Joint bi-modal image reconstruction of DOT and XCT with an extended Mumford-Shah functional

TL;DR

This paper introduces a novel joint bi-modal image reconstruction method using an extended Mumford-Shah functional based on feature similarity, improving image quality in multi-modality medical imaging such as DOT and XCT.

Contribution

It proposes a new image similarity measure derived from Tversky's feature similarity theory, integrated into an extended Mumford-Shah functional for joint reconstruction.

Findings

Improved image quality by over 10% SSIM compared to single modality reconstruction

Effective incorporation of edge-based feature similarity in joint reconstruction

Validated on three numerical phantoms with positive results

Abstract

Feature similarity measures are indispensable for joint image reconstruction in multi-modality medical imaging, which enable joint multi-modal image reconstruction (JmmIR) by communication of feature information from one modality to another, and vice versa. In this work, we establish an image similarity measure in terms of image edges from Tversky's theory of feature similarity in psychology. For joint bi-modal image reconstruction (JbmIR), it is found that this image similarity measure is an extended Mumford-Shah functional with a-priori edge information proposed previously from the perspective of regularization approach. This image similarity measure consists of Hausdorff measures of the common and different parts of image edges from both modalities. By construction, it posits that two images are more similar if they have more common edges and fewer unique/distinctive features, and will not force the nonexistent structures to be reconstructed when applied to JbmIR. With the Gamma-approximation of the JbmIR functional, an alternating minimization method is proposed for the JbmIR of diffuse optical tomography and x-ray computed tomography. The performance of the proposed method is evaluated by three numerical phantoms. It is found that the proposed method improves the reconstructed image quality by more than 10% compared to single modality image reconstruction (SmIR) in terms of the structural similarity index measure (SSIM)