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Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction

2024-06-06 17:58:00
Chen-Yu Yen, Raghav Singhal, Umang Sharma, Rajesh Ranganath, Sumit Chopra, Lerrel Pinto


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。



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