Sparse Correlated Diffusion Imaging: A New Computational Diffusion MRI Modality for Prostate Cancer Detection

Abstract

Diffusion weighted imaging (DWI) is a promising magnetic resonance
imaging (MRI) modality with wide applications in diagnosis
of different types of diseases such as prostate cancer. DWI provides
a large amount of imaging data which often makes it difficult
to interpret accurately, mainly due to the fact that much of information
in diffusion imaging cannot be deciphered by human experts
alone. Computational diffusion MRI (CD-MRI) aims to leverage
computational means to generate imagery from diffusion signals
which are easier to interpret by human experts. Recently, a
new CD-MRI modality called correlated diffusion imaging (CDI) has
been proposed which takes advantage of the joint correlation of diffusion
signal attenuation across multiple gradient pulse strengths
and timings to improve the separability of cancerous and healthy
tissues. In this paper, we propose a new CD-MRI modality called
Sparse CDI (sCDI) where an optimally sparse subset of diffusion
signals contributes to the formation of the final diffusion signal leading
to further separation of cancerous and healthy tissue in prostate
gland compared to CDI and conventional DWI.