Tomography最新文献

Cardiac computed tomography (CT) has rapidly advanced, becoming an invaluable tool for diagnosing and prognosticating various cardiovascular diseases. While echocardiography and cardiac magnetic resonance imaging (CMR) remain the gold standards for myocardial assessment, modern CT technologies offer enhanced spatial resolution, making it an essential tool in clinical practice. Cardiac CT has expanded beyond coronary artery disease evaluation, now playing a key role in assessing cardiomyopathies and structural heart diseases. Innovations like photon-counting CT enable precise estimation of myocardial extracellular volume, facilitating the detection of infiltrative disorders and myocardial fibrosis. Additionally, CT-based myocardial strain analysis allows for the classification of impaired myocardial contractility, while quantifying cardiac volumes and function remains crucial in cardiomyopathy evaluation. This review explores the emerging role of cardiac CT in cardiomyopathy phenotyping, emphasizing recent technological advancements.

Background: Predictive models like Residual Neural Networks (ResNets) can use Magnetic Resonance Imaging (MRI) data to identify cervix tumors likely to recur after radiotherapy (RT) with high accuracy. However, there persists a lack of insight into model selections (explainability). In this study, we explored whether model features could be used to generate simulated images as a method of model explainability.

Methods: T2W MRI data were collected for twenty-seven women with cervix cancer who received RT from the TCGA-CESC database. Simulated images were generated as follows: [A] a ResNet model was trained to identify recurrent cervix cancer; [B] a model was evaluated on T2W MRI data for subjects to obtain corresponding feature maps; [C] most important feature maps were determined for each image; [D] feature maps were combined across all images to generate a simulated image; [E] the final image was reviewed by a radiation oncologist and an initial algorithm to identify the likelihood of recurrence.

Results: Predictive feature maps from the ResNet model (93% accuracy) were used to generate simulated images. Simulated images passed through the model were identified as recurrent and non-recurrent cervix tumors after radiotherapy. A radiation oncologist identified the simulated images as cervix tumors with characteristics of aggressive Cervical Cancer. These images also contained multiple MRI features not considered clinically relevant.

Conclusion: This simple method was able to generate simulated MRI data that mimicked recurrent and non-recurrent cervix cancer tumor images. These generated images could be useful for evaluating the explainability of predictive models and to assist radiologists with the identification of features likely to predict disease course.

Purpose: In cancers imaged using contrast-enhanced protocols, such as hepatocellular carcinoma (HCC), formal guidelines rely on measurements of lesion size (in mm) and radiographic density (in Hounsfield units [HU]) to evaluate response to treatment. However, the variability of these measurements across different contrast enhancement phases remains poorly understood. This limits the ability of clinicians to discern whether measurement changes are accurate.

Methods: In this study, we investigated the variability of maximal lesion diameter and mean lesion density of HCC lesions on CT scans across four different contrast enhancement phases: non-contrast-enhanced phase (NCE), early arterial phase (E-AP), late arterial phase (L-AP), and portal venous phase (PVP). HCC lesions were independently segmented by two expert radiologists. For each pair of a lesion's scan timepoints, one was selected randomly as the baseline measurement and the other as the repeat measurement. Both absolute and relative differences in measurements were calculated, as were the coefficients of variance (CVs). Analysis was further stratified by both contrast enhancement phase and lesion diameter.

Results: Lesion diameter was found to have a CV of 5.11% (95% CI: 4.20-6.01%). About a fifth of the measurement's relative changes were greater than 10%. Although there was no significant difference in diameter measurements across different phases, there was a significant negative correlation (R = -0.303, p-value = 0.030) between lesion diameter and percent difference in diameter measurement. Lesion density measurements varied significantly across all phases, with the greatest relative difference of 47% in the late arterial phase and a CV of 22.84% (21.48-24.20%). The overall CV for lesion density measurements was 26.19% (24.66-27.72%).

Conclusions: Changes in tumor diameter measurements within 10% may simply be due to variability, and lesion density is highly sensitive to contrast timing. This highlights the importance of paying attention to these two variables when evaluating tumor response in both clinical trials and practice.

Background/objectives: This study aimed to investigate the predictive power of integrated longitudinal amyloid positron emission tomography (PET) and brain magnetic resonance imaging (MRI) data for determining the likelihood of conversion to Alzheimer's disease (AD) in patients with mild cognitive impairment (MCI).

Methods: We included 180 patients with MCI from the Alzheimer's Disease Neuroimaging Initiative, with baseline and 2-year follow-up scans obtained using F-18 florbetapir PET and MRI. Patients were categorized as converters (progressing to AD) or nonconverters based on a 6-year follow-up. Quantitative analyses included the calculation of amyloid burden using the standardized uptake value ratio (SUVR), brain amyloid smoothing scores (BASSs), brain atrophy indices (BAIs), and their integration into shape features. Longitudinal changes and receiver operating characteristic analyses assessed the predictive power of these biomarkers.

Results: Among 180 patients with MCI, 76 (42.2%) were converters, who exhibited significantly higher baseline and 2-year follow-up values for SUVR, BASS, BAI, and shape features than nonconverters (p < 0.001). Shape features demonstrated the highest predictive accuracy for conversion, with areas under the curve of 0.891 at baseline and 0.898 at 2 years. Percent change analyses revealed significant increases in brain atrophy; amyloid deposition changes showed a paradoxical decrease in converters. Additionally, strong associations were observed between longitudinal changes in shape features and neuropsychological test results.

Conclusions: The integration of amyloid PET and MRI biomarkers enhances the prediction of AD progression in patients with MCI. These findings support the potential of combined imaging approaches for early diagnosis and targeted interventions in AD.

Background/objectives: The risk of hemorrhage during CT-guided lung biopsy has not been systematically studied in cases where ground-glass opacities (GGO) are present in the access route or when biopsies are performed in highly perfused, dependent lung areas. While patient positioning has been studied for pneumothorax prevention, its role in minimizing hemorrhage risk remains unexplored. This study aimed to determine whether GGOs in the access route and biopsies in dependent lung areas are risk factors for pulmonary hemorrhage during CT-guided lung biopsy.

Methods: A retrospective analysis was conducted on 115 CT-guided lung biopsies performed at a single center (2020-2023). Patients were categorized based on post-interventional hemorrhage exceeding 2 cm (Grade 2 or higher). We evaluated the presence of GGOs in the access route and biopsy location (dependent vs. non-dependent areas) using chi square, Fisher's exact, and Mann-Whitney U tests. Univariate and multivariate logistic regression analyses were conducted to evaluate risk factors for pulmonary hemorrhage.

Results: Pulmonary hemorrhage beyond 2 cm occurred in 30 of 115 patients (26%). GGOs in the access route were identified in 67% of these cases (p < 0.01), and hemorrhage occurred more frequently when biopsies were performed in dependent lung areas (63% vs. 40%, p = 0.03). Multivariable analysis showed that GGOs in the access route (OR 5.169, 95% CI 1.889-14.144, p = 0.001) and biopsies in dependent areas (OR 4.064, 95% CI 1.477-11.186, p < 0.001) independently increased hemorrhage risk.

Conclusions: GGOs in the access route and dependent lung area biopsies are independent risk factors for hemorrhage during CT-guided lung biopsy.

Background/objectives: Holotomography is an advanced imaging technique that enables high-resolution, three-dimensional visualization of microscopic specimens without the need for fixation or staining. Here we aim to apply holotomography technology to image live Hypsibius exemplaris in their native state, avoiding invasive sample preparation procedures and phototoxic effects associated with other imaging modalities.

Methods: We use a low concentration of 7% ethanol for tardigrade sedation and sample preparation. Holotomographic images were obtained and reconstructed using the Tomocube HT-X1 system, enabling high-resolution visualization of tardigrade anatomical structures.

Results: We captured detailed, label-free holotomography images of both external and internal structures of tardigrade, including the digestive tract, brain, ovary, claws, salivary glands, and musculature.

Conclusions: Our findings highlight holotomography as a complementary high-resolution imaging modality that effectively addresses the challenges faced with traditional imaging techniques in tardigrade research.

Rationale: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response of NAC for patients with LABC before initiating treatment would be valuable to customize therapies and ensure the delivery of effective care.

Objective: Our objective was to develop predictive measures of tumor response to NAC prior to starting for LABC using machine learning and textural computed tomography (CT) features in different level of frequencies.

Materials and methods: A total of 851 textural biomarkers were determined from CT images and their wavelet coefficients for 117 patients with LABC to evaluate the response to NAC. A machine learning pipeline was designed to classify response to NAC treatment for patients with LABC. For training predictive models, three models including all features (wavelet and original image features), only wavelet and only original-image features were considered. We determined features from CT images in different level of frequencies using wavelet transform. Additionally, we conducted a comparison of feature selection methods including mRMR, Relief, Rref QR decomposition, nonnegative matrix factorization and perturbation theory feature selection techniques.

Results: Of the 117 patients with LABC evaluated, 82 (70%) had clinical-pathological response to chemotherapy and 35 (30%) had no response to chemotherapy. The best performance for hold-out data splitting was obtained using the KNN classifier using the Top-5 features, which were obtained by mRMR, for all features (accuracy = 77%, specificity = 80%, sensitivity = 56%, and balanced-accuracy = 68%). Likewise, the best performance for leave-one-out data splitting could be obtained by the KNN classifier using the Top-5 features, which was obtained by mRMR, for all features (accuracy = 75%, specificity = 76%, sensitivity = 62%, and balanced-accuracy = 72%).

Conclusions: The combination of original textural features and wavelet features results in a greater predictive accuracy of NAC response for LABC patients. This predictive model can be utilized to predict treatment outcomes prior to starting, and clinicians can use it as a recommender system to modify treatment.

Background: Digital tomosynthesis (DTS) is an advanced imaging modality that enhances diagnostic accuracy by offering three-dimensional visualization from two-dimensional projections, which is particularly beneficial in breast and lung imaging. However, this increased imaging capability raises concerns about patient exposure to ionizing radiation.

Methods: This study explores the energy and angular dependence of thermoluminescent dosimeters (TLDs), specifically TLD100H, to improve the accuracy of organ dose assessment during DTS. Using a comprehensive experimental approach, organ doses were measured in both DTS and traditional RX modes.

Results: The results showed lung doses of approximately 3.21 mGy for the left lung and 3.32 mGy for the right lung during DTS, aligning with the existing literature. In contrast, the RX mode yielded significantly lower lung doses of 0.33 mGy. The heart dose during DTS was measured at 2.81 mGy, corroborating findings from similar studies.

Conclusions: These results reinforce the reliability of TLD100H dosimetry in assessing radiation exposure and highlight the need for optimizing imaging protocols to minimize doses. Overall, this study contributes to the ongoing dialogue on enhancing patient safety in diagnostic imaging and advocates for collaboration among medical physicists, radiologists, and technologists to establish best practices.

Background/objectives: While ancillary tests for brain death diagnosis are not routinely recommended in guidelines, they may be necessary in specific clinical scenarios. Computed tomography angiography (CTA) is particularly advantageous in pediatric patients due to its noninvasive nature, accessibility, and rapid provision of anatomical information. This study aims to assess the diagnostic sensitivity of a revised venous system (ICV-SPV) utilizing a 4-point scoring system in children clinically diagnosed with brain death.

Materials and methods: A total of 43 pediatric patients clinically diagnosed with brain death who underwent CTA were retrospectively analyzed. Imaging was performed using a standardized brain death protocol. Three distinct 4-point scoring systems (A20-V60, A60-V60, ICV-SPV) were utilized to assess vessel opacification in different imaging phases. To evaluate age-dependent sensitivity, patients were categorized into three age groups: 26 days-1 year, 2-6 years, and 6-18 years. The sensitivity of each 4-point scoring system in diagnosing brain death was calculated for all age groups.

Results: The revised venous scoring system (ICV-SPV) demonstrated the highest overall sensitivity in confirming brain death across all age groups, significantly outperforming the reference 4-point scoring systems. Furthermore, the ICV-SPV system exhibited the greatest sensitivity in patients with cranial defects.

Conclusions: The revised 4-point venous CTA scoring system, which relies on the absence of ICV and SPV opacification, is a reliable tool for confirming cerebral circulatory arrest in pediatric patients with clinical brain death.