Assessment of Variability in Liver Tumor Contrast in MRI for Radiation Therapy
Purpose: To investigate the inter-patient and inter-sequence variation in liver tumor contrast in MRI and the feasibility of improving the liver tumor contrast by using an in-house developed multi-source adaptive fusion method for use in MRI-based treatment planning.
Methods and Materials: MR-images from 29 patients were retrospectively reviewed in this study. The imaging sequences acquired by a 1.5T GE and 3T Siemens MR scanner consisted of T1-w, T1-w, Post C, T2-w, T2/T1-w, and DWI. Using an in-house developed MSAF algorithm, we created fused images for a smaller subset of 12 patients using T1-w, T2-w, T2/T1-w, and DWI 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 (Itum-IBG)/SDBG, where Itum and IBG are the mean values of the tumor and the BG respectively, and SDBG is the standard deviation of the BG. To assess variation in tumor to tissue CNR for each image type an inter-patient coefficient-of-variation(CV) was calculated across all patients, as well as an inter-sequence CV. CV was calculated using the following: CV = σ/µ, where σ and µ are the standard deviation, and mean CNR for a single image sequence, respectively. These values were calculated for the original sequence types and fusion-images and compared.
Results: Our results from the 29 patients showed large inter-patient and inter-sequence variability, ranging from 86.90% to 67.03%, and 134.67% to 1.22% respectively. The T1-w, T1-w, Post Contrast, T2-w, T2/T1-w, DWI, and CT CV was 85.25%, 84.11%, 67.03%, 81.78%, 86.90%, and 74.30% respectively. Tumor CNR ranged from 0.95 to 4.47 with mean (± SD) CNR for T1-w, T1-w, Post Contrast, T2-w, T2/T1-w, DWI, and CT of 1.90 (±1.60), 2.12 (±1.42), 3.59 (±2.94), 1.95 (±1.70), 4.47 (±3.32), and 0.95 (±0.81) respectively. In the smaller subset of 12 patients, our results show a reduction in the inter-patient CV when using the in-house algorithm to obtain a tumor enhanced – fusion image. The inter-patient CV for T1-w, T2-w, T2/T1-w, DWI, Balanced Anatomy – Fusion, and Tumor Enhanced – Fusion was 94.16%, 112.73%, 105.69%, 124.23%, and 67.94% respectively. Tumor-CNR was significantly enhanced for each patient when using the in-house algorithm to obtain a tumor-enhanced image. The mean (± SD) CNR for T1-w, T2-w, T2/T1-w, Balanced Anatomy – Fusion, and Tumor Enhanced – Fusion was 2.11 (±1.99), 3.89 (±4.38), 3.71 (±3.92), 5.73 (±7.12), and 17.01 (±11.55) respectively.
Conclusion: The in-house multi-source adaptive fusion algorithm has the potential to increase the liver tumor contrast, as well as, improve the consistency for use in MRI based radiation therapy treatment planning.
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Rights for Collection: Masters Theses