Toru Higaki

Clonal chromosomal aberrations accompanied by strong telomerase activity in immortalization of human B-lymphoblastoid cell lines transformed by Epstein-Barr virus

Human B-lymphoblastoid cell lines transformed by Epstein-Barr (EBV-LCLs) are considered to be immortalized, although most of them show a normal diploid karyotype. Recently, we and others have shown that only part of EBV-LCLs is immortalized by developing strong telomerase activity that stabilizes the telomeres. In this study, we investigated the change in karyotypes during immortalization. All the eight immortalized cell lines developed clonal chromosomal aberrations accompanied by the development of strong telomerase activity. Interestingly, abnormal chromosomes were not shared among the immortalized cell lines. These results strongly suggest that chromosomal rearrangements and induction of strong telomerase activity are two events that take place in parallel in the process of immortalization of EBV-LCLs, and indicate that EBV-LCLs are clearly divided into two distinct groups, pre-immortal cell lines mostly with a normal diploid karyotype and post-immortal cell lines with a clonally abnormal karyotype.

Coronary Artery Stent Evaluation with Model-based Iterative Reconstruction at Coronary CT Angiography

RATIONALE AND OBJECTIVES This study aims to compare the image quality of coronary artery stent scans on computed tomography images reconstructed with forward projected model-based iterative reconstruction solution (FIRST) and adaptive iterative dose reduction 3D (AIDR 3D). MATERIALS AND METHODS Coronary computed tomography angiography scans of 23 patients with 32 coronary stents were used. The images were reconstructed with AIDR 3D and FIRST. We generated computed tomography attenuation profiles across the stents and measured the width of the edge rise distance and the edge rise slope (ERS). We also calculated the stent lumen attenuation increase ratio (SAIR) and measured visible stent lumen diameters. Two radiologists visually evaluated the image quality of the stents using a 4-point scale (1 = poor, 4 = excellent). RESULTS There was no significant difference in the edge rise distance between the two reconstruction methods (P = 0.36). The ERS on FIRST images was greater than the ERS on AIDR 3D images (325.2 HU/mm vs 224.4 HU/mm; P <0.01). The rate of the visible stent lumen diameter compared to the true diameter on FIRST images was higher than that on AIDR 3D images (51.4% vs 47.3%, P <0.01). The SAIR on FIRST images was lower than the SAIR on AIDR 3D images (0.19 vs 0.30, P <0.01). The mean image quality scores for AIDR 3D and FIRST images were 3.18 and 3.63, respectively; the difference was also significant (P <0.01). CONCLUSION The image quality of coronary artery stent scans is better on FIRST than on AIDR 3D images.

Lung cancer screening with ultra-low dose CT using full iterative reconstruction

PurposeTo investigate the diagnostic capability of ultra-low-dose CT (ULDCT) with full iterative reconstruction (f-IR) for lung cancer screening.Materials and methodsAll underwent ULDCT and/or low-dose CT (LD-CT) on a 320-detector scanner. ULDCT images were reconstructed with f-IR. We qualitatively and quantitatively studied 95 nodules in 69 subjects. Two radiologists classified the nodules on ULDCT images as solid-, part-solid-, and pure ground-glass (PGG) and recorded their mean size. Their findings were compared with the reference standard. The observer performance study included 7 other radiologists and 35 subjects with- and 15 without nodules. The results were analyzed by AFROC analysis.ResultsIn the qualitative study, the kappa values between observers 1 and 2, respectively, and the reference standard were 0.70 and 0.83; the intra-class correlation coefficients for the nodule diameter between the reference standard and their measurements were 0.84 and 0.90. The 95% confidence interval (CI) for the area under the curve (AUC) difference for nodule detection on LDCT and ULDCT was −0.03 to 0.07. The 95% CI crossed the 0 difference in the AUC but not the pre-defined non-inferiority margin of −0.08.ConclusionThe diagnostic ability of ULDCT using f-IR is comparable to LDCT.

Measurement of electron density and effective atomic number by dual-energy scan using a 320-detector computed tomography scanner with raw data-based analysis: a phantom study.

Abstract We evaluated the accuracy of the electron densities and effective atomic numbers determined by raw data-based dual-energy analysis on a 320-detector computed tomography scanner. The mean (SD) errors between the measured and true electron densities and between the measured and true effective atomic numbers were 1.3% (1.5%) and 3.1% (3.2%), respectively. Electron densities and effective atomic numbers can be determined with high accuracy, which may help to improve accuracy in radiotherapy treatment planning.

Visualization of simulated small vessels on computed tomography using a model-based iterative reconstruction technique

This article describes a quantitative evaluation of visualizing small vessels using several image reconstruction methods in computed tomography. Simulated vessels with diameters of 1–6 mm made by 3D printer was scanned using 320-row detector computed tomography (CT). Hybrid iterative reconstruction (hybrid IR) and model-based iterative reconstruction (MBIR) were performed for the image reconstruction.

Effects of Iterative Reconstruction Algorithms on Computer-assisted Detection (CAD) Software for Lung Nodules in Ultra-low-dose CT for Lung Cancer Screening

RATIONALE AND OBJECTIVES This study aimed to evaluate the effects of iterative reconstruction (IR) algorithms on computer-assisted detection (CAD) software for lung nodules in ultra-low-dose computed tomography (ULD-CT) for lung cancer screening. MATERIALS AND METHODS We selected 85 subjects who underwent both a low-dose CT (LD-CT) scan and an additional ULD-CT scan in our lung cancer screening program for high-risk populations. The LD-CT scans were reconstructed with filtered back projection (FBP; LD-FBP). The ULD-CT scans were reconstructed with FBP (ULD-FBP), adaptive iterative dose reduction 3D (AIDR 3D; ULD-AIDR 3D), and forward projected model-based IR solution (FIRST; ULD-FIRST). CAD software for lung nodules was applied to each image dataset, and the performance of the CAD software was compared among the different IR algorithms. RESULTS The mean volume CT dose indexes were 3.02 mGy (LD-CT) and 0.30 mGy (ULD-CT). For overall nodules, the sensitivities of CAD software at 3.0 false positives per case were 78.7% (LD-FBP), 9.3% (ULD-FBP), 69.4% (ULD-AIDR 3D), and 77.8% (ULD-FIRST). Statistical analysis showed that the sensitivities of ULD-AIDR 3D and ULD-FIRST were significantly higher than that of ULD-FBP (P < .001). CONCLUSIONS The performance of CAD software in ULD-CT was improved by using IR algorithms. In particular, the performance of CAD in ULD-FIRST was almost equivalent to that in LD-FBP.

Clinically essential requirement for brain CT with iterative reconstruction

The Editor We read with great interest the article titled “Six iterative reconstruction algorithms in brain CT: a phantom study on image quality at different radiation dose levels” by Love et al.1 They evaluated six iterative reconstruction (IR) algorithms and concluded that while all improved the image quality of brain CT scans, they exhibited different strengths and weaknesses. The authors also reported that model-based IR algorithms have a potential for a further dose reduction. We would like to comment from a clinical perspective. While MRI is now the primary imaging modality for most neurological diseases, many patients with ischaemic stroke and intracranial haemorrhage undergo CT because their general status tends to be serious, and the examination time is much shorter with CT than with MRI. Subtle subarachnoid haemorrhage between cranial bone and the brain surface is occasionally overlooked because of beam-hardening artefacts from the cranial bone. Although Love et al used Catphan® (The Phantom Laboratory, Greenwich, NY) with an external bone-mimicking ring to simulate beam hardening attributable to the cranial bone, they failed to evaluate the degree of beam-hardening artefacts adjacent to the ring on their scans. For a valid extrapolation of their phantom results, the effect of each IR algorithm on beam-hardening artefacts must be investigated. Another important application of brain CT is the diagnosis of ischaemic stroke. Early findings of ischaemia on brain CT scans include “obscuration of the lentiform nucleus”2 and the “insular ribbon sign”,3 and radiologists must differentiate between the brain grey matter and white matter. However, the difference in the CT number between the grey matter and white matter is as little as 5–10 HU and very small even in normal subjects. To detect such minute CT number differences between the grey matter and white matter, the image noise must be less than at least 5 HU. Based on the information that Love et al present in Table 3, to obtain an image noise <5 HU, the radiation dose must be 84 mGy or larger for all reconstruction algorithms on all systems. Considering the urgency and life-threatening nature of ischaemic stroke, the focus must be on the acquisition of low-noise CT images rather than on a radiation dose reduction. We think that drastic image noise reduction is difficult at the current technical level of CT. Therefore, we recommend that efforts be directed at improving the image quality of brain CT scans acquired with standard or lower radiation doses by developing new reconstruction algorithms rather than by pursuing the possibility of a radiation dose reduction.

Aortic and Hepatic Contrast Enhancement During Hepatic-Arterial and Portal Venous Phase Computed Tomography Scanning: Multivariate Linear Regression Analysis Using Age, Sex, Total Body Weight, Height, and Cardiac Output.

Objective We evaluated the effect of the age, sex, total body weight (TBW), height (HT) and cardiac output (CO) of patients on aortic and hepatic contrast enhancement during hepatic-arterial phase (HAP) and portal venous phase (PVP) computed tomography (CT) scanning. Methods This prospective study received institutional review board approval; prior informed consent to participate was obtained from all 168 patients. All were examined using our routine protocol; the contrast material was 600 mg/kg iodine. Cardiac output was measured with a portable electrical velocimeter within 5 minutes of starting the CT scan. We calculated contrast enhancement (per gram of iodine: [INCREMENT]HU/gI) of the abdominal aorta during the HAP and of the liver parenchyma during the PVP. We performed univariate and multivariate linear regression analysis between all patient characteristics and the [INCREMENT]HU/gI of aortic- and liver parenchymal enhancement. Results Univariate linear regression analysis demonstrated statistically significant correlations between the [INCREMENT]HU/gI and the age, sex, TBW, HT, and CO (all P < 0.001). However, multivariate linear regression analysis showed that only the TBW and CO were of independent predictive value (P < 0.001). Also, only the CO was independently and negatively related to aortic enhancement during HAP and to liver parenchymal enhancement when the contrast material injection protocol was adjusted for the TBW (P < 0.001). Conclusion By multivariate linear regression analysis only the TBW and CO were significantly correlated with aortic and liver parenchymal enhancement; the age, sex, and HT were not. The CO was the only independent factor affecting aortic and liver parenchymal enhancement at hepatic CT when the protocol was adjusted for the TBW.

Preliminary Results of High-precision Computed Diffusion Weighted Imaging for the Diagnosis of Hepatocellular Carcinoma at 3 Tesla

Objective To compare the utility of high-precision computed diffusion-weighted imaging (hc-DWI) and conventional computed DWI (cc-DWI) for the diagnosis of hepatocellular carcinoma (HCC) at 3 T. Methods We subjected 75 HCC patients to DWI (b-value 150 and 600 s/mm2). To generate hc-DWI we applied non-rigid image registration to avoid the mis-registration of images obtained with different b-values. We defined c-DWI with a b-value of 1500 s/mm2 using DWI with b-value 150 and 600 s/mm2 as cc-DWI, and c-DWI with b-value 1500 s/mm2 using registered DWI with b-value 150 and 600 s/mm2 as hc-DWI. A radiologist recorded the contrast ratio (CR) between HCC and the surrounding hepatic parenchyma. Results The CR for HCC was significantly higher on hc- than cc-DWIs (median 2.0 vs. 1.8, P < 0.01). Conclusion The CR of HCC can be improved with image registration, indicating that hc-DWI is more useful than cc-DWI for the diagnosis of HCC.

Neural Network Convolution (NNC) for Converting Ultra-Low-Dose to "Virtual" High-Dose CT Images.

To reduce radiation dose in CT, we developed a novel deep-learning technique, neural network convolution (NNC), for converting ultra-low-dose (ULD) to “virtual” high-dose (HD) CT images with less noise or artifact. NNC is a supervised image-based machine-learning (ML) technique consisting of a neural network regression model. Unlike other typical deep learning, NNC can learn thus output desired images, as opposed to class labels. We trained our NNC with ULDCT (0.1 mSv) and corresponding “teaching” HDCT (5.7 mSv) of an anthropomorphic chest phantom. Once trained, our NNC no longer require HDCT, and it provides “virtual” HDCT where noise and artifact are substantially reduced. To test our NNC, we collected ULDCT (0.1 mSv) of 12 patients with 3 different vendor CT scanners. To determine a dose reduction rate of our NNC, we acquired 6 CT scans of the anthropomorphic chest phantom at 6 different radiation doses (0.1–3.0 mSv). Our NNC reduced noise and streak artifacts in ULDCT substantially, while maintaining anatomic structures and pathologies such as vessels and nodules. With our NNC, the image quality of ULDCT (0.1 mSv) images was improved at the level equivalent to 1.1 mSv CT images, which corresponds to 91% dose reduction.