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Proceedings Paper

Pairwise mixture model for unmixing partial volume effect in multi-voxel MR spectroscopy of brain tumour patients
Author(s): Nathan Olliverre; Muhammad Asad; Guang Yang; Franklyn Howe; Gregory Slabaugh
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Paper Abstract

Multi-Voxel Magnetic Resonance Spectroscopy (MV-MRS) provides an important and insightful technique for the examination of the chemical composition of brain tissue, making it an attractive medical imaging modality for the examination of brain tumours. MRS, however, is affected by the issue of the Partial Volume Effect (PVE), where the signals of multiple tissue types can be found within a single voxel and provides an obstacle to the interpretation of the data. The PVE results from the low resolution achieved in MV-MRS images relating to the signal to noise ratio (SNR). To counteract PVE, this paper proposes a novel Pairwise Mixture Model (PMM), that extends a recently reported Signal Mixture Model (SMM) for representing the MV-MRS signal as normal, low or high grade tissue types. Inspired by Conditional Random Field (CRF) and its continuous variant the PMM incorporates the surrounding voxel neighbourhood into an optimisation problem, the solution of which provides an estimation to a set of coefficients. The values of the estimated coefficients represents the amount of each tissue type (normal, low or high) found within a voxel. These coefficients can then be visualised as a nosological rendering using a coloured grid representing the MV-MRS image overlaid on top of a structural image, such as a Magnetic Resonance Image (MRI). Experimental results show an accuracy of 92.69% in classifying patient tumours as either low or high grade compared against the histopathology for each patient. Compared to 91.96% achieved by the SMM, the proposed PMM method demonstrates the importance of incorporating spatial coherence into the estimation as well as its potential clinical usage.

Paper Details

Date Published: 3 March 2017
PDF: 13 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101341R (3 March 2017); doi: 10.1117/12.2255026
Show Author Affiliations
Nathan Olliverre, City Univ. London (United Kingdom)
Muhammad Asad, City Univ. London (United Kingdom)
Guang Yang, Royal Brompton Hospital (United Kingdom)
Imperial College London (United Kingdom)
St. George's, Univ. of London (United Kingdom)
Franklyn Howe, St. George's, Univ. of London (United Kingdom)
Gregory Slabaugh, City Univ. London (United Kingdom)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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