Limited parameter denoising for low-dose X-ray computed tomography using deep reinforcement learning

Patwari M, Gutjahr R, Raupach R, Maier A (2022)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2022

Journal

Medical Physics Wiley

Book Volume: 49

Pages Range: 4540 - 4553

Journal Issue: 7

URI: https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.15643

DOI: 10.1002/mp.15643

Open Access Link: https://doi.org/10.1002/mp.15643

Abstract

Background

The use of deep learning has successfully solved several problems in the field of medical imaging. Deep learning has been applied to the CT denoising problem successfully. However, the use of deep learning requires large amounts of data to train deep convolutional networks (CNNs). Moreover, due to large parameter count, such deep CNNs may cause unexpected results.

Purpose

In this study, we introduce a novel CT denoising framework, which has interpretable behaviour, and provides useful results with limited data.

Methods

We employ bilateral filtering in both the projection and volume domains to remove noise. To account for non-stationary noise, we tune the σ parameters of the volume for every projection view, and for every volume pixel. The tuning is carried out by two deep CNNs. Due to impracticality of labelling, the two deep CNNs are trained via a Deep-Q reinforcement learning task. The reward for the task is generated by using a custom reward function represented by a neural network. Our experiments were carried out on abdominal scans for the Mayo Clinic TCIA dataset, and the AAPM Low Dose CT Grand Challenge.

Results

Our denoising framework has excellent denoising performance increasing the PSNR from 28.53 to 28.93, and increasing the SSIM from 0.8952 to 0.9204. We outperform several state-of-the-art deep CNNs, which have several orders of magnitude higher number of parameters (p-value (PSNR) = 0.000, p-value (SSIM) = 0.000). Our method does not introduce any blurring, which is introduced by MSE loss based methods, or any deep learning artifacts, which are introduced by WGAN based models. Our ablation studies show that parameter tuning and using our reward network results in the best possible results.

Conclusions

We present a novel CT denoising framework, which focuses on interpretability to deliver good denoising performance, especially with limited data. Our method outperforms state-of-the-art deep neural networks. Future work will be focused on accelerating our method, and generalizing to different geometries and body parts.

Authors with CRIS profile

Mayank Patwari Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Siemens AG, Healthcare Sector DE Germany (DE) Siemens Healthineers DE Germany (DE)

How to cite

APA:

Patwari, M., Gutjahr, R., Raupach, R., & Maier, A. (2022). Limited parameter denoising for low-dose X-ray computed tomography using deep reinforcement learning. Medical Physics, 49(7), 4540 - 4553. https://dx.doi.org/10.1002/mp.15643

MLA:

Patwari, Mayank, et al. "Limited parameter denoising for low-dose X-ray computed tomography using deep reinforcement learning." Medical Physics 49.7 (2022): 4540 - 4553.

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