Dictionary learning compressed sensing reconstruction: pilot validation of accelerated echo planar J-resolved spectroscopic imaging in prostate cancer
Abstract
This study aimed at developing dictionary learning (DL) based compressed sensing (CS) reconstruction for randomly undersampled five-dimensional (5D) MR Spectroscopic Imaging (3D spatial + 2D spectral) data acquired in prostate cancer patients and healthy controls, and test its feasibility at 8x and 12x undersampling factors.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 23, 2022
- Source ID
- 10.1007/s10334-022-01029-z
Entities
People
- Ajin Joy
- Andres Saucedo
- Ely Felker
- M Albert Thomas
- Manoj K. Sarma
- Neil R Wilson
- Rajakumar Nagarajan
- Robert E. Reiter
- Steven S. Raman
- Zohaib Iqbal
Organizations
- National Cancer Institute
- National Heart, Lung, and Blood Institute
- National Institute of Mental Health