Blake E. Zimmerman
Scientific Computing and Imaging Institute
7 Papers
21 Citations
Blake E. Zimmerman is an academic researcher from Scientific Computing and Imaging Institute. The author has contributed to research in topics: Image registration & Fiducial marker. The author has an hindex of 3, co-authored 7 publications. Previous affiliations of Blake E. Zimmerman include University of Utah.
Chat about Author
Papers
Real-Time 2D-3D Deformable Registration with Deep Learning and Application to Lung Radiotherapy Targeting
Markus D. Foote,Blake E. Zimmerman,Amit Sawant,Sarang Joshi +3 more
- 02 Jun 2019
TL;DR: In this article, a patient-specific motion subspace and a deep convolutional neural network were designed to recover anatomical positions from a single fluoroscopic projection in real-time.
21
Real-Time 2D-3D Deformable Registration with Deep Learning and Application to Lung Radiotherapy Targeting
TL;DR: A patient-specific motion subspace and a deep convolutional neural network are designed to recover anatomical positions from a single fluoroscopic projection in real-time and approximate the nonlinear inverse of a diffeomorphic deformation composed with radiographic projection.
13
Learning Multiparametric Biomarkers for Assessing MR-Guided Focused Ultrasound Treatment of Malignant Tumors
Blake E. Zimmerman,Sara L. Johnson,Henrik Odéen,Jill E. Shea,Markus D. Foote,Nicole Winkler,Sarang Joshi,Allison Payne +7 more
TL;DR: In this paper, a deep convolutional neural network was trained on non-contrast multiparametric MR images using the nonperfused volume (NPV) biomarker from follow-up MR imaging (3-5 days after MRgFUS treatment) as the accurate label of nonviable tissue.
6
•Posted Content
Real-Time Patient-Specific Lung Radiotherapy Targeting using Deep Learning.
Markus D. Foote,Blake E. Zimmerman,Amit Sawant,Sarang Joshi +3 more
- 22 Jul 2018
TL;DR: A deep convolutional neural network and subspace motion tracking is used to recover anatomical positions from a single radiograph projection in real-time to define the patient-specific deformation of the lungs from a baseline anatomic position.
5
•Posted Content
Learning Multiparametric Biomarkers for Assessing MR-Guided Focused Ultrasound Treatments Using Volume-Conserving Registration.
Blake E. Zimmerman,Blake E. Zimmerman,Sara L. Johnson,Henrik Odéen,Jill E. Shea,Markus D. Foote,Markus D. Foote,Nicole Winkler,Sarang Joshi,Sarang Joshi,Allison Payne +10 more
TL;DR: A novel, noncontrast, learned multiparametric MR biomarker that can be used during treatment for intratreatment assessment, validated in a VX2 rabbit tumor model is presented.
3