  Magnetic Resonance (MR) imaging, despite its proven diagnostic utility,
remains an inaccessible imaging modality for disease surveillance at the
population level. A major factor rendering MR inaccessible is lengthy scan
times. An MR scanner collects measurements associated with the underlying
anatomy in the Fourier space, also known as the k-space. Creating a
high-fidelity image requires collecting large quantities of such measurements,
increasing the scan time. Traditionally to accelerate an MR scan, image
reconstruction from under-sampled k-space data is the method of choice.
However, recent works show the feasibility of bypassing image reconstruction
and directly learning to detect disease directly from a sparser learned subset
of the k-space measurements. In this work, we propose Adaptive Sampling for MR
(ASMR), a sampling method that learns an adaptive policy to sequentially select
k-space samples to optimize for target disease detection. On 6 out of 8
pathology classification tasks spanning the Knee, Brain, and Prostate MR scans,
ASMR reaches within 2% of the performance of a fully sampled classifier while
using only 8% of the k-space, as well as outperforming prior state-of-the-art
work in k-space sampling such as EMRT, LOUPE, and DPS.
