Assessment of splanchnic blood flow using magnetic resonance imaging

BACKGROUND AND AIMS: The splanchnic circulation has an important function in the body under both physiological and pathophysiological conditions. Despite its importance, no reliable noninvasive procedures for estimating splanchnic circulation have been established. The aim of this study was to evaluate MRI as a tool for assessing intra-abdominal blood flows of the aorta, portal vein (VPO) and the major intestinal and hepatic vessels. METHODS: In nine healthy volunteers, the proximal aorta (AOP) and distal abdominal aorta (AOD), superior mesenteric artery (SAM), celiac trunk (CTR), hepatic arteries (common and proper hepatic arteries, AHC and AHP, respectively), and VPO were localized on contrast-enhanced magnetic resonance angiography images. Volumetric flow was measured using a two-dimensional cine echocardiogram-gated phase contrast technique. Measurements were taken before and 30 min after continuous intravenous infusion of somatostatin (250 microg/h) and were independently evaluated by two investigators. RESULTS: Blood flow measured by MRI in the VPO, SAM, AOP, AHP, and CTR significantly decreased after drug infusion. Flows in the AOD and AHC showed a tendency to decrease (P>0.05). Interrater agreement on flows in MRI was very good for large vessels (VPO, AOP, and AOD), with a concordance correlation coefficient of 0.94, as well as for smaller vessels such as the CTR, AHC, AHP, and SAM (concordance correlation coefficient =0.78). CONCLUSION: Somatostatin-induced blood flow changes in the splanchnic region were reliably detected by MRI. MRI may be useful for the noninvasive assessment of blood flow changes in the splanchnic region.

Ruler Based Automatic C-Arm Image Stitching Without Overlapping Constraint

In this paper, we propose a new method for stitching multiple fluoroscopic images taken by a C-arm instrument. We employ an X-ray radiolucent ruler with numbered graduations while acquiring the images, and the image stitching is based on detecting and matching ruler parts in the images to the corresponding parts of a virtual ruler. To achieve this goal, we first detect the regular spaced graduations on the ruler and the numbers. After graduation labeling, for each image, we have the location and the associated number for every graduation on the ruler. Then, we initialize the panoramic X-ray image with the virtual ruler, and we “paste” each image by aligning the detected ruler part on the original image, to the corresponding part of the virtual ruler on the panoramic image. Our method is based on ruler matching but without the requirement of matching similar feature points in pairwise images, and thus, we do not necessarily require overlap between the images. We tested our method on eight different datasets of X-ray images, including long bones and a complete spine. Qualitative and quantitative experiments show that our method achieves good results.