Tomography最新文献

Background: Femoroacetabular impingement (FAI) is a condition caused by abnormal contact between the femur head and the acetabulum, which damages the labrum and articular cartilage. While the prevalence and the type of impingement may vary across human groups, the variability among populations with short height or with a high prevalence of overweight has not yet been explored. Latin American studies have rarely been conducted in reference to this condition, including the Mayan and mestizo populations from the Yucatan Peninsula.

Objective: We aimed to describe the prevalence of morphological changes in femoroacetabular impingement by measuring radiological angles in abdominopelvic tomography studies in a sample of patients from a population with short height.

Methods: In this prospective study, patients with programmed abdominopelvic tomography unrelated to femoroacetabular impingement but with consistent symptoms were included. Among the 98 patients, the overall prevalence of unrelated femoroacetabular impingement was 47%, and the pincer-type was the most frequent. The cam-type occurred more frequently among individuals with taller stature compared to their peers. Alpha and Wiberg angles predicted cam- and pincer-type, respectively, with over 0.95 area under the curve values in ROC analyses. The inter-rater agreement in the study was >91%.

Conclusions: In a patient population from Yucatan, Mexico, attending ambulatory consultations unrelated to femoroacetabular impingement, an overall morphological changes prevalence of 47% was observed. Angle measurements using tomographic techniques can be used to predict cam- and pincer-type femoroacetabular impingement. Average stature was observed to be shorter in patients with cam-type femoroacetabular impingement, but body mass index did not vary between groups.

Background: Accurate reconstruction of internal temperature fields from surface temperature data is critical for applications such as non-invasive thermal imaging, particularly in scenarios involving small temperature gradients, like those in the human body. Methods: In this study, we employed 3D convolutional neural networks (CNNs) to predict internal temperature fields. The network's performance was evaluated under both ideal and non-ideal conditions, incorporating noise and background temperature variations. A physics-informed loss function embedding the heat equation was used in conjunction with statistical uncertainty during training to simulate realistic scenarios. Results: The CNN achieved high accuracy for small phantoms (e.g., 10 cm in diameter). However, under non-ideal conditions, the network's predictive capacity diminished in larger domains, particularly in regions distant from the surface. The introduction of physical constraints in the training processes improved the model's robustness in noisy environments, enabling accurate reconstruction of hot-spots in deeper regions where traditional CNNs struggled. Conclusions: Combining deep learning with physical constraints provides a robust framework for non-invasive thermal imaging and other applications requiring high-precision temperature field reconstruction, particularly under non-ideal conditions.

Background: Assessment of skeletal maturity is a common clinical practice to investigate adolescent growth and endocrine disorders. The distal radius and ulna (DRU) maturity classification is a practical and easy-to-use scheme that was designed for adolescent idiopathic scoliosis clinical management and presents high sensitivity in predicting the growth peak and cessation among adolescents. However, time-consuming and error-prone manual assessment limits DRU in clinical application. Methods: In this study, we propose a multi-task learning framework with an attention mechanism for the joint segmentation and classification of the distal radius and ulna in hand X-ray images. The proposed framework consists of two sub-networks: an encoder-decoder structure with attention gates for segmentation and a slight convolutional network for classification. Results: With a transfer learning strategy, the proposed framework improved DRU segmentation and classification over the single task learning counterparts and previously reported methods, achieving an accuracy of 94.3% and 90.8% for radius and ulna maturity grading. Findings: Our automatic DRU assessment platform covers the whole process of growth acceleration and cessation during puberty. Upon incorporation into advanced scoliosis progression prognostic tools, clinical decision making will be potentially improved in the conservative and operative management of scoliosis patients.

Background: Early diagnosis of depression is crucial for effective treatment and suicide prevention. Traditional methods rely on self-report questionnaires and clinical assessments, lacking objective biomarkers. Combining functional magnetic resonance imaging (fMRI) with artificial intelligence can enhance depression diagnosis using neuroimaging indicators, but depression-specific fMRI datasets are often small and imbalanced, posing challenges for classification models. New Method: We propose the Spatio-Temporal Aggregation Network (STANet) for diagnosing depression by integrating convolutional neural networks (CNN) and recurrent neural networks (RNN) to capture both temporal and spatial features of brain activity. STANet comprises the following steps: (1) Aggregate spatio-temporal information via independent component analysis (ICA). (2) Utilize multi-scale deep convolution to capture detailed features. (3) Balance data using the synthetic minority over-sampling technique (SMOTE) to generate new samples for minority classes. (4) Employ the attention-Fourier gate recurrent unit (AFGRU) classifier to capture long-term dependencies, with an adaptive weight assignment mechanism to enhance model generalization. Results: STANet achieves superior depression diagnostic performance, with 82.38% accuracy and a 90.72% AUC. The Spatio-Temporal Feature Aggregation module enhances classification by capturing deeper features at multiple scales. The AFGRU classifier, with adaptive weights and a stacked Gated Recurrent Unit (GRU), attains higher accuracy and AUC. SMOTE outperforms other oversampling methods. Additionally, spatio-temporal aggregated features achieve better performance compared to using only temporal or spatial features. Comparison with existing methods: STANet significantly outperforms traditional classifiers, deep learning classifiers, and functional connectivity-based classifiers. Conclusions: The successful performance of STANet contributes to enhancing the diagnosis and treatment assessment of depression in clinical settings on imbalanced and small fMRI.

Objective: The purpose of this study was to explore the impact of pericardial T1 mapping as a potential supportive non-contrast cardiovascular magnetic resonance (CMR) parameter in the diagnosis of acute pericarditis. Additionally, we investigated the relationship between T1 mapping values in acute pericarditis patients and their demographic data, cardiovascular risk factors, clinical parameters, cardiac biomarkers, and cardiac function.

Method: This retrospective study included CMR scans in 35 consecutive patients with acute pericarditis (26 males, 45.54 ± 23.38 years). Moreover, we included 17 sex- and age-matched healthy controls (12 males, mean age 47.78 ±19.38 years). CMR-derived pericardial T1 mapping values, which included all pericardial structures within the pericardial layers-encompassing both pericardial effusion and pericardial layer thickness-were analyzed and compared between acute pericarditis patients and controls.

Results: Compared to the matched control group, acute pericarditis patients demonstrated significantly lower pericardial T1 mapping values (2137 ms ± 519 vs. 3268 ms ± 362, p = 0.001). In the multivariable analysis, the pericardial T1 mapping value was independently associated with the severity of pericardial late gadolinium enhancement (LGE) (β coefficient = -3.271, p = 0.003). The receiver operating characteristic curve analysis showed that the diagnostic performance of pericardial T1 mapping in discriminating acute pericarditis patients was excellent, with an area under the curve of 0.97 (95% CI = 0.94-0.98), using a threshold of 2862.5 ms.

Conclusions: Pericardial T1 mapping values could serve as an additional non-contrast CMR parameter for identifying patients with acute pericarditis, demonstrating an independent association with the severity of pericardial LGE.

Background/Objectives: Photon-counting detector computed tomography (PCD-CT) offers energy-resolved CT data with enhanced resolution, reduced electronic noise, and improved tissue contrast. This study aimed to evaluate the visibility of intracranial perforating arteries on ultra-high-resolution (UHR) CT angiography (CTA) on PCD-CT. Methods: A retrospective analysis of intracranial UHR PCD-CTA was performed for 30 patients. The image quality from four UHR PCD-CTA reconstruction methods [kernel Hv40 and Hv72, with and without quantum iterative reconstruction (QIR)] was assessed for the lenticulostriate arteries (LSAs) and pontine arteries (PAs). A subjective evaluation included peripheral visibility, vessel sharpness, and image noise, while objective analysis focused on the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Results: Peripheral LSAs were well visualized across all reconstruction methods, with no significant differences between them. Vessel sharpness and image noise varied significantly (p < 0.0001); sharper LSAs and more noise were seen with kernel Hv72 compared to kernel Hv40 (p < 0.05). A similar pattern was observed for PAs, though peripheral visibility was lower than that for LSAs. The SNR and CNR were the highest in the presence of kernel Hv72 with QIR, and lowest with kernel Hv72 without QIR, compared to kernel Hv40 (p < 0.05). Conclusions: UHR PCD-CTA provided a good visualization of the intracranial perforating arteries, particularly LSAs. The vessel sharpness and image noise varied by reconstruction method, in which kernel Hv72 with QIR offered the optimal visualization.

Background: This multicenter and retrospective study investigated the additive value of tumor morphologic features derived from the functional tumor volume (FTV) tumor mask at pre-treatment (T0) and the early treatment time point (T1) in the prediction of pathologic outcomes for breast cancer patients undergoing neoadjuvant chemotherapy.

Methods: A total of 910 patients enrolled in the multicenter I-SPY 2 trial were included. FTV and tumor morphologic features were calculated from the dynamic contrast-enhanced (DCE) MRI. A poor response was defined as a residual cancer burden (RCB) class III (RCB-III) at surgical excision. The area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive performance. The analysis was performed in the full cohort and in individual sub-cohorts stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status.

Results: In the full cohort, the AUCs for the use of the FTV ratio and clinicopathologic data were 0.64 ± 0.03 (mean ± SD [standard deviation]). With morphologic features, the AUC increased significantly to 0.76 ± 0.04 (p < 0.001). The ratio of the surface area to volume ratio between T0 and T1 was found to be the most contributing feature. All top contributing features were from T1. An improvement was also observed in the HR+/HER2- and triple-negative sub-cohorts. The AUC increased significantly from 0.56 ± 0.05 to 0.70 ± 0.06 (p < 0.001) and from 0.65 ± 0.06 to 0.73 ± 0.06 (p < 0.001), respectively, when adding morphologic features.

Conclusion: Tumor morphologic features can improve the prediction of RCB-III compared to using FTV only at the early treatment time point.

Background: Preclinical cell tracking is enhanced with a multimodal imaging approach. Bioluminescence imaging (BLI) is a highly sensitive optical modality that relies on engineering cells to constitutively express a luciferase gene. Magnetic particle imaging (MPI) is a newer imaging modality that directly detects superparamagnetic iron oxide (SPIO) particles used to label cells. Here, we compare BLI and MPI for imaging cells in vitro and in vivo.

Methods: Mouse 4T1 breast carcinoma cells were transduced to express firefly luciferase, labeled with SPIO (ProMag), and imaged as cell samples after subcutaneous injection into mice.

Results: For cell samples, the BLI and MPI signals were strongly correlated with cell number. Both modalities presented limitations for imaging cells in vivo. For BLI, weak signal penetration, signal attenuation, and scattering prevented the detection of cells for mice with hair and for cells far from the tissue surface. For MPI, background signals obscured the detection of low cell numbers due to the limited dynamic range, and cell numbers could not be accurately quantified from in vivo images.

Conclusions: It is important to understand the shortcomings of these imaging modalities to develop strategies to improve cellular detection sensitivity.

Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal radiology, leading to an improvement in diagnostic and disease management capabilities. This narrative review seeks to evaluate the current standing of AI in abdominal imaging, with a focus on recent literature contributions. This work explores the diagnosis and characterization of hepatobiliary, pancreatic, gastric, colonic, and other pathologies. In addition, the role of AI has been observed to help differentiate renal, adrenal, and splenic disorders. Furthermore, workflow optimization strategies and quantitative imaging techniques used for the measurement and characterization of tissue properties, including radiomics and deep learning, are highlighted. An assessment of how these advancements enable more precise diagnosis, tumor description, and body composition evaluation is presented, which ultimately advances the clinical effectiveness and productivity of radiology. Despite the advancements of AI in abdominal imaging, technical, ethical, and legal challenges persist, and these challenges, as well as opportunities for future development, are highlighted.

The topic of quantitative imaging in radiation therapy was presented as a "Masterclass" at the 2023 annual meeting of the American Society of Radiation Oncology (ASTRO). Dual-energy computed tomography (CT) and single-positron computed tomography were reviewed in detail as the first portion of the meeting session, with data showing utility in many aspects of radiation oncology including treatment planning and dose response. Positron emission tomography/CT scans evaluating the functional volume of lung tissue so as to provide optimal avoidance of healthy lungs were presented second. Advanced brain imaging was then discussed in the context of different forms of magnetic resonance scanning methods as the third area noted with significant discussion of ongoing research programs. Quantitative image analysis was presented to provide clinical utility for the analysis of patients with head and neck cancer. Finally, quality assurance was reviewed for different forms of quantitative imaging given the critical nature of imaging when numerical valuation, not just relative contrast, plays a crucial role in clinical process and decision-making. Conclusions and thoughts are shared in the conclusion, noting strong data supporting the use of quantitative imaging in radiation therapy going forward and that more studies are needed to move the field forward.