2015 JRNL PP Mohammad Javad Shafiee - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
A promising, recently explored, alternative to
ultra-high -value diffusion weighted imaging (UHB-DWI) is apparent
ultra-high -value diffusion-weighted image reconstruction
(AUHB-DWR), where a computational model is used to assist in
the reconstruction of apparent DW images at ultra-high -values.
Firstly, we present a novel approach to AUHB-DWR that aims
to improve image quality. We formulate the reconstruction of an
apparent DW image as a hidden conditional random field (HCRF)
in which tissue model diffusion parameters act as hidden states in
this random field. The second contribution of this paper is a new
generation of fully connected conditional random fields, called the
hidden stochastically fully connected conditional random fields
(HSFCRF) that allows for efficient inference with significantly
reduced computational complexity via stochastic clique structures.
The proposed AUHB-DWR algorithms, HCRF and HSFCRF,
are evaluated quantitatively in nine different patient cases using
Fisher's criteria, probability of error, and coefficient of variation
metrics to validate its effectiveness for the purpose of improving
intensity delineation between expert identified suspected cancerous
and healthy tissue within the prostate gland. The proposed
methods are also examined using a prostate phantom, where the
apparent ultra-high -value DW images reconstructed using the
tested AUHB-DWR methods are compared with real captured
UHB-DWI. The results illustrate that the proposed AUHB-DWR
methods has improved reconstruction quality and improved intensity
delineation compared with existing AUHB-DWR approaches.