DDM 2 : SELF-SUPERVISED DIFFUSION MRI DENOIS-ING WITH GENERATIVE DIFFUSION MODELS

Abstract

Magnetic resonance imaging (MRI) is a common and life-saving medical imaging technique. However, acquiring high signal-to-noise ratio MRI scans requires long scan times, resulting in increased costs and patient discomfort, and decreased throughput. Thus, there is great interest in denoising MRI scans, especially for the subtype of diffusion MRI scans that are severely SNR-limited. While most prior MRI denoising methods are supervised in nature, acquiring supervised training datasets for the multitude of anatomies, MRI scanners, and scan parameters proves impractical. Here, we propose Denoising Diffusion Models for Denoising Diffusion MRI (DDM 2 ), a self-supervised denoising method for MRI denoising using diffusion denoising generative models. Our three-stage framework integrates statisticbased denoising theory into diffusion models and performs denoising through conditional generation. During inference, we represent input noisy measurements as a sample from an intermediate posterior distribution within the diffusion Markov chain. We conduct experiments on 4 real-world in-vivo diffusion MRI datasets and show that our DDM 2 demonstrates superior denoising performances ascertained with clinically-relevant visual qualitative and quantitative metrics. Our source codes are available at:

1. INTRODUCTION

Magnetic resonance imaging (MRI) is a non-invasive clinical imaging modality that can provide life-saving diagnostic information. Diffusion MRI is a subtype of MRI commonly used in oncologic and neurologic disorders (Bihan, 2003; Bihan et al., 2006) , which can quantitatively assess microstructural anatomical details. However, diffusion MRI scans suffer from severe signal to noise ratio (SNR) deficits, hindering diagnostic and quantitative accuracy. Image SNR can be improved either by lowering image resolution, which further reduces diagnostic utility, or by increasing the total scan time, which already can require 10+ minutes of time in the MRI scanner. Thus, there is large interest in decreasing diffusion MRI scan times to improve patient throughout in hospitals and the patient experience. While image SNR is governed by the underlying MRI physics, applying postprocessing denoising techniques to fast and low-SNR MRI acquisitions can improve overall image SNR. Developing such methods to improve SNR of diffusion MRI scans is an unsolved problem that may improve the efficacy of the millions of such scans performed in routine clinical practice annually. Supervised machine learning techniques have previously been proposed for MRI denoising, however, such methods are limited by their clinical feasibility. It is clinically impractical to acquire paired highand low-SNR diffusion MRI scans across various anatomies (e.g., brain, abdomen, etc), diffusion weighting factors (and resultant SNR variations), MRI field strength and vendors, and clinical usecases. Such large distributional shifts across heterogeneous use-cases leads to fundamental drop in model performance (Darestani et al., 2021) . The diversity of data and the need for effective denoising methods motivates the use of unsupervised denoising techniques for diffusion MRI. To address these challenges, the major contributions of our work are three-fold: (i) We propose DDM 2 for unsupervised denoising of diffusion MRI scans using diffusion denoising models. Our threestage self-supervised approach couples statistical self-denoising techniques into the diffusion models (shown in Figure 1 Left); (ii) DDM 2 allows representing noisy inputs as samples from an intermediate state in the diffusion Markov chain to generate fine-grained denoised images without requiring ground truth references; (iii) We evaluate our method on four real-world diffusion MRI datasets

availability

https://github.com/StanfordMIMI

