This is the official repository of "Progressive Knowledge Distillation for Automatic Perfusion Parameter Maps Generation from Low Temporal Resolution CT Perfusion Images," presented at MICCAI 2024.

Progressive Knowledge Distillation for Automatic Perfusion Parameter Maps Generation from Low Temporal Resolution CT Perfusion Images
Moo Hyun (Kyle) Son¹, Juyoung (Justin) Bae¹, Elizabeth Tong², Hao Chen^1
1The Hong Kong University of Science and Technology (HKUST)  ²Stanford University

Abstract. Perfusion Parameter Maps (PPMs), generated from Computer 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.

Paper · Code · Poster.

To get started with this project, clone this repository to your local machine using the following command:

git clone https://github.com/mhson-kyle/progressive-kd.git
cd progressive-kd

Before Training the model, make sure you have the following requirements installed:

pip install -r requirements.txt

1. Prepare your dataset in the required format
2. Adjust the configuration files to suit your training needs
3. Run the following command to train the model:

CUDA_VISIBLE_DEVICES=0 python train.py --config path/to/config.yaml

To evaluate the model, run the following command:

CUDA_VISIBLE_DEVICES=0 python test.py --experiment_dir path/to/experiment_dir --ckpt best.h5 --stride_xy 16 --stride_z 4

Please refer to the paper for more details on the datasets and preprocessing steps. Will be updated soon.

Will be updated soon.

Below shows the predicted perfusion parameter using the Progressive Knowledge Distillation.

If you find this repository useful, please consider citing:

@inproceedings{Son2024Progressive,
  title={Progressive Knowledge Distillation for Automatic Perfusion Parameter Maps Generation from Low Temporal Resolution CT Perfusion Images},
  author={Son, Moo Hyun and Bae, Juyoung and Tong, Elizabeth and Chen, Hao},
  booktitle={Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2024},
  pages={611--621},
  year={2024},
  publisher={Springer}
}