Search Results - "Desai, Arjun D."

Utility of deep learning super‐resolution in the context of osteoarthritis MRI biomarkers by Chaudhari, Akshay S., Stevens, Kathryn J., Wood, Jeff P., Chakraborty, Amit K., Gibbons, Eric K., Fang, Zhongnan, Desai, Arjun D., Lee, Jin Hyung, Gold, Garry E., Hargreaves, Brian A.

“…Background Super‐resolution is an emerging method for enhancing MRI resolution; however, its impact on image quality is still unknown. Purpose To evaluate MRI…”
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Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach by Zambrano Chaves, Juan M., Wentland, Andrew L., Desai, Arjun D., Banerjee, Imon, Kaur, Gurkiran, Correa, Ramon, Boutin, Robert D., Maron, David J., Rodriguez, Fatima, Sandhu, Alexander T., Rubin, Daniel, Chaudhari, Akshay S., Patel, Bhavik N.

“…Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD) events—the leading cause of global mortality—have known limitations…”
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The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset by Desai, Arjun D, Caliva, Francesco, Iriondo, Claudia, Mortazi, Aliasghar, Jambawalikar, Sachin, Bagci, Ulas, Perslev, Mathias, Igel, Christian, Dam, Erik B, Gaj, Sibaji, Yang, Mingrui, Li, Xiaojuan, Deniz, Cem M, Juras, Vladimir, Regatte, Ravinder, Gold, Garry E, Hargreaves, Brian A, Pedoia, Valentina, Chaudhari, Akshay S

“…To organize a multi-institute knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant…”
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Improving Data-Efficiency and Robustness of Medical Imaging Segmentation Using Inpainting-Based Self-Supervised Learning by Dominic, Jeffrey, Bhaskhar, Nandita, Desai, Arjun D, Schmidt, Andrew, Rubin, Elka, Gunel, Beliz, Gold, Garry E, Hargreaves, Brian A, Lenchik, Leon, Boutin, Robert, Chaudhari, Akshay S

“…We systematically evaluate the training methodology and efficacy of two inpainting-based pretext tasks of context prediction and context restoration for…”
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Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning by Desai, Arjun D, Ozturkler, Batu M, Sandino, Christopher M, Boutin, Robert, Willis, Marc, Vasanawala, Shreyas, Hargreaves, Brian A, Ré, Christopher, Pauly, John M, Chaudhari, Akshay S

“…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…”
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Reproducibility of Quantitative Double-Echo Steady-State T2 Mapping of Knee Cartilage by Williams, Ashley A, Asay, Jessica L, Asare, Daniella, Desai, Arjun D, Gold, Garry E, Hargreaves, Brian A, Chaudhari, Akshay S, Chu, Constance R

“…Cartilage T2 can detect joints at risk of developing osteoarthritis. The quantitative double-echo steady state (qDESS) sequence is attractive for knee…”
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Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry by Schmidt, Andrew M., Desai, Arjun D., Watkins, Lauren E., Crowder, Hollis A., Black, Marianne S., Mazzoli, Valentina, Rubin, Elka B., Lu, Quin, MacKay, James W., Boutin, Robert D., Kogan, Feliks, Gold, Garry E., Hargreaves, Brian A., Chaudhari, Akshay S.

“…Background Deep learning (DL)‐based automatic segmentation models can expedite manual segmentation yet require resource‐intensive fine‐tuning before deployment…”
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Reproducibility of Quantitative Double-Echo Steady-State T 2 Mapping of Knee Cartilage by Williams, Ashley A, Asay, Jessica L, Asare, Daniella, Desai, Arjun D, Gold, Garry E, Hargreaves, Brian A, Chaudhari, Akshay S, Chu, Constance R

“…Cartilage T can detect joints at risk of developing osteoarthritis. The quantitative double-echo steady state (qDESS) sequence is attractive for knee cartilage…”
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Effects of the Competitive Season and Off‐Season on Knee Articular Cartilage in Collegiate Basketball Players Using Quantitative MRI: A Multicenter Study by Rubin, Elka B., Mazzoli, Valentina, Black, Marianne S., Young, Katherine, Desai, Arjun D., Koff, Matthew F., Sreedhar, Ashwin, Kogan, Feliks, Safran, Marc R., Vincentini, Dominic J., Knox, Katelin A., Yamada, Tomoo, McCabe, Andrew, Majumdar, Sharmila, Potter, Hollis G., Gold, Garry E.

“…Background Injuries to the articular cartilage in the knee are common in jumping athletes, particularly high‐level basketball players. Unfortunately, these are…”
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Rapid volumetric gagCEST imaging of knee articular cartilage at 3 T: evaluation of improved dynamic range and an osteoarthritic population by Watkins, Lauren E., Rubin, Elka B., Mazzoli, Valentina, Uhlrich, Scott D., Desai, Arjun D., Black, Marianne, Ho, Gabe K., Delp, Scott L., Levenston, Marc E., Beaupré, Gary S., Gold, Garry E., Kogan, Feliks

“…Chemical exchange saturation transfer of glycosaminoglycans, gagCEST, is a quantitative MR technique that has potential for assessing cartilage proteoglycan…”
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Open-source, machine and deep learning-based automated algorithm for gestational age estimation through smartphone lens imaging by Desai, Arjun D, Peng, Chunlei, Fang, Leyuan, Mukherjee, Dibyendu, Yeung, Andrew, Jaffe, Stephanie J, Griffin, Jennifer B, Farsiu, Sina

“…Gestational age estimation at time of birth is critical for determining the degree of prematurity of the infant and for administering appropriate postnatal…”
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B1 Field inhomogeneity correction for qDESS T2 mapping: application to rapid bilateral knee imaging by Barbieri, Marco, Watkins, Lauren E., Mazzoli, Valentina, Desai, Arjun D., Rubin, Elka, Schmidt, Andrew, Gold, Garry Evan, Hargreaves, Brian Andrew, Chaudhari, Akshay Sanjay, Kogan, Feliks

“…Purpose T 2 mapping is a powerful tool for studying osteoarthritis (OA) changes and bilateral imaging may be useful in investigating the role of between-knee…”
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B_1$$ Field inhomogeneity correction for qDESS $$T_2$$ mapping: application to rapid bilateral knee imaging by Barbieri, Marco, Watkins, Lauren E., Mazzoli, Valentina, Desai, Arjun D., Rubin, Elka, Schmidt, Andrew, Gold, Garry Evan, Hargreaves, Brian Andrew, Chaudhari, Akshay Sanjay, Kogan, Feliks

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Rapid Volumetric gagCEST Imaging of Knee Articular Cartilage at 3T: Evaluation of Improved Dynamic Range and an Osteoarthritic Population by Watkins, Lauren E, Rubin, Elka B, Mazzoli, Valentina, Uhlrich, Scott D, Desai, Arjun D, Black, Marianne, Ho, Gabe K, Delp, Scott L, Levenston, Marc E, Beaupré, Gary S, Gold, Garry E, Kogan, Feliks

“…Chemical exchange saturation transfer of glycosaminoglycans (GAG), gagCEST, is a quantitative MR technique that has potential for assessing cartilage…”
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[Formula: see text] Field inhomogeneity correction for qDESS [Formula: see text] mapping: application to rapid bilateral knee imaging by Barbieri, Marco, Watkins, Lauren E, Mazzoli, Valentina, Desai, Arjun D, Rubin, Elka, Schmidt, Andrew, Gold, Garry Evan, Hargreaves, Brian Andrew, Chaudhari, Akshay Sanjay, Kogan, Feliks

“…[Formula: see text] mapping is a powerful tool for studying osteoarthritis (OA) changes and bilateral imaging may be useful in investigating the role of…”
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Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction by Gunel, Beliz, Sahiner, Arda, Desai, Arjun D, Chaudhari, Akshay S, Vasanawala, Shreyas, Pilanci, Mert, Pauly, John

“…Unrolled neural networks have enabled state-of-the-art reconstruction performance and fast inference times for the accelerated magnetic resonance imaging (MRI)…”
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Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning by Dominic, Jeffrey, Bhaskhar, Nandita, Desai, Arjun D, Schmidt, Andrew, Rubin, Elka, Gunel, Beliz, Gold, Garry E, Hargreaves, Brian A, Lenchik, Leon, Boutin, Robert, Chaudhari, Akshay S

“…Although supervised learning has enabled high performance for image segmentation, it requires a large amount of labeled training data, which can be difficult…”
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GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction by Ozturkler, Batu, Sahiner, Arda, Ergen, Tolga, Desai, Arjun D, Sandino, Christopher M, Vasanawala, Shreyas, Pauly, John M, Mardani, Morteza, Pilanci, Mert

“…Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by…”
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VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction by Desai, Arjun D, Gunel, Beliz, Ozturkler, Batu M, Beg, Harris, Vasanawala, Shreyas, Hargreaves, Brian A, Ré, Christopher, Pauly, John M, Chaudhari, Akshay S

“…Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance…”
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Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning by Desai, Arjun D, Ozturkler, Batu M, Sandino, Christopher M, Boutin, Robert, Willis, Marc, Vasanawala, Shreyas, Hargreaves, Brian A, Ré, Christopher M, Pauly, John M, Chaudhari, Akshay S

“…Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of…”
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