Journal of Computer Assisted Tomography最新文献

Background: Bariatric surgery is associated with decreasing bone mineral density (BMD).

Objective: To assess the long-term vertebral BMD, measured by opportunistic quantitative CT (QCT), and body mass index (BMI) in patients undergoing proximal laparoscopic Roux-en-Y surgery (LRYGB).

Methods: In 62 patients undergoing LRYGB, opportunistic QCT measurements were performed extracting BMD and BMI on day 1 and years 1, 3, and 5 postoperatively.Primarily, one-way analyses of variance were performed on dependent variables BMI and BMD, with imaging interval defined as an independent factor. Student-Newman-Keuls tests performed pairwise comparisons of imaging interval permutations for BMI/BMD.Secondarily, analyses of covariance were used on dependent variables BMI and BMD, with imaging interval as an independent factor and gender/age as well as BMD/BMI, respectively, as covariates.

Results: A total of 227 opportunistic QCT measurements in 62 patients were performed without the need of a phantom or extra software.The BMD decreased substantially and continuously during 1-, 3-, and 5-year follow-up observations, reaching statistical significance in pairwise comparisons for 3- and 5-year follow-up visits compared to initial BMD values as well as the 5-year follow-up visit compared to the 1-year BMD values, P < 0.001. Age and BMI were significant covariates, P < 0.001.The BMI decreased within 1 year and stayed constant until a slight increase at 5 years was observed. Statistical significance in pairwise comparisons for first-year and 3- and 5-year follow-up visits was reached compared to initial BMI values, P < 0.001. For the BMI assessment, none of the covariates reached statistical significance.

Conclusion: Opportunistic QCT is suited for the calculation and follow-up of BMD. There was a continuous decrease of BMD after LRYGB over 5 years post-surgery, whereas BMI decreased in the first year and stayed constant thereafter. Older patients with lower BMI seem particularly prone to an accelerated BMD loss.

Purpose: This study aimed to enhance the predictability of local tumor progression (LTP) postthermal ablation in patients with colorectal cancer liver metastases (CRLMs). A sophisticated approach integrating magnetic resonance imaging (MRI) Δ-radiomics and clinical feature-based modeling was employed.

Materials and methods: In this retrospective study, 37 patients with CRLM were included, encompassing a total of 57 tumors. Radiomics features were derived by delineating the images of lesions pretreatment and images of the ablation zones posttreatment. The change in these features, termed Δ-radiomics, was calculated by subtracting preprocedure values from postprocedure values. Three models were developed using the least absolute shrinkage and selection operators (LASSO) and logistic regression: the preoperative lesion model, the postoperative ablation area model, and the Δ model. Additionally, a composite model incorporating identified clinical features predictive of early treatment success was created to assess its prognostic utility for LTP.

Results: LTP was observed in 20 out of the 57 lesions (35%). The clinical model identified, tumor size (P = 0.010), and ΔCEA (P = 0.044) as factors significantly associated with increased LTP risk postsurgery. Among the three models, the Δ model demonstrated the highest AUC value (T2WI AUC in training, 0.856; Delay AUC, 0.909; T2WI AUC in testing, 0.812; Delay AUC, 0.875), whereas the combined model yielded optimal performance (T2WI AUC in training, 0.911; Delay AUC, 0.954; T2WI AUC in testing, 0.847; Delay AUC, 0.917). Despite its superior AUC values, no significant difference was noted when comparing the performance of the combined model across the two sequences (P = 0.6087).

Conclusions: Combined models incorporating clinical data and Δ-radiomics features serve as valuable indicators for predicting LTP following thermal ablation in patients with CRLM.

Abstract: This review aims to provide valuable insights into how energy consumption in magnetic resonance imaging (MRI) and computed tomography (CT) scanners can be effectively monitored, managed, and reduced, thereby contributing to more sustainable medical imaging practices. Demand for advanced imaging technologies such as MRI and CT scanners continues to increase, and understanding the resultant impact on greenhouse gas emissions requires a thorough evaluation of their energy consumption. This review examines the energy monitoring and consumption characteristics of MRI and CT scanners, highlighting potential approaches for energy savings. An overview of MRI and CT principles, hardware components, and their associated energy consumption is provided. After addressing the technical aspects, the hardware and software requirements essential for accurate energy metering are detailed. Baseline measurements of energy consumption data are then provided as a foundation to understand current usage patterns and identify areas for improvement. Ongoing efforts to reduce energy consumption are categorized into 3 main strategies: operations, scanner design enhancements, and active scanning techniques, including accelerated MRI protocols. Ultimately, we emphasize that achieving sustainability in medical imaging requires collaboration across disciplines. By incorporating eco-friendly design in new imaging equipment, we can reduce the environmental impact, promote sustainability, and set a health care industry standard for a healthier planet.

Aim: This study aimed to analyze the differences in radiomic features of the anterior scalene muscle and evaluate the diagnostic performance of MRI-based radiomics model for neurogenic thoracic outlet syndrome (NTOS).

Materials and methods: Imaging data of patients with NTOS who underwent preoperative brachial plexus magnetic resonance neurography were collected and were randomly divided into training and test groups. The anterior scalene muscle area was sliced in the T1WI sequence as the region of interest for the extraction of radiomics features. The most significant features were identified using feature selection and dimensionality-reduction methods. Various machine learning algorithms were applied to construct regression models. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC).

Results: Totally, 267 radiomics features were extracted, of which 57 showed significant differences (P ≤ 0.05) between the abnormal and normal anterior scalene muscle groups. The least absolute shrinkage and selection operator regression model identified 13 optimal radiomic features with nonzero coefficients for constructing the model. In the training set, the AUROCs of diagnostic models built by different machine learning algorithms, ranked from highest to lowest, were as follows: support vector machine (SVM), 0.953; multilayer perception (MLP), 0.936; logistic regression (LR), 0.926; light gradient boosting machine (LightGBM), 0.906; and K-nearest neighbors (KNN), 0.813. In the testing set, the rankings were as follows: LR, 0.933; SVM, 0.886; KNN, 0.843; LightGBM, 0.824; and MLP, 0.706.

Conclusions: NTOS is attributed to anterior scalene muscle abnormalities and exhibits distinct radiomic features. Integrating these features with machine learning can improve traditional manual image interpretation, offering further clarity in NTOS diagnosis.

Abstract: Iodinated contrast media (ICM) is an integral and ubiquitous component of modern diagnostic imaging. Although most radiology practices are familiar with ICM administration and physiological excretion, they may be less aware of how much ICM is wasted on a per exam basis. Furthermore, radiologists may not recognize the environmental fate of discarded ICM waste. In an evolving world where medical practices are increasingly cognizant of their environmental footprint and radiology practices are considered high consumers of resources, it behooves the radiology community to understand the ICM lifecycle and ways to mitigate unnecessary waste. This review article explains the origin and environmental fate of discarded ICM, with special focus on wastewater contamination. Secondly, the article focuses on feasible options to both optimize use and decrease consumable waste. Specifically, the article addresses ICM vial size inventory diversification, multi-use ICM vials, syringeless contrast injectors, and the potential for using multi-energy imaging (dual-energy or photon counting CT) to accomplish these goals. Finally, the authors share their institutional experience participating in an ICM recycling program and its current departmental impact.

Abstract: Artificial intelligence (AI) is a rapidly expanding field of interest to radiologists for its utility as an adjunct in detecting and reporting disease and its potential influence on the role of radiologists and their practices. As radiology educators, we are responsible for developing and providing access to curricular elements that will prepare residents to be good stewards of artificial intelligence resources while also acquiring fundamental knowledge and skills that are essential to daily practice. Residency programs should consider collaborative approaches as well as solicit support from national societies in the development and curation of their AI curricula.

Objective: This study aimed to investigate the value of radiomics analysis in the precise diagnosis of triple-negative breast cancer (TNBC) based on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) maps.

Methods: This retrospective study included 326 patients with pathologically proven breast cancer (TNBC: 129, non-TNBC: 197). The lesions were segmented using the ITK-SNAP software, and whole-volume radiomics features were extracted using a radiomics platform. Radiomics features were obtained from DCE-MRI and ADC maps. The least absolute shrinkage and selection operator regression method was employed for feature selection. Three prediction models were constructed using a support vector machine classifier: Model A (based on the selected features of the ADC maps), Model B (based on the selected features of DCE-MRI), and Model C (based on the selected features of both combined). Receiver operating characteristic curves were used to evaluate the diagnostic performance of the conventional MR image model and the 3 radiomics models in predicting TNBC.

Results: In the training dataset, the AUCs for the conventional MR image model and the 3 radiomics models were 0.749, 0.801, 0.847, and 0.896. The AUCs for the conventional MR image model and 3 radiomics models in the validation dataset were 0.693, 0.742, 0.793, and 0.876, respectively.

Conclusions: Radiomics based on the combination of whole volume DCE-MRI and ADC maps is a promising tool for distinguishing between TNBC and non-TNBC.

Objective: The aim of the study is to investigate the efficacy of amide proton transfer-weighted (APT) imaging combined with serum squamous cell carcinoma antigen (SCC-Ag) in grading cervical cancer.

Methods: Sixty-three patients with surgically confirmed cervical SCC were enrolled and categorized into 3 groups: highly differentiated (G1), moderately differentiated (G2), and poorly differentiated (G3). The diagnostic efficacies of APT imaging and serum SCC-Ag, alone or in combination, for grading cervical SCC were compared.

Results: The APT values measured by the 2 observers were in excellent agreement (intraclass correlation coefficient >0.75). Mean (± standard deviation) APT values for the high, moderate, and poor differentiation groups were 2.542 ± 0.215% (95% confidence interval [CI]: 2.423-2.677), 2.784 ± 0.175% (95% CI: 2.701-2.856), and 3.120 ± 0.221% (95% CI: 2.950-3.250), respectively. APT values for groups G2 and G3 were significantly higher than those for G1 (P < 0.05). APT values for identifying cervical SCC in groups G1 and G2, G2 and G3, and G1 and G3, had areas under the receiver operating characteristic curve, sensitivities, and specificities of 0.815 (95% confidence interval [CI]: 0.674-0.914), 82.1%, and 72.2%, 0.882 (95% CI: 0.751-0.959), 70.6%, and 92.7%, and 0.961 (95% CI: 0.835-0.998), 94.1%, and 94.4%, respectively. APT values were significantly and positively correlated with the histological grade of cervical SCC (Spearman's correlation [rs] = 0.731, P < 0.01). Serum SCC-Ag levels for the high, moderate, and poor differentiation groups were 1.60 (0.88-4.63) ng/mL, 4.10 (1.85-6.98) ng/mL, and 26.10 (9.65-70.00) ng/mL, respectively. The differences were statistically significant only between groups G1 and G3 and G2 and G3 (P < 0.05), whereas the differences between groups G1 and G2 were not statistically significant (P > 0.05). Spearman's analysis revealed a positive correlation between SCC-Ag levels and the histological grade of cervical SCC (rs = 0.573, P < 0.01). The diagnostic efficacy of APT imaging for the histological grading of cervical SCC was better than that of serum SCC-Ag, and the discriminatory efficacy of the combination of the 2 parameters was better than that of either alone.

Conclusions: The diagnostic efficacy of APT imaging was better than that of serum SCC-Ag, and the combined diagnostic utility of APT and SCC-Ag was better than that of the individual parameters.

Objective: Patient characteristics, iodine injection, and scanning parameters can impact the quality and consistency of contrast enhancement of hepatic parenchyma in CT imaging. Improving the consistency and adequacy of contrast enhancement can enhance diagnostic accuracy and reduce clinical practice variability, with added positive implications for safety and cost-effectiveness in the use of contrast medium. We developed a clinical tool that uses patient attributes (height, weight, sex, age) to predict hepatic enhancement and suggest alternative injection/scanning parameters to optimize the procedure.

Methods: The tool was based on a previously validated neural network prediction model that suggested adjustments for patients with predicted insufficient enhancement. We conducted a prospective clinical study in which we tested this tool in 24 patients aiming for a target portal-venous parenchyma CT number of 110 HU ± 10 HU.

Results: Out of the 24 patients, 15 received adjustments to their iodine contrast injection parameters, resulting in median reductions of 8.8% in volume and 9.1% in injection rate. The scan delays were reduced by an average of 42.6%. We compared the results with the patients' previous scans and found that the tool improved consistency and reduced the number of underenhanced patients. The median enhancement remained relatively unchanged, but the number of underenhanced patients was reduced by half, and all previously overenhanced patients received enhancement reductions.

Conclusions: Our study showed that the proposed patient-informed clinical framework can predict optimal contrast enhancement and suggest empiric injection/scanning parameters to achieve consistent and sufficient contrast enhancement of hepatic parenchyma. The described GUI-based tool can prospectively inform clinical decision-making predicting optimal patient's hepatic parenchyma contrast enhancement. This reduces instances of nondiagnostic/insufficient enhancement in patients.

Objective: This study aimed to evaluate the image quality and visualization of hepatocellular carcinoma (HCC) on arterial phase computed tomography (CT) using the contrast enhancement (CE)-boost technique.

Methods: This retrospective study included 527 consecutive patients who underwent dynamic liver CT between June 2021 and February 2022. Quantitative and qualitative image analyses were performed on 486 patients after excluding 41 patients. HCC conspicuity was evaluated in 40 of the 486 patients with at least one HCC in the liver. Iodinated images obtained by subtracting nonenhanced images from arterial phase images were combined to generate CE-boost images. For quantitative image analysis, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured for the liver, pancreas, muscles, and aorta. For qualitative analysis, the overall image quality and noise were graded using a 3-point scale. Artifact, sharpness, and HCC lesion conspicuity were assessed using a 5-point scale. The paired-sample t test was used to compare quantitative measures, whereas the Wilcoxon signed-rank test was used to compare qualitative measures.

Results: The mean SNR and CNR of the aorta, liver, pancreas, and muscle were significantly higher, and the image noise was significantly lower in the CE-boost images than in the conventional images (P < 0.001). The mean CNR of HCC was also significantly higher in the CE-boost images than in the conventional images (P < 0.001). In the qualitative analysis, CE-boost images showed higher scores for HCC lesion conspicuity than conventional images (P < 0.001).

Conclusions: The overall image quality and visibility of HCC were improved using the CE-boost technique.