Journal of Computer Assisted Tomography最新文献

Purpose: Fluoroscopic-guided lumbar puncture (FG-LP) is a common neuroradiologic procedure. Traditionally, a minimum platelet count (MPC) of 50,000/μL for this procedure has been required; however, we recently adopted a lower MPC threshold of 20,000/μL. The purpose of this study was to compare adverse events in patients undergoing FG-LP with MPCs above to those below the conventional 50,000/μL threshold.

Materials: This was an institutional review board-approved, retrospective study on adult patients with hematologic malignancy undergoing FG-LP in the neuroradiology division between May 2021 and December 2022, after lowering the minimal required MPC to 20,000/μL. Recorded data included indication for FG-LP, preprocedure and postprocedure MPC, need for and number of platelet transfusions within 24 hours of FG-LP, presence of traumatic tap, FG-LP-related complications, and any platelet transfusion-related adverse event. Patients were classified into 2 groups based on MPC: (1) those above 50,000/μL and (2) those below 50,000/μL. Descriptive statistics were used comparing these 2 groups.

Results: One hundred twenty-eight patients underwent FG-LP, with 46 having an MPC between 20,000 and 50,000/μL and 82 having an MPC above 50,000/μL. No postprocedural complications were encountered in either group. Traumatic taps occurred in 10/46 (22%)​ with MPC below 50,000/μL versus 10/82 (12%)​ in those with MPC above 50,000/μL. Forty of 46 patients (87%) were transfused with platelets within 24 hours prior to FG-LP. One patient developed a transfusion-related reaction.

Conclusion: Lowering the MPC threshold from 50,000/μL to 20,000/μL for FG-LP did not result in a higher incidence of spinal hematoma.

Purpose: This study aimed to compare the image quality of chest computed tomography (CT) scans for COVID-19 pneumonia using forward-projected model-based iterative reconstruction solution-LUNG (FIRST-LUNG) with filtered back projection (FBP) and hybrid iterative reconstruction (HIR).

Method: The CT images of 44 inpatients diagnosed with COVID-19 pneumonia between December 2022 and June 2023 were retrospectively analyzed. The CT images were reconstructed using FBP, HIR, and FIRST-LUNG-MILD/STANDARD/STRONG. The CT values and noise of the lumen of the main trachea and erector spine muscle were measured for each group. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Subjective evaluations included overall image quality, noise, streak artifact, visualization of normal lung structures, and abnormal CT features. One-way analysis of variance was used to compare the objective and subjective indicators among the five groups. The task-based transfer function was derived for three distinct contrasts representing anatomical structures, lower-contrast lesion, and higher-contrast lesion.

Results: The results of the study demonstrated significant differences in image noise, SNR, and CNR among the five groups ( P < 0.001). The FBP images exhibited the highest levels of noise and the lowest SNR and CNR among the five groups ( P < 0.001). When compared to the FBP and HIR groups, the noise was lower in the FIRST-LUNG-MILD/STANDARD/STRONG group, while the SNR and CNR were higher ( P < 0.001). The subjective overall image quality score of FIRST-LUNG-MILD/STANDARD was significantly better than FBP and FIRST-LUNG-STRONG ( P < 0.001). FIRST-LUNG-MILD was superior to FBP, HIR, FIRST-LUNG-STANDARD, and FIRST-LUNG-STRONG in visualizing proximal and peripheral bronchovascular and subpleural vessels ( P < 0.05). Additionally, FIRST-LUNG-MILD achieved the best scores in evaluating abnormal lung structure ( P < 0.001). The overall interobserver agreement was substantial (intraclass correlation coefficient = 0.891). The task-based transfer function 50% values of FIRST reconstructions are consistently higher compared to FBP and HIR.

Conclusions: The FIRST-LUNG-MILD/STANDARD algorithm can enhance the image quality of chest CT in patients with COVID-19 pneumonia, while preserving important details of the lesions, better than the FBP and HIR algorithms. After evaluating various COVID-19 pneumonia lesions and considering the improvement in image quality, we recommend using the FIRST-LUNG-MILD reconstruction for diagnosing COVID-19 pneumonia.

Background: The present study aims to evaluate the postprocessing image quality of a deep-learning (DL)-based automatic bone removal algorithm in the real clinical practice for cervical computed tomography angiography (CTA).

Materials and methods: A total of 100 patients (31 females, 61.4 ± 12.4 years old) who had performed cervical CTA from January 2022 to July 2022 were included retrospectively. Three different types of scanners were used. Ipsilateral cervical artery was divided into 10 segments. The performance of the DL algorithm and conventional algorithm in terms of bone removal and vascular integrity was independently evaluated by two radiologists for each segment. The difference in the performance between the two algorithms was compared. Inter- and intrarater consistency were assessed, and the correlation between the degree of carotid artery stenosis and the rank of bone removal and vascular integrity was analyzed.

Results: Significant differences were observed in the rankings of bone removal and vascular integrity between the two algorithms on most segments on both sides. Compared to DL algorithm, the conventional algorithm showed a higher correlation between the degree of carotid artery stenosis and vascular integrity ( r = -0.264 vs r = -0.180). The inter- and intrarater consistency of DL algorithm were found to be higher than or equal to those of conventional algorithm.

Conclusions: The DL algorithm for bone removal in cervical CTA demonstrated significantly better performance than conventional postprocessing method, particularly in the segments with complex anatomical structures and adjacent to bone.

Objective: Elastofibroma dorsi (ED) is an uncommon benign tumor that is commonly incidentally discovered on thoracic imaging and at times misinterpreted as a more aggressive lesion. The objective of the study is to characterize the typical cross-sectional imaging findings of elastofibroma dorsi and quantify the risk of masquerading malignancy.

Methods: Retrospective search of radiology and pathology reports over a 12-year period identified 409 cases of suspected ED. Pertinent imaging was reviewed with a focus on computed tomography (CT) and magnetic resonance imaging (MRI), specifically assessing lesion location, presence of interspersed fat, and appearances on follow-up.

Results: Typical imaging appearances of 310 ED, including 10% with pathologic confirmation, were that of a mass deep to the serratus anterior (98%) and near the scapular tip (98%). Intralesional interspersed fat was present in 87% of cases imaged with CT and in 90% of cases imaged with MRI. In the 43 cases imaged with both modalities, 8 (19%) did not have interspersed fat on CT, but 7 (88%) of these did have interspersed fat on MRI. Twelve tumors (benign and malignant) were included, of which only 17% were deep to serratus anterior and 25% were at the scapular tip, P = 0.0001 and P < 0.0001 versus ED. Only a single tumor contained interspersed fat, P < 0.001 versus ED, which had benign pathology on biopsy.

Conclusions: Elastofibroma dorsi can be diagnosed with a high degree of certainty in the presence of classic location and imaging characteristics, obviating the need for further imaging or biopsy.

Background: Studies have shown that the type of low-grade glioma is associated with its shape. The traditional diagnostic method involves extraction of the tumor shape from MRIs and diagnosing the type of glioma based on corresponding relationship between the glioma shape and type. This method is affected by the MRI background, tumor pixel size, and doctors' professional level, leading to misdiagnoses and missed diagnoses. With the help of deep learning algorithms, the shape of a glioma can be automatically segmented, thereby assisting doctors to focus more on the diagnosis of glioma and improving diagnostic efficiency. The segmentation of glioma MRIs using traditional deep learning algorithms exhibits limited accuracy, thereby impeding the effectiveness of assisting doctors in the diagnosis. The primary objective of our research is to facilitate the segmentation of low-grade glioma MRIs for medical practitioners through the utilization of deep learning algorithms.

Methods: In this study, a UNet glioma segmentation network that incorporates multiattention gates was proposed to address this limitation. The UNet-based algorithm in the coding part integrated the attention gate into the hierarchical structure of the network to suppress the features of irrelevant regions and reduce the feature redundancy. In the decoding part, by adding attention gates in the fusion process of low- and high-level features, important feature information was highlighted, model parameters were reduced, and model sensitivity and accuracy were improved.

Results: The network model performed image segmentation on the glioma MRI dataset, and the accuracy and average intersection ratio (mIoU) of the algorithm segmentation reached 99.7%, 87.3%, 99.7%, and 87.6%.

Conclusions: Compared with the UNet, PSPNet, and Attention UNet network models, this network model has obvious advantages in accuracy, mIoU, and loss convergence. It can serve as a standard for assisting doctors in diagnosis.

Objective: This study aims to evaluate, on one MRI vendor's platform, the impact of deep learning (DL)-based reconstruction techniques on MRI radiomic features compared to conventional image reconstruction techniques.

Methods: Under IRB approval and informed consent, we prospectively collected undersampled coronal T2-weighted MR images of the abdomen (1.5 T; Philips Healthcare) from 17 pediatric and adult subjects and reconstructed them using a conventional image reconstruction technique (compressed sensitivity encoding [C-SENSE]) and two DL-based reconstruction techniques (SmartSpeed [Philips Healthcare, US FDA cleared] and SmartSpeed with Super Resolution [SmartSpeed-SuperRes, not US FDA cleared to date]). Eight regions of interest (ROIs) across organs/tissues (liver, spleen, kidney, pancreas, fat, and muscle) were manually placed. Eighty-six MRI radiomic features were then extracted. Pearson's correlation coefficients (PCCs) and intraclass correlation coefficients (ICCs) were calculated between (A) C-SENSE versus SmartSpeed, and (B) C-SENSE versus SmartSpeed-SuperRes. To quantify the impact from the perspective of the whole MR image, cross-ROI mean PCCs and ICCs were calculated for individual radiomic features. The impact of image reconstruction on individual radiomic features in different organs/tissues was evaluated using ANOVA analyses.

Results: According to cross-ROI mean PCCs, 50 out of 86 radiomic features were highly correlated (PCC, ≥0.8) between SmartSpeed and C-SENSE, whereas only 15 radiomic features were highly correlated between SmartSpeed-SuperRes and C-SENSE reconstructions. According to cross-ROI mean ICCs, 58 out of 86 radiomic features had high agreements (ICC ≥0.75) between SmartSpeed and C-SENSE, whereas only 9 radiomic features had high agreements between SmartSpeed-SuperRes and C-SENSE reconstructions. For SmartSpeed reconstruction, the psoas muscle ROI appeared to be impacted most with the lowest median (IQR) correlation of 0.57 (0.25). The circular liver ROI was impacted most by SmartSpeed-SuperRes (PCC, 0.60 [0.22]). ANOVA analyses suggest that the impact of DL reconstruction algorithms on radiomic features varies significantly among different organs/tissues ( P < 0.001).

Conclusions: MRI radiomic features are significantly altered by DL-based reconstruction compared to a conventional reconstruction technique. The impact of DL reconstruction algorithms on radiomic features varies significantly between different organs/tissues.

Objective: We devised a split-bolus injection and imaging protocol for pulmonary artery and vein separation computed tomography (CT) angiography based on time enhancement curve characterization. Furthermore, we aimed to evaluate the contrast enhancement effect and success rate of blood vessel separation between the pulmonary artery and vein of this proposed protocol.

Methods: In this study, 102 patients (45 patients with the standard protocol and 57 patients with the proposed protocol) who underwent pulmonary arteriovenous computed tomography angiography were included. The CT values of various vessels, CT value difference between the pulmonary trunk and left atrium, and coefficient of variation in pulmonary arteries and veins were obtained from images of the standard and proposed protocols.

Results: The CT values in the proposed protocol for the pulmonary trunk were significantly higher than those in the standard protocol (487.3 [415.5-546.9] HU vs. 293.0 [259.0-350.0] HU, P < 0.01). The CT value difference between the pulmonary trunk and left atrium in the proposed protocol was significantly higher than that in the conventional protocol (211.3 [158.0-265.7] HU vs. 32 [-30.0-55.0] HU, P < 0.01). The coefficient of variation in the proposed protocol was 0.08 (0.06-0.10) and 0.09 (0.08-0.11) in pulmonary arteries and 0.08 (0.06-0.09) and 0.09 (0.07-0.12) in pulmonary veins, respectively.

Conclusions: The proposed protocol achieved separation between the pulmonary artery and vein in many patients, making it useful for the preoperative assessment of individual thoracic anatomy.

Objective: The aim of this study was to explore whether machine learning model based on computed tomography (CT) radiomics and clinical characteristics can differentiate Epstein-Barr virus-associated gastric cancer (EBVaGC) from non-EBVaGC.

Methods: Contrast-enhanced CT images were collected from 158 patients with GC (46 EBV-positive, 112 EBV-negative) between April 2018 and February 2023. Radiomics features were extracted from the volumes of interest. A radiomics signature was built based on radiomics features by the least absolute shrinkage and selection operator logistic regression algorithm. Multivariate analyses were used to identify significant clinicoradiological variables. We developed 6 ML models for EBVaGC, including logistic regression, Extreme Gradient Boosting, random forest (RF), support vector machine, Gaussian Naive Bayes, and K-nearest neighbor algorithm. The area under the receiver operating characteristic curve (AUC), the area under the precision-recall curves (AP), calibration plots, and decision curve analysis were applied to assess the effectiveness of each model.

Results: Six ML models achieved AUC of 0.706-0.854 and AP of 0.480-0.793 for predicting EBV status in GC. With an AUC of 0.854 and an AP of 0.793, the RF model performed the best. The forest plot of the AUC score revealed that the RF model had the most stable performance, with a standard deviation of 0.003 for AUC score. RF also performed well in the testing dataset, with an AUC of 0.832 (95% confidence interval: 0.679-0.951), accuracy of 0.833, sensitivity of 0.857, and specificity of 0.824, respectively.

Conclusions: The RF model based on clinical variables and Rad_score can serve as a noninvasive tool to evaluate the EBV status of gastric cancer.

Objective: This study aimed to evaluate a vendor-specific correction software for apparent diffusion coefficient (ADC) bias due to gradient nonlinearity in breast diffusion-weighted magnetic resonance imaging using an ice-water phantom.

Methods: The phantom consists of 5 plastic tubes with a length of 100 mm and a diameter of 15 mm, filled with distilled water and immersed in an ice-water bath. Diffusion-weighted images were acquired by echo-planar imaging sequence on a 3.0-T scanner. ADC maps with and without correction were calculated using 4 b -values (0, 100, 600, and 800 s/mm 2 ). The mean ADCs were measured using a rectangular profile with 5 × 40 pixels in the anterior-posterior (AP) and a square region of interest with 5 × 5 pixels in the right-left (RL) and superior-inferior (SI) directions on the ADC map. ADC was compared with and without correction using a paired t test. Additionally, ADC of the ice-water phantom was measured at the magnet isocenter.

Results: ADC increased in the AP and RL directions and decreased in the SI direction with increasing distance from the isocenter before correction. After the correction, ADC at the off-center positions in the AP, RL, and SI directions was reduced to within 5% of the expected value. There were significant differences in the ADC at the off-center positions without and with correction ( P < 0.001); however, ADC at the magnet isocenter did not vary after correction (1.08 ± 0.02 × 10 -3 mm 2 /s).

Conclusions: The vendor-specific software corrected the ADC bias due to gradient nonlinearity at the off-center positions in the AP, RL, and SI directions. Therefore, the software will contribute to the accurate ADC assessment in breast DWI.

Objective: The objectives of this research were to assess the effectiveness of computer-assisted detection reading (CADR) and structured reports in monitoring patients with multiple sclerosis (MS) and to evaluate the role of radiology technicians in this context.

Methods: Eighty-seven patients with MS who underwent at least 2 sequential magnetic resonance imaging (MRI) follow-ups analyzed by 2 radiologists and a technician. Progression of disease (POD) was identified through the emergence of T2 fluid-attenuated inversion recovery white matter hyperintensities or contrast enhancements and evaluated both qualitatively (progression vs stability) and quantitatively (count of new white matter hyperintensities).

Results: CADR increased the accuracy by 11%, enhancing interobserver consensus on qualitative progression and saving approximately 2 minutes per examination. Although structured reports did not improve these metrics, it may improve clinical communication and permit technicians to achieve approximately 80% accuracy in MRI readings.

Conclusions: The use of CADR improves the accuracy, agreement, and interpretation time in MRI follow-ups of MS. With the help of computer tools, radiology technicians could represent a significant aid in the follow-up of these patients.