Tomography最新文献

Objectives: To evaluate the effectiveness of breast MRI, including diffusion-weighted imaging (DWI), in detecting residual lesions in patients with malignancy after excisional biopsy.

Methods: From January 2018 to December 2023, 3T breast MRI was performed to assess lesion morphology, residual size, and enhancement kinetics. The apparent diffusion coefficient (ADC) values were measured, and the diagnostic outcomes of CE-MRI, CE-MRI with DWI, mammography (MG), and ultrasound (US) were compared with clinical and histopathological data.

Results: A total of 152 lesions were analyzed, with 36.2% showing residual malignancy. Both CE-MRI and CE-MRI with DWI effectively identified residual lesions, with significant differences in morphology, size, kinetic patterns, and ADC values (all p < 0.001). CE-MRI with DWI showed a sensitivity of 90.9% and an NPV of 93.6%, compared with 89.1% sensitivity and 92.2% NPV for CE-MRI alone. Sensitivities for MG and US were 57.1% and 38.7%, with NPVs of 64.7% and 59.6%, respectively. Diagnostic accuracy was highest for CE-MRI with DWI (80.9%), followed by CE-MRI (79.0%), MG (60.3%), and US (59.7%). The AUC for CE-MRI with DWI (0.831) was slightly higher than CE-MRI alone (0.811), though not significant (p = 0.095). AUCs for MG and US were lower at 0.623 and 0.563, with no significant difference between MG and US (p = 0.234).

Conclusions: CE-MRI with DWI and CE-MRI alone were comparable and demonstrated excellent performance in discriminating between women with and without residual disease. Integrating CE-MRI with DWI could become a standard protocol for patients with suspected residual malignancy after excisional biopsy.

Background/Objectives: Although multiple magnetic resonance imaging (MRI) indices are known to be sensitive to the noninvasive assessment of myelin integrity, their relative sensitivities have not been directly compared. This study aimed to identify the most sensitive MRI index for characterizing myelin composition in the spinal cord's gray matter (GM) and white matter (WM). Methods: MRI was performed on a deer's ex vivo cervical spinal cord. Quantitative indices known to be sensitive to myelin, including the myelin water fraction (MWF), magnetization transfer ratio (MTR), the signal ratio between T1- and T2-weighted images (T1W/T2W), fractional anisotropy (FA), mean diffusivity (MD), electrical conductivity (σ), and T1, T2, and T1ρ relaxation times were calculated. Their mean values were compared using repeated measures analysis of variance (ANOVA) and post hoc Bonferroni tests or Friedman and post hoc Wilcoxon tests to identify differences across GM and WM columns possessing distinct myelin distributions, as revealed by histological analysis. Relationships among the indices were examined using Spearman's rank-order correlation analysis. Corrected p < 0.05 was considered statistically significant. Results: All indices except σ differed significantly between GM and all WM columns. Two of the three WM columns had significantly different MWF, FA, MD, and T2, whereas one WM column had significantly different MTR, σ, T1, and T1ρ from the others. A significant moderate to very strong correlation was observed among most indices. Conclusions: The sensitivity of MRI indices in distinguishing spinal cord regions varied. A strategic combination of two or more indices may allow the accurate differentiation of spinal cord regions.

Background/objectives: The aim was to investigate the association between variations in the dorsal pancreatic artery (DPA) and intrapancreatic arcade anatomy with Whipple procedure outcomes and postoperative complications.

Methods: This retrospective study was conducted with 362 patients who underwent a Whipple procedure at the Department of Gastroenterological Surgery of Adana City Training and Research Hospital between January 2018 and April 2024. All data collected from medical records were compared and statistically analyzed according to the patients' survival status and arcade subtypes.

Results: After excluding cases that did not meet the study criteria, a total of 284 patients were included in the study. DPA was visualized in 55.98% (159/284) of patients, while the intrapancreatic arcade was observed in 25% (71/284). The most common origin of the DPA was the splenic artery in 69.2% (n = 110) of patients, followed by the superior mesenteric artery in 17.6% (n = 28). The frequency of intrapancreatic arcade anatomy variations was as follows: type 1: 28.2% (n = 20), type 2: 49.3% (n = 35) and type 3: 22.5% (n = 16). Arcade type 4 anatomy was not detected. Postoperative pancreatic fistula (POPF) complication was found to be statistically significantly higher in patients with type 3 anatomy (p = 0.042). The 90-day mortality and long-term mortality rates did not differ among the groups based on the variations in both DPA and intrapancreatic arcade anatomy types.

Conclusions: Patients with intrapancreatic arcade type 3 anatomy had a higher risk of POPF complications. Determination of preoperative arcade type by computed tomography (CT) angiography may help to predict the risk of POPF.

Background/Objectives: The increased SNR available at 7T combined with fast readout trajectories enables accelerated spectroscopic imaging acquisitions for clinical applications. In this report, we evaluate the performance of a Hadamard slice encoding strategy with a 2D rosette trajectory for multi-slice fast spectroscopic imaging at 7T. Methods: Moderate-TE (~40 ms) spin echo and J-refocused polarization transfer sequences were acquired with simultaneous Hadamard multi-slice excitations and rosette in-plane encoding. The moderate spin echo sequence, which targets singlet compounds (i.e., N-acetyl aspartate, creatine, and choline), uses cascaded multi-slice RF excitation pulses to minimize the chemical shift dispersion error. The J-refocused sequence targets coupled spin systems (i.e., glutamate and myo-inositol) using simultaneous multi-slice excitation to maintain the same TE across all slices. A modified Hadamard slice encoding strategy was used to decrease the peak RF pulse amplitude of the simultaneous multi-slice excitation pulse for the J-refocused acquisition. Results: The accuracy of multi-slice and single-slice rosette spectroscopic imaging (RSI) is comparable to conventional Cartesian-encoded spectroscopic imaging (CSI). Spectral analyses for the J-refocused studies of glutamate and myo-inositol show that the Cramer Rao lower bounds are not significantly different between the fast RSI and conventional CSI studies. Linear regressions of creatine/N-acetyl aspartate and glutamate/N-acetyl aspartate with tissue gray matter content are consistent with literature values. Conclusions: With minimal gradient demands and fast acquisition times, the 2.2 min to 9 min for single- to four-slice RSI acquisitions are well tolerated by healthy subjects and tumor patients, and show results that are consistent with clinical outcomes.

This commentary examines Topological Data Analysis (TDA) in radiology imaging, highlighting its revolutionary potential in medical image interpretation. TDA, which is grounded in mathematical topology, provides novel insights into complex, high-dimensional radiological data through persistent homology and topological features. We explore TDA's applications across medical imaging domains, including tumor characterization, cardiovascular imaging, and COVID-19 detection, where it demonstrates 15-20% improvements over traditional methods. The synergy between TDA and artificial intelligence presents promising opportunities for enhanced diagnostic accuracy. While implementation challenges exist, TDA's ability to uncover hidden patterns positions it as a transformative tool in modern radiology.

Background/objectives: Mummy studies allow to reconstruct the characteristic of a population in a specific spatiotemporal context, in terms of living conditions, pathologies and death. Radiology represents an efficient diagnostic technique able to establish the preservation state of mummified organs and to estimate the patient's pathological conditions. However, the radiological approach shows some limitations. Although bone structures are easy to differentiate, soft tissue components are much more challenging, especially when they overlap. For this reason, computed tomography, a well-established approach that achieves optimal image contrast and three-dimensional reconstruction, has been introduced. This original article focuses attention on the role of virtual dissection as a promising technology for exploring human mummy anatomy and considers the potential of environmental scanning electron microscopy and X-ray spectroscopy as complementary approaches useful to understand the state of preservation of mummified remains.

Methods: Ancient mummy corps have been analyzed through Anatomage Table 10 and environmental scanning electron microscope equipped with X-ray spectrometer; Results: Anatomage Table 10 through various volumetric renderings allows us to describe spine alteration due to osteoarthritis, dental state, and other clinical-pathological characteristics of different mummies. Environmental scanning electron microscope, with the advantage of observing mummified samples without prior specimen preparation, details on the state of tissue fragments. Skin, tendon and muscle show a preserved morphology and keratinocytes, collagen fibers and tendon structures are easily recognizable. Furthermore, X-ray spectrometer reveals in our tissue remains, the presence of compounds related to soil contamination. This investigation identifies a plethora of organic and inorganic substances where the mummies were found, providing crucial information about the mummification environment.

Conclusions: These morphological and analytical techniques make it possible to study mummified bodies and describe their anatomical details in real size, in a non-invasive and innovative way, demonstrating that these interdisciplinary approaches could have great potential for improving knowledge in the study of ancient corpses.

Objectives: Accurate kidney and tumor segmentation of computed tomography (CT) scans is vital for diagnosis and treatment, but manual methods are time-consuming and inconsistent, highlighting the value of AI automation. This study develops a fully automated AI model using vision transformers (ViTs) and convolutional neural networks (CNNs) to detect and segment kidneys and kidney tumors in Contrast-Enhanced (CECT) scans, with a focus on improving sensitivity for small, indistinct tumors.

Methods: The segmentation framework employs a ViT-based model for the kidney organ, followed by a 3D UNet model with enhanced connections and attention mechanisms for tumor detection and segmentation. Two CECT datasets were used: a public dataset (KiTS23: 489 scans) and a private institutional dataset (Private: 592 scans). The AI model was trained on 389 public scans, with validation performed on the remaining 100 scans and external validation performed on all 592 private scans. Tumors were categorized by TNM staging as small (≤4 cm) (KiTS23: 54%, Private: 41%), medium (>4 cm to ≤7 cm) (KiTS23: 24%, Private: 35%), and large (>7 cm) (KiTS23: 22%, Private: 24%) for detailed evaluation.

Results: Kidney and kidney tumor segmentations were evaluated against manual annotations as the reference standard. The model achieved a Dice score of 0.97 ± 0.02 for kidney organ segmentation. For tumor detection and segmentation on the KiTS23 dataset, the sensitivities and average false-positive rates per patient were as follows: 0.90 and 0.23 for small tumors, 1.0 and 0.08 for medium tumors, and 0.96 and 0.04 for large tumors. The corresponding Dice scores were 0.84 ± 0.11, 0.89 ± 0.07, and 0.91 ± 0.06, respectively. External validation on the private data confirmed the model's effectiveness, achieving the following sensitivities and average false-positive rates per patient: 0.89 and 0.15 for small tumors, 0.99 and 0.03 for medium tumors, and 1.0 and 0.01 for large tumors. The corresponding Dice scores were 0.84 ± 0.08, 0.89 ± 0.08, and 0.92 ± 0.06.

Conclusions: The proposed model demonstrates consistent and robust performance in segmenting kidneys and kidney tumors of various sizes, with effective generalization to unseen data. This underscores the model's significant potential for clinical integration, offering enhanced diagnostic precision and reliability in radiological assessments.

Background: Anesthesia can significantly impact positron emission tomography (PET) neuroimaging in preclinical studies. Therefore, understanding these effects is crucial for accurate interpretation of the results. In this experiment, we investigate the effect of [¹⁸F]-labeled glucose analog fluorodeoxyglucose ([¹⁸F]FDG) uptake in the brains of rats anesthetized with two commonly used anesthetics for rodents: isoflurane, an inhalation anesthetic, and Hypnorm-Dormicum, a combination injection anesthetic.

Materials and methods: Female adult Sprague Dawley rats were randomly assigned to one of two anesthesia groups: isoflurane or Hypnorm-Dormicum. The rats were submitted to dynamic [¹⁸F]FDG PET scan. The whole brain [¹⁸F]FDG standard uptake value (SUV) and the brain voxel-based analysis were performed.

Results: The dynamic [¹⁸F]FDG data revealed that the brain SUV was 38% lower in the isoflurane group after 40 min of image (2.085 ± 0.3563 vs. 3.369 ± 0.5577, p = 0.0008). In voxel-based analysis between groups, the maps collaborate with SUV data, revealing a reduction in [¹⁸F]FDG uptake in the isoflurane group, primarily in the cortical regions, with additional small increases observed in the midbrain and cerebellum.

Discussion and conclusions: The observed differences in [¹⁸F]FDG uptake in the brain may be attributed to variations in metabolic activity. These results underscore the necessity for careful consideration of anesthetic choice and its impact on neuroimaging outcomes in future research.

Background: Computed tomography scans are widely used in everyday medical practice due to speed, image reliability, and detectability of a wide range of pathologies. Each scan exposes the patient to a radiation dose, and performing a fast estimation of the effective dose (E) is an important step for radiological safety. The aim of this work is to estimate E from patient and CT acquisition parameters in the absence of a dose-tracking software exploiting machine learning.

Methods: In total, 69,037 CT acquisitions were collected with the dose-tracking software (DTS) available at our institution. E calculated by DTS was chosen as the target value for prediction. Different machine learning algorithms were selected, optimizing parameters to achieve the best performance for each algorithm. Effective dose was also estimated using DLP and k-factors, and with multiple linear regression. Mean absolute error (MAE, mean absolute percentage error (MAPE), and R² were used to evaluate predictions in the test set and in an external dataset of 3800 acquisitions.

Results: The random forest regressor (MAE: 0.416 mSv; MAPE: 7%; and R²: 0.98) showed best performances over the neural network and the support vector machine. However, all three machine learning algorithms outperformed effective dose estimation using k-factors (MAE: 2.06; MAPE: 26%) or multiple linear regression (MAE: 0.98; MAPE: 44.4%). The random forest regressor on the external dataset showed an MAE of 0.215 mSv and an MAPE of 7.1%.

Conclusions: Our work demonstrated that machine learning models trained with data calculated by a dose-tracking software can provide good estimates of the effective dose just from patient and scanner parameters, without the need for a Monte Carlo approach.

Due to the increasing number of people working at computers in professional settings, the incidence of lumbar disc herniation is increasing.

Background/objectives: The early diagnosis and treatment of lumbar disc herniation is much more likely to yield favorable results, allowing the hernia to be treated before it develops further. The aim of this study was to classify lumbar disc herniations in a computer-aided, fully automated manner using magnetic resonance images (MRIs).

Methods: This study presents a hybrid method integrating residual network (ResNet50), grey wolf optimization (GWO), and machine learning classifiers such as multi-layer perceptron (MLP) and support vector machine (SVM) to improve classification performance. The proposed approach begins with feature extraction using ResNet50, a deep convolutional neural network known for its robust feature representation capabilities. ResNet50's residual connections allow for effective training and high-quality feature extraction from input images. Following feature extraction, the GWO algorithm, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to optimize the feature set by selecting the most relevant features. Finally, the optimized feature set is fed into machine learning classifiers (MLP and SVM) for classification. The use of various activation functions (e.g., ReLU, identity, logistic, and tanh) in MLP and various kernel functions (e.g., linear, rbf, sigmoid, and polynomial) in SVM allows for a thorough evaluation of the classifiers' performance.

Results: The proposed methodology demonstrates significant improvements in metrics such as accuracy, precision, recall, and F1 score, outperforming traditional approaches in several cases. These results highlight the effectiveness of combining deep learning-based feature extraction with optimization and machine learning classifiers.

Conclusions: Compared to other methods, such as capsule networks (CapsNet), EfficientNetB6, and DenseNet169, the proposed ResNet50-GWO-SVM approach achieved superior performance across all metrics, including accuracy, precision, recall, and F1 score, demonstrating its robustness and effectiveness in classification tasks.