Temporal and volumetric denoising via quantile sparse image prior

TL;DR

This paper presents a novel regularization technique called QuaSI prior for denoising volumetric medical images, effectively handling different noise types in OCT and CT data within a variational framework.

Contribution

The introduction of the QuaSI prior as a universal, structure-preserving regularizer for medical image denoising, applicable across multiple modalities and data dimensions.

Findings

Effective denoising of OCT and CT images demonstrated

Outperforms existing methods in preserving structures and reducing noise

Efficient optimization via ADMM and linearized quantile filter

Abstract

This paper introduces an universal and structure-preserving regularization term, called quantile sparse image (QuaSI) prior. The prior is suitable for denoising images from various medical imaging modalities. We demonstrate its effectiveness on volumetric optical coherence tomography (OCT) and computed tomography (CT) data, which show different noise and image characteristics. OCT offers high-resolution scans of the human retina but is inherently impaired by speckle noise. CT on the other hand has a lower resolution and shows high-frequency noise. For the purpose of denoising, we propose a variational framework based on the QuaSI prior and a Huber data fidelity model that can handle 3-D and 3-D+t data. Efficient optimization is facilitated through the use of an alternating direction method of multipliers (ADMM) scheme and the linearization of the quantile filter. Experiments on multiple datasets emphasize the excellent performance of the proposed method.