Progressive Knowledge Distillation for Automatic Perfusion Parameter Maps Generation from Low Temporal Resolution CT Perfusion Images

Moo Hyun Son, Juyoung Bae, Elizabeth Tong, Hao Chen

Abstract: Perfusion Parameter Maps (PPMs), generated from Computed Tomography Perfusion (CTP) scans, deliver detailed measurements of cerebral blood flow and volume, crucial for the early identification and strategic treatment of cerebrovascular diseases. However, the acquisition of PPMs involves significant challenges. Firstly, the accuracy of these maps heavily relies on the manual selection of Arterial Input Function (AIF) information. Secondly, patients are subjected to considerable radiation exposure during the scanning process. In response, previous researches have attempted to automate AIF selection and reduce radiation exposure of CTP by lowering temporal resolution, utilizing deep learning to predict PPMs from automated AIF selection and temporal resolutions as low as \(\frac{1}{3}\). However, the effectiveness of these approaches remains marginally significant. In this paper, we push the limits and propose a novel framework, Progressive Knowledge Distillation (PKD), to generate accurate PPMs from \(\frac{1}{16}\) standard temporal resolution CTP scans. PKD uses a series of teacher networks, each trained on different temporal resolutions, for knowledge distillation. Initially, the student network learns from a teacher with low temporal resolution; as the student is trained, the teacher is scaled to a higher temporal resolution. This progressive approach aims to reduce the large initial knowledge gap between the teacher and the student. Experimental results demonstrate that PKD can generate PPMs comparable to full-resolution ground truth, outperforming current deep learning frameworks. Our code is available at https://github.com/mhson-kyle/progressive-kd.

External IDs:dblp:conf/miccai/SonBTC24