Tomography最新文献

Objective: To determine the added value of digital breast tomosynthesis (DBT) in the assessment of lesions detected by contrast-enhanced mammography (CEM).

Material and methods: A retrospective study was conducted in a tertiary university medical center. All CEM studies including DBT performed between January 2016 and December 2020 were included. Lesions were categorized and scored by four dedicated breast radiologists according to the recent CEM and DBT supplements to the Breast Imaging Reporting and Data System (BIRADS) lexicon. Changes in the BIRADS score of CEM-detected lesions with the addition of DBT were evaluated according to the pathology results and 1-year follow-up imaging study.

Results: BIRADS scores of CEM-detected lesions were upgraded toward the lesion's pathology with the addition of DBT (p > 0.0001), overall and for each reader. The difference in BIRADS scores before and after the addition of DBT was more significant for readers who were less experienced. The reason for changes in the BIRADS score was better lesion margin visibility. The main BIRADS descriptors applied in the malignant lesions were spiculations, calcifications, architectural distortion, and sharp or obscured margins.

Conclusions: The addition of DBT to CEM provides valuable information on the enhancing lesion, leading to a more accurate BIRADS score.

Background: Bone assessment using the MRI DEAL-IQ sequence may have the potential to serve as a substitute for evaluating bone strength by quantifying the bone marrow hematopoietic region (R2*) and marrow adiposity (proton density fat fraction: PDFF). Higher body mass index (BMI) is associated with increased bone mineral density (BMD) in the proximal femur; however, the relationship between BMI and R2* or PDFF remains unclear. Herein, we investigated the correlation between BMI and MRI IDEAL-IQ based R2* or PDFF of the proximal femur.

Methods: A retrospective single-cohort study was conducted on 217 patients diagnosed with non-metastatic prostate cancer between September 2019 and December 2022 who underwent MRI. The correlation between BMI and R2* or PDFF of the proximal femur was analyzed using Spearman's rank correlation test.

Results: Among 217 patients (median age, 74 years; median BMI, 23.8 kg/m²), there was a significant positive correlation between BMI and R2* at the right and left proximal femur (r = 0.2686, p < 0.0001; r = 0.2755, p < 0.0001, respectively). Furthermore, BMI and PDFF showed a significant negative correlation (r = -0.239, p = 0.0004; r = -0.2212, p = 0.001, respectively).

Conclusion: In elderly men, the increased loading on the proximal femur due to elevated BMI was observed to promote a decrease in bone marrow adiposity in the proximal femur, causing a tendency for a transition from fatty marrow to red marrow with hematopoietic activity. These results indicate that the MRI IDEAL-IQ sequence may be valuable for assessing bone quality deterioration in the proximal femur.

The aim of this study was to show for the first time that low-frequency 3D-transmitted ultrasound tomography (3D UT, volography) can differentiate breast tissue types using tissue properties, accurately measure glandular and ductal volumes in vivo, and measure variation over time. Data were collected for 400 QT breast scans on 24 women (ages 18-71), including four (4) postmenopausal subjects, 6-10 times over 2+ months of observation. The date of onset of menopause was noted, and the cases were further subdivided into three (3) classes: pre-, post-, and peri-menopausal. The ducts and glands were segmented using breast speed of sound, attenuation, and reflectivity images and followed over several menstrual cycles. The coefficient of variation (CoV) for glandular tissue in premenopausal women was significantly larger than for postmenopausal women, whereas this is not true for the ductal CoV. The glandular standard deviation (SD) is significantly larger in premenopausal women vs. postmenopausal women, whereas this is not true for ductal tissue. We conclude that ducts do not appreciably change over the menstrual cycle in either pre- or post-menopausal subjects, whereas glands change significantly over the cycle in pre-menopausal women, and 3D UT can differentiate ducts from glands in vivo.

Background: The arterial input function (AIF) is vital for myocardial blood flow quantification in cardiac MRI to indicate the input time-concentration curve of a contrast agent. Inaccurate AIFs can significantly affect perfusion quantification. Purpose: When only saturated and biased AIFs are measured, this work investigates multiple ways of leveraging tissue curve information, including using AIF + tissue curves as inputs and optimizing the loss function for deep neural network training. Methods: Simulated data were generated using a 12-parameter AIF mathematical model for the AIF. Tissue curves were created from true AIFs combined with compartment-model parameters from a random distribution. Using Bloch simulations, a dictionary was constructed for a saturation-recovery 3D radial stack-of-stars sequence, accounting for deviations such as flip angle, T2* effects, and residual longitudinal magnetization after the saturation. A preliminary simulation study established the optimal tissue curve number using a bidirectional long short-term memory (Bi-LSTM) network with just AIF loss. Further optimization of the loss function involves comparing just AIF loss, AIF with compartment-model-based parameter loss, and AIF with compartment-model tissue loss. The optimized network was examined with both simulation and hybrid data, which included in vivo 3D stack-of-star datasets for testing. The AIF peak value accuracy and ktrans results were assessed. Results: Increasing the number of tissue curves can be beneficial when added tissue curves can provide extra information. Using just the AIF loss outperforms the other two proposed losses, including adding either a compartment-model-based tissue loss or a compartment-model parameter loss to the AIF loss. With the simulated data, the Bi-LSTM network reduced the AIF peak error from -23.6 ± 24.4% of the AIF using the dictionary method to 0.2 ± 7.2% (AIF input only) and 0.3 ± 2.5% (AIF + ten tissue curve inputs) of the network AIF. The corresponding ktrans error was reduced from -13.5 ± 8.8% to -0.6 ± 6.6% and 0.3 ± 2.1%. With the hybrid data (simulated data for training; in vivo data for testing), the AIF peak error was 15.0 ± 5.3% and the corresponding ktrans error was 20.7 ± 11.6% for the AIF using the dictionary method. The hybrid data revealed that using the AIF + tissue inputs reduced errors, with peak error (1.3 ± 11.1%) and ktrans error (-2.4 ± 6.7%). Conclusions: Integrating tissue curves with AIF curves into network inputs improves the precision of AI-driven AIF corrections. This result was seen both with simulated data and with applying the network trained only on simulated data to a limited in vivo test dataset.

This brief report aimed to show the utility of photon-counting technology alongside standard cranial imaging protocols for visualizing shunt valves in a patient's cranial computed tomography scan. Photon-counting CT scans with cranial protocols were retrospectively surveyed and four types of shunt valves were encountered: proGAV 2.0^®, M.blue^®, Codman Certas^®, and proSA^®. These scans were compared with those obtained from non-photon-counting scanners at different time points for the same patients. The analysis of these findings demonstrated the usefulness of photon-counting technology for the clear and precise visualization of shunt valves without any additional radiation or special reconstruction patterns. The enhanced utility of photon-counting is highlighted by providing superior spatial resolution compared to other CT detectors. This technology facilitates a more accurate characterization of shunt valves and may support the detection of subtle abnormalities and a precise assessment of shunt valves.

Objective: This study investigates the correlation between patient body metrics and radiation dose in abdominopelvic CT scans, aiming to identify significant predictors of radiation exposure. Methods: Employing a cross-sectional analysis of patient data, including BMI, abdominal fat, waist, abdomen, and hip circumference, we analyzed their relationship with the following dose metrics: the CTDI_vol, DLP, and SSDE. Results: Results from the analysis of various body measurements revealed that BMI, abdominal fat, and waist circumference are strongly correlated with increased radiation doses. Notably, the SSDE, as a more patient-centric dose metric, showed significant positive correlations, especially with waist circumference, suggesting its potential as a key predictor for optimizing radiation doses. Conclusions: The findings suggest that incorporating patient-specific body metrics into CT dosimetry could enhance personalized care and radiation safety. Conclusively, this study highlights the necessity for tailored imaging protocols based on individual body metrics to optimize radiation exposure, encouraging further research into predictive models and the integration of these metrics into clinical practice for improved patient management.

Quantifying an imaging modality's ability to reproduce results is important for establishing its utility. In magnetic resonance spectroscopic imaging (MRSI), new acquisition protocols are regularly introduced which improve upon their precursors with respect to signal-to-noise ratio (SNR), total acquisition duration, and nominal voxel resolution. This study has quantified the within-subject and between-subject reproducibility of one such new protocol (reduced-field-of-view density-weighted concentric ring trajectory (rFOV-DW-CRT) MRSI) by calculating the coefficient of variance of data acquired from a test-retest experiment. The posterior cingulate cortex (PCC) and the right superior corona radiata (SCR) were selected as the regions of interest (ROIs) for grey matter (GM) and white matter (WM), respectively. CVs for between-subject and within-subject were consistently around or below 15% for Glx, tCho, and Myo-Ins, and below 5% for tNAA and tCr.

The aim of this paper is to investigate whether a multifractal analysis can be applied to study choroidal blood vessels and help ophthalmologists in the early diagnosis of retinitis pigmentosa (RP). In a case study, we used spectral domain optical coherence tomography (SDOCT), which is a noninvasive and highly sensitive imaging technique of the retina and choroid. The image of a choroidal branching pattern can be regarded as a multifractal. Therefore, we calculated the generalized Renyi point-centered dimensions, which are considered a measure of the inhomogeneity of data, to prove that it increases in patients with RP as compared to those in the control group.

An increasing amount of molecular imaging studies are ordered each year for an oncologic population that continues to expand and increase in age. The importance of these studies in dictating further care for oncologic patients underscores the necessity of differentiating benign from malignant findings, particularly for a population in whom incidental findings are common. The aim of this review is to provide pictorial examples of benign musculoskeletal pathologies which may be found on molecular imaging and which may be mistaken for malignant processes. Imaging examples are provided in the form of radiographs, bone scintigraphy, computed tomography, and fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) scans. Special attention is paid to specific features that help narrow the differential diagnosis and distinguish benign from malignant processes, with the goal of avoiding unnecessary invasive procedures.

Detailed visualization of the cribriform plate is challenging due to its intricate structure. This study investigates how computed tomography (CT) with a novel photon counting (PC) detector enhance cribriform plate visualization compared to traditionally used energy-integrated detectors in patients. A total of 40 patients were included in a retrospective analysis, with half of them undergoing PC CT (Naeotom Alpha Siemens Healthineers, Forchheim, Germany) and the other half undergoing CT scans using an energy-integrated detector (Somatom Sensation 64, Siemens, Forchheim, Germany) in which the cribriform plate was visualized with a temporal bone protocol. Both groups of scans were evaluated for signal-to-noise ratio, radiation dose, the imaging quality of the whole scan overall, and, separately, the cribriform plate and the clarity of volume rendering reconstructions. Two independent observers conducted a qualitative analysis using a Likert scale. The results consistently demonstrated excellent imaging of the cribriform plate with the PC CT scanner, surpassing traditional technology. The visualization provided by PC CT allowed for precise anatomical assessment of the cribriform plate on multiplanar reconstructions and volume rendering imaging with reduced radiation dose (by approximately 50% per slice) and higher signal-to-noise ratio (by approximately 75%). In conclusion, photon-counting technology provides the possibility of better imaging of the cribriform plate in adult patients. This enhanced imaging could be utilized in skull base-associated pathologies, such as cerebrospinal fluid leaks, to visualize them more reliably for precise treatment.