Journal of Computer Assisted Tomography最新文献

Purpose: The aim of the study is to investigate the feasibility of using dual-source computed tomography (CT) combined with low flow rate and low tube voltage for postchemotherapy image assessment in cancer patients.

Methods: Ninety patients undergoing contrast-enhanced CT scans of the upper abdomen were prospectively enrolled and randomly assigned to groups A, B, and C (n = 30 each). In group A, patients underwent scans at 120 kVp with 448 mgI/kg. Patients in group B underwent scans at 100 kVp with 336 mgI/kg. Patient in group C underwent scans at 70 kVp with of 224 mgI/kg. Quantitative measurements including the CT number, standard deviation of CT number, signal-to-noise ratio, contrast-to-noise ratio, subjective reader scores, and the volume and flow rate of contrast agent were evaluated for each group.

Results: There was no statistically significant difference in the subjective image scores within the three groups except for the kidney (all P > 0.05). Group C showed significantly higher CT values, lower noise levels, and higher signal-to-noise ratio and contrast-to-noise ratio values in the majority of the regions of interest compared to the other groups (P < 0.05). In group C, the contrast agent dose was decreased by 46% compared to group A (79.48 ± 12.24 vs 42.7 ± 8.6, P < 0.01), and the contrast agent injection rate was reduced by 22% (2.7 ± 0.41 vs 2.1 ± 0.4, P < 0.01).

Conclusions: The use of 70 kVp tube voltage combined with low iodine flow rates prove to be a more effective approach in solving the challenge of compromised blood vessels in postchemotherapy tumor patients, without reducing image quality and diagnostic confidence.

Objective: This study aimed to develop and assess the precision of a radiomics signature based on computed tomography imaging for predicting segmental chromosomal aberrations (SCAs) status at 1p36 and 11q23 in neuroblastoma.

Methods: Eighty-seven pediatric patients diagnosed with neuroblastoma and with confirmed genetic testing for SCAs status at 1p36 and 11q23 were enrolled and randomly stratified into a training set and a test set. Radiomics features were extracted from 3-phase computed tomography images and analyzed using various statistical methods. An optimal set of radiomics features was selected using a least absolute shrinkage and selection operator regression model to calculate the radiomics score for each patient. The radiomics signature was validated using receiver operating characteristic curves to obtain the area under the curve and 95% confidence interval (CI).

Results: Eight radiomics features were carefully selected and used to compute the radiomics score, which demonstrated a statistically significant distinction between the SCAs and non-SCAs groups in both sets. The radiomics signature achieved an area under the curve of 0.869 (95% CI, 0.788-0.943) and 0.883 (95% CI, 0.753-0.978) in the training and test sets, respectively. The accuracy of the radiomics signature was 0.817 and 0.778 in the training and test sets, respectively. The Hosmer-Lemeshow test confirmed that the radiomics signature was well calibrated.

Conclusions: Computed tomography-based radiomics signature has the potential to predict SCAs at 1p36 and 11q23 in neuroblastoma.

Objective: A simulation-based supervised deep neural network (DNN) can accurately estimate cerebral blood flow (CBF) and arterial transit time (ATT) from multidelay arterial spin labeling signals. However, the performance of deep learning depends on the characteristics of the training data set. We aimed to investigate the effects of the ground truth (GT) ranges of CBF and ATT on the performance of the DNN when training data were prepared using arterial spin labeling signal simulation.

Methods: Deep neural networks were individually trained using 36 patterns of the training data sets. Simulation test data (1,000,000 points), 17 healthy volunteers, and 1 patient with moyamoya disease were included. The simulation test data were used to evaluate accuracy, precision, and noise immunity of the DNN. The best-performing DNN was determined by the normalized mean absolute error (NMAE), normalized root mean squared error (NRMSE), and normalized coefficient of variation over repeated training (CV Net ). Cerebral blood flow and ATT values and their histograms were compared between the GT and predicted values. For the in vivo data, the dependency of the predicted values on the GT ranges was visually evaluated by comparing CBF and ATT maps between the best-performing DNN and the other DNNs. Moreover, using the synthesized noisy images, noise immunity was compared between the best-performing DNN based on the simulation study and a conventional method.

Results: The simulation study showed that a network trained by the GT of CBF and ATT in the ranges of 0 to 120 mL/100 g/min and 0 to 4500 milliseconds, respectively, had the highest performance (NMAE CBF , 0.150; NRMSE CBF , 0.231; CV NET CBF , 0.028; NMAE ATT , 0.158; NRMSE ATT , 0.257; and CV NET ATT , 0.028). Although the predicted CBF and ATT varied with the GT range of the training data sets, the appropriate settings preserved the accuracy, precision, and noise immunity of the DNN. In addition, the same results were observed in in vivo studies.

Conclusions: The GT ranges to prepare the training data affected the performance of the simulation-based supervised DNNs. The predicted CBF and ATT values depended on the GT range; inappropriate settings degraded the accuracy, whereas appropriate settings of the GT range provided accurate and precise estimates.

Objective: Salivary gland lesions show overlapping morphological findings and types of time/intensity curves. This research aimed to evaluate the role of 2-phase multislice spiral computed tomography (MSCT) texture analysis in differentiating between benign and malignant salivary gland lesions.

Methods: In this prospective study, MSCT was carried out on 90 patients. Each lesion was segmented on axial computed tomography (CT) images manually, and 33 texture features and morphological CT features were assessed. Logistic regression analysis was used to confirm predictors of malignancy ( P < 0.05 was considered to be statistically significant), followed by receiver operating characteristics analysis to assess the diagnostic performance.

Results: Univariate logistic regression analysis revealed that morphological CT features (shape, size, and invasion of adjacent tissues) and 17 CT texture parameters had significant differences between benign and malignant lesions ( P < 0.05). Multivariate binary logistic regression demonstrated that shape, invasion of adjacent tissues, entropy, and inverse difference moment were independent factors for malignant tumors. The diagnostic accuracy values of multivariate binary logistic models based on morphological parameters, CT texture features, and a combination of both were 87.8%, 90%, and 93.3%, respectively.

Conclusions: Two-phase MSCT texture analysis was conducive to differentiating between malignant and benign neoplasms in the salivary gland, especially when combined with morphological CT features.

Objective: The aim of this study was to evaluate the association between medial patellar plica (MPP) syndrome and the morphological features of the MPP, including length, width, and thickness, on knee magnetic resonance imaging (MRI).

Materials and methods: From 2018 to 2022, 167 patients diagnosed with isolated MPP syndrome based on both MRI and arthroscopic findings were included in the "study group" and 226 patients without knee pathology on both MRI and physical examination were included in the "control group." Finally, 393 patients (mean age, 38.9 ± 5.7 years) with 405 knee MRI examinations were included. Morphological MR features of MPP were assessed, including width, length, and thickness. Multivariate regression and receiver operating characteristic analyses were performed to identify the factors associated with MPP syndrome.

Results: The mean thickness of MPP was significantly higher in the study group than control group (2.3 ± 0.5 mm vs 1.0 ± 0.8 mm, P < 0.001). Moreover, on multivariate analysis, MPP thickness was the only significant factor associated with MPP syndrome (odds ratio, 6.452; 95% confidence interval, 0.816-15.073; P = 0.002). On receiver operating characteristic analysis, thickness ≥1.8 mm was estimated as the optimal cutoff for predicting MPP syndrome with sensitivity of 75.9%, specificity of 65.4%, and area under the curve of 0.727 (95% confidence interval, 0.667-0.788; P < 0.001).

Conclusions: Measurement of MPP thickness on MRI could be a morphological predictor of MPP syndrome.

Objective: This study aimed to address the gap in knowledge assessing the impact of visceral and subcutaneous body fat on 3-dimensional computed tomography imaging in patients with greater trochanteric pain syndrome (GTPS) in comparison with those primarily diagnosed with osteoarthritis (OA).

Materials and methods: We evaluated adult patients with a confirmed diagnosis of GTPS from our institutional hip-preservation clinic spanning 2011 to 2022. Selection criteria included their initial clinic visit for hip pain and a concurrent pelvis computed tomography scan. These patients were age- and sex-matched to mild-moderate OA patients selected randomly from the database. Visceral and subcutaneous fat areas were measured volumetrically from the sacroiliac joint to the lesser trochanter using an independent software. Interreader reliability was also calculated.

Results: A total of 93 patients met the study criteria, of which 37 belonged to the GTPS group and 56 belonged to the OA group. Both groups were sex and race matched. Average age in GTPS and OA groups was 59.3 years and 56 years, respectively. For GTPS group, average body mass index was 28.9 kg/m 2 , and for the OA group, average body mass index was 29.9 kg/m 2 , with no significant difference ( P > 0.05). Two-sample t test showed no significant differences in the visceral fat, subcutaneous fat, or the visceral fat to total fat volume ratio between the GTPS and OA groups. There was excellent interreader reliability.

Conclusions: Our results indicate that there is no significant difference in fat distribution and volumes among GTPS and OA patients. This suggests that being overweight or obese may not be directly linked or contribute to the onset of GTPS. Other factors, such as gluteal tendinopathy, bursitis, or iliotibial band syndrome, might be responsible and need further investigation.

Objective: Prostate cancer and interstitial lung abnormality (ILA) share similar risk factor, which is men and older age. The purpose of this study was to investigate the prevalence of pretreatment ILA among prostate cancer patients who underwent abdominal computed tomography (CT) within 1 year at their first visit to the urology department. In addition, we aimed to assess the association between pretreatment ILA and long-term survival in prostate cancer patients.

Methods: This study was conducted in patients who had a first visit for prostate cancer at urology department between 2005 and 2016 and underwent an abdominal CT within 1 year. A thoracic radiologist evaluated the presence of ILA through inspecting the lung base scanned on an abdominal CT. The association between pretreatment ILA and survival was assessed using Kaplan-Meier analysis with log-rank test. Specific survival rates at 12, 36, and 60 months according to the presence of ILA were evaluated using z -test. Cox regression analysis was used to assess the risk factors of mortality.

Results: A total of 173 patients were included (mean age, 70.23 ± 7.98 years). Pretreatment ILA was observed in 10.4% of patients. Patients with ILA were more likely to be older and current smokers. Pretreatment ILA was associated with poor survival ( P < 0.001). Age ≥70 years (hazards ratio [HR], 1.98; 95% confidence interval [CI], 1.24-3.16; P = 0.004), metastatic stage (HR, 2.26; 95% CI, 1.36-3.74; P = 0.002), and ILA (HR, 1.96; 95% CI, 1.06-3.60; P = 0.031) were the independent risk factors of mortality. An ILA (HR, 3.94; 95% CI, 1.78-8.72; P = 0.001) was the only independent risk factor of mortality in localized stage prostate cancer patients.

Conclusions: This study provides important insights into the unexplored effect of pretreatment ILA in prostate cancer patients. Pretreatment ILAs were observed considerably in the lung bases scanned on the abdominal CT scans among prostate cancer patients. Furthermore, pretreatment ILAs were the risk factor of mortality. Therefore, lung bases should be routinely inspected in the abdominal CT scans of prostate cancer patients. This result may help clinicians in establishing personalized management strategy of prostate cancer patients.

Abstract: We describe early ex vivo proof-of-concept testing of a novel system composed of a disposable endorectal coil and converging multichannel needle guide with a reusable clamp stand, embedded electronics, and baseplate to allow for endorectal magnetic resonance (MR) imaging and in-bore MRI-targeted biopsy of the prostate as a single integrated procedure. Using prostate phantoms imaged with standard T 2 -weighted sequences in a Siemens 3T Prisma MR scanner, we measured the signal-to-noise ratio in successive 1-cm distances from the novel coil and from a commercially available inflatable balloon coil and measured the lateral and longitudinal deviation of the tip of a deployed MR compatible needle from the intended target point. Signal-to-noise ratio obtained with the novel system was significantly better than the inflatable balloon coil at each of five 1-cm intervals, with a mean improvement of 78% ( P < 0.05). In a representative sampling of 15 guidance channels, the mean lateral deviation for MR imaging-guided needle positioning was 1.7 mm and the mean longitudinal deviation was 2.0 mm. Our ex vivo results suggest that our novel system provides significantly improved signal-to-noise ratio when compared with an inflatable balloon coil and is capable of accurate MRI-guided needle deployment.

Objective: This study aimed to evaluate the correlation between the estimated body weight obtained from 2 easy-to-perform methods and the actual body weight at different computed tomography (CT) levels and determine the best reference site for estimating body weight.

Methods: A total of 862 patients from a public database of whole-body positron emission tomography/CT studies were retrospectively analyzed. Two methods for estimating body weight at 10 single-slice CT levels were evaluated: a linear regression model using total cross-sectional body area and a deep learning-based model. The accuracy of body weight estimation was evaluated using the mean absolute error (MAE), root mean square error (RMSE), and Spearman rank correlation coefficient ( ρ ).

Results: In the linear regression models, the estimated body weight at the T5 level correlated best with the actual body weight (MAE, 5.39 kg; RMSE, 7.01 kg; ρ = 0.912). The deep learning-based models showed the best accuracy at the L5 level (MAE, 6.72 kg; RMSE, 8.82 kg; ρ = 0.865).

Conclusions: Although both methods were feasible for estimating body weight at different single-slice CT levels, the linear regression model using total cross-sectional body area at the T5 level as an input variable was the most favorable method for single-slice CT analysis for estimating body weight.

Abstract: Substance abuse continues to be prevalent nationwide and can lead to a myriad of chest pathologies. Imaging findings are vast and can include nodules, masses, ground-glass opacities, airspace disease, and cysts. Radiologists with awareness of these manifestations can assist in early identification of disease in situations where information is unable to be obtained from the patient. This review focuses on thoracic imaging findings associated with various forms of substance abuse, which are organized by portal of entry into the thorax: inhalation, ingestion, and injection.