Dr. Chaudhari is an Assistant Professor of research in the Integrative Biomedical Imaging Informatics at Stanford (IBIIS) section in the Department of Radiology. His primary research interests lie at the intersection of artificial intelligence and medical imaging. Dr. Chaudhari graduated from UCSD with a B.S. in Bioengineering in 2012. He completed his Ph.D. from Stanford University’s Department of Bioengineering in 2017, focusing on novel MRI methods for musculoskeletal imaging; supported through the National Science Foundation Graduate Research Fellowship, the Whitaker Fellowship, and the Siebel Fellowship. Dr. Chaudhari trained as a postdoctoral fellow in Radiology at Stanford University, where he combined machine learning with medical imaging acquisition and analysis. Dr. Chaudhari has won many awards, including the W.S. Moore Young Investigator Award, the Junior Fellow Award, and an Outstanding Teacher Award from the ISMRM. He has 6 additional young investigator awards for his work on advanced medical imaging acquisition and analysis techniques. Dr. Chaudhari is the Associate Director of Research and Education at the Stanford AIMI Center and is an internal advisory board member of the Precision Health and Integrated Diagnostics Center.

Academic Appointments

Honors & Awards

  • Junior Fellow, International Society for Magnetic Resonance in Medicine (2020)
  • W.S. Moore Young Investigator Award, International Society for Magnetic Resonance in Medicine (2019)
  • Best Young Investigator Award, 12th Intl. Workshop on Osteoarthritis (2019)
  • Best Emerging Investigator, Imaging Elevated Symposium (2019)
  • 2nd - 'Best Science' Presentation, ISMRM and RSNA Workshop on Value in MRI (2018)
  • 2nd – ‘Best Value’ Presentation, ISMRM and RSNA Workshop on Value in MRI (2018)
  • 2x Magna Cum Laude Merit Award, International Society for Magnetic Resonance in Medicine Annual Meeting (2018)
  • Best Healthcare Poster, NVIDIA GPU Technology Conference (2018)
  • Best Junior Investigator Abstract, 11th Intl. Workshop on Osteoarthritis (2018)
  • Best Overall Poster, NVIDIA GPU Technology Conference (2018)
  • Editor’s Monthly Pick, Magnetic Resonance in Medicine (2018)
  • Outstanding Teacher Award, International Society for Magnetic Resonance in Medicine Annual Meeting (2018)
  • Best Young Investigator Award, 10th Intl. Workshop on Osteoarthritis (2017)
  • Biodesign NEXT Fellow, Stanford Biodesign (2017)
  • Magna Cum Laude Merit Award, International Society for Magnetic Resonance in Medicine (2017)
  • Best Young Investigator Award, 9h Intl. Workshop on Osteoarthritis (2016)
  • Mobile Biodesign Innovation Award, Stanford Biodesign (2016)
  • Siebel Scholar for Engineering Leadership, Siebel Foundation (2016)
  • Award of Merit for Highly Rated Trainee Abstract, 8th Intl. Workshop on Osteoarthritis (2015)
  • Whitaker International Program Summer Fellow, Whitaker Foundation (2015)
  • Best Poster, Center for Biomedical Imaging at Stanford Symposium (2014)
  • Graduate Research Fellow, National Science Foundation (2012)
  • Best Undergraduate Research Poster, University of California San Diego Bioengineering Day (2011)
  • Chuao Chocolate Alumni Scholar, University of California San Diego (2010)
  • Most Informative Poster, Genentech Summer Intern Poster Expo (2010)
  • Outstanding UCSD Junior, Genentech Process Research and Development (2010)
  • Best Oral Presentation, Biomedical Engineering Society Lab Expo (2009)
  • Gordon Scholar, Jacobs School of Engineering (UCSD) (2009)

Research & Scholarship

Current Research and Scholarly Interests

Dr. Chaudhari is interested in the application of artificial intelligence techniques to all aspects of medical imaging, including automated schedule and reading prioritization, image reconstruction, quantitative analysis, and prediction of patient outcomes. His interests range from developing novel data-efficient machine learning algorithms to clinical deployment and validation of patient outcomes, both for medical imaging acquisition and subsequent analysis. He is also exploring combining imaging with clinical, natural language, and time series data.


2020-21 Courses

Stanford Advisees


All Publications

  • Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort. Magma (New York, N.Y.) Wirth, W., Eckstein, F., Kemnitz, J., Baumgartner, C. F., Konukoglu, E., Fuerst, D., Chaudhari, A. S. 2020


    OBJECTIVE: To evaluate the agreement, accuracy, and longitudinal reproducibility of quantitative cartilage morphometry from 2D U-Net-based automated segmentations for 3T coronal fast low angle shot (corFLASH) and sagittal double echo at steady-state (sagDESS) MRI.METHODS: 2D U-Nets were trained using manual, quality-controlled femorotibial cartilage segmentations available for 92 Osteoarthritis Initiative healthy reference cohort participants from both corFLASH and sagDESS (n=50/21/21 training/validation/test-set). Cartilage morphometry was computed from automated and manual segmentations for knees from the test-set. Agreement and accuracy were evaluated from baseline visits (dice similarity coefficient: DSC, correlation analysis, systematic offset). The longitudinal reproducibility was assessed from year-1 and -2 follow-up visits (root-mean-squared coefficient of variation, RMSCV%).RESULTS: Automated segmentations showed high agreement (DSC 0.89-0.92) and high correlations (r≥0.92) with manual ground truth for both corFLASH and sagDESS and only small systematic offsets (≤10.1%). The automated measurements showed a similar test-retest reproducibility over 1year (RMSCV% 1.0-4.5%) as manual measurements (RMSCV% 0.5-2.5%).DISCUSSION: The 2D U-Net-based automated segmentation method yielded high agreement compared with manual segmentation and also demonstrated high accuracy and longitudinal test-retest reproducibility for morphometric analysis of articular cartilage derived from it, using both corFLASH and sagDESS.

    View details for DOI 10.1007/s10334-020-00889-7

    View details for PubMedID 33025284

  • Diagnostic Accuracy of Quantitative Multi-Contrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement. AJR. American journal of roentgenology Chaudhari, A. S., Grissom, M. J., Fang, Z., Sveinsson, B., Lee, J. H., Gold, G. E., Hargreaves, B. A., Stevens, K. J. 2020


    Potential approaches for abbreviated knee MRI, including prospective acceleration with deep learning, have achieved limited clinical implementation to date.The objective of this study was to evaluate the inter-reader agreement of conventional knee MRI and a 5-minute 3D quantitative double-echo steady-state (qDESS) sequence with automatic T2 mapping and deep-learning super-resolution (DLSR) augmentation, as well as to compare the diagnostic performance of the two methods with respect to findings from arthroscopic surgery.A total of 51 patients with knee pain underwent knee MRI that included an additional 3D qDESS sequence with automatic T2 mapping. Fourier interpolation was followed by prospective DLSR to enhance qDESS slice-resolution twofold. A musculoskeletal radiologist and a radiology resident performed retrospective independent evaluations of the articular cartilage, menisci, ligaments, bones, extensor mechanism, and synovium using conventional MRI. Following a two-month washout period, the readers reviewed qDESS images alone, followed by qDESS with the automatic T2 maps. Inter-reader agreement between conventional MRI and qDESS was computed using percent agreement and Cohen's Kappa. The sensitivity and specificity of conventional MRI, qDESS alone, and qDESS+T2 were compared with arthroscopic findings using exact McNemar's tests.Conventional MRI and qDESS demonstrated 92% agreement in evaluation of articular cartilage, menisci, ligaments, bones, extensor mechanism, and synovium combined. Kappa was 0.79 (0.76-0.81) across all imaging findings. In the 43/51 patients who underwent arthroscopy, sensitivity and specificity were not significantly different (p=0.23-1.00) between conventional MRI (sensitivity: 58%-93%; specificity: 27%-87%) and qDESS alone (sensitivity: 54%-90%; specificity: 23%-91%) for cartilage, menisci, ligaments, and synovium. Sensitivity and specificity for grade 1 cartilage lesions were 33%/56% for conventional MRI, 23%/53% for qDESS (p=0.81), and 46%/39% for qDESS+T2 (p=0.80); for grade 2A lesions, 27%/53% for conventional MRI, 26%/52% for qDESS (p=0.02), and 58%/40% for qDESS+T2 (p<0.001).qDESS prospectively enhanced with deep learning had strong inter-reader agreement with conventional knee MRI and near-equivalent diagnostic performance with respect to arthroscopy. The ability of qDESS to automatically generate T2 maps increases sensitivity for cartilage abnormalities. Clinical Impact: qDESS using prospective artificial intelligence image quality enhancement may facilitate an abbreviated knee MRI protocol while generating quantitative T2 maps.

    View details for DOI 10.2214/AJR.20.24172

    View details for PubMedID 32755384

  • Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices. Journal of magnetic resonance imaging : JMRI Chaudhari, A. S., Sandino, C. M., Cole, E. K., Larson, D. B., Gold, G. E., Vasanawala, S. S., Lungren, M. P., Hargreaves, B. A., Langlotz, C. P. 2020


    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.

    View details for DOI 10.1002/jmri.27331

    View details for PubMedID 32830874

  • Rapid Knee MRI Acquisition and Analysis Techniques for Imaging Osteoarthritis. Journal of magnetic resonance imaging : JMRI Chaudhari, A. S., Kogan, F., Pedoia, V., Majumdar, S., Gold, G. E., Hargreaves, B. A. 2019


    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.

    View details for DOI 10.1002/jmri.26991

    View details for PubMedID 31755191

  • Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers. Journal of magnetic resonance imaging : JMRI Chaudhari, A. S., Stevens, K. J., Wood, J. P., Chakraborty, A. K., Gibbons, E. K., Fang, Z., Desai, A. D., Lee, J. H., Gold, G. E., Hargreaves, B. A. 2019


    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 naive interpolation and may improve image quality without biasing quantitative biomarkers.LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019.

    View details for DOI 10.1002/jmri.26872

    View details for PubMedID 31313397

  • 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 JOURNAL OF MAGNETIC RESONANCE IMAGING Chaudhari, A. S., Stevens, K. J., Sveinsson, B., Wood, J. P., Beaulieu, C. F., Oei, E. G., Rosenberg, J. K., Kogan, F., Alley, M. T., Gold, G. E., Hargreaves, B. A. 2019; 49 (7): E183–E194

    View details for DOI 10.1002/jmri.26582

    View details for Web of Science ID 000474612300018

  • Super-resolution musculoskeletal MRI using deep learning. Magnetic resonance in medicine Chaudhari, A. S., Fang, Z., Kogan, F., Wood, J., Stevens, K. J., Gibbons, E. K., Lee, J. H., Gold, G. E., Hargreaves, B. A. 2018


    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 (kappa) 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 (kappa=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.

    View details for PubMedID 29582464

  • Five-minute knee MRI for simultaneous morphometry and T2 relaxometry of cartilage and meniscus and for semiquantitative radiological assessment using double-echo in steady-state at 3T. Journal of magnetic resonance imaging : JMRI Chaudhari, A. S., Black, M. S., Eijgenraam, S., Wirth, W., Maschek, S., Sveinsson, B., Eckstein, F., Oei, E. H., Gold, G. E., Hargreaves, B. A. 2018; 47 (5): 1328–41


    Biomarkers for assessing osteoarthritis activity necessitate multiple MRI sequences with long acquisition times.To perform 5-minute simultaneous morphometry (thickness/volume measurements) and T2 relaxometry of both cartilage and meniscus, and semiquantitative MRI Osteoarthritis Knee Scoring (MOAKS).Prospective.Fifteen healthy volunteers for morphometry and T2 measurements, and 15 patients (five each Kellgren-Lawrence grades 0/2/3) for MOAKS assessment.A 5-minute double-echo steady-state (DESS) sequence was evaluated for generating quantitative and semiquantitative osteoarthritis biomarkers at 3T.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.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.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).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.2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1328-1341.

    View details for PubMedID 29090500

    View details for PubMedCentralID PMC5899635

  • connective tissues in the knee using ultrashort echo-time double-echo steady-state (UTEDESS). Magnetic resonance in medicine Chaudhari, A. S., Sveinsson, B., Moran, C. J., McWalter, E. J., Johnson, E. M., Zhang, T., Gold, G. E., Hargreaves, B. A. 2017


    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.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).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.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, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

    View details for DOI 10.1002/mrm.26577

    View details for PubMedID 28074498

  • Improving in vivo human cerebral cortical surface reconstruction using data-driven super-resolution. Cerebral cortex (New York, N.Y. : 1991) Tian, Q., Bilgic, B., Fan, Q., Ngamsombat, C., Zaretskaya, N., Fultz, N. E., Ohringer, N. A., Chaudhari, A. S., Hu, Y., Witzel, T., Setsompop, K., Polimeni, J. R., Huang, S. Y. 2020


    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 100mum at the single-subject level and below 50mum at the group level for the simulated data, and below 200mum at the single-subject level and below 100mum at the group level for the empirical data, making the accuracy of cortical surfaces derived from super-resolution sufficient for most applications.

    View details for DOI 10.1093/cercor/bhaa237

    View details for PubMedID 32887984

  • Layer-specific analysis of femorotibial cartilage t2 relaxation time based on registration of segmented double echo steady state (dess) to multi-echo-spin-echo (mese) images. Magma (New York, N.Y.) Furst, D., Wirth, W., Chaudhari, A., Eckstein, F. 2020


    OBJECTIVE: To develop and validate a 3D registration approach by which double echo steady state (DESS) MR images with cartilage thickness segmentations are used to extract the cartilage transverse relaxation time (T2) from multi-echo-spin-echo (MESE) MR images, without direct segmentations for MESE.MATERIALS AND METHODS: Manual DESS segmentations of 89 healthy reference knees (healthy) and 60 knees with early radiographic osteoarthritis (early ROA) from the Osteoarthritis Initiative were registered to corresponding MESE images that had independent direct T2 segmentations. For validation purposes, (a) regression analysis of deep and superficial cartilage T2 was performed and (b) between-group differences between healthy vs. early ROA knees were compared for registered vs. direct MESE analysis.RESULTS: Moderate to high correlations were observed for the deep (r=0.80) and the superficial T2 (r=0.81), with statistically significant between-group differences (ROA vs. healthy) of+1.4ms (p=0.002) vs.+1.3ms (p<0.001) for registered vs. direct T2 segmentation in the deep, and+1.3ms (p=0.002) vs.+2.3ms (p<0.001) in the superficial layer.DISCUSSION: This registration approach enables extracting cartilage T2 from MESE scans using DESS (cartilage thickness) segmentations, avoiding the need for direct MESE T2 segmentations.

    View details for DOI 10.1007/s10334-020-00852-6

    View details for PubMedID 32458188

  • 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 & research Eckstein, F., Chaudhari, A. S., Fuerst, D., Gaisberger, M., Kemnitz, J., Baumgartner, C. F., Konukoglu, E., Hunter, D. J., Wirth, W. 2020


    To study the longitudinal performance of fully automated cartilage segmentation in knees with radiographic osteoarthritis (ROA). We evaluate the sensitivity to change in progressor knees from the Foundation National Institutes of Health OA Biomarkers Consortium between the automated and previously reported manual expert segmentation, and whether differences in progression rates between predefined cohorts can be detected by the fully automated approach.The Osteoarthritis 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 WOMAC pain (≥9 on a 0-100 scale) after two years from baseline (n=194), whereas non-progressor knees did not have either of both (n=200). Deep learning automated algorithms trained on ROA or healthy reference (HRC) knees were used to automatically segment medial (MFTC) and lateral femorotibial cartilage on baseline and two-year follow-up MRIs. Findings were compared with previously published manual expert segmentation.The MFTC cartilage loss in the progressor cohort was -181±245µm by manual (SRM=-0.74), -144±200µm by ROA-based model (SRM=-0.72), and -69±231µm by HRC-based model segmentation (SRM=-0.30). The Cohen's D for rates of progression between progressor vs. non-progressor cohort was -0.84 (p<0.001) for manual, -0.68 (p<0.001) for automated ROA-model, and -0.14 (p=0.18) for automated HRC-model segmentation.A fully automated deep learning segmentation approach not only displayed similar sensitivity to change of longitudinal cartilage thickness loss in knee OA as manual expert segmentation, but also effectively differentiates longitudinal rates of cartilage thickness loss between cohorts with different progression profiles.

    View details for DOI 10.1002/acr.24539

    View details for PubMedID 33337584

  • Preoperative MRI of Articular Cartilage in the Knee: A Practical Approach. The journal of knee surgery Fritz, R. C., Chaudhari, A. S., Boutin, R. D. 2020; 33 (11): 1088–99


    Articular cartilage of the knee can be evaluated with high accuracy by magnetic resonance imaging (MRI) in preoperative patients with knee pain, but image quality and reporting are variable. This article discusses the normal MRI appearance of articular cartilage as well as the common MRI abnormalities of knee cartilage that may be considered for operative treatment. This article focuses on a practical approach to preoperative MRI of knee articular cartilage using routine MRI techniques. Current and future directions of knee MRI related to articular cartilage are also discussed.

    View details for DOI 10.1055/s-0040-1716719

    View details for PubMedID 33124010

  • Time-saving opportunities in knee osteoarthritis: T2 mapping and structural imaging of the knee using a single 5-min MRI scan. European radiology Eijgenraam, S. M., Chaudhari, A. S., Reijman, M., Bierma-Zeinstra, S. M., Hargreaves, B. A., Runhaar, J., Heijboer, F. W., Gold, G. E., Oei, E. H. 2019


    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±4.3ms 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±4.7ms 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 T 2values 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.

    View details for DOI 10.1007/s00330-019-06542-9

    View details for PubMedID 31844957

  • Evaluation of a Flexible 12-Channel Screen-printed Pediatric MRI Coil RADIOLOGY Winkler, S., Corea, J., Lechene, B., O'Brien, K., Bonanni, J., Chauelhari, A., Alley, M., Taviani, V., Grafendorfer, T., Robb, F., Seem, G., Pauly, J., Lustig, M., Arias, A., Vasanawala, S. 2019; 291 (1): 179–84
  • Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning MAGNETIC RESONANCE IN MEDICINE Gibbons, E. K., Hodgson, K. K., Chaudhari, A. S., Richards, L. G., Majersik, J. J., Adluru, G., DiBella, E. R. 2019; 81 (4): 2399–2411

    View details for DOI 10.1002/mrm.27568

    View details for Web of Science ID 000462092100015

  • Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain. Magma (New York, N.Y.) Kemnitz, J., Baumgartner, C. F., Eckstein, F., Chaudhari, A., Ruhdorfer, A., Wirth, W., Eder, S. K., Konukoglu, E. 2019


    Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a fully automated approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study.The segmentation method is based on U-Net architecture trained on 250 manually segmented thighs from the Osteoarthritis Initiative (OAI). The clinical evaluation is performed on a hold-out test set bilateral thighs of 48 subjects with unilateral knee pain.The segmentation time of the method is < 1 s and demonstrated high agreement with the manual method (dice similarity coeffcient: 0.96 ± 0.01). In the clinical study, the automated method shows that similar to manual segmentation (- 5.7 ± 7.9%, p < 0.001, effect size: 0.69), painful knees display significantly lower quadriceps CSAs than contralateral painless knees (- 5.6 ± 7.6%, p < 0.001, effect size: 0.73).Automated segmentation of thigh muscle and adipose tissues has high agreement with manual segmentations and can replicate the effect size seen in a clinical study on osteoarthritic pain.

    View details for DOI 10.1007/s10334-019-00816-5

    View details for PubMedID 31872357

  • 3D Ultrashort TE MRI for Evaluation of Cartilaginous Endplate of Cervical Disk In Vivo: Feasibility and Correlation With Disk Degeneration in T2-Weighted Spin-Echo Sequence AMERICAN JOURNAL OF ROENTGENOLOGY Kim, Y., Cha, J., Shin, Y., Chaudhari, A. S., Suh, Y., Yoon, S., Gold, G. E. 2018; 210 (5): 1131–40


    The purpose of this study was to evaluate the feasibility of 3D ultrashort TE (UTE) MRI in depicting the cartilaginous endplate (CEP) and its abnormalities and to investigate the association between CEP abnormalities and disk degeneration on T2-weighted spin-echo (SE) MR images in cervical disks in vivo.Eight healthy volunteers and 70 patients were examined using 3-T MRI with the 3D UTE cones trajectory technique (TR/TE, 16.1/0.032, 6.6). In the volunteer study, quantitative and qualitative assessments of CEP depiction were conducted for the 3D UTE and T2-weighted SE imaging. In the patient study, CEP abnormalities were analyzed. Intersequence agreement between the images obtained with the first-echo 3D UTE sequence and the images created by subtracting the second-echo from the first-echo 3D UTE sequence (subtracted 3D UTE) and the intraobserver and interobserver agreements for 3D UTE overall were also tested. The CEP abnormalities on the 3D UTE images correlated with the Miyazaki grading of the T2-weighted SE images.In the volunteer study, the CEP was well visualized on 3D UTE images but not on T2-weighted SE images (p < 0.001). In the patient study, for evaluation of CEP abnormalities, intersequence agreements were substantial to almost perfect, intraobserver agreements were substantial to almost perfect, and interobserver agreements were moderate to substantial (p < 0.001). All of the CEP abnormalities correlated with the Miyazaki grade with statistical significance (p < 0.001).Three-dimensional UTE MRI feasibly depicts the CEP and CEP abnormalities, which may be associated with the severity of disk degeneration on T2-weighted SE MRI.

    View details for PubMedID 29629793

  • Simultaneous bilateral-knee MR imaging. Magnetic resonance in medicine Kogan, F., Levine, E., Chaudhari, A. S., Monu, U. D., Epperson, K., Oei, E. H., Gold, G. E., Hargreaves, B. A. 2018; 80 (2): 529–37


    To demonstrate and evaluate the scan time and quantitative accuracy of simultaneous bilateral-knee imaging compared with single-knee acquisitions.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.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)).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.

    View details for PubMedID 29250856

    View details for PubMedCentralID PMC5910219

  • A simple analytic method for estimating T2 in the knee from DESS. Magnetic resonance imaging SVEINSSON, B., Chaudhari, A. S., Gold, G. E., Hargreaves, B. A. 2016; 38: 63-70


    To introduce a simple analytical formula for estimating T2 from a single Double-Echo in Steady-State (DESS) scan.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.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.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.

    View details for DOI 10.1016/j.mri.2016.12.018

    View details for PubMedID 28017730

    View details for PubMedCentralID PMC5360502

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