Christopher Sandino
I am a Ph.D. student at Stanford University where I work on computational magnetic resonance imaging and artificial intelligence. I am advised by Shreyas Vasanawala and funded by the NSF Graduate Research Fellowship.
I did my undergrad at University of Southern California where I worked with Krishna Nayak on cardiac MRI for a few years. I've also interned at the National Institutes of Health and GE Healthcare. This summer I will be working at Apple AI Research 
  
  
  
  
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News
- January 17, 2020: Our tutorial on deep learning-based MR image reconstruction was published in IEEE Signal Processing Magazine! See paper and code.
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Research
Broadly, I'm interested in developing signal processing and artificial intelligence approaches to solve problems in computational sensing. Currently, I focus on upstream AI methods to improve pediatric magnetic resonance imaging with respect to speed and diagnostic accuracy. Representative papers are highlighted.
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Accelerating dynamic MRI using a physics-driven deep learning reconstruction
Christopher Sandino,
Peng Lai,
Shreyas Vasanawala,
Joseph Cheng
Presented at ISMRM 2019 (Magna Cum Laude). Submitted to MRM, 2019.
Designed a convolutional neural network architecture to reconstruct dynamic MR images from highly undersampled raw data by jointly leveraging physics-based signal models and deep 3D priors. Using this technique, standard MRI scans which once took 5-6 breath-holds now take a single breath-hold allowing for faster and higher accuracy assessment of heart function.
    
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High-dimensional blood flow imaging with motion-robust acquisition
Christopher Sandino,
Frank Ong,
Joseph Cheng,
Marc Alley,
Shreyas Vasanawala
Presented at ISMRM 2018 (Magna Cum Laude).
Developed a motion-robust MRI acquisition scheme to enable measurement of velocity vector fields as a function of 3D space and time. We leverage low-rank compressed representations and a stochastic algorithm to reconstruct 100GB datasets acquired from each scan. Our technique has enabled fast, free-breathing, and comprehensive abdominal MRI exams for pediatric patients in as little as 5 minutes.
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Cardiac tissue characterization using magnetic tissue parameter mapping
Christopher Sandino,
Peter Kellman,
Andrew Arai,
Michael Hansen,
Hui Xue
Presented at ISMRM 2015. Published in JCMR, 2015.
Developed a more robust method for estimating T2*, a magnetic tissue parameter which can be used to identify iron overload in the heart. We also formulated a method to derive "confidence" maps using noise statistics measured with a short and simple calibration scan. This technique is implemented in medical image reconstruction framework, Gadgetron, and is being used at over 30 clinical sites worldwide.
    
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