Peer-Reviewed Manuscripts

For a complete list of publications, please visit my Google Scholar profile.

  1. Govil, S., Crabb, B., Deng, Y., Dal Toso, L., Puyol-Antón, E., Pushparajah, K., Hegde, S., Perry, J., Omens, J., & Hsiao, A. (2023). A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot. Journal of Cardiovascular Magnetic Resonance.
  2. Gamboa, N., Crabb, B., Henson, J., Cole, K., Weaver, B., Karsy, M., & Jensen, R. (2022). High-grade glioma imaging volumes and survival: a single-institution analysis of 101 patients after resection using intraoperative MRI. Journal of Neuro-Oncology.
  3. Crabb, B., Hamrick, F., Campbell, J., Vignolles-Jeong, J., Magill, S., Prevedello, D., Carrau, R., Otto, B., Hardesty, D., & Couldwell, W. (2022). Machine Learning–Based Analysis and Prediction of Unplanned 30-Day Readmissions After Pituitary Adenoma Resection: A Multi-Institutional Retrospective Study With External Validation. Neurosurgery.
  4. Crabb, B., Hamrick, F., Richards, T., Eiswirth, P., Noo, F., Hsiao, A., & Fine, G. (2022). Deep Learning Subtraction Angiography: Improved Generalizability with Transfer Learning. Journal of Vascular and Interventional Radiology.
  5. Crabb, B., Lyons, A., Bale, M., Martin, V., Berger, B., Mann, S., West, W., Brown, A., Peacock, J., & Leung, D. (2020). Comparison of International Classification of Diseases and Related Health Problems, Tenth Revision Codes With Electronic Medical Records Among Patients With Symptoms of Coronavirus Disease 2019. JAMA Network Open.
  6. Schumacher, W., Mathews, M., Larson, S., Lemmon, C., Campbell, K., Crabb, B., Chicoine, B., Beauvais, L., & Perry, M. (2016). Organocatalysis by site-isolated N-heterocyclic carbenes doped into the UIO-67 framework. Polyhedron.



Conference Proceedings


  1. Crabb, B., Govil, S., Hegde, S., Perry, J., Young, A., Omens, J., Kim, N., Valdez-Jasso, D., & Contijoch, F. (2022). Biventricular Statistical Shape Atlas of Unloaded Reference Geometries: A Novel Method to Control for Hemodynamic Variations in End-diastolic Pressure. International Mechanical Engineering Congress and Exposition.
  2. Crabb, B. & Olson, N. (2018). SlideSeg: a Python module for the creation of annotated image repositories from whole slide images. SPIE Medical Imaging: Digital Pathology.
  3. Ward, C., Harguess, J., Crabb, B., & Parameswaran, S. (2017). Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN). SPIE Applications of Digital Image Processing XL.



Oral Presentations


  1. Crabb, B. (2024). Trials, Abstracts, and What It Takes to Publish an Award-Winning Article: Deep Learning Subtraction Angiography. Oral Presentation at the 2024 Society of Interventional Radiology annual meeting.
  2. Tandon, A., Deng, L., Nasopoulou, A., Crabb, B., Young, A., Hussain, T., Karamlou, T., Zahka, K., & Nguyen, C. (2024). Adverse Biventricular Shape and Exercise Capacity in repaired Tetralogy of Fallot. Oral Presentation at the 2024 Society of Cardiovascular Magnetic Resonance Global Conference.
  3. Crabb, B., Govil, S., Hegde, S., Perry, J., Young, A., Omens, J., Kim, N., Valdez-Jasso, D., & Contijoch, F. (2022). Biventricular Statistical Shape Atlas of Unloaded Reference Geometries: A Novel Method to Control for Hemodynamic Variations in End-diastolic Pressure. Oral Presentation at the ASME International Mechanical Engineering Congress and Exposition.
  4. Crabb, B., Chandrupatla, R., & Masutani, E. (2022). Deep Learning Synthetic Strain - Sensing Biventricular Dysfunction in Tetralogy of Fallot. Oral presentation at the 2022 Radiological Society of North America annual meeting.
  5. Crabb, B., Chandrupatla, R., & Masutani, E. (2022). Deep Learning Analysis and Unsupervised Clustering of Left Ventricular Mechanics in Tetralogy of Fallot. Oral presentation at the 2022 North American Society for Cardiovascular Imaging annual meeting.
  6. Crabb, B., McCulloch, A., & Hsiao, A. (2022). Quantitative Analysis of Cardiac MRI: From Deep Learning Synthetic Strain to Bi-Ventricular Computational Modeling. Oral presentation at the 42nd Annual Sarnoff Cardiovascular Research Foundation Scientific Meeting.
  7. Crabb, B. (2022). Machine Learning–based Analysis and Prediction of Unplanned 30-Day Readmissions after Pituitary Adenoma Resection: A Multi-Institutional Retrospective Study with External Validation”. Invited Speaker, Congress of Neurological Surgeons Journal Club Podcast.
  8. Crabb, B., Hamrick, F., & Campbell, J. (2021). Predicting Readmission Following Pituitary Adenoma Resection: A Machine Learning Approach. Oral presentation at the Weill Cornell Medicine Medical Student Neurological Surgery Research Symposium.
  9. Crabb, B., Noo, F., & Fine, G. (2020). Image Synthesis for Motion Correction in Digital Subtraction Angiography (DSA) Using a Generative Adversarial Network (GAN). Oral Presentation at the Society of Interventional Radiology’s 2020 Annual Scientific Meeting.
  10. Crabb, B., Olpin, J., & Fine, G. (2019). A Case of Budd-Chiari Syndrome. Clinical case presentation at the Imaging Elevated: Utah Symposium for Emerging Investigators.
  11. Crabb, B., Shoukry, M., Beauvais, L., & Bennett, M. (2017). Exploratory Coordination Chemistry: Metal Nitride Nanoclusters with Cobalt, Bismuth, and Titanium Species. Oral presentation at the 2017 Point Loma Nazarene University Honors Scholars Conference.



Poster Presentations


  1. Crabb, B., Noo, F., & Fine, G. (2023). Fully Automated Dynamic Frame Rate Adjustment in Digital Subtraction Angiography. Poster presented at the 2023 Radiological Society of North America Annual Scientific Meeting, Chicago, IL.
  2. Crabb, B., Chandrupatla, R., Masutani, E., You, S., Govil, S., Atkins, M., Lorenzatti, D., Hahn, L., Hegde, S., McCulloch, A., Raimondi, F., & Hsiao, A. (2022). Deep Learning Left Ventricular Mechanical Analysis - Sensing Bi-Ventricular Dysfunction in Tetralogy of Fallot. Poster presented at the Rady Children's 11th Annual Pediatric Research Symposium, San Diego, CA.
  3. Crabb, B., Mills, M., & Williams, L. (2022). Learning from Machine Learning: Evaluating How Contextual Interference Impacts the Learning of a Radiology Task in Machines and Radiologists.. Poster presented at the Imaging Elevated: Utah Symposium for Emerging Investigators, Salt Lake City, UT.
  4. Crabb, B., Govil, S., Young, A., Hegde, S., Perry, J., Omens, J., Hsiao, A., & McCulloch, A. (2021). Towards Fully Automated Cardiac Statistical Shape Modeling: A Deep-Learning Based MRI View and Phase Selection Tool. Poster presented at the 2021 Cardiac Physiome Workshop, Auckland, NZL.
  5. Crabb, B., Hammrick, F., Noo, F., & Fine, G. (2021). Motion Correction in Digital Subtraction Angiography using Generative Adversarial Networks: An Implementation and Evaluation of the Gradient-Consistency Loss Function. Poster presented at the 2021 Radiological Society of North America Annual Scientific Meeting, Chicago, IL.
  6. Crabb, B., Noo, F., & Fine, G. (2019). Motion Correction in Digital Subtraction Angiography Using Machine Learning. Poster presented at the 2019 Radiological Society of North America Annual Scientific Meeting, Chicago, IL.
  7. Crabb, B. & Ward, C. (2018). Improving Semantic Segmentation Results using a CRF-RNN. Poster presented at the Naval Applications of Machine Learning Workshop, San Diego, CA.
  8. Crabb, B. & Olson, N. (2018). SlideSeg: A Python module for the creation of annotated image repositories from whole slide images. Poster presentated at the 2018 Society of Photo-Optical Instrumentation Engineers Medical Imaging Conference, Houston, TX.
  9. Crabb, B., Campbell, K., & Beauvais, L. (2017). Preparation of metal-organic frameworks containing N-heterocylic carbene catalysts. Poster presented at the 253rd ACS National Meeting and Exposition, San Francisco, CA.