Abstract
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.
Abstract (translated)
磁共振(MR)成像是一种经过证明的疾病诊断价值很高的成像方式,但其在人群水平上的疾病监测仍然是一种无法访问的成像方式。使MR无法访问的一个主要因素是漫长的扫描时间。一个MR扫描机在傅里叶空间中收集与 underlying anatomy 相关的测量值,也称为 k-空间。创建高保真度的图像需要收集大量这样的测量值,增加扫描时间。通常,为了加速MR扫描,从欠采样k-空间数据进行图像重构是首选方法。然而,最近的工作表明,通过跳过图像重构,直接从k-空间学习来检测疾病是可行的。 在这项工作中,我们提出了Adaptive Sampling for MR (ASMR),一种学习自适应策略以选择k-空间样本来优化目标疾病检测的采样方法。在Knee、Brain和Prostate MR扫描的8个病理分类任务中,ASMR在仅使用8%的k-空间以及8%的样本量时,可以达到与完全采样分类器相同的性能,同时还超过了诸如EMRT、LOUPE和DPS等先前的k-空间采样工作的表现。
URL
https://arxiv.org/abs/2406.04318