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Evaluation of a Multi-Source Adaptive Fusion Algorithm for Improving MR Tumor Contrast in Liver Cancer Patients

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B Moore

B Moore*, L Zhang , F Yin , B Czito , M Palta , J Cai , Duke University Medical Center, Durham, NC


TU-C1-GePD-J(B)-5 (Tuesday, August 1, 2017) 9:30 AM - 10:00 AM Room: Joint Imaging-Therapy ePoster Lounge - B

Purpose: To evaluate the efficacy of an in-house developed multi-source adaptive fusion(MSAF) method for enhancing and improving the consistency of liver tumor contrast for the purpose of MRI-based treatment planning.

Methods: MR images from 5 patients were retrospectively reviewed in this study. The imaging sequences acquired by a 3T Siemens MR scanner consisted of T1-w, T2-w, T2/T1-w, and DWI. Using an in-house developed MSAF algorithm, we created fused images using these as inputs. Two fusion-images were obtained for each patient by implementing either an input-driven or output-driven fusion optimization method. Once a fusion-image was obtained an analysis was performed on each original image, and the fusion-image for each patient to calculate the tumor-to-tissue contrast-to-noise ratio(CNR) by contouring the tumor and a liver background-region(BG) in a homogeneous region of the liver using this in-house algorithm. CNR was calculated by (I_tum-I_BG)/SD_BG, where I_tum and I_BG are the mean values of the tumor and the BG respectively, and SD_BG is the standard deviation of the BG. To assess variation in tumor to tissue CNR for each image type a coefficient-of-variation (CV) was calculated across all patients. CV was calculated by CNR_avg/CNR_SD where CNR_avg and CNR_SD are the mean, and standard deviation of the CNR for a single image sequence, respectively. These values were calculated for the original sequence types and fusion-images and compared.

Results: Our preliminary results show a reduction in the CV when using the in-house algorithm to obtain a balanced anatomy image (T1-w:99.96%, T2-w:91.26%, T2/T1-w:119.54%, Fusion-Balanced Anatomy:79.55%). Tumor-CNR was significantly enhanced for each patient when using the in-house algorithm to obtain a tumor-enhanced image. The average CNR values for each sequence are as follows: T1-w:2.27+/-2.27, T2-w:5.73+/-6.28, T2/T1-w:1.85+/-1.55, Fusion–Balanced Anatomy:8.27+/-10.39, Fusion–Tumor Enhanced:20.06+/-16.05.

Conclusion: The in-house MSAF algorithm has the potential to increase the liver tumor contrast, as well as, improve the consistency.

Funding Support, Disclosures, and Conflict of Interest: Varian Medical Systems

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