Japanese Journal of Radiological Technology
Online ISSN : 1881-4883
Print ISSN : 0369-4305
ISSN-L : 0369-4305
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Volume 72, Issue 2
Displaying 1-10 of 10 articles from this issue
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Opening Article
Originals
  • Novel Index (Hepatic Receptor: IHR) to Evaluate Hepatic Functional Reserve Using 99mTc-GSA Scintigraphy
    Daisuke Hasegawa, Hideo Onishi, Norikazu Matsutomo
    2016 Volume 72 Issue 2 Pages 121-127
    Published: 2016
    Released on J-STAGE: February 20, 2016
    DOIhttps://doi.org/10.6009/jjrt.2016_JSRT_72.2.121
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    Purpose: This study aimed to evaluate the novel index of hepatic receptor (IHR) on the regression analysis derived from time activity curve of the liver for hepatic functional reserve. Methods: Sixty patients had undergone 99mTc-galactosyl serum albumin (99mTc-GSA) scintigraphy in the retrospective clinical study. Time activity curves for liver were obtained by region of interest (ROI) on the whole liver. A novel hepatic functional predictor was calculated with multiple regression analysis of time activity curves. In the multiple regression function, the objective variables were the indocyanine green (ICG) retention rate at 15 min, and the explanatory variables were the liver counts in 3-min intervals until end from beginning. Then, this result was defined by IHR, and we analyzed the correlation between IHR and ICG, uptake ratio of the heart at 15 minutes to that at 3 minutes (HH15), uptake ratio of the liver to the liver plus heart at 15 minutes (LHL15), and index of convexity (IOC). Results: Regression function of IHR was derived as follows: IHR=0.025×L(6)−0.052×L(12)+0.027×L(27). The multiple regression analysis indicated that liver counts at 6 min, 12 min, and 27 min were significantly related to objective variables. The correlation coefficient between IHR and ICG was 0.774, and the correlation coefficient between ICG and conventional indices (HH15, LHL15, and IOC) were 0.837, 0.773, and 0.793, respectively. IHR had good correlation with HH15, LHL15, and IOC. Conclusions: The finding results suggested that IHR would provide clinical benefit for hepatic functional assessment in the 99mTc-GSA scintigraphy.
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  • Magnetic Resonance Elastography: Measurement of Hepatic Stiffness Using Different Direct Inverse Problem Reconstruction Methods in Healthy Volunteers and Patients with Liver Disease
    Shigeyoshi Saito, Keiko Tanaka, Takashi Hashido
    2016 Volume 72 Issue 2 Pages 128-138
    Published: 2016
    Released on J-STAGE: February 20, 2016
    DOIhttps://doi.org/10.6009/jjrt.2016_JSRT_72.2.128
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    The purpose of this study was to compare the mean hepatic stiffness values obtained by the application of two different direct inverse problem reconstruction methods to magnetic resonance elastography (MRE). Thirteen healthy men (23.2±2.1 years) and 16 patients with liver diseases (78.9±4.3 years; 12 men and 4 women) were examined for this study using a 3.0 T-MRI. The healthy volunteers underwent three consecutive scans, two 70-Hz waveform and a 50-Hz waveform scans. On the other hand, the patients with liver disease underwent scanning using the 70-Hz waveform only. The MRE data for each subject was processed twice for calculation of the mean hepatic stiffness (Pa), once using the multiscale direct inversion (MSDI) and once using the multimodel direct inversion (MMDI). There were no significant differences in the mean stiffness values among the scans obtained with two 70-Hz and different waveforms. However, the mean stiffness values obtained with the MSDI technique (with mask: 2895.3±255.8 Pa, without mask: 2940.6±265.4 Pa) were larger than those obtained with the MMDI technique (with mask: 2614.0±242.1 Pa, without mask: 2699.2±273.5 Pa). The reproducibility of measurements obtained using the two techniques was high for both the healthy volunteers [intraclass correlation coefficients (ICCs): 0.840–0.953] and the patients (ICC: 0.830–0.995). These results suggest that knowledge of the characteristics of different direct inversion algorithms is important for longitudinal liver stiffness assessments such as the comparison of different scanners and evaluation of the response to fibrosis therapy.
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  • A Novel Evaluation Method for Displacement between Carbon Beam Axis and Positioning X-ray Axis Using an Imaging Plate
    Yuji Kurosawa, Yoshiki Kubota, Eri Takeshita, Ryosuke Okada, Yoshiaki ...
    2016 Volume 72 Issue 2 Pages 139-148
    Published: 2016
    Released on J-STAGE: February 20, 2016
    DOIhttps://doi.org/10.6009/jjrt.2016_JSRT_72.2.139
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    Purpose: We developed an evaluation method for easily calculating displacement directly between the carbon beam axis and positioning X-ray axis. Methods: A verification image was acquired by irradiating an imaging plate with a carbon beam and X-ray. The X-ray passed through a lead plate inserted in the range compensator holder. The displacement was calculated on the verification image from the center of a wire irradiated with carbon using a multi leaf collimator (MLC) and a wire irradiated with X-ray also using MLC. The accuracy of the method was evaluated by moving the carbon beam axis, the X-ray axis, and the setup angle. The weekly changes of vertical and lateral beams in all rooms were also evaluated. Results: The displacements of the carbon beam axis and the setup angle did not influence the calculation results, whereas the displacement of the X-ray axis did (R=0.999). The displacements including weekly changes were all less than 1.00 mm. Conclusion: An evaluation method for calculating the displacement directly and simply between the carbon beam axis and positioning X-ray axis was developed and verified. The weekly changes of displacement between axes were evaluated to be acceptable at our facility.
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  • Image Data Mining for Pattern Classification and Visualization of Morphological Changes in Brain MR Images
    Saki Murakawa, Rie Ikuta, Yoshikazu Uchiyama, Junji Shiraishi
    2016 Volume 72 Issue 2 Pages 149-156
    Published: 2016
    Released on J-STAGE: February 20, 2016
    DOIhttps://doi.org/10.6009/jjrt.2016_JSRT_72.2.149
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    Hospital information systems (HISs) and picture archiving and communication systems (PACSs) are archiving large amounts of data (i.e., “big data”) that are not being used. Therefore, many research projects in progress are trying to use “big data” for the development of early diagnosis, prediction of disease onset, and personalized therapies. In this study, we propose a new method for image data mining to identify regularities and abnormalities in the large image data sets. We used 70 archived magnetic resonance (MR) images that were acquired using three-dimensional magnetization-prepared rapid acquisition with gradient echo (3D MP-RAGE). These images were obtained from the Alzheimer’s disease neuroimaging initiative (ADNI) database. For anatomical standardization of the data, we used the statistical parametric mapping (SPM) software. Using a similarity matrix based on cross-correlation coefficients (CCs) calculated from an anatomical region and a hierarchical clustering technique, we classified all the abnormal cases into five groups. The Z score map identified the difference between a standard normal brain and each of those from the Alzheimer’s groups. In addition, the scatter plot obtained from two similarity matrixes visualized the regularities and abnormalities in the image data sets. Image features identified using our method could be useful for understanding of image findings associated with Alzheimer’s disease.
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  • The Detectability of Iterative CT Reconstruction for Low-contrast Lesions in Hyperacute Cerebral Infarction: Assessment with Newly Developed Dedicated Head Phantoms
    Kazuaki Nishimura, Chikako Tanaka, Mari Fukao, Takashi Ueguchi, Mitsuo ...
    2016 Volume 72 Issue 2 Pages 157-164
    Published: 2016
    Released on J-STAGE: February 20, 2016
    DOIhttps://doi.org/10.6009/jjrt.2016_JSRT_72.2.157
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    Iterative reconstruction techniques, such as adaptive statistical iterative reconstruction (ASiR), improve the contrast-to-noise ratio of computed tomography (CT) images; however, underlying anatomical structures may nevertheless hamper detectability of low-contrast areas in clinical situations, despite using such a technique. We therefore conducted a phantom study to investigate the efficacy of ASiR in improving the detectability of low-contrast areas in the presence of brain anatomical structures. We developed dedicated head phantoms simulating hyperacute cerebral infarction and confirmed that their CT numbers were sufficiently reproducible and that observer performance in detecting low-contrast areas using these phantoms more closely resembled that in clinical situations than that using a simple phantom. The efficacy of ASiR in improving low-contrast detectability was evaluated via receiver operating characteristics analysis. The mean area under the curve (AUC) values at ASiR blend rates of 0%, 30%, 60%, and 100% were 0.57, 0.57, 0.59, and 0.59 at 200 mA; 0.83, 0.84, 0.84, and 0.90 at 500 mA; and 0.79, 0.77, 0.76, and 0.79 at 800 mA, respectively. No significant differences were noted in AUC values among ASiR blend rates at any mA setting, suggesting that ASiR does not improve the detectability of subtle low-contrast lesions seen in hyperacute cerebral infarction in clinical situations.
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