We recently generated a high-resolution supratentorial vascular topographic atlas using diffusion-weighed MRI in a population of large artery infarcts. These MRI-based topographic maps are not easily applicable to CT scans, because the standard-reference-lines for axial image orientation (i.e., anterior-posterior commissure line versus orbito-meatal line, respectively) are ‘not parallel’ to each other. Moreover, current, widely-used CT-based vascular topographic diagrams omit demarcation of the inter-territorial border-zones. Thus, we aimed to generate a CT-specific high-resolution atlas, showing the supratentorial cerebrovascular territories and the inter-territorial border-zones in a statistically rigorous way. The diffusion-weighted MRI lesion atlas is based on 1160 patients (67.0 ± 13.3 years old, 53.7% men) with acute (50%) stenosis or occlusion of a single large cerebral artery: anterior, middle, or posterior cerebral artery. We developed a software package enabling the transformation of our MR-based atlas into a re-oriented CT space corresponding to the axial slice orientations used in clinical practice. Infarct volumes are individually mapped to the three vascular territories on the CT template-set, generating brain maps showing the voxelwise frequency of infarct by the affected parent vessel. We then mapped the three vascular territories collectively, generating a dataset of Certainty-Index (CI) maps to reflect the likelihood of a voxel being a member of a specific vascular territory. Border-zones could be defined by using either relative infarct frequencies or CI differences. The topographic vascular territory atlas, revised for CT, will allow for easier and more accurate delineation of arterial territories and borders on CT images.

Conventional digital displays show images with much less dynamic range than that of human visual perception. High-dynamic range (HDR) displays are being developed for viewing images with higher dynamic range than that of conventional low-dynamic range (LDR) images. However, to view existing LDR images on HDR displays, an inverse tone mapping operator (ITMO), which is a process to extend the dynamic range of LDR images, is required. In this paper, we propose an adaptive ITMO by effectively learning the differences between LDR and HDR images using a sequential learning process: dynamic range learning followed by color difference learning. Our proposed method enables visualization of colors similar to real-world colors better than conventional ITMOs by learning color differences based on the human visual system properties. For the objective comparison, seven different evaluation metrics optimized for HDR image evaluation were used. Our method resulted in 10%-25% improved HDR-visual difference predictor-2.2 values over those of other ITMOs. Other metrics also demonstrated the superior performance of our method. For the subjective comparison, eight human observers evaluated the estimated HDR images in terms of color appearance and overall preferences. Our method received an average 4.7 out of 5 score, whereas other ITMOs received below 3.5 scores in the evaluation of color appearance. The objective and subjective evaluations' results showed that our proposed method outperformed the conventional ITMOs in estimating the dynamic range and color appearance of ground-truth HDR images.

This paper proposes a randomized variable probability pattern in under-sampling acquisition for medical image matching which is a method that can perform the quantitative analysis of tissue parameters. For high-speed estimation of tissue parameters, random under-sampling with less than the Nyquist rate in k-space is required. This study presents an accurate parameter mapping method for under-sampled data by using various randomized probability pattern. In comparison to the fixed probability pattern, the proposed method shows improved estimation results with reduced artifacts such as ghosting effects due to the undersampling scheme.