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. 2010 Jun 21:2010:932-935.
doi: 10.1109/ISBI.2010.5490140.

MR CONTRAST SYNTHESIS FOR LESION SEGMENTATION

Snehashis Roy  1 , Aaron Carass, Navid Shiee, Dzung L Pham, Jerry L Prince
Affiliations

MR CONTRAST SYNTHESIS FOR LESION SEGMENTATION

Snehashis Roy et al. Proc IEEE Int Symp Biomed Imaging. .

Abstract

The magnetic resonance contrast of a neuroimaging data set has strong impact on the utility of the data in image analysis tasks, such as registration and segmentation. Lengthy acquisition times often prevent routine acquisition of multiple MR contrast images, and opportunities for detailed analysis using these data would seem to be irrevocably lost. This paper describes an example based approach which uses patch matching from a multiple contrast atlas with the intended goal of generating an alternate MR contrast image, thus effectively simulating alternative pulse sequences from one another. In this paper, we deal specifically with Fluid Attenuated Inversion Recovery (FLAIR) sequence generation from T1 and T2 pulse sequences. The applicability of this synthetic FLAIR for estimating white matter lesions segmentation is demonstrated.

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Figures

Fig. 1
Fig. 1
(a) a T1 weighted spoiled gradient recalled (SPGR) image, (b) the T2 and (c) FLAIR acquisitions of the same subject.
Fig. 2
Fig. 2
From the subject we take the ith patch pair {fT1(i), fT2(i)} and identify the best possible matching pairs {gT1(j), gT2(j); j ∈ Ω}. The corresponding FLAIR patches {gFL(j); j ∈ Ω} are recombined using a non-local means approach to generate the synthetic FLAIR patch f̂FL(i). The merging of all such patches generate the synthetic FLAIR f̂FL.
Fig. 3
Fig. 3
Synthetic FLAIR generation from real data for a subject with no lesions. (a) T1 atlas (gT1), (b) T2 atlas (gT2), (c) FLAIR atlas (gFL), (d) subject T1 (fT1), (e) subject T2 (fT2), (f) subject true FLAIR (fFL), (h) Synthetic FLAIR (f̂FL) generated from the subjects T1 and T2. (g) shows the Universal Image Quality Index (UQI) and the Visual Information Fidelity (VIF) between the true and synthetic FLAIRs for each slice of the image volume. The maximum possible score for each measure is 1.
Fig. 4
Fig. 4
(a) Subject T1, (b) true FLAIR, (c) segmented lesions using T1 + true FLAIR, (d) T1, (e) T2, (f) segmented lesions using T1 + T2, (g) Segmentation of T1 which is the spatial prior, (h) Synthetic FLAIR, (i) segmented lesions using the T1 + synthetic FLAIR. The Dice coefficient between (c) and (f) is 0.49, while it is 0.75 between (c) and (i).
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