Shouhei Hanaoka

Development of automatic visceral fat volume calculation software for CT volume data.

Objective. To develop automatic visceral fat volume calculation software for computed tomography (CT) volume data and to evaluate its feasibility. Methods. A total of 24 sets of whole-body CT volume data and anthropometric measurements were obtained, with three sets for each of four BMI categories (under 20, 20 to 25, 25 to 30, and over 30) in both sexes. True visceral fat volumes were defined on the basis of manual segmentation of the whole-body CT volume data by an experienced radiologist. Software to automatically calculate visceral fat volumes was developed using a region segmentation technique based on morphological analysis with CT value threshold. Automatically calculated visceral fat volumes were evaluated in terms of the correlation coefficient with the true volumes and the error relative to the true volume. Results. Automatic visceral fat volume calculation results of all 24 data sets were obtained successfully and the average calculation time was 252.7 seconds/case. The correlation coefficients between the true visceral fat volume and the automatically calculated visceral fat volume were over 0.999. Conclusions. The newly developed software is feasible for calculating visceral fat volumes in a reasonable time and was proved to have high accuracy.

Hepatic Segments and Vasculature: Projecting CT Anatomy onto Angiograms.

Hepatic transarterial interventional therapies such as chemoembolization and radiation embolization are important treatment options for hepatocellular carcinoma. Understanding the anatomy of individual arterial branches and hepatic segments is critical for selecting the correct embolization technique for treatment and to avoid complications. The authors describe the morphologic characteristics of hepatic arterial branches (and their mimickers) and hepatic segments on conventional angiograms. These vessels and segments include the celiac artery, the common and proper hepatic arteries, the left and right hepatic arteries and branches, the caudate lobe, and the portal vein and branches. Mimickers of hepatic arteries include the cystic, accessory left gastric, and right gastric arteries, as well as branches of the left gastric artery that resemble segmental branches of the replaced left hepatic artery. The authors describe how each segmental branch of the hepatic artery and the area it supplies correlates at computed tomography (CT) and angiography. Finally, the authors demonstrate how the vascular anatomy changes with the respiratory cycle by creating a virtual movie from calculations with dynamic CT data, in which the arterial and venous phases are acquired at end expiration and inspiration, respectively. Each segmental branch of the hepatic artery has morphologic characteristics that help distinguish it from mimickers. The location of each hepatic segment can be estimated if the artery supplying the segment can be correctly identified on angiograms. Notably, morphologic differences in the hepatic artery system caused by respiration should be recognized.Hepatic transarterial interventional therapies such as chemoembolization and radiation embolization are important treatment options for hepatocellular carcinoma. Understanding the anatomy of individual arterial branches and hepatic segments is critical for selecting the correct embolization technique for treatment and to avoid complications. The authors describe the morphologic characteristics of hepatic arterial branches (and their mimickers) and hepatic segments on conventional angiograms. These vessels and segments include the celiac artery, the common and proper hepatic arteries, the left and right hepatic arteries and branches, the caudate lobe, and the portal vein and branches. Mimickers of hepatic arteries include the cystic, accessory left gastric, and right gastric arteries, as well as branches of the left gastric artery that resemble segmental branches of the replaced left hepatic artery. The authors describe how each segmental branch of the hepatic artery and the area it supplies correlates at computed tomography (CT) and angiography. Finally, the authors demonstrate how the vascular anatomy changes with the respiratory cycle by creating a virtual movie from calculations with dynamic CT data, in which the arterial and venous phases are acquired at end expiration and inspiration, respectively. Each segmental branch of the hepatic artery has morphologic characteristics that help distinguish it from mimickers. The location of each hepatic segment can be estimated if the artery supplying the segment can be correctly identified on angiograms. Notably, morphologic differences in the hepatic artery system caused by respiration should be recognized.

A unified framework for concurrent detection of anatomical landmarks for medical image understanding

Anatomical landmarks are useful as the primitive anatomical knowledge for medical image understanding. In this study, we construct a unified framework for automated detection of anatomical landmarks distributed within the human body. Our framework includes the following three elements; (1) initial candidate detection based on a local appearance matching technique based on appearance models built by PCA and the generative learning, (2) false positive elimination using classifier ensembles trained by MadaBoost, and (3) final landmark set determination based on a combination optimization method by Gibbs sampling with a priori knowledge of inter-landmark distances. In evaluation of our methods with 50 data sets of body trunk CT, the average sensitivity in detecting candidates of 165 landmarks was 0.948 ± 0.084 while 55 landmarks were detected with 100 % sensitivity. Initially, the amount of false positives per landmark was 462.2 ± 865.1 per case on average, then they were reduced to 152.8 ± 363.9 per case by the MadaBoost classifier ensembles without miss-elimination of the true landmarks. Finally 89.1 % of landmarks were correctly selected by the final combination optimization. These results showed that our framework is promising for an initial step for the subsequent anatomical structure recognition.

High-resolution CT with new model-based iterative reconstruction with resolution preference algorithm in evaluations of lung nodules: Comparison with conventional model-based iterative reconstruction and adaptive statistical iterative reconstruction

OBJECTIVE To compare the image quality of high-resolution computed tomography (HRCT) for evaluating lung nodules reconstructed with the new version of model-based iterative reconstruction and spatial resolution preference algorithm (MBIRn) vs. conventional model-based iterative reconstruction (MBIRc) and adaptive statistical iterative reconstruction (ASIR). MATERIALS AND METHODS This retrospective clinical study was approved by our institutional review board and included 70 lung nodules in 58 patients (mean age, 71.2±10.9years; 34 men and 24 women). HRCT of lung nodules were reconstructed using MBIRn, MBIRc and ASIR. Objective image noise was measured by placing the regions of interest on lung parenchyma. Two blinded radiologists performed subjective image analyses. RESULTS Significant improvements in the following points were observed in MBIRn compared with ASIR (p<0.005): objective image noise (24.4±8.0 vs. 37.7±10.4), subjective image noise, streak artifacts, and adequateness for evaluating internal characteristics and borders of nodules. The sharpness of small vessels and bronchi and diagnostic acceptability with MBIRn were significantly better than with MBIRc and ASIR (p<0.008). CONCLUSION HRCT reconstructed with MBIRn provides diagnostically more acceptable images for the detailed analyses of lung nodules compared with MBIRc and ASIR.

Radiology reading-caused fatigue and measurement of eye strain with critical flicker fusion frequency

PurposeThe aim of this study was to investigate eye fatigue that could impair diagnostic accuracy by measuring the critical flicker fusion frequency (CFFF) before and after reading.Materials and methodsCFFF was measured before and after about 4 h of health checkup reading in seven healthy volunteer radiologists. A questionnaire was also completed on duration of sleep the night before the experiment, average duration of sleep, and subjective fatigue using a visual analog scale (corrected to a 0–1 scale, 0 indicating the worst fatigue ever experienced).ResultsAfter-reading subjective fatigue was significantly greater (before 0.52 ± 0.15, after 0.42 ± 0.15), and CFFF was significantly lower (before 40.9 ± 2.4, after 39.9 ± 2.0). There was no significant correlation between subjective fatigue and CFFF, either before or after or between before- and after-reading differences in subjective fatigue and CFFF. Shorter duration of sleep the night before significantly correlated with lower CFFF (Pearson’s correlation coefficient): before 0.42, P = 0.0047; after 0.52, P = 0.0003.ConclusionCFFF declines after reading and can be considered useful as an indicator of fatigue induced by radiology reading. CFFF declines significantly when sleep is reduced the day before reading without correlation with subjective fatigue, meaning that sleep deprivation can cause an unaware decline in visual function.

Automated segmentation method for spinal column based on a dual elliptic column model and its application for virtual spinal straightening.

Segmentation of vertebral bones in computed tomographic data is important as a first stage of image-based radiological tasks. However, it is a challenging problem to segment an affected spine correctly. In this study, we propose a new method of segmentation of thoracic and lumbar vertebral bodies from thin-slice computed tomographic images. Especially, we focus on a deformable model-based segmentation scheme to confirm the feasibility in clinical data sets with various bone diseases, such as bone metastases and scoliosis. As an application of this algorithm, virtual straightening of the thoracolumbar spine is also performed. Results on a database of 16 patients indicate the applicability of our method to spines affected by scoliosis and multiple bone metastases.

Automatic detection of over 100 anatomical landmarks in medical CT images: A framework with independent detectors and combinatorial optimization

&NA; An automatic detection method for 197 anatomically defined landmarks in computed tomography (CT) volumes is presented. The proposed method can handle missed landmarks caused by detection failure, a limited imaging range and other problems using a novel combinatorial optimization framework with a two‐stage sampling algorithm. After a list of candidates is generated by each landmark detector, the best combination of candidates is searched for by a combinatorial optimization algorithm using a landmark point distribution model (L‐PDM) to provide prior knowledge. Optimization is performed by simulated annealing and iterative Gibbs sampling. Prior to each cycle of Gibbs sampling, another sampling algorithm is processed to estimate the spatial distribution of each target landmark, so that landmark positions without any correct detector‐derived candidates can be estimated. The proposed method was evaluated using 104 CT volumes with various imaging ranges. The overall average detection distance error was 6.6 mm, and 83.8, 93.2 and 96.5% of landmarks were detected within 10, 15 and 20 mm from the ground truth, respectively. The proposed method worked even when most of the landmarks were outside of the imaging range. The identification accuracy of the vertebral centroid was also evaluated using public datasets and the proposed method could identify 70% of vertebrae including severely diseased ones. From these results, the feasibility of our framework in detecting multiple landmarks in various CT datasets was validated. HighlightsNovel landmark detection framework for over 100 anatomical landmarks.Handling missing landmarks caused by detection failure or a limited imaging range.Estimation of the positions of missing landmarks.A stochastic model of a general landmark detector.Validation with various CT datasets, e.g. different imaging ranges. Graphical abstract Figure. Image, graphical abstract

Whole vertebral bone segmentation method with a statistical intensity-shape model based approach

An automatic segmentation algorithm for the vertebrae in human body CT images is presented. Especially we focused on constructing and utilizing 4 different statistical intensity-shape combined models for the cervical, upper / lower thoracic and lumbar vertebrae, respectively. For this purpose, two previously reported methods were combined: a deformable model-based initial segmentation method and a statistical shape-intensity model-based precise segmentation method. The former is used as a pre-processing to detect the position and orientation of each vertebra, which determines the initial condition for the latter precise segmentation method. The precise segmentation method needs prior knowledge on both the intensities and the shapes of the objects. After PCA analysis of such shape-intensity expressions obtained from training image sets, vertebrae were parametrically modeled as a linear combination of the principal component vectors. The segmentation of each target vertebra was performed as fitting of this parametric model to the target image by maximum a posteriori estimation, combined with the geodesic active contour method. In the experimental result by using 10 cases, the initial segmentation was successful in 6 cases and only partially failed in 4 cases (2 in the cervical area and 2 in the lumbo-sacral). In the precise segmentation, the mean error distances were 2.078, 1.416, 0.777, 0.939 mm for cervical, upper and lower thoracic, lumbar spines, respectively. In conclusion, our automatic segmentation algorithm for the vertebrae in human body CT images showed a fair performance for cervical, thoracic and lumbar vertebrae.

Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography

The usefulness of computer‐assisted detection (CAD) for detecting cerebral aneurysms has been reported; therefore, the improved performance of CAD will help to detect cerebral aneurysms.

Feasibility Study of a Generalized Framework for Developing Computer-Aided Detection Systems—a New Paradigm

We propose a generalized framework for developing computer-aided detection (CADe) systems whose characteristics depend only on those of the training dataset. The purpose of this study is to show the feasibility of the framework. Two different CADe systems were experimentally developed by a prototype of the framework, but with different training datasets. The CADe systems include four components; preprocessing, candidate area extraction, candidate detection, and candidate classification. Four pretrained algorithms with dedicated optimization/setting methods corresponding to the respective components were prepared in advance. The pretrained algorithms were sequentially trained in the order of processing of the components. In this study, two different datasets, brain MRA with cerebral aneurysms and chest CT with lung nodules, were collected to develop two different types of CADe systems in the framework. The performances of the developed CADe systems were evaluated by threefold cross-validation. The CADe systems for detecting cerebral aneurysms in brain MRAs and for detecting lung nodules in chest CTs were successfully developed using the respective datasets. The framework was shown to be feasible by the successful development of the two different types of CADe systems. The feasibility of this framework shows promise for a new paradigm in the development of CADe systems: development of CADe systems without any lesion specific algorithm designing.