a 5-min qDESS scan. We measured T2 relaxation times in four cartilage and four meniscus regions of interest (ROIs) and performed structural OA evaluation with the MRI Osteoarthritis Knee Score (MOAKS) using qDESS with multiplanar reformatting. Between-group differences in T2 values and MOAKS were calculated using ANOVA. Correlations of the reference standard (i.e., radiographic knee OA) with T2 and MOAKS were assessed with correlation analyses for ordinal variables. Results In cartilage, mean T2 values were 36.1 ± SD 4.3, 40.6 ± 5.9, and 47.1 ± 4.3 ms for no, mild, and moderate OA, respectively (p < 0.001). In menisci, mean T2 values were 15 ± 3.6, 17.5 ± 3.8, and 20.6 ± 4.7 ms for no, mild, and moderate OA, respectively (p < 0.001). Statistically significant correlations were found between radiographic OA and T2 and between radiographic OA and MOAKS in all ROIs (p < 0.05). Conclusion Quantitative T2 and structural assessment of cartilage and meniscus, using a single 5-min qDESS scan, can distinguish between different grades of radiographic OA, demonstrating the potential of qDESS as an efficient tool for OA imaging. Key Points • Quantitative T2values of cartilage and meniscus as well as structural assessment of the knee with a single 5-min quantitative double-echo steady-state (qDESS) scan can distinguish between different grades of knee osteoarthritis (OA). • Quantitative and structural qDESS-based measurements correlate significantly with the reference standard, radiographic degree of OA, for all cartilage and meniscus regions. • By providing quantitative measurements and diagnostic image quality in one rapid MRI scan, qDESS has great potential for application in large-scale clinical trials in knee OA. Electronic supplementary material The online version of this article (10.1007/s00330-019-06542-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Susanne M Eijgenraam
- Deptartment of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Dr. Molewaterplein 40, Room Nd-547, 3015, GD, Rotterdam, The Netherlands.,Deptartment of Orthopedic Surgery, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | | | - Max Reijman
- Deptartment of Orthopedic Surgery, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Sita M A Bierma-Zeinstra
- Deptartment of Orthopedic Surgery, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.,Deptartment of General Practice, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Brian A Hargreaves
- Deptartment of Radiology, Stanford University, Stanford, CA, USA.,Deptartment of Electrical Engineering, Stanford University, Stanford, CA, USA.,Deptartment of Bioengineering, Stanford University, Stanford, CA, USA
| | - Jos Runhaar
- Deptartment of General Practice, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Frank W J Heijboer
- Deptartment of Orthopedic Surgery, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Garry E Gold
- Deptartment of Radiology, Stanford University, Stanford, CA, USA.,Deptartment of Bioengineering, Stanford University, Stanford, CA, USA.,Deptartment of Orthopedic Surgery, Stanford University, Stanford, CA, USA
| | - Edwin H G Oei
- Deptartment of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Dr. Molewaterplein 40, Room Nd-547, 3015, GD, Rotterdam, The Netherlands.
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Chaudhari AS, Stevens KJ, Sveinsson B, Wood JP, Beaulieu CF, Oei EH, Rosenberg JK, Kogan F, Alley MT, Gold GE, Hargreaves BA. Combined 5-minute double-echo in steady-state with separated echoes and 2-minute proton-density-weighted 2D FSE sequence for comprehensive whole-joint knee MRI assessment. J Magn Reson Imaging 2019; 49:e183-e194. [PMID: 30582251 PMCID: PMC7850298 DOI: 10.1002/jmri.26582] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 11/01/2018] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Clinical knee MRI protocols require upwards of 15 minutes of scan time. PURPOSE/HYPOTHESIS To compare the imaging appearance of knee abnormalities depicted with a 5-minute 3D double-echo in steady-state (DESS) sequence with separate echo images, with that of a routine clinical knee MRI protocol. A secondary goal was to compare the imaging appearance of knee abnormalities depicted with 5-minute DESS paired with a 2-minute coronal proton-density fat-saturated (PDFS) sequence. STUDY TYPE Prospective. SUBJECTS Thirty-six consecutive patients (19 male) referred for a routine knee MRI. FIELD STRENGTH/SEQUENCES DESS and PDFS at 3T. ASSESSMENT Five musculoskeletal radiologists evaluated all images for the presence of internal knee derangement using DESS, DESS+PDFS, and the conventional imaging protocol, and their associated diagnostic confidence of the reading. STATISTICAL TESTS Differences in positive and negative percent agreement (PPA and NPA, respectively) and 95% confidence intervals (CIs) for DESS and DESS+PDFS compared with the conventional protocol were calculated and tested using exact McNemar tests. The percentage of observations where DESS or DESS+PDFS had equivalent confidence ratings to DESS+Conv were tested with exact symmetry tests. Interreader agreement was calculated using Krippendorff's alpha. RESULTS DESS had a PPA of 90% (88-92% CI) and NPA of 99% (99-99% CI). DESS+PDFS had increased PPA of 99% (95-99% CI) and NPA of 100% (99-100% CI) compared with DESS (both P < 0.001). DESS had equivalent diagnostic confidence to DESS+Conv in 94% of findings, whereas DESS+PDFS had equivalent diagnostic confidence in 99% of findings (both P < 0.001). All readers had moderate concordance for all three protocols (Krippendorff's alpha 47-48%). DATA CONCLUSION Both 1) 5-minute 3D-DESS with separated echoes and 2) 5-minute 3D-DESS paired with a 2-minute coronal PDFS sequence depicted knee abnormalities similarly to a routine clinical knee MRI protocol, which may be a promising technique for abbreviated knee MRI. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
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Affiliation(s)
- Akshay S. Chaudhari
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Kathryn J. Stevens
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Bragi Sveinsson
- Department of Radiology, Stanford University, Stanford, California, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Jeff P. Wood
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Christopher F. Beaulieu
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Edwin H.G. Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | | | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Marcus T. Alley
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Brian A. Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
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Gibbons EK, Hodgson KK, Chaudhari AS, Richards LG, Majersik JJ, Adluru G, DiBella EVR. Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning. Magn Reson Med 2018; 81:2399-2411. [PMID: 30426558 DOI: 10.1002/mrm.27568] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 09/20/2018] [Accepted: 09/23/2018] [Indexed: 12/11/2022]
Abstract
PURPOSE To develop a robust multidimensional deep-learning based method to simultaneously generate accurate neurite orientation dispersion and density imaging (NODDI) and generalized fractional anisotropy (GFA) parameter maps from undersampled q-space datasets for use in stroke imaging. METHODS Traditional diffusion spectrum imaging (DSI) capable of producing accurate NODDI and GFA parameter maps requires hundreds of q-space samples which renders the scan time clinically untenable. A convolutional neural network (CNN) was trained to generated NODDI and GFA parameter maps simultaneously from 10× undersampled q-space data. A total of 48 DSI scans from 15 stroke patients and 14 normal subjects were acquired for training, validating, and testing this method. The proposed network was compared to previously proposed voxel-wise machine learning based approaches for q-space imaging. Network-generated images were used to predict stroke functional outcome measures. RESULTS The proposed network achieves significant performance advantages compared to previously proposed machine learning approaches, showing significant improvements across image quality metrics. Generating these parameter maps using CNNs also comes with the computational benefits of only needing to generate and train a single network instead of multiple networks for each parameter type. Post-stroke outcome prediction metrics do not appreciably change when using images generated from this proposed technique. Over three test participants, the predicted stroke functional outcome scores were within 1-6% of the clinical evaluations. CONCLUSIONS Estimates of NODDI and GFA parameters estimated simultaneously with a deep learning network from highly undersampled q-space data were improved compared to other state-of-the-art methods providing a 10-fold reduction scan time compared to conventional methods.
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Affiliation(s)
- Eric K Gibbons
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah
| | - Kyler K Hodgson
- Department of Bioengineering, University of Utah, Salt Lake City, Utah
| | | | - Lorie G Richards
- Department of Occupational and Recreational Therapies, University of Utah, Salt Lake City, Utah
| | | | - Ganesh Adluru
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah
| | - Edward V R DiBella
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah
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Chaudhari AS, Fang Z, Kogan F, Wood J, Stevens KJ, Gibbons EK, Lee JH, Gold GE, Hargreaves BA. Super-resolution musculoskeletal MRI using deep learning. Magn Reson Med 2018; 80:2139-2154. [PMID: 29582464 PMCID: PMC6107420 DOI: 10.1002/mrm.27178] [Citation(s) in RCA: 171] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2017] [Revised: 02/14/2018] [Accepted: 02/22/2018] [Indexed: 12/22/2022]
Abstract
PURPOSE To develop a super-resolution technique using convolutional neural networks for generating thin-slice knee MR images from thicker input slices, and compare this method with alternative through-plane interpolation methods. METHODS We implemented a 3D convolutional neural network entitled DeepResolve to learn residual-based transformations between high-resolution thin-slice images and lower-resolution thick-slice images at the same center locations. DeepResolve was trained using 124 double echo in steady-state (DESS) data sets with 0.7-mm slice thickness and tested on 17 patients. Ground-truth images were compared with DeepResolve, clinically used tricubic interpolation, and Fourier interpolation methods, along with state-of-the-art single-image sparse-coding super-resolution. Comparisons were performed using structural similarity, peak SNR, and RMS error image quality metrics for a multitude of thin-slice downsampling factors. Two musculoskeletal radiologists ranked the 3 data sets and reviewed the diagnostic quality of the DeepResolve, tricubic interpolation, and ground-truth images for sharpness, contrast, artifacts, SNR, and overall diagnostic quality. Mann-Whitney U tests evaluated differences among the quantitative image metrics, reader scores, and rankings. Cohen's Kappa (κ) evaluated interreader reliability. RESULTS DeepResolve had significantly better structural similarity, peak SNR, and RMS error than tricubic interpolation, Fourier interpolation, and sparse-coding super-resolution for all downsampling factors (p < .05, except 4 × and 8 × sparse-coding super-resolution downsampling factors). In the reader study, DeepResolve significantly outperformed (p < .01) tricubic interpolation in all image quality categories and overall image ranking. Both readers had substantial scoring agreement (κ = 0.73). CONCLUSION DeepResolve was capable of resolving high-resolution thin-slice knee MRI from lower-resolution thicker slices, achieving superior quantitative and qualitative diagnostic performance to both conventionally used and state-of-the-art methods.
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Affiliation(s)
- Akshay S. Chaudhari
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | | | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jeff Wood
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Kathryn J Stevens
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Eric K. Gibbons
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Jin Hyung Lee
- Department of Bioengineering, Stanford University, Stanford, California, USA
- LVIS Corporation, Palo Alto, California, USA
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Brian A. Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
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Kogan F, Levine E, Chaudhari AS, Monu UD, Epperson K, Oei EHG, Gold GE, Hargreaves BA. Simultaneous bilateral-knee MR imaging. Magn Reson Med 2017; 80:529-537. [PMID: 29250856 DOI: 10.1002/mrm.27045] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 11/19/2017] [Accepted: 11/21/2017] [Indexed: 12/23/2022]
Abstract
PURPOSE To demonstrate and evaluate the scan time and quantitative accuracy of simultaneous bilateral-knee imaging compared with single-knee acquisitions. METHODS Hardware modifications and safety testing was performed to enable MR imaging with two 16-channel flexible coil arrays. Noise covariance and sensitivity-encoding g-factor maps for the dual-coil-array configuration were computed to evaluate coil cross-talk and noise amplification. Ten healthy volunteers were imaged on a 3T MRI scanner with both dual-coil-array bilateral-knee and single-coil-array single-knee configurations. Two experienced musculoskeletal radiologists compared the relative image quality between blinded image pairs acquired with each configuration. Differences in T2 relaxation time measurements between dual-coil-array and single-coil-array acquisitions were compared with the standard repeatability of single-coil-array measurements using a Bland-Altman analysis. RESULTS The mean g-factors for the dual-coil-array configuration were low for accelerations up to 6 in the right-left direction, and minimal cross-talk was observed between the two coil arrays. Image quality ratings of various joint tissues showed no difference in 89% (95% confidence interval: 85-93%) of rated image pairs, with only small differences ("slightly better" or "slightly worse") in image quality observed. The T2 relaxation time measurements between the dual-coil-array configuration and the single-coil configuration showed similar limits of agreement and concordance correlation coefficients (limits of agreement: -0.93 to 1.99 ms; CCC: 0.97 (95% confidence interval: 0.96-0.98)), to the repeatability of single-coil-array measurements (limits of agreement: -2.07 to 1.96 ms; CCC: 0.97 (95% confidence interval: 0.95-0.98)). CONCLUSION A bilateral coil-array setup can image both knees simultaneously in similar scan times as conventional unilateral knee scans, with comparable image quality and quantitative accuracy. This has the potential to improve the value of MRI knee evaluations. Magn Reson Med 80:529-537, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Evan Levine
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Akshay S Chaudhari
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Uchechukwuka D Monu
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Kevin Epperson
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Edwin H G Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, Netherlands
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Bioengineering, Stanford University, Stanford, California, USA.,Department of Orthopedic Surgery, Stanford University, Stanford, California, USA
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA.,Department of Bioengineering, Stanford University, Stanford, California, USA
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Chaudhari AS, Black MS, Eijgenraam S, Wirth W, Maschek S, Sveinsson B, Eckstein F, Oei EHG, Gold GE, Hargreaves BA. Five-minute knee MRI for simultaneous morphometry and T 2 relaxometry of cartilage and meniscus and for semiquantitative radiological assessment using double-echo in steady-state at 3T. J Magn Reson Imaging 2017; 47:1328-1341. [PMID: 29090500 DOI: 10.1002/jmri.25883] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Accepted: 10/14/2017] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Biomarkers for assessing osteoarthritis activity necessitate multiple MRI sequences with long acquisition times. PURPOSE To perform 5-minute simultaneous morphometry (thickness/volume measurements) and T2 relaxometry of both cartilage and meniscus, and semiquantitative MRI Osteoarthritis Knee Scoring (MOAKS). STUDY TYPE Prospective. SUBJECTS Fifteen healthy volunteers for morphometry and T2 measurements, and 15 patients (five each Kellgren-Lawrence grades 0/2/3) for MOAKS assessment. FIELD STRENGTH/SEQUENCE A 5-minute double-echo steady-state (DESS) sequence was evaluated for generating quantitative and semiquantitative osteoarthritis biomarkers at 3T. ASSESSMENT Flip angle simulations evaluated tissue signals and sensitivity of T2 measurements. Morphometry and T2 reproducibility was compared against morphometry-optimized and relaxometry-optimized sequences. Repeatability was assessed by scanning five volunteers twice. MOAKS reproducibility was compared to MOAKS derived from a clinical knee MRI protocol by two readers. STATISTICAL TESTS Coefficients of variation (CVs), concordance confidence intervals (CCI), and Wilcoxon signed-rank tests compared morphometry and relaxometry measurements with their reference standards. DESS MOAKS positive percent agreement (PPA), negative percentage agreement (NPA), and interreader agreement was calculated using the clinical protocol as a reference. Biomarker variations between Kellgren-Lawrence groups were evaluated using Wilcoxon rank-sum tests. RESULTS Cartilage thickness (P = 0.65), cartilage T2 (P = 0.69), and meniscus T2 (P = 0.06) did not significantly differ from their reference standard (with a 20° DESS flip angle). DESS slightly overestimated meniscus volume (P < 0.001). Accuracy and repeatability CVs were <3.3%, except the meniscus T2 accuracy (7.6%). DESS MOAKS had substantial interreader agreement and high PPA/NPA values of 87%/90%. Bone marrow lesions and menisci had slightly lower PPAs. Cartilage and meniscus T2 , and MOAKS (cartilage surface area, osteophytes, cysts, and total score) was higher in Kellgren-Lawrence groups 2 and 3 than group 0 (P < 0.05). DATA CONCLUSION The 5-minute DESS sequence permits MOAKS assessment for a majority of tissues, along with repeatable and reproducible simultaneous cartilage and meniscus T2 relaxometry and morphometry measurements. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1328-1341.
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Affiliation(s)
- Akshay S Chaudhari
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Marianne S Black
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Mechanical Engineering, Stanford University, Stanford, California, USA
| | - Susanne Eijgenraam
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, Netherlands
| | - Wolfgang Wirth
- Institute of Anatomy, Paracelsus Medical University Salzburg and Nuremberg, Salzburg, Austria.,Chondrometrics GmbH, Ainring, Germany
| | - Susanne Maschek
- Institute of Anatomy, Paracelsus Medical University Salzburg and Nuremberg, Salzburg, Austria.,Chondrometrics GmbH, Ainring, Germany
| | - Bragi Sveinsson
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Felix Eckstein
- Institute of Anatomy, Paracelsus Medical University Salzburg and Nuremberg, Salzburg, Austria.,Chondrometrics GmbH, Ainring, Germany
| | - Edwin H G Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, Netherlands
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Bioengineering, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
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Chaudhari AS, Sveinsson B, Moran CJ, McWalter EJ, Johnson EM, Zhang T, Gold GE, Hargreaves BA. Imaging and T 2 relaxometry of short-T 2 connective tissues in the knee using ultrashort echo-time double-echo steady-state (UTEDESS). Magn Reson Med 2017; 78:2136-2148. [PMID: 28074498 DOI: 10.1002/mrm.26577] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Revised: 10/26/2016] [Accepted: 11/19/2016] [Indexed: 12/15/2022]
Abstract
PURPOSE To develop a radial, double-echo steady-state (DESS) sequence with ultra-short echo-time (UTE) capabilities for T2 measurement of short-T2 tissues along with simultaneous rapid, signal-to-noise ratio (SNR)-efficient, and high-isotropic-resolution morphological knee imaging. METHODS THe 3D radial UTE readouts were incorporated into DESS, termed UTEDESS. Multiple-echo-time UTEDESS was used for performing T2 relaxometry for short-T2 tendons, ligaments, and menisci; and for Dixon water-fat imaging. In vivo T2 estimate repeatability and SNR efficiency for UTEDESS and Cartesian DESS were compared. The impact of coil combination methods on short-T2 measurements was evaluated by means of simulations. UTEDESS T2 measurements were compared with T2 measurements from Cartesian DESS, multi-echo spin-echo (MESE), and fast spin-echo (FSE). RESULTS UTEDESS produced isotropic resolution images with high SNR efficiency in all short-T2 tissues. Simulations and experiments demonstrated that sum-of-squares coil combinations overestimated short-T2 measurements. UTEDESS measurements of meniscal T2 were comparable to DESS, MESE, and FSE measurements while the tendon and ligament measurements were less biased than those from Cartesian DESS. Average UTEDESS T2 repeatability variation was under 10% in all tissues. CONCLUSION The T2 measurements of short-T2 tissues and high-resolution morphological imaging provided by UTEDESS makes it promising for studying the whole knee, both in routine clinical examinations and longitudinal studies. Magn Reson Med 78:2136-2148, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Akshay S Chaudhari
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Bragi Sveinsson
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Catherine J Moran
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Emily J McWalter
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Ethan M Johnson
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Tao Zhang
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Bioengineering, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
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Sveinsson B, Chaudhari AS, Gold GE, Hargreaves BA. A simple analytic method for estimating T2 in the knee from DESS. Magn Reson Imaging 2016; 38:63-70. [PMID: 28017730 DOI: 10.1016/j.mri.2016.12.018] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 12/19/2016] [Accepted: 12/20/2016] [Indexed: 11/16/2022]
Abstract
PURPOSE To introduce a simple analytical formula for estimating T2 from a single Double-Echo in Steady-State (DESS) scan. METHODS Extended Phase Graph (EPG) modeling was used to develop a straightforward linear approximation of the relationship between the two DESS signals, enabling accurate T2 estimation from one DESS scan. Simulations were performed to demonstrate cancellation of different echo pathways to validate this simple model. The resulting analytic formula was compared to previous methods for T2 estimation using DESS and fast spin-echo scans in agar phantoms and knee cartilage in three volunteers and three patients. The DESS approach allows 3D (256×256×44) T2-mapping with fat suppression in scan times of 3-4min. RESULTS The simulations demonstrated that the model approximates the true signal very well. If the T1 is within 20% of the assumed T1, the T2 estimation error was shown to be less than 5% for typical scans. The inherent residual error in the model was demonstrated to be small both due to signal decay and opposing signal contributions. The estimated T2 from the linear relationship agrees well with reference scans, both for the phantoms and in vivo. The method resulted in less underestimation of T2 than previous single-scan approaches, with processing times 60 times faster than using a numerical fit. CONCLUSION A simplified relationship between the two DESS signals allows for rapid 3D T2 quantification with DESS that is accurate, yet also simple. The simplicity of the method allows for immediate T2 estimation in cartilage during the MRI examination.
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Affiliation(s)
- B Sveinsson
- Department of Radiology, Stanford University, Stanford, CA, United States.
| | - A S Chaudhari
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - G E Gold
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - B A Hargreaves
- Department of Radiology, Stanford University, Stanford, CA, United States
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Peng LX, Ivetac A, Van S, Zhao G, Chaudhari AS, Yu L, Howell SB, McCammon JA, Gough DA. Characterization of a clinical polymer-drug conjugate using multiscale modeling. Biopolymers 2010; 93:936-51. [PMID: 20564048 PMCID: PMC3099131 DOI: 10.1002/bip.21474] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The molecular conformation of certain therapeutic agents has been shown to affect the ability to gain access to target cells, suggesting potential value in defining conformation of candidate molecules. This study explores how the shape and size of poly-γ-glutamyl-glutamate paclitaxel (PGG-PTX), an amphiphilic polymer-drug with potential chemotherapeutic applications, can be systematically controlled by varying hydrophobic and hydrophilic entities. Eighteen different formulations of PGG-PTX varying in three PTX loading fractions (f(PTX)) of 0.18, 0.24, and 0.37 and six spatial arrangements of PTX ('clusters', 'ends', 'even', 'middle', 'random', and 'side') were explored. Molecular dynamics (MD) simulations of all-atom (AA) models of PGG-PTX were run until a statistical equilibrium was reached at 100 ns and then continued as coarse-grained (CG) models until a statistical equilibrium was reached at an effective time of 800 ns. Circular dichroism spectroscopy was used to suggest initial modeling configurations. Results show that a PGG-PTX molecule has a strong tendency to form coil shapes, regardless of the PTX loading fraction and spatial PTX arrangement, although globular shapes exist at f(PTX) = 0.24. Also, less uniform PTX arrangements such as 'ends', 'middle', and 'side' produce coil geometries with more curvature. The prominence of coil shapes over globules suggests that PGG-PTX may confer a long circulation half-life and high propensity for accumulation to tumor endothelia. This multiscale modeling approach may be advantageous for the design of cancer therapeutic delivery systems. © 2010 Wiley Periodicals, Inc. Biopolymers 93: 936-951, 2010.
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Affiliation(s)
- Lili X. Peng
- Department of Bioengineering, University of California at San Diego, La Jolla, CA
| | - Anthony Ivetac
- Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA
| | - Sang Van
- Nitto Denko Technical Corporation, Oceanside, CA
| | - Gang Zhao
- Nitto Denko Technical Corporation, Oceanside, CA
| | - Akshay S. Chaudhari
- Department of Bioengineering, University of California at San Diego, La Jolla, CA
| | - Lei Yu
- Nitto Denko Technical Corporation, Oceanside, CA
| | - Stephen B. Howell
- Moores Cancer Center, University of California at San Diego, La Jolla, CA
| | - J. Andrew McCammon
- Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA
| | - David A. Gough
- Department of Bioengineering, University of California at San Diego, La Jolla, CA
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Viswanathan CL, Chaudhari AS. Synthesis and evaluation of uterine relaxant activity for a novel series of substituted p-hydroxyphenylethanolamines. Bioorg Med Chem 2006; 14:6581-5. [PMID: 16824765 DOI: 10.1016/j.bmc.2006.06.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2006] [Revised: 06/02/2006] [Accepted: 06/03/2006] [Indexed: 10/24/2022]
Abstract
Novel racemic 1-(4-hydroxyphenyl)-2-[3-(substituted phenoxy)-2-hydroxy-1-propyl]aminopropan-1-ol hydrochlorides (9a-h) were synthesized by condensing racemic 1-(p-hydroxyphenyl)-2-aminopropan-1-ol hydrochloride (6) with substituted aryloxymethyloxiranes (8a-h) in DMF in presence of anhydrous potassium carbonate and then reacting with dry hydrogen chloride gas. They were evaluated for uterine relaxant activity in vitro on isolated rat uterus and in vivo in pregnant rats. Their cAMP releasing potential was studied using rat uterus tissue homogenates by cAMP [3H] assay and cardiac stimulant potential was evaluated in dog. All compounds exhibited potent uterine relaxant activity in vitro and produced a significant delay in the onset of labour in pregnant rats; their cAMP releasing potential was higher than isoxsuprine hydrochloride except for 9b and 9c. Finally insignificant cardiac stimulant potential was noted for these compounds when compared to isoxsuprine hydrochloride.
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Affiliation(s)
- C L Viswanathan
- Department of Pharmaceutical Chemistry, Bombay College of Pharmacy, Kalina, Santacruz (E), Mumbai-400 098, India.
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Viswanathan CL, Kodgule MM, Chaudhari AS. Design, synthesis and evaluation of racemic 1-(4-hydroxyphenyl)-2-[3-(substituted phenoxy)-2-hydroxy-1-propyl]amino-1-propanol hydrochlorides as novel uterine relaxants. Bioorg Med Chem Lett 2005; 15:3532-5. [PMID: 15967663 DOI: 10.1016/j.bmcl.2005.05.047] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2004] [Revised: 05/15/2005] [Accepted: 05/19/2005] [Indexed: 11/27/2022]
Abstract
Novel 1-(4-hydroxyphenyl)-2-[3-(substituted phenoxy)-2-hydroxy-1-propyl]amino-1-propanol hydrochlorides were designed based on the pharmacophore for potent uterine relaxant activity and by utilizing the principles of structural hybridization. The designed molecules were synthesized as racemates by a novel route and were evaluated for uterine relaxant activity in vitro on isolated rat uterus and in vivo in pregnant rats. Their cAMP-releasing potential was studied using rat uterus tissue homogenates by the cAMP [(3)H] assay, and cardiac stimulant potential was evaluated on isolated guinea pig right atrium. All compounds exhibited potent uterine relaxant activity in vitro and produced a significant delay in the onset of labour in pregnant rats; their cAMP-releasing potential was slightly less, while their cardiac stimulant potential was insignificant as compared to isoxsuprine hydrochloride.
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Affiliation(s)
- C L Viswanathan
- Department of Pharmaceutical Chemistry, Bombay College of Pharmacy, Kalina, Santacruz (E), Mumbai 400 098, India.
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Abstract
A D-galactosamine polymer having a molar ratio of galactosamine–acetyl–phosphorus (1.0:1.0:0.35) has been isolated from the cell wall of Neisseria sicca. Its homogeneity was established by free-boundary electrophoresis, ultracentrifugal sedimentation, and gel permeation chromatography. Molecular weight estimation by reducing end group assay gave a value of [Formula: see text] 18 500. Methylation analysis indicated that the main glycosidic linkages were (1 → 4) and (1 → 6) with a minimum of 10% branch points. The glycosidic bonds were indicated as being in the α configuration by the high positive optical rotation of the polysaccharide and also from infrared spectrum evidence. Both the acetyl and phosphate contents were relatively unchanged by the Hakamori methylation procedure. Although galactosamine polymers have been found in a number of fungi, the present instance appears to be the first report of their presence in bacteria.
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