Ryujiro Yokoyama

Diffusion-weighted imaging of the liver: Optimizing b value for the detection and characterization of benign and malignant hepatic lesions

To determine the optimal b values required for diffusion‐weighted (DW) imaging of the liver in the detection and characterization of benign and malignant hepatic lesions.

Evaluating local hepatocellular carcinoma recurrence post-transcatheter arterial chemoembolization: Is diffusion-weighted MRI reliable as an indicator?

To evaluate the detectability of local hepatocellular carcinoma (HCC) recurrence after transcatheter arterial chemoembolization (TACE) by diffusion‐weighted MR imaging in correlation with those of gadolinium‐enhanced MR imaging.

MDCT of the Liver and Hypervascular Hepatocellular Carcinomas: Optimizing Scan Delays for Bolus-Tracking Techniques of Hepatic Arterial and Portal Venous Phases

OBJECTIVE The purpose of our study was to determine the optimal scan delays required for hepatic arterial and portal venous phase imaging and for the detection of hypervascular hepatocellular carcinomas (HCCs) in contrast-enhanced MDCT of the liver using a bolus-tracking program. SUBJECTS AND METHODS CT images (2.5-mm collimation, 5-mm thickness with no intersectional gap) detected an increase in the CT value of 50 H in the lower thoracic aorta. The images were obtained after an IV bolus injection of 2 mL/kg of nonionic iodine contrast material (300 mg I/mL) at 4 mL/s in 171 patients, who were prospectively randomized into three groups with scans commencing at 5, 20, and 45 seconds; 10, 25, and 50 seconds; and 15, 30, and 55 seconds for the first (acquisition time: 4.3 seconds), second (4.3 seconds), and third (9.1 seconds) phases, respectively, after a bolus-tracking program. CT values of the aorta, spleen, proximal portal veins, liver parenchyma, and hepatic veins were measured. Increases in CT values from unenhanced to contrast-enhanced CT were assessed using a contrast enhancement index (CEI). Spleen-to-liver and HCC-to-liver contrasts were also assessed. A qualitative degree of contrast enhancement in each organ was prospectively assessed by two independent radiologists. RESULTS At 10-15 seconds, the CEI of the aorta reached 300-336 H and that of the spleen reached 97-108 H without significant enhancement of liver parenchyma (15-25 H). The CEI of the proximal portal veins moderately increased (75-104 H) at 10-15 seconds, but no significant enhancement of hepatic veins was observed (24-51 H). The CEI of liver parenchyma peaked (59-63 H) at 45-55 seconds, when the CEIs of the aorta (117-125 H) and spleen (73-82 H) decreased. Spleen-to-liver contrast (81-84 H) was highest at 10-20 seconds and HCC-to-liver contrast (39-44 H) was highest at 10-15 seconds. The qualitative results correlated well with quantitative results. CONCLUSION The optimal scan delays for hepatic arterial and portal venous phases after the bolus-tracking program detected threshold enhancement by 50 H in the lower thoracic aorta for the detection of hypervascular HCCs were 10-15 and 45-55 seconds, respectively.

Constructing a probabilistic model for automated liver region segmentation using non-contrast x-ray torso CT images

A probabilistic model was proposed in this research for fully-automated segmentation of liver region in non-contrast X-ray torso CT images. This probabilistic model was composed of two kinds of probability that show the location and density (CT number) of the liver in CT images. The probability of the liver on the spatial location was constructed from a number of CT scans in which the liver regions were pre-segmented manually as gold standards. The probability of the liver on density was estimated specifically using a Gaussian function. The proposed probabilistic model was used for automated liver segmentation from non-contrast CT images. 132 cases of the CT scans were used for the probabilistic model construction and then this model was applied to segment liver region based on a leave-one-out method. The performances of the probabilistic model were evaluated by comparing the segmented liver with the gold standard in each CT case. The validity and usefulness of the proposed model were proved.

Optimizing scan delays of fixed duration contrast injection in contrast-enhanced biphasic multidetector-row CT for the liver and the detection of hypervascular hepatocellular carcinoma

Objective: To determine the optimal scan delay required for fixed duration contrast injection in contrast-enhanced biphasic multidetector-row CT for the liver and the detection of hypervascular hepatocellular carcinoma (HCC). Methods: CT images (2.5-mm collimation, 5-mm thickness with no intersectional gap) were obtained after an intravenous bolus injection of 2 mL/kg of nonionic iodine contrast material (300 mg I/mL) for a fixed 30-second injection in 206 patients, who were prospectively randomized into four groups, for which scans were initiated at −5, 15, and 35 seconds; at 0, 20, and 40 seconds; at 5, 25, and 45 seconds; or at 10, 30, and 50 seconds for the first (acquisition time: 4.3 seconds), second (4.3 seconds), and third (9.1 seconds) phases, respectively, after the completion of contrast injection. Mean CT values (HU) of the abdominal aorta, spleen, main portal veins, liver parenchyma, and hepatic veins were measured. Increases in CT values between pre- and post-contrast CTs (ΔHU) for the organs, and spleen-to-liver and HCC-to-liver contrast differences (δHU) were assessed. Results: Abdominal aorta reached 273-301 ΔHU at −5 to 10 seconds with a peak (301 ΔHU) at 5 seconds. Spleen peaked (115 ΔHU) at 10 seconds. Liver parenchyma were enhanced weakly (11-34 ΔHU) at −5 to 10 seconds, exceeded 50 ΔHU at 20 seconds, peaked (61 ΔHU) at 30 seconds, and then plateaued (54-58 ΔHU) at 35-50 seconds. Hepatic veins were enhanced weakly (14-37 ΔHU) at −5 to 10 seconds, and reached 67 ΔHU at 15 seconds. Spleen-to-liver (65-69 δHU) and HCC-to-liver (31-34 δHU) contrast differences were highest at 5-10 seconds. Qualitative results corresponded well with quantitative results. Conclusions: For the detection of hypervascular HCCs, the optimal scan delay after a 30-second contrast injection of the hepatic arterial phase, was found to range from 5 to 10 seconds, and that of the portal venous phase was 35 seconds or somewhat longer.

Overestimating the size of hepatic malignancy on helical CT during arterial portography : Equilibrium phase CT and pathology

PURPOSE Overestimating the size of hepatic malignancy with helical CT during arterial portography (CTAP) can be a potential pitfall in determining liver resection area. We evaluated the prevalence and extent of overestimation of hepatic malignancy on CTAP in correlation with helical equilibrium phase CT (EPCT) and pathologic findings. METHOD CTAP and EPCT in 47 histologically proven malignant hepatic tumors [33 hepatocellular carcinomas (HCCs) and 14 metastases] in 39 patients were retrospectively studied. Nineteen tumors were resected and pathologically evaluated. RESULTS The size overestimation ratios (CTAP/EPCT) ranged from 1.02 to 1.56 (mean +/- SD 1.24 +/- 0.16) in HCC and from 1.00 to 2.48 (1.34 +/- 0.42) in metastasis. In 19 surgical specimens, the overestimation ratios (CTAP/specimen) ranged from 1.05 to 1.45 (1.20 +/- 0.13) in HCC and from 1.10 to 1.38 (1.22 +/- 0.10) in metastasis. Histopathologically, flattening of parenchymal structures (100%), atrophy of hepatic cords (95%), sinusoidal congestions (95%), fibrosis and ductular proliferation (58%), and no tumor were seen in peritumoral parenchyma corresponding to perilesional perfusion defects with CTAP. CONCLUSION CTAP frequently and significantly overestimates the size of malignant hepatic tumors. This phenomenon is attributable to either benign histopathological changes in the perilesional liver parenchyma caused by parenchymal compression or portal venous obstruction by malignant liver tumors or to a siphoning effect by hypervascular neoplasms.

Nonpathological focal enhancements on spiral CT hepatic angiography

Abstract.Background: To assess the frequency and characteristics of nonpathological focal enhancements seen on spiral computed tomographic (CT) hepatic angiography (CTA). Methods: Spiral CTA and spiral CT arterial portography (CTAP) were performed in 31 patients with suspected liver malignancy prior to potential liver resection. The CTA images were retrospectively reviewed for focal enhancements by two radiologists. After determining nonpathological focal enhancements on CTA images based on the other radiographic tests, surgical exploration including intraoperative sonography, follow-up imagings, the frequency, size, site, and shape of nonpathological focal enhancements with CTA were assessed. Results: Thirty-six nonpathological focal enhancements with CTA from 4 to 23 (mean = 11.4) mm were seen in 14 (45.2%) of 31 patients. Thirteen (36.1%) of 36 nonpathological focal enhancements with CTA were not depicted with CTAP. Nonpathological focal enhancements with CTA were frequent in Couinaud segments III (27.8%), V (22.2%), and VI (19.4%). Twenty-three (63.9%) of 36 nonpathological focal enhancements were located in the edge of the liver. Shapes of 36 nonpathological focal enhancements with CTA included circular (n = 16), worm (n = 7), irregular (n = 6), dot (n = 6), and wedge (n = 1). Conclusion: In nearly half of patients, spiral CTA shows various shapes of small nonpathological focal enhancements more frequently in the liver edge.

Automatic recognition of lung lobes and fissures from multislice CT images

Computer-aided diagnosis (CAD) has been expected to help radiologists to improve the accuracy of abnormality detection and reduce the burden during CT image interpretations. In order to realize such functions, automated segmentations of the target organ regions are always required by CAD systems. This paper describes a fully automatic processing procedure, which is designed to identify inter-lobe fissures and divide lung into five lobe regions. The lung fissures are disappeared very fuzzy and indefinite in CT images, so that it is very difficult to extract fissures directly based on its CT values. We propose a method to solve this problem using the anatomy knowledge of human lung. We extract lung region firstly and then recognize the structures of lung vessels and bronchus. Based on anatomy knowledge, we classify the vessels and bronchus on a lobe-by-lobe basis and estimate the boundary of each lobe region as the initial fissure locations. Within those locations, we extract lung fissures precisely based on an edge detection method and divide lung regions into five lung lobes lastly. The performance of the proposed method was evaluated using 9 patient cases of high-resolution multi-slice chest CT images; the improvement has been confirmed with the reliable recognition results.

Automatic localization of solid organs on 3D CT images by a collaborative majority voting decision based on ensemble learning

PURPOSE Organ segmentation is an essential step in the development of computer-aided diagnosis/surgery systems based on computed tomography (CT) images. A universal segmentation approach/scheme that can adapt to different organ segmentations can substantially increase the efficiency and robustness of such computer-aided systems. However, this is a very challenging problem. An initial determination of the approximate position and range of a target organ in CT images is prerequisite for precise organ segmentations. In this study, we have proposed a universal approach that enables automatic localization of the approximate position and range of different solid organs in the torso region on three-dimensional (3D) CT scans. METHODS The location of a target organ in a 3D CT scan is presented as a 3D rectangle that bounds the organ region tightly and accurately. Our goal was to automatically and effectively detect such a target organ-specific 3D rectangle. In our proposed approach, multiple 2D detectors are trained using ensemble learning and their outputs are combined using a collaborative majority voting in 3D to accomplish the robust organ localizations. RESULTS We applied this approach to localize the heart, liver, spleen, left-kidney, and right-kidney regions independently using a CT image database that includes 660 torso CT scans. In the experiment, we manually labeled the abovementioned target organs from 101 3D CT scans as training samples and used our proposed approach to localize the 5 kinds of target organs separately on the remaining 559 torso CT scans. The localization results of each organ were evaluated quantitatively by comparing with the corresponding ground truths obtained from the target organs that were manually labeled by human operators. Experimental results showed that success rates of such organ localizations were distributed from 99% to 75% of the 559 test CT scans. We compared the performance of our approach with an atlas-based approach. The errors of the detected organ-center-positions in the successful CT scans by our approach had a mean value of 5.14 voxels, and those errors were much smaller than the results (mean value about 25 voxels) from the atlas-based approach. The potential usefulness of the proposed organ localization was also shown in a preliminary investigation of left kidney segmentation in non-contrast CT images. CONCLUSIONS We proposed an approach to accomplish automatic localizations of major solid organs on torso CT scans. The accuracy of localizations, flexibility of localizations of different organs, robustness to contrast and non-contrast CT images, and normal and abnormal patient cases, and computing efficiency were validated on the basis of a large number of torso CT scans.

Improving the Classification of Cirrhotic Liver by using Texture Features

We have been developing a computer-aided diagnosis (CAD) system for distinguishing the cirrhosis in MR images by shape and texture analysis. Two shape features are calculated from a segmented liver region, and seven texture features are quantified by using grey level difference method (GLDM) within the small region-of-interests (ROIs). The degree of cirrhosis is derived from integrating the shape and texture features of the liver into a three-layer feed-forward artificial neural network (ANN). A liver is regarded as cirrhosis if the percentage of the ROIs with a degree over 0.5 is greater than 50%. The initial experimental result showed that the ANN can learn all of the patterns in the training data sets. In testing of the whole liver regions, 82% cirrhosis and 100% normal cases were correctly differentiated from 18 test cases, that indicates our proposed method is effective to the cirrhosis prediction on MRI