Search Results - "Sandino, Christopher M"

Accelerating cardiac cine MRI using a deep learning‐based ESPIRiT reconstruction by Sandino, Christopher M., Lai, Peng, Vasanawala, Shreyas S., Cheng, Joseph Y.

“…Purpose To propose a novel combined parallel imaging and deep learning‐based reconstruction framework for robust reconstruction of highly accelerated 2D…”
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Free-breathing Accelerated Cardiac MRI Using Deep Learning: Validation in Children and Young Adults by Zucker, Evan J., Sandino, Christopher M., Kino, Aya, Lai, Peng, Vasanawala, Shreyas S.

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Reconstruction of undersampled 3D non‐Cartesian image‐based navigators for coronary MRA using an unrolled deep learning model by Malavé, Mario O., Baron, Corey A., Koundinyan, Srivathsan P., Sandino, Christopher M., Ong, Frank, Cheng, Joseph Y., Nishimura, Dwight G.

“…Purpose To rapidly reconstruct undersampled 3D non‐Cartesian image‐based navigators (iNAVs) using an unrolled deep learning (DL) model, enabling nonrigid…”
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Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices by Chaudhari, Akshay S., Sandino, Christopher M., Cole, Elizabeth K., Larson, David B., Gold, Garry E., Vasanawala, Shreyas S., Lungren, Matthew P., Hargreaves, Brian A., Langlotz, Curtis P.

“…Artificial intelligence algorithms based on principles of deep learning (DL) have made a large impact on the acquisition, reconstruction, and interpretation of…”
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Accelerated two-dimensional phase-contrast for cardiovascular MRI using deep learning-based reconstruction with complex difference estimation by Oscanoa, Julio A, Middione, Matthew J, Syed, Ali B, Sandino, Christopher M, Vasanawala, Shreyas S, Ennis, Daniel B

“…To develop and validate a deep learning-based reconstruction framework for highly accelerated two-dimensional (2D) phase contrast (PC-MRI) data with accurate…”
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Coil sketching for computationally efficient MR iterative reconstruction by Oscanoa, Julio A., Ong, Frank, Iyer, Siddharth S., Li, Zhitao, Sandino, Christopher M., Ozturkler, Batu, Ennis, Daniel B., Pilanci, Mert, Vasanawala, Shreyas S.

“…Abstract Purpose Parallel imaging and compressed sensing reconstructions of large MRI datasets often have a prohibitive computational cost that bottlenecks…”
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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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Free-breathing R 2 ∗ mapping of hepatic iron overload in children using 3D multi-echo UTE cones MRI by Kee, Youngwook, Sandino, Christopher M, Syed, Ali B, Cheng, Joseph Y, Shimakawa, Ann, Colgan, Timothy J, Hernando, Diego, Vasanawala, Shreyas S

“…To enable motion-robust, ungated, free-breathing mapping of hepatic iron overload in children with 3D multi-echo UTE cones MRI. A golden-ratio re-ordered 3D…”
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Near‐silent distortionless DWI using magnetization‐prepared RUFIS by Yuan, Jianmin, Hu, Yuxin, Menini, Anne, Sandino, Christopher M., Sandberg, Jesse, Sheth, Vipul, Moran, Catherine J., Alley, Marcus, Lustig, Michael, Hargreaves, Brian, Vasanawala, Shreyas

“…Purpose To develop a near‐silent and distortionless DWI (sd‐DWI) sequence using magnetization‐prepared rotating ultrafast imaging sequence. Methods A rotating…”
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Free‐breathing R2∗ mapping of hepatic iron overload in children using 3D multi‐echo UTE cones MRI by Kee, Youngwook, Sandino, Christopher M., Syed, Ali B., Cheng, Joseph Y., Shimakawa, Ann, Colgan, Timothy J., Hernando, Diego, Vasanawala, Shreyas S.

“…Purpose To enable motion‐robust, ungated, free‐breathing R2∗ mapping of hepatic iron overload in children with 3D multi‐echo UTE cones MRI. Methods A…”
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Diffusion‐weighted double‐echo steady‐state with a three‐dimensional cones trajectory for non‐contrast‐enhanced breast MRI by Moran, Catherine J., Cheng, Joseph Y., Sandino, Christopher M., Carl, Michael, Alley, Marcus T., Rosenberg, Jarrett, Daniel, Bruce L., Pittman, Sarah M., Rosen, Eric L., Hargreaves, Brian A.

“…The image quality limitations of echo‐planar diffusion‐weighted imaging (DWI) are an obstacle to its widespread adoption in the breast. Steady‐state DWI is an…”
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Rosette Trajectories Enable Ungated, Motion-Robust, Simultaneous Cardiac and Liver T 2 Iron Assessment by Bush, Adam M, Sandino, Christopher M, Ramachandran, Shreya, Ong, Frank, Dwork, Nicholas, Zucker, Evan J, Syed, Ali B, Pauly, John M, Alley, Marcus T, Vasanawala, Shreyas S

“…Quantitative T * MRI is the standard of care for the assessment of iron overload. However, patient motion corrupts T * estimates. To develop and evaluate a…”
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Rosette Trajectories Enable Ungated, Motion‐Robust, Simultaneous Cardiac and Liver T2 Iron Assessment by Bush, Adam M., Sandino, Christopher M., Ramachandran, Shreya, Ong, Frank, Dwork, Nicholas, Zucker, Evan J., Syed, Ali B., Pauly, John M., Alley, Marcus T., Vasanawala, Shreyas S.

“…Background Quantitative T2* MRI is the standard of care for the assessment of iron overload. However, patient motion corrupts T2* estimates. Purpose To develop…”
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Upstream Machine Learning in Radiology by Sandino, Christopher M., Cole, Elizabeth K., Alkan, Cagan, Chaudhari, Akshay S., Loening, Andreas M., Hyun, Dongwoon, Dahl, Jeremy, Imran, Abdullah-Al-Zubaer, Wang, Adam S., Vasanawala, Shreyas S.

“…Machine learning (ML) and Artificial intelligence (AI) has the potential to dramatically improve radiology practice at multiple stages of the imaging pipeline…”
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Myocardial T2 mapping: influence of noise on accuracy and precision by Sandino, Christopher M, Kellman, Peter, Arai, Andrew E, Hansen, Michael S, Xue, Hui

“…Pixel-wise, parametric T2* mapping is emerging as a means of automatic measurement of iron content in tissues. It enables quick, intuitive interpretation and…”
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Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges by Ma, Jeffrey J., Nakarmi, Ukash, Kin, Cedric Yue Sik, Sandino, Christopher M., Cheng, Joseph Y., Syed, Ali B., Wei, Peter, Pauly, John M., Vasanawala, Shreyas S.

“…Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of…”
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Free-breathing T2 mapping using respiratory motion corrected averaging by Kellman, Peter, Xue, Hui, Spottiswoode, Bruce S, Sandino, Christopher M, Hansen, Michael S, Abdel-Gadir, Amna, Treibel, Thomas A, Rosmini, Stefania, Mancini, Christine, Bandettini, W Patricia, McGill, Laura-Ann, Gatehouse, Peter, Moon, James C, Pennell, Dudley J, Arai, Andrew E

“…Pixel-wise T2* maps based on breath-held segmented image acquisition are prone to ghost artifacts in instances of poor breath-holding or cardiac arrhythmia…”
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Free‐breathing mapping of hepatic iron overload in children using 3D multi‐echo UTE cones MRI by Kee, Youngwook, Sandino, Christopher M., Syed, Ali B., Cheng, Joseph Y., Shimakawa, Ann, Colgan, Timothy J., Hernando, Diego, Vasanawala, Shreyas S.

“…Purpose To enable motion‐robust, ungated, free‐breathing mapping of hepatic iron overload in children with 3D multi‐echo UTE cones MRI. Methods A golden‐ratio…”
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Prospective Deployment of Artificial Intelligence in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices by Chaudhari, Akshay S., Sandino, Christopher M., Cole, Elizabeth K., Larson, David B., Gold, Garry E., Vasanawala, Shreyas S., Lungren, Matthew P., Hargreaves, Brian A., Langlotz, Curtis P.

“…Artificial intelligence algorithms based on principles of deep learning (DL) have made a large impact on the acquisition, reconstruction, and interpretation of…”
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MAEEG: Masked Auto-encoder for EEG Representation Learning by Chien, Hsiang-Yun Sherry, Goh, Hanlin, Sandino, Christopher M, Cheng, Joseph Y

“…Decoding information from bio-signals such as EEG, using machine learning has been a challenge due to the small data-sets and difficulty to obtain labels. We…”
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