Association between interstitial lung abnormality and mortality in patients with esophageal cancer.

PURPOSE: To investigate the relationship between interstitial lung abnormalities (ILAs) and mortality in patients with esophageal cancer and the cause of mortality. MATERIALS AND METHODS: This retrospective study investigated patients with esophageal cancer from January 2011 to December 2015. ILAs were visually scored on baseline CT using a 3-point scale (0 = non-ILA, 1 = indeterminate for ILA, and 2 = ILA). ILAs were classified into subcategories of non-subpleural, subpleural non-fibrotic, and subpleural fibrotic. Five-year overall survival (OS) was compared between patients with and without ILAs using the multivariable Cox proportional hazards model. Subgroup analyses were performed based on cancer stage and ILA subcategories. The prevalences of treatment complications and death due to esophageal cancer and pneumonia/respiratory failure were analyzed using Fisher's exact test. RESULTS: A total of 478 patients with esophageal cancer (age, 66.8 years ± 8.6 [standard deviation]; 64 women) were evaluated in this study. Among them, 267 patients showed no ILAs, 125 patients were indeterminate for ILAs, and 86 patients showed ILAs. ILAs were a significant factor for shorter OS (hazard ratio [HR] = 1.68, 95% confidence interval [CI] 1.10-2.55, P = 0.016) in the multivariable Cox proportional hazards model adjusting for age, sex, smoking history, clinical stage, and histology. On subgroup analysis using patients with clinical stage IVB, the presence of ILAs was a significant factor (HR = 3.78, 95% CI 1.67-8.54, P = 0.001). Subpleural fibrotic ILAs were significantly associated with shorter OS (HR = 2.22, 95% CI 1.25-3.93, P = 0.006). There was no significant difference in treatment complications. Patients with ILAs showed a higher prevalence of death due to pneumonia/respiratory failure than those without ILAs (non-ILA, 2/95 [2%]; ILA, 5/39 [13%]; P = 0.022). The prevalence of death due to esophageal cancer was similar in patients with and without ILA (non-ILA, 82/95 [86%]; ILA 32/39 [82%]; P = 0.596). CONCLUSION: ILAs were significantly associated with shorter survival in patients with esophageal cancer.

The evaluation of the reduction of radiation dose via deep learning-based reconstruction for cadaveric human lung CT images.

To compare the quality of CT images of the lung reconstructed using deep learning-based reconstruction (True Fidelity Image: TFI ™; GE Healthcare) to filtered back projection (FBP), and to determine the minimum tube current-time product in TFI without compromising image quality. Four cadaveric human lungs were scanned on CT at 120 kVp and different tube current-time products (10, 25, 50, 75, 100, and 175 mAs) and reconstructed with TFI and FBP. Two image evaluations were performed by three independent radiologists. In the first experiment, using the same tube current-time product, a side-by-side TFI and FBP comparison was performed. Images were evaluated with regard to noise, streak artifacts, and overall image quality. Overall image quality was evaluated in view of whole image quality. In the second experiment, CT images reconstructed using TFI and FBP with five different tube current-time products were displayed in random order, which were evaluated with reference to the 175 mAs-FBP image. Images were scored with regard to normal structure, abnormal findings, noise, streak artifacts, and overall image quality. Median scores from three radiologists were statistically analyzed. Quantitative evaluation of noise was performed by setting regions of interest (ROIs) in air. In first experiment, overall image quality was improved, and noise was decreased in images of TFI compared to that of FBP for all tube current-time products. In second experiment, scores of all evaluation items except for small vessels in images of 25 mAs-TFI were almost the same as that of 175 mAs-FBP (all p > 0.31). Using TFI instead of FBP, at least 85% radiation dose reduction could be possible without any degradation in the image quality.

CT-based radiomics analysis for differentiation between thymoma and thymic carcinoma.

Background: The purpose of our study was to differentiate between thymoma and thymic carcinoma using a radiomics analysis based on the computed tomography (CT) image features. Methods: The CT images of 61 patients with thymic epithelial tumors (TETs) who underwent contrast-enhanced CT with slice thickness <1 mm were analyzed. Pathological examination of the surgical specimens revealed thymoma in 45 and thymic carcinoma in 16. Tumor volume and the ratio of major axis to minor axis were calculated using a computer-aided diagnostic software. Sixty-one different radiomics features in the segmented CT images were extracted, then filtered and minimized by least absolute shrinkage and selection operator (LASSO) regression to select the optimal radiomics features for predicting thymic carcinoma. The association between the quantitative values and a diagnosis of thymic carcinoma were analyzed with logistic regression models. Parameters identified as significant in univariate analysis were included in multiple analyses. Receiver-operating characteristic (ROC) curves were assessed to evaluate the diagnostic performance. Results: Thymic carcinoma was significantly predominant in men (P=0.001). Optimal radiomics features for predicting thymic carcinoma were as follows: gray-level co-occurrence matrix (GLCM)-homogeneity, GLCM-energy, compactness, large zone high gray-level emphasis (LZHGE), solidity, size of minor axis, and kurtosis. Multiple logistic regression analysis of these features revealed solidity and GLCM-energy as independent indicators associated with thymic carcinoma [odds ratio, 14.7 and 14.3; 95% confidence interval (CI): 1.6-139.0 and 3.0-68.7; and P=0.045 and 0.002, respectively]. Area under the curve (AUC) for diagnosing thymic carcinoma was 0.882 (sensitivity, 81.2%; specificity, 91.1%). Multivariate analysis adjusted for sex similarly revealed two features (solidity and GLCM-energy) as independent indicators associated with thymic carcinoma (odds ratio, 14.6 and 23.9; 95% CI: 2.4-89.2 and 1.9-302.8; P=0.004 and 0.014, respectively). Adjusted AUC for diagnosing thymic carcinoma was 0.921 (95% CI: 0.82-0.97): sensitivity, 62.5% and specificity, 100%. Conclusions: Two texture features (GLCM-energy and solidity) were significant predictors of thymic carcinoma.

Detectability of pulmonary ossifications in fibrotic lung on ultra-high-resolution CT using 2048 matrix size and 0.25-mm slice thickness.

To investigate the prevalence of nodular pulmonary ossifications (POs) in patients with honeycombing on ultra-high-resolution CT (UHRCT) and to compare the detectability of nodular POs between images reconstructed using the ultra-high-resolution setting (UHR-setting) and those using the conventional setting (C-setting) on UHRCT. Twenty patients with honeycombing in the lung were evaluated retrospectively. All patients underwent non-contrast-enhanced UHRCT. Images were reconstructed with UHR-setting (matrix, 2048 × 2048; slice thickness, 0.25 mm) and with C-setting (matrix size, 512 × 512; slice thickness, 0.5 mm). Two chest radiologists independently recorded the number of nodular POs (< 4 mm diameter) in each lung lobes. Each lobe was classified as one of the following five categories according to the number of POs: C0, none; C1, 1-4 POs; C2, 5-9 POs; C3, 10-49 POs; and C4, ≥ 50 POs. The maximum CT values of the POs were measured and compared between the two settings. PO categories were significantly higher with UHR-setting than with C-setting (p < 0.001). Maximum CT values were significantly higher with UHR-setting than with C-setting (p < 0.001). Nodular POs were seen in 80% or more of patients with honeycombing and more easily detected in images reconstructed with UHR-setting than in those with C-setting.

Quantitative volumetry of ground-glass nodules on high-spatial-resolution CT with 0.25-mm section thickness and 1024 matrix: Phantom and clinical studies.

OBJECTIVES: To compare high-resolution (HR) and conventional (C) settings of high-spatial-resolution computed tomography (CT) for software volumetry of ground-glass nodules (GGNs) in phantoms and patients. METHODS: We placed -800 and -630 HU spherical GGN-mimic nodules in 28 different positions in phantoms and scanned them individually. Additionally, 60 GGNs in 45 patients were assessed retrospectively. Images were reconstructed using the HR-setting (matrix size, 1024; slice thickness, 0.25 mm) and C-setting (matrix size, 512; slice thickness, 0.5 mm). We measured the GGN volume and mass using software. In the phantom study, the absolute percentage error (APE) was calculated as the absolute difference between Vernier caliper measurement-based and software-based volumes. In patients, we measured the density (mean, maximum, and minimum) and classified GGNs into low- and high-attenuation GGNs. RESULTS: In images of the -800 HU, but not -630 HU, phantom nodules, the volumes and masses differed significantly between the two settings (both p < 0.01). The APE was significantly lower in the HR-setting than in the C-setting (p < 0.01). In patients, volumes did not differ significantly between settings (p = 0.59). Although the mean attenuation was not significantly different, the maximum and minimum values were significantly increased and decreased, respectively, in the HR-setting (both p < 0.01). The volumes of both low-attenuation and high-attenuation GGNs were not significantly different between settings (p = 0.78 and 0.39, respectively). CONCLUSION: The HR-setting might yield a more accurate volume for phantom GGN of -800 HU and influence the detection of maximum and minimum CT attenuation.

Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT.

OBJECTIVES: To develop a deep learning-based algorithm to detect aortic dissection (AD) and evaluate the diagnostic ability of the algorithm compared with those of radiologists. METHODS: Included in the study were 170 patients (85 with AD and 85 without AD). An AD detection algorithm was developed using a convolutional neural network with Xception architecture. Of the patient data, 80% were used for training and validation and 20% were used for testing. Fivefold cross-validation was performed to evaluate the method. An average of 6688 non-contrast-enhanced CT images (slice thickness, 5 mm) were used for training. A radiologist reviewed both contrast-enhanced and non-contrast-enhanced images and identified the slices of AD. The identified slices were used as ground truth. Receiver operating characteristic curve and area under the curve (AUC) analysis was performed. Five radiologists independently evaluated the images. The accuracy, sensitivity, and specificity of the algorithm and those of the radiologists were compared. RESULTS: The AUC of the developed algorithm was 0.940, and a cutoff value of 0.400 provided accuracy of 90.0%, sensitivity of 91.8%, and specificity of 88.2%. For the radiologists, median (range) accuracy, sensitivity, and specificity were 88.8 (83.5-94.1)%, 90.6 (83.5-94.1)%, and 94.1 (72.9-97.6)%, respectively. There was no significant difference in performance in terms of accuracy, sensitivity, or specificity between the algorithm and the average performance of the radiologists (p > 0.05). CONCLUSIONS: The developed algorithm showed comparable diagnostic performance to radiologists for detecting AD, which suggests the potential of the proposed method to support clinical practice by reducing missed ADs. KEY POINTS: • A deep learning-based algorithm for detecting aortic dissection was developed using the non-contrast-enhanced CT images of 170 patients. • The algorithm had an AUC of 0.940 for detecting aortic dissection. • The accuracy, sensitivity, and specificity of the algorithm were comparable to those of radiologists.

Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network.

OBJECTIVES: To compare diagnostic performance for pulmonary invasive adenocarcinoma among radiologists with and without three-dimensional convolutional neural network (3D-CNN). METHODS: Enrolled were 285 patients with adenocarcinoma in situ (AIS, n = 75), minimally invasive adenocarcinoma (MIA, n = 58), and invasive adenocarcinoma (IVA, n = 152). A 3D-CNN model was constructed with seven convolution-pooling and two max-pooling layers and fully connected layers, in which batch normalization, residual connection, and global average pooling were used. Only the flipping process was performed for augmentation. The output layer comprised two nodes for two conditions (AIS/MIA and IVA) according to prognosis. Diagnostic performance of the 3D-CNN model in 285 patients was calculated using nested 10-fold cross-validation. In 90 of 285 patients, results from each radiologist (R1, R2, and R3; with 9, 14, and 26 years of experience, respectively) with and without the 3D-CNN model were statistically compared. RESULTS: Without the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 70.0%, 52.1%, and 90.5%; R2, 72.2%, 75%, and 69%; and R3, 74.4%, 89.6%, and 57.1%, respectively. With the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 72.2%, 77.1%, and 66.7%; R2, 74.4%, 85.4%, and 61.9%; and R3, 74.4%, 93.8%, and 52.4%, respectively. Diagnostic performance of each radiologist with and without the 3D-CNN model had no significant difference (p > 0.88), but the accuracy of R1 and R2 was significantly higher with than without the 3D-CNN model (p < 0.01). CONCLUSIONS: The 3D-CNN model can support a less-experienced radiologist to improve diagnostic accuracy for pulmonary invasive adenocarcinoma without deteriorating any diagnostic performances. KEY POINTS: • The 3D-CNN model is a non-invasive method for predicting pulmonary invasive adenocarcinoma in CT images with high sensitivity. • Diagnostic accuracy by a less-experienced radiologist was better with the 3D-CNN model than without the model.

Combination of Deep Learning-Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation.

OBJECTIVE. The objective of our study was to assess the effect of the combination of deep learning-based denoising (DLD) and iterative reconstruction (IR) on image quality and Lung Imaging Reporting and Data System (Lung-RADS) evaluation on chest ultra-low-dose CT (ULDCT). MATERIALS AND METHODS. Forty-one patients with 252 nodules were evaluated retrospectively. All patients underwent ULDCT (mean ± SD, 0.19 ± 0.01 mSv) and standard-dose CT (SDCT) (6.46 ± 2.28 mSv). ULDCT images were reconstructed using hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR), and they were postprocessed using DLD (i.e., HIR-DLD and MBIR-DLD). SDCT images were reconstructed using filtered back projection. Three independent radiologists subjectively evaluated HIR, HIR-DLD, MBIR, and MBIR-DLD images on a 5-point scale in terms of noise, streak artifact, nodule edge, clarity of small vessels, homogeneity of the normal lung parenchyma, and overall image quality. Two radiologists independently evaluated the nodules according to Lung-RADS using HIR, MBIR, HIR-DLD, and MBIR-DLD ULDCT images and SDCT images. The median scores for subjective analysis were analyzed using Wilcoxon signed rank test with Bonferroni correction. Intraobserver agreement for Lung-RADS category between ULDCT and SDCT was evaluated using the weighted kappa coefficient. RESULTS. In the subjective analysis, ULDCT with DLD showed significantly better scores than did ULDCT without DLD (p < 0.001), and MBIR-DLD showed the best scores among the ULDCT images (p < 0.001) for all items. In the Lung-RADS evaluation, HIR showed fair or moderate agreement (reader 1 and reader 2: κw = 0.46 and 0.32, respectively); MBIR, moderate or good agreement (κw = 0.68 and 0.57); HIR-DLD, moderate agreement (κw = 0.53 and 0.48); and MBIR-DLD, good agreement (κw = 0.70 and 0.72). CONCLUSION. DLD improved the image quality of both HIR and MBIR on ULDCT. MBIR-DLD was superior to HIR_DLD for image quality and for Lung-RADS evaluation.

Lung Adenocarcinoma at CT with 0.25-mm Section Thickness and a 2048 Matrix: High-Spatial-Resolution Imaging for Predicting Invasiveness.

Background High-spatial-resolution (HSR) CT provides detailed information and clear delineation of lung anatomy and disease states. HSR CT may have high diagnostic performance for predicting invasiveness of lung adenocarcinoma. Purpose To examine the diagnostic performance of HSR CT in predicting the invasiveness of lung adenocarcinoma. Materials and Methods In this retrospective study, 89 consecutive patients with adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IVA) were included who underwent surgery for lung cancer between January 2018 and December 2019. All patients underwent HSR CT with 0.25-mm section thickness and a 2048 matrix. Two independent observers evaluated the images for the presence or absence of the following HSR CT findings: lobulation, spiculation, pleural indentation, vessel convergence, homogeneity of ground-glass opacity, reticulation, irregularity and centrality of solid portion, and air bronchiologram (irregularity, disruption, or dilatation). The total diameter (≤1.6 cm or >1.6 cm) and the longest diameter of the solid portion (≤0.8 cm or >0.8 cm) were evaluated. Logistic regression models were used to identify findings associated with MIA plus IVA. Receiver operating characteristic analysis was performed to determine diagnostic performance. Results Eighty-nine patients (mean, 69 years ± 11 [standard deviation]; 49 men) were evaluated. The size of the nodules with invasion was a mean of 2.5 cm ± 1.2. Univariable analysis revealed lobulation, spiculation, pleural indentation, irregular and central solid portion, air bronchiologram with disruption and/or irregular dilatation, and total and solid portion diameters as associated with MIA plus IVA (all, P < .05). After adjustment for age, sex, and pack-years of smoking, disruption of air bronchogram and solid portion diameter greater than 0.8 cm remained as predictors of invasiveness (P = .001 and P = .02, respectively). The diagnostic performance of these two findings combined were as follows: sensitivity of 97% (59 of 61 patients; 95% confidence interval: 94%, 100%) and specificity of 86% (19 of 22 patients; 95% confidence interval: 65%, 97%), with an area under the curve of 0.94. Conclusion Using high-spatial-resolution CT, disruption of air bronchiologram and a solid portion greater than 0.8 cm were independently associated with a greater likelihood of invasiveness in lung adenocarcinoma. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lynch and Oh in this issue.

The effect of the reconstruction algorithm for the pulmonary nodule detection under the metal artifact caused by a pacemaker.

The aim was to compare the effects of metal artifacts from a pacemaker on pulmonary nodule detection among computed tomography (CT) images reconstructed using filtered back projection (FBP), single-energy metal artifact reduction (SEMAR), and forward-projected model-based iterative reconstruction solution (FIRST).Nine simulated nodules were placed inside a chest phantom with a pacemaker. CT images reconstructed using FBP, SEMAR, and FIRST were acquired at low and standard dose, and were evaluated by 2 independent radiologists.FIRST demonstrated the most significantly improved metal artifact and nodule detection on low dose CT (P < .0032), except at 10 mA and 5-mm thickness. At standard-dose CT, SEMAR showed the most significant metal artifact reduction (P < .00001). In terms of nodule detection, no significant differences were observed between FIRST and SEMAR (P = .161).With a pacemaker present, FIRST showed the best nodule detection ability at low-dose CT and SEMAR is comparable to FIRST at standard dose CT.

Ultra high-resolution computed tomography with 1024-matrix: Comparison with 512-matrix for the evaluation of pulmonary nodules.

PURPOSE: To determine whether a 1024-matrix provides superior image quality for the evaluation of pulmonary nodules. MATERIALS AND METHODS: Prospective evaluation conducted between December 2017 and April 2018, during which CT images showing lung nodules of more than 6 mm and less than 30 mmm were reconstructed with 2 different protocols: 0.5-mm thickness, 512 × 512 matrix, 34.5-cm field of view (FOV) (0.5-512 protocol); and 2-mm thickness, 1024 × 1024 matrix, 34.5-cm FOV (2-1024 protocol). Lung nodule characteristics such as margin, lobulation, pleural indentation, spiculation as well as peripheral vessels and bronchioles visibility and overall image quality were evaluated by three chest radiologists, using a 5-point scale. Image noise was evaluated by measuring the standard deviation in the region of interest for each image. RESULTS: A total of 89 nodules were evaluated. The 2-1024 protocol performed significantly better for the subjective evaluation of pulmonary nodules (p = 0.006 ∼ p < 0.0001). However, image noise was significantly higher both subjectively and objectively (p = 0.036, p < 0.0001). CONCLUSION: The use of a 2-1024 protocol does not increase the amount of images and allows better assessment of pulmonary nodules, despite noise increase.

Influence of field of view size on image quality: ultra-high-resolution CT vs. conventional high-resolution CT.

OBJECTIVES: This study was conducted in order to compare the effect of field of view (FOV) size on image quality between ultra-high-resolution CT (U-HRCT) and conventional high-resolution CT (HRCT). METHODS: Eleven cadaveric lungs were scanned with U-HRCT and conventional HRCT and reconstructed with five FOVs (40, 80, 160, 240, and 320 mm). Three radiologists evaluated and scored the images. Three image evaluations were performed, comparing the image quality with the five FOVs with respect to the 160-mm FOV. The first evaluation was performed on conventional HRCT images, and the second evaluation on U-HRCT images. Images were scored on normal structure, abnormal findings, and overall image quality. The third evaluation was a comparison of the images obtained with conventional HRCT and U-HRCT, with scoring performed on overall image quality. Quantitative evaluation of noise was performed by setting ROIs. RESULTS: In conventional HRCT, image quality was improved when the FOV was reduced to 160 mm. In U-HRCT, image quality, except for noise, improved when the FOV was reduced to 80 mm. In the third evaluation, overall image quality was improved in U-HRCT over conventional HRCT at all FOVs. Noise of U-HRCT increased with respect to conventional HRCT when the FOV was reduced from 160 to 40 mm. However, at 240- and 320-mm FOVs, the noise of U-HRCT and conventional HRCT showed no differences. CONCLUSIONS: In conventional HRCT, image quality did not improve when the FOV was reduced below 160 mm. However, in U-HRCT, image quality improved even when the FOV was reduced to 80 mm. KEY POINTS: • Reducing the size of the field of view to 160 mm improves diagnostic imaging quality in high-resolution CT. • In ultra-high-resolution CT, improvements in image quality can be obtained by reducing the size of the field of view to 80 mm. • Ultra-high-resolution CT produces images of higher quality compared with conventional HRCT irrespective of the size of the field of view.

Ultra-low-dose chest computed tomography for interstitial lung disease using model-based iterative reconstruction with or without the lung setting.

The aim of this study was to assess the effects of reconstruction on the image quality and quantitative analysis for interstitial lung disease (ILD) using filtered back projection (FBP) and model-based iterative reconstruction (MBIR) with the lung setting and the conventional setting on ultra-low-dose computed tomography (CT).Fifty-two patients with known ILD were prospectively enrolled and underwent CT at an ultra-low dose (0.18 ±â€Š0.02 mSv) and a standard dose (7.01 ±â€Š2.66 mSv). Ultra-low-dose CT was reconstructed using FBP (uFBP) and MBIR with the lung setting (uMBIR-Lung) and the conventional setting (uMBIR-Stnd). Standard-dose CT was reconstructed using FBP (sFBP). Three radiologists subjectively evaluated the images on a 3-point scale (1 = worst, 3 = best). For objective image quality analysis, regions of interest were placed in the lung parenchyma and the axillary fat, and standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were evaluated. For 32 patients with clinically diagnosed idiopathic interstitial pneumonia, quantitative measurements including total lung volume (TLV) and the percentage of ILD volume (%ILDV) were obtained. The medians of 3 radiologists' scores were analyzed using the Wilcoxon signed-rank test and the objective noise was analyzed using the paired t test. The Bonferroni correction was used for multiple comparisons. The quantitative measurements were analyzed using the Bland-Altman method.uMBIR-Lung scored better than uMBIR-Stnd and worse than sFBP (P < .001), except for noise and streak artifact in subjective analysis. The SD decreased significantly in the order of uMBIR-Stnd, uMBIR-Lung, sFBP, and uFBP (P < .001). The SNR and CNR increased significantly in the order of uMBIR-Stnd, uMBIR-Lung, sFBP, and uFBP (P < .001). For TLV, there was no significant bias between ultra-low-dose MBIRs and sFBP (P > .3). For %ILDV, there was no significant bias between uMBIR-Lung and sFBP (p = 0.8), but uMBIR-Stnd showed significantly lower %ILDV than sFBP (P = .013).uMBIR-Lung provided more appropriate image quality than uMBIR-Stnd. Although inferior to standard-dose CT for image quality, uMBIR-Lung showed equivalent CT quantitative measurements to standard-dose CT.

Subjective and objective comparisons of image quality between ultra-high-resolution CT and conventional area detector CT in phantoms and cadaveric human lungs.

OBJECTIVES: To compare the image quality of the lungs between ultra-high-resolution CT (U-HRCT) and conventional area detector CT (AD-CT) images. METHODS: Image data of slit phantoms (0.35, 0.30, and 0.15 mm) and 11 cadaveric human lungs were acquired by both U-HRCT and AD-CT devices. U-HRCT images were obtained with three acquisition modes: normal mode (U-HRCTN: 896 channels, 0.5 mm × 80 rows; 512 matrix), super-high-resolution mode (U-HRCTSHR: 1792 channels, 0.25 mm × 160 rows; 1024 matrix), and volume mode (U-HRCTSHR-VOL: non-helical acquisition with U-HRCTSHR). AD-CT images were obtained with the same conditions as U-HRCTN. Three independent observers scored normal anatomical structures (vessels and bronchi), abnormal CT findings (faint nodules, solid nodules, ground-glass opacity, consolidation, emphysema, interlobular septal thickening, intralobular reticular opacities, bronchovascular bundle thickening, bronchiectasis, and honeycombing), noise, artifacts, and overall image quality on a 3-point scale (1 = worst, 2 = equal, 3 = best) compared with U-HRCTN. Noise values were calculated quantitatively. RESULTS: U-HRCT could depict a 0.15-mm slit. Both U-HRCTSHR and U-HRCTSHR-VOL significantly improved visualization of normal anatomical structures and abnormal CT findings, except for intralobular reticular opacities and reduced artifacts, compared with AD-CT (p < 0.014). Visually, U-HRCTSHR-VOL has less noise than U-HRCTSHR and AD-CT (p < 0.00001). Quantitative noise values were significantly higher in the following order: U-HRCTSHR (mean, 30.41), U-HRCTSHR-VOL (26.84), AD-CT (16.03), and U-HRCTN (15.14) (p < 0.0001). U-HRCTSHR and U-HRCTSHR-VOL resulted in significantly higher overall image quality than AD-CT and were almost equal to U-HRCTN (p < 0.0001). CONCLUSIONS: Both U-HRCTSHR and U-HRCTSHR-VOL can provide higher image quality than AD-CT, while U-HRCTSHR-VOL was less noisy than U-HRCTSHR. KEY POINTS: • Ultra-high-resolution CT (U-HRCT) can improve spatial resolution. • U-HRCT can reduce streak and dark band artifacts. • U-HRCT can provide higher image quality than conventional area detector CT. • In U-HRCT, the volume mode is less noisy than the super-high-resolution mode. • U-HRCT may provide more detailed information about the lung anatomy and pathology.

Influence of gantry rotation time and scan mode on image quality in ultra-high-resolution CT system.

OBJECTIVES: To investigate the image quality of helical scan (HS) mode and non-helical scan (non-HS) mode on ultra-high-resolution CT in different gantry rotation time. METHODS: non-HS with 0.35 s/rot (non-HS200 mA/0.35 s). Three observers compared each non-HS image with HS image, and scored non-HS images by using 3-point scale, paying attention to normal findings, abnormal findings, noise, streak artifact, and overall image quality. Statistical analysis was performed with Steel-Dwass test. RESULTS: Overall image quality (score: 2.45) and noise (score: 2.42) of non-HS 200 mA/1.5s was statistically best (p < 0.0005). Overall Image quality and noise of non-HS200 mA/0.75 s (score: 2.0) was comparable to that of HS200 mA/1.5 s. CTDIvol of HS200 mA/1.5 s is 23.2 mGy. CTDIvol of non-HS200 mA/1.5 s, non-HS200 mA/0.75 s, non-HS200 mA/0.35 s is 19.2 mGy, 9.8 mGy, 4.7 mGy. CONCLUSION: Overall image quality and noise of non-helical scan is better than that of helical scan in the same rotation time. Overall Image quality of non-HS200 mA/0.75 s is comparable to that of HS200 mA/1.5 s, though the radiation dose of non-HS200 mA/0.75 s is lower than that of HS200 mA/1.5 s.

Effect of Matrix Size on the Image Quality of Ultra-high-resolution CT of the Lung: Comparison of 512 × 512, 1024 × 1024, and 2048 × 2048.

RATIONALE AND OBJECTIVES: This study aimed to assess the effect of matrix size on the spatial resolution and image quality of ultra-high-resolution computed tomography (U-HRCT). MATERIALS AND METHODS: Slit phantoms and 11 cadaveric lungs were scanned on U-HRCT. Slit phantom scans were reconstructed using a 20-mm field of view (FOV) with 1024 matrix size and a 320-mm FOV with 512, 1024, and 2048 matrix sizes. Cadaveric lung scans were reconstructed using 512, 1024, and 2048 matrix sizes. Three observers subjectively scored the images on a three-point scale (1 = worst, 3 = best), in terms of overall image quality, noise, streak artifact, vessel, bronchi, and image findings. The median score of the three observers was evaluated by Wilcoxon signed-rank test with Bonferroni correction. Noise was measured quantitatively and evaluated with the Tukey test. A P value of <.05 was considered significant. RESULTS: The maximum spatial resolution was 0.14 mm; among the 320-mm FOV images, the 2048 matrix had the highest resolution and was significantly better than the 1024 matrix in terms of overall quality, solid nodule, ground-glass opacity, emphysema, intralobular reticulation, honeycombing, and clarity of vessels (P < .05). Both the 2048 and 1024 matrices performed significantly better than the 512 matrix (P < .001), except for noise and streak artifact. The visual and quantitative noise decreased significantly in the order of 512, 1024, and 2048 (P < .001). CONCLUSION: In U-HRCT scans, a large matrix size maintained the spatial resolution and improved the image quality and assessment of lung diseases, despite an increase in image noise, when compared to a 512 matrix size.

Pathophysiology, Diagnosis, and Treatment of Radiation Necrosis in the Brain.

New radiation modalities have made it possible to prolong the survival of individuals with malignant brain tumors, but symptomatic radiation necrosis becomes a serious problem that can negatively affect a patient's quality of life through severe and lifelong effects. Here we review the relevant literature and introduce our original concept of the pathophysiology of brain radiation necrosis following the treatment of brain, head, and neck tumors. Regarding the pathophysiology of radiation necrosis, we introduce two major hypotheses: glial cell damage or vascular damage. For the differential diagnosis of radiation necrosis and tumor recurrence, we focus on the role of positron emission tomography. Finally, in accord with our hypothesis regarding the pathophysiology, we describe the promising effects of the anti-vascular endothelial growth factor antibody bevacizumab on symptomatic radiation necrosis in the brain.

Intratumoral and peritumoral post-irradiation changes, but not viable tumor tissue, may respond to bevacizumab in previously irradiated meningiomas.

The efficacy of bevacizumab has not been determined for treatment-refractory meningiomas. We treated meningiomas with low-dose bevacizumab and compared the radiological responses of non-irradiated meningiomas with previously irradiated meningiomas. In addition, we assessed intraparenchymal radiation necrosis following bevacizumab treatment.Six patients with meningiomas (three anaplastic, one atypical, and two grade I) who were previously treated with multiple sessions of radiotherapy and subsequently developed perilesional edema were treated with bevacizumab. Of six patients, two patients with anaplastic meningiomas developed three tumors following radiotherapy, which were defined as non-irradiated tumors. There were 12 pre-existing extra-axial tumors that were previously irradiated. Some of these tumors demonstrated adjacent intraparenchymal contrast enhancement. These tumors were defined as post-irradiated tumors. Four patients had intraparenchymal radiation necrosis. Low-dose bevacizumab was administered biweekly over 3-6 cycles to all patients.Four tumors decreased in contrast-enhanced volume, nine tumors were unchanged, and two tumors progressed. Of the three non-irradiated tumors, two tumors increased in volume (126 % and 198 %) and one tumor was stable (-5 %). The median reduction rates determined by contrast volume were -31 % and -71 % in post-irradiated tumors and radiation necrosis, respectively. Non-irradiated tumors had a significantly poorer response to bevacizumab than post-irradiated tumors and radiation necrosis (p = 0.0013 and p = 0.0005, respectively, Tukey-Kramer test).Low-dose bevacizumab did not demonstrate efficacy in the treatment of non-irradiated meningiomas. Responses to low-dose bevacizumab could be related to its effect on post-irradiation changes, rather than its effect on biologically active tumor tissue in post-irradiated meningiomas. Radiological responses to low-dose bevacizumab may distinguish biologically active tumors from post-irradiation changes in progressive meningiomas following radiotherapy.

Reversible hypopituitarism with pituitary tuberculoma.

A 50-year-old woman presented with a headache and nausea. A sellar and suprasellar mass was detected on MRI; the tumor was heterogeneously enhanced with gadolinium, and the pituitary stalk was slightly thickened. Laboratory tests revealed severe growth hormone, luteinizing hormone, follicle-stimulating hormone and thyroid-stimulating hormone deficiencies. A pathological examination of the tumor showed scattered granulomas with central necrosis and Langhans giant cells. Tuberculin skin and QuantiFERON TB-Gold tests (QFT-2G) were positive. Accordingly, we diagnosed the patient with pituitary tuberculoma presenting with pituitary dysfunction. Following treatment with antituberculous drugs, the pituitary hormone function normalized and the pituitary tuberculoma disappeared.