Diffusion weighted imaging (DWI) is a promising magnetic resonanceimaging (MRI) modality with wide applications in diagnosisof different types of diseases such as prostate cancer. DWI providesa large amount of imaging data which often makes it difficultto interpret accurately, mainly due to the fact that much of informationin diffusion imaging cannot be deciphered by human expertsalone. Computational diffusion MRI (CD-MRI) aims to leveragecomputational means to generate imagery from diffusion signalswhich are easier to interpret by human experts. Recently, anew CD-MRI modality called correlated diffusion imaging (CDI) hasbeen proposed which takes advantage of the joint correlation of diffusionsignal attenuation across multiple gradient pulse strengthsand timings to improve the separability of cancerous and healthytissues. In this paper, we propose a new CD-MRI modality calledSparse CDI (sCDI) where an optimally sparse subset of diffusionsignals contributes to the formation of the final diffusion signal leadingto further separation of cancerous and healthy tissue in prostategland compared to CDI and conventional DWI.
Sparse Correlated Diffusion Imaging: A New Computational Diffusion MRI Modality for Prostate Cancer Detection
