Tetsuhiro Miyara

CT Scans of the Chest in Carriers of Human T-cell Lymphotropic Virus Type 1 : Presence of Interstitial Pneumonia

RATIONALE AND OBJECTIVES To evaluate pulmonary findings on computed tomography (CT) scans in carriers of human T-lymphotropic virus type 1 (HTLV-1). MATERIALS AND METHODS This retrospective study was approved by the Institutional Review Board at each institution, and informed consent was waived. Patients who were diagnosed with adult T-cell lymphoma/leukemia or collagen vascular disease were excluded from the study. Chest CT of 106 HTLV-1 carriers (54 females and 52 males; age range 44-94 years) were initially evaluated by two chest radiologists. Assessed CT findings included centrilobular nodules, thickening of bronchovascular bundles, ground-glass opacity, bronchiectasis, interlobular septal thickening, consolidation, honeycombing, crazy-paving appearance, enlarged lymph nodes, pleural effusion, and pericardial effusion. Three chest radiologists secondarily evaluated the CT scans with the abnormal findings to judge the presence of interstitial pneumonia patterns or a bronchiolitis/bronchitis pattern. RESULTS Abnormal CT findings were found in 65 (61.3%) patients, including ground-glass opacity (n = 33), bronchiectasis (n = 28), centrilobular nodules (n = 25), and interlobular septal thickening (n = 19). Honeycombing (n = 5) and crazy-paving appearance (n = 3) were also observed. Based on the CT findings, 10 subjects were diagnosed with interstitial pneumonia (usual interstitial pneumonia pattern, n = 3; nonspecific interstitial pneumonia pattern, n = 5; organizing pneumonia pattern, n = 2; respectively). Twenty subjects were diagnosed with the bronchitis/bronchiolitis pattern. CONCLUSION Although the bronchiolitis/bronchitis pattern is predominant on chest CT in HTLV-1 carriers, the HTLV-1 infection is associated with various interstitial pneumonias.

Lung nodule detection performance in five observers on computed tomography (CT) with adaptive iterative dose reduction using three-dimensional processing (AIDR 3D) in a Japanese multicenter study: Comparison between ultra-low-dose CT and low-dose CT by receiver-operating characteristic analysis.

PURPOSE To compare lung nodule detection performance (LNDP) in computed tomography (CT) with adaptive iterative dose reduction using three dimensional processing (AIDR3D) between ultra-low dose CT (ULDCT) and low dose CT (LDCT). MATERIALS AND METHODS This was part of the Area-detector Computed Tomography for the Investigation of Thoracic Diseases (ACTIve) Study, a multicenter research project being conducted in Japan. Institutional Review Board approved this study and informed consent was obtained. Eighty-three subjects (body mass index, 23.3 ± 3.2) underwent chest CT at 6 institutions using identical scanners and protocols. In a single visit, each subject was scanned using different tube currents: 240, 120 and 20 mA (3.52, 1.74 and 0.29 mSv, respectively). Axial CT images with 2-mm thickness/increment were reconstructed using AIDR3D. Standard of reference (SOR) was determined based on CT images at 240 mA by consensus reading of 2 board-certificated radiologists as to the presence of lung nodules with the longest diameter (LD) of more than 3mm. Another 5 radiologists independently assessed and recorded presence/absence of lung nodules and their locations by continuously-distributed rating in CT images at 20 mA (ULDCT) and 120 mA (LDCT). Receiver-operating characteristic (ROC) analysis was used to evaluate LNDP of both methods in total and also in subgroups classified by LD (>4, 6 and 8 mm) and nodular characteristics (solid and ground glass nodules). RESULTS For SOR, 161 solid and 60 ground glass nodules were identified. No significant difference in LNDP for entire solid nodules was demonstrated between both methods, as area under ROC curve (AUC) was 0.844 ± 0.017 in ULDCT and 0.876 ± 0.026 in LDCT (p=0.057). For ground glass nodules with LD 8mm or more, LNDP was similar between both methods, as AUC 0.899 ± 0.038 in ULDCT and 0.941 ± 0.030 in LDCT. (p=0.144). CONCLUSION ULDCT using AIDR3D with an equivalent radiation dose to chest x-ray could have comparable LNDP to LDCT with AIDR3D except for smaller ground glass nodules in cases with normal range body habitus.

Adaptive Iterative Dose Reduction Using Three Dimensional Processing (AIDR3D) improves chest CT image quality and reduces radiation exposure.

Objective To assess the advantages of Adaptive Iterative Dose Reduction using Three Dimensional Processing (AIDR3D) for image quality improvement and dose reduction for chest computed tomography (CT). Methods Institutional Review Boards approved this study and informed consent was obtained. Eighty-eight subjects underwent chest CT at five institutions using identical scanners and protocols. During a single visit, each subject was scanned using different tube currents: 240, 120, and 60 mA. Scan data were converted to images using AIDR3D and a conventional reconstruction mode (without AIDR3D). Using a 5-point scale from 1 (non-diagnostic) to 5 (excellent), three blinded observers independently evaluated image quality for three lung zones, four patterns of lung disease (nodule/mass, emphysema, bronchiolitis, and diffuse lung disease), and three mediastinal measurements (small structure visibility, streak artifacts, and shoulder artifacts). Differences in these scores were assessed by Scheffes test. Results At each tube current, scans using AIDR3D had higher scores than those without AIDR3D, which were significant for lung zones (p<0.0001) and all mediastinal measurements (p<0.01). For lung diseases, significant improvements with AIDR3D were frequently observed at 120 and 60 mA. Scans with AIDR3D at 120 mA had significantly higher scores than those without AIDR3D at 240 mA for lung zones and mediastinal streak artifacts (p<0.0001), and slightly higher or equal scores for all other measurements. Scans with AIDR3D at 60 mA were also judged superior or equivalent to those without AIDR3D at 120 mA. Conclusion For chest CT, AIDR3D provides better image quality and can reduce radiation exposure by 50%.

Efficacy of diffusion-weighted magnetic resonance imaging in the diagnosis of middle ear cholesteatoma

OBJECTIVE This study evaluated the usefulness of diffusion-weighted magnetic resonance imaging (DWI) in the diagnosis of middle ear cholesteatoma. METHODS We performed DWI on 73 patients suspected of having middle ear cholesteatoma, including 21 revision cases. Magnetic resonance imaging was performed with 1.5T units using diffusion-weighted spin-echo-type echo planar imaging (DWI). RESULTS Of 73 subjects, 59 had cholesteatoma that consisted of 41 primary acquired cholesteatoma, 13 had residual and/or recurrent cholesteatoma, four had congenital cholesteatoma, and one had iatrogenic cholesteatoma. Positive DWI findings were observed in 42 subjects and negative findings in 31 subjects. The sensitivity, specificity, and positive and negative predictive values of DWI for cholesteatoma were 69.4%, 92.8%, 97.5%, and 41.9%, respectively. In the case of 34 patients who were positive for cholesteatoma on both otoscopic and CT examinations, 33 were diagnosed with cholesteatoma. Of the remaining 39 subjects with one or both negative results for cholesteatoma, the sensitivity, specificity, positive predictive value, and negative predictive value of DWI were 57.6%, 92.3%, 93.7%, and 52.1%, respectively. Cholesteatoma mass diameters were less than 5mm in 10 out of 18 subjects with both cholesteatoma and negative DWI findings. Of the 21 subjects who received revision surgery, the sensitivity, specificity, and positive and negative predictive values of DWI for residual or recurrent acquired cholesteatoma were 71.4%, 100%, 100%, and 63.6%, respectively. CONCLUSIONS Since DWI clearly showed high specificity and positive predictive value, it is useful for diagnosing middle ear cholesteatoma, including postoperative recurrent cholesteatoma of 5mm diameter or larger. DWI could sufficiently detect cholesteatoma with one or both negative results on otoscopic and CT examinations, but it was difficult to detect cholesteatoma of less than 5mm diameter using DWI owing to the tiny mass and small volume of debris.

Lung Image Quality with 320-row Wide-volume CT Scans: The Effect of Prospective ECG-gating and Comparisons with 64-row Helical CT Scans

RATIONALES AND OBJECTIVES To evaluate the image quality of 320-row wide-volume (WV) computed tomography (CT) scans in comparison with 64-row helical scans for the lung. MATERIALS AND METHODS The Institutional Review Board of each institution approved this prospective, multicenter study and informed consent was obtained. A total of 73 subjects underwent two types of chest CT, including 320-row WV scans and 64-row helical scans. Both scans used the same tube voltage, tube current, exposure time setting, and slice thickness. The helical scans were not electrocardiogram (ECG)-gated. For the WV scans, prospective ECG-gating was used for 38 subjects, whereas the other 35 subjects did not have ECG-gating. Using a 5-point scale from 1 (nondiagnostic) to 5 (excellent), three blinded observers independently evaluated image quality for five lobes and the lingula. The differences in the scores between WV scans and helical scans were compared using Wilcoxons signed-rank test. RESULTS The WV scans with ECG-gating had significantly higher scores than 64-row helical scans for all lobes and lingula (right lower lobe, P < .01; other lobes and lingula, P < .0001, respectively). The 320-row WV scans without ECG-gating also had significantly higher scores than 64-row helical scans (P < .05), except for nonsignificant differences for the left upper lobe. CONCLUSIONS Lung image quality of ECG-gated WV scans, which do not require any additional radiation exposure, is better than that of non-ECG-gated 64-row helical scans. Non-ECG-gated 320-row WV scans are comparable or slightly superior to non-ECG-gated 64-row helical scans.

Iterative reconstruction for quantitative computed tomography analysis of emphysema: consistent results using different tube currents.

Purpose To assess the advantages of iterative reconstruction for quantitative computed tomography (CT) analysis of pulmonary emphysema. Materials and methods Twenty-two patients with pulmonary emphysema underwent chest CT imaging using identical scanners with three different tube currents: 240, 120, and 60 mA. Scan data were converted to CT images using Adaptive Iterative Dose Reduction using Three Dimensional Processing (AIDR3D) and a conventional filtered-back projection mode. Thus, six scans with and without AIDR3D were generated per patient. All other scanning and reconstruction settings were fixed. The percent low attenuation area (LAA%; < −950 Hounsfield units) and the lung density 15th percentile were automatically measured using a commercial workstation. Comparisons of LAA% and 15th percentile results between scans with and without using AIDR3D were made by Wilcoxon signed-rank tests. Associations between body weight and measurement errors among these scans were evaluated by Spearman rank correlation analysis. Results Overall, scan series without AIDR3D had higher LAA% and lower 15th percentile values than those with AIDR3D at each tube current (P<0.0001). For scan series without AIDR3D, lower tube currents resulted in higher LAA% values and lower 15th percentiles. The extent of emphysema was significantly different between each pair among scans when not using AIDR3D (LAA%, P<0.0001; 15th percentile, P<0.01), but was not significantly different between each pair among scans when using AIDR3D. On scans without using AIDR3D, measurement errors between different tube current settings were significantly correlated with patients’ body weights (P<0.05), whereas these errors between scans when using AIDR3D were insignificantly or minimally correlated with body weight. Conclusion The extent of emphysema was more consistent across different tube currents when CT scans were converted to CT images using AIDR3D than using a conventional filtered-back projection method.

Sub-solid Nodule Detection Performance on Reduced-dose Computed Tomography with Iterative Reduction: Comparison Between 20 mA (7 mAs) and 120 mA (42 mAs) Regarding Nodular Size and Characteristics and Association with Size-specific Dose Estimate

RATIONALE AND OBJECTIVES This study aimed to compare sub-solid nodule detection performances (SSNDP) on chest computed tomography (CT) with Adaptive Iterative Dose Reduction using Three Dimensional Processing (AIDR 3D) between 7 mAs (0.21 mSv) and 42 mAs (1.28 mSv) in total and in subgroups classified by nodular size, characteristics, and location, and analyze the association of SSNDP with size-specific dose estimate (SSDE). MATERIALS AND METHODS As part of the Area-detector Computed Tomography for the Investigation of Thoracic Diseases Study, a Japanese multicenter research project, 68 subjects underwent chest CT with 120 kV, 0.35 seconds per rotation, and three tube currents: 240 mA (84 mAs), 120 mA (42 mAs), and 20 mA (7 mAs). The research committee of the study project outlined and approved our study protocols. The institutional review board of each institution approved this study. Axial 2-mm-thick CT images were reconstructed using AIDR 3D. Standard reference was determined by CT images at 84 mAs. Four radiologists recorded SSN presence by continuously distributed rating on CT at 7 mAs and 42 mAs. Receiver operating characteristic analysis was used to evaluate SSNDP at both doses in total and in subgroups classified by nodular longest diameter (LD) (≥5 mm), characteristics (pure and part-solid), and locations (ventral, intermediate, or dorsal; central or peripheral; and upper, middle, or lower). Detection sensitivity was compared among five groups of SSNs classified based on particular SSDE to nodule on CT with AIDR 3D at 7 mAs. RESULTS Twenty-two part-solid and 86 pure SSNs were identified. For larger SSNs (LD ≥ 5 mm) as well as subgroups classified by nodular locations and part-solid nodules, SSNDP was similar in both methods (area under the receiver operating characteristics curve: 0.96 ± 0.02 in CT at 7 mAs and 0.97 ± 0.01 in CT at 42 mAs), with acceptable interobserver agreements in five locations. For larger SSNs (LD ≥ 5 mm), on CT at 42 mAs, no significant differences in detection sensitivity were found among the five groups classified by SSDE, whereas on CT with 7 mAs, four groups with SSDE of 0.65 or higher were superior in detection sensitivity to the other group, with SSDE less than 0.65 mGy. CONCLUSIONS For SSNs with 5 mm or more in cases with normal range of body habitus, CT at 7 mAs was demonstrated to have comparable SSNDP to CT at 42 mAs regardless of nodular location and characteristics, and SSDE higher than 0.65 mGy is desirable to obtain sufficient SSNDP.

Pulmonary nodules: a quantitative method of diagnosis by evaluating nodule perimeter difference to approximate oval using three-dimensional CT images.

The purpose of this study was to investigate whether maximum nodule perimeter to the approximate oval could discriminate benign nodules from malignancy. Measurement of maximum nodule perimeter difference to the approximate oval was performed using volume-rendering images of three directions of each pulmonary nodule. The margin was then traced manually and our custom software delineated the approximate oval automatically. The maximum nodule perimeter difference was 26.5±23.3 mm for malignant and 16.6±16.9 mm for benign nodules, showing an almost statistically significant difference (P=.07). This study suggests that the maximum nodule perimeter difference to the approximate oval of the malignant nodules has a tendency to be longer than benign nodules.

Pulmonary metastases from angiosarcoma: a spectrum of CT findings

Background Though a few reports have summarized the computed tomography (CT) findings of pulmonary metastases from angiosarcoma, the detailed CT findings of cysts are not well known, except for their characteristic thin walls. Purpose To retrospectively summarize the CT findings of pulmonary metastases from angiosarcoma, focusing mainly on the CT findings of cysts. Material and Methods Thirty-three patients with pulmonary metastases from angiosarcoma were selected retrospectively. Two radiologists reviewed and assessed patients’ chest CT images on a consensus basis for nodules, cysts, the CT halo sign, pneumothorax, pleural effusion, and enlarged lymph nodes. Cysts were also evaluated by wall thickness and smoothness, air-fluid levels, and vessels or bronchi penetrating the cysts. The relationship between cysts and pneumothorax was assessed using the Chi-square test. Results Nodules were found in 28 (85%) patients. Cysts were found in 19 (58%) patients; 17 had thin and smooth walls, 10 had thin and irregular walls, and four had thick and irregular walls. In addition, 12 patients showed vessels or bronchi penetrating the cysts, and six showed air-fluid levels. The CT halo sign, pneumothorax, pleural effusion, and mediastinal lymphadenopathy were seen in 19 (58%), 16 (48%), 26 (78.8%), and five (15.2%) patients, respectively. Pneumothorax occurred significantly more frequently in patients with cysts (P = 0.002). Conclusion Cysts showed variability in their walls, and air-fluid levels and vessels or bronchi penetrating the cysts appeared to be characteristic findings, which may be useful for detection and accurate diagnosis in patients with pulmonary metastases from angiosarcoma.

320-Row wide volume CT significantly reduces density heterogeneity observed in the descending aorta: Comparisons with 64-row helical CT

The aim of this study was to compare density heterogeneity on wide volume (WV) scans with that on helical CT scans. 22 subjects underwent chest CT using 320-WV and 64-helical modes. Density heterogeneity of the descending aorta was evaluated quantitatively and qualitatively. At qualitative assessment, the heterogeneity was judged to be smaller on WV scans than on helical scans (p<0.0001). Mean changes in aortic density between two contiguous slices were 1.64 HU (3.40%) on WV scans and 2.29 HU (5.19%) on helical scans (p<0.0001). CT density of thoracic organs is more homogeneous and reliable on WV scans than on helical scans.