MR Imaging Research Articles

Association of quantitative histopathology measurements with antemortem medial temporal lobe cortical thickness in the Alzheimer's disease continuum.

The medial temporal lobe (MTL) is a hotspot for neuropathology, and measurements of MTL atrophy are often used as a biomarker for cognitive decline associated with neurodegenerative disease. Due to the aggregation of multiple proteinopathies in this region, the specific relationship of MTL atrophy to distinct neuropathologies is not well understood. Here, we develop two quantitative algorithms using deep learning to measure phosphorylated tau (p-tau) and TDP-43 (pTDP-43) pathology, which are both known to accumulate in the MTL and are associated with MTL neurodegeneration. We focus on these pathologies in the context of Alzheimer's disease (AD) and limbic predominant age-related TDP-43 encephalopathy (LATE) and apply our deep learning algorithms to distinct histology sections, on which MTL subregions were digitally annotated. We demonstrate that both quantitative pathology measures show high agreement with expert visual ratings of pathology and discriminate well between pathology stages. In 140 cases with antemortem MR imaging, we compare the association of semi-quantitative and quantitative postmortem measures of these pathologies in the hippocampus with in vivo structural measures of the MTL and its subregions. We find widespread associations of p-tau pathology with MTL subregional structural measures, whereas pTDP-43 pathology had more limited associations with the hippocampus and entorhinal cortex. Quantitative measurements of p-tau pathology resulted in a significantly better model of antemortem structural measures than semi-quantitative ratings and showed strong associations with cortical thickness and volume. By providing a more granular measure of pathology, the quantitative p-tau measures also showed a significant negative association with structure in a severe AD subgroup where semi-quantitative ratings displayed a ceiling effect. Our findings demonstrate the advantages of using quantitative neuropathology to understand the relationship of pathology to structure, particularly for p-tau, and motivate the use of quantitative pathology measurements in future studies.

Synthetic lumbar MRI can aid in diagnosis and treatment strategies based on self-pix networks.

CT and MR tools are commonly used to diagnose lumbar fractures (LF). However, numerous limitations have been found in practice. The aims of this study were to innovate and develop a spinal disease-specific neural network and to evaluate whether synthetic MRI of the LF affected clinical diagnosis and treatment strategies. A total of 675 LF patients who met the inclusion and exclusion criteria were included in the study. For each participant, two mid-sagittal CT and T2-weighted MR images were selected; 1350 pairs of LF images were also included. A new Self-pix based on Pix2pix and Self-Attention was constructed. A total of 1350 pairs of CT and MR images, which were randomly divided into a training group (1147 pairs) and a test group (203 pairs), were fed into Pix2pix and Self-pix. The quantitative evaluation included PSNR and SSIM (PSNR1 and SSIM1: real MR images and Pix2pix-generated MR images; PSNR2 and SSIM2: real MR images and Self-pix-generated MR images). The qualitative evaluation, including accurate diagnosis of acute fractures and accurate selection of treatment strategies based on Self-pix-generated MRI, was performed by three spine surgeons. In the LF group, PSNR1 and PSNR2 were 10.884 and 11.021 (p < 0.001), and SSIM1 and SSIM2 were 0.766 and 0.771 (p < 0.001), respectively. In the ROI group, PSNR1 and PSNR2 were 12.350 and 12.670 (p = 0.004), and SSIM1 and SSIM2 were 0.816 and 0.832 (p = 0.005), respectively. According to the qualitative evaluation, Self-pix-generated MRI showed no significant difference from real MRI in identifying acute fractures (p = 0.689), with a good sensitivity of 84.36% and specificity of 96.65%. No difference in treatment strategy was found between the Self-pix-generated MRI group and the real MRI group (p = 0.135). In this study, a disease-specific GAN named Self-pix was developed, which demonstrated better image generation performance compared to traditional GAN. The spine surgeon could accurately diagnose LF and select treatment strategies based on Self-pix-generated T2 MR images.

Risk factors and impact of occult and skip metastasis in early-stage oral tongue squamous cell carcinoma.

To investigate risk factors associated with occult lymph node metastases (ONM) and skip metastasis in early-stage oral tongue squamous cell carcinoma (OTSCC) patients. Meanwhile, to analyze the contribution of metastatic nodes to survival outcomes. 544 OTSCC patients who were clinically staged T1-T2N0 with pathologic results from May 2018 to January 2024 were enrolled. Those with ONM were divided into subgroups with or without skip metastasis. Clinical, laboratorial, radiological and pathological factors between groups were analyzed by using univariate analysis and multivariate logistic analysis. The association of tumor growth behavior with the metastatic pattern of lymph nodes was summarized. Additionally, disease free survival (DFS) among different groups were compared using Kaplan-Meier analysis. Tumor growth behavior was associated with ONM. Tumor thickness with a threshold of 6.4mm was not inferior to histological depth of invasion in predicting ONM. Only 1.3% of patients had nodal involvement of neck level IV or V. The DFS of patients with ONM were significantly reduced than those without ONM (P<0.001). The DFS between patients with and without skip metastasis exhibited no statistical significance(P = 0.246). The 1-year, 2-year recurrence rates of patients with or without ONM were 31.9%, 37.5%, 10.1% and 14.0%, correspondingly. Tumor thickness with a threshold of 6.4mm could be used as a preoperative predictor for ONM. Elective neck dissection of level I - III might be sufficient for early stage OTSCC patients. OTSCC patients with ONM should be closely observed during the first 2 years after surgery. The risk of ONM in early stage OTSCC patients might be predicted by tumor thickness calculated on MR imaging. Elective neck dissection of level I - III could remove micrometastases timely and effectively.

Non-osseous coalition of accessory anterolateral talar facet-A case series.

The accessory anterolateral talar facet (AALTF) is an anatomical variation that broadens the apex of the lateral talar process. Studies have associated it with other tarsal coalitions as a cause of rigid painful flatfoot. However, a coalition of an AALTF has never been described before. With the advent of MRI, there has been an increase in the detection of these accessory facets. Nevertheless, owing to its small size and unfamiliarity, it is not easily identified. Proper assessment of imaging findings and associated conditions is needed to confirm the diagnosis. Imaging findings in a series of four patients with painful rigid flatfoot revealed the presence of an AALTF with adjacent marrow edema and reduced joint space. This was associated with hindfoot valgus and subfibular impingement. Radiographic suspicion of a non-osseous coalition of AALTF was raised, which was confirmed intraoperatively in one of the patients and demonstrated on CT and MR images in the other patients. This case series presents rare cases of non-osseous coalition of AALTF with a new radiographic sign, the 'reverse tip of the iceberg sign'.

Cortical surface-based morphometry in cognitive impairment in sporadic cerebral small vessel disease

Background: Analysis of structural MR images plays a key role in assessing the underlying substrate of cognitive impairment (CI) in sporadic age-related cerebral small vessel disease (SVD), leading to up to 45% of all cases of dementia. The variety of MR-morphometry results in SVD requires extensive research and comparisons with clinical data. Aims: to assess the characteristics of brain atrophy in cognitive impairment in patients with SVD using surface-based morphometry (SBM). Materials and methods: A prospective study was conducted assessing patients with SVD and CI of varying severity (subjective - subCI, moderate - MCI and dementia) and a group of volunteers matched by gender and age. MRI was performed with subsequent analysis of MRI signs of SVD and calculation of the general SVD index and processing of T1mpr images using the SBM method with general and regional quantitative assessment of the brain, including the thickness of the cerebral cortex. Results: the main group with SVD consisted of 173 patients, the control group included 47 healthy volunteers. As the severity of structural changes in the brain and the severity of CI increased, there was a significant (p0.05) decrease in the thickness of the cortex of certain regions according to a similar pattern: the cingulate gyri, mainly their posterior sections; medial and middle sections of the frontal lobes, various parts of the insular cortex, temporo-parietal areas (especially the supramarginal gyri). The volume of the brain itself (total volume, gray and white matter) in SVD had significant differences only with controls, but not between groups of patients with different severity of CI. The volume of white matter hyperintensities was significantly different between the groups with dementia and MCI, dementia and sub-CI (p 0.0001). Conclusions: The data obtained during the study confirm the secondary/mixed nature of atrophy in SVD. The wide variety of regions with significant thinning of the cortex limits the specification of the progression of CI in SVD based on their atrophy. This allows the quantitative measurement of the cortex to be used only as an auxiliary method in assessing the prediction of SVD progression.

Quantitative mapping of renal oxygen consumption using pseudo-continuous arterial spin labeling and quantitative susceptibility mapping in humans.

To propose a new method for quantitatively mapping the renal metabolic rate of oxygen (RMRO2) and to evaluate the proposed method using a caffeine challenge. Pseudo-continuous arterial spin labeling (pCASL) and QSM sequences were used to obtain MR images in the kidney. Six healthy volunteers were scanned on caffeine and control days. The pCASL and QSM images were registered using DICOM information and rigid translation. The Fick principle was applied to estimate RMRO2. The results on caffeine and control days were compared to evaluate the capability of the proposed method to estimate renal oxygen consumption. A paired t-test was used to assess the statistical significance. Estimated renal blood flow (RBF), QSM, and RMRO2 maps were consistent with those reported in the literature. RMRO2 values were higher than the cerebral metabolic rate of oxygen (CMRO2) and were significantly reduced on the caffeine days compared to the control days, consistent with findings from non-MRI literature. The feasibility of measuring renal oxygen consumption using pCASL and QSM images was demonstrated. To the best of our knowledge, this work provides quantitative maps of renal oxygen consumption in humans for the first time. The results were consistent with the literature, including the statistically significant reduction in renal oxygen consumption with caffeine challenge. These findings suggest the potential utility of our technique in measuring renal oxygen consumption noninvasively, especially for patients with complications associated with contrast agents.

Congenital malformations of the inner ear - CT, DVT and MR Imaging

Congenital malformations of the inner ear - CT, DVT and MR Imaging

Three contrasts in 3 min: Rapid, high-resolution, and bone-selective UTE MRI for craniofacial imaging with automated deep-learning skull segmentation.

Ultrashort echo time (UTE) MRI can be a radiation-free alternative to CT for craniofacial imaging of pediatric patients. However, unlike CT, bone-specific MR imaging is limited by long scan times, relatively low spatial resolution, and a time-consuming bone segmentation workflow. A rapid, high-resolution UTE technique for brain and skull imaging in conjunction with an automatic segmentation pipeline was developed. A dual-RF, dual-echo UTE sequence was optimized for rapid scan time (3 min) and smaller voxel size (0.65 mm3). A weighted least-squares conjugate gradient method for computing the bone-selective image improves bone specificity while retaining bone sensitivity. Additionally, a deep-learning U-Net model was trained to automatically segment the skull from the bone-selective images. Ten healthy adult volunteers (six male, age 31.5 ± 10 years) and three pediatric patients (two male, ages 12 to 15 years) were scanned at 3 T. Clinical CT for the three patients were obtained for validation. Similarities in 3D skull reconstructions relative to clinical standard CT were evaluated based on the Dice similarity coefficient and Hausdorff distance. Craniometric measurements were used to assess geometric accuracy of the 3D skull renderings. The weighted least-squares method produces images with enhanced bone specificity, suppression of soft tissue, and separation from air at the sinuses when validated against CT in pediatric patients. Dice similarity coefficient overlap was 0.86 ± 0.05, and the 95th percentile Hausdorff distance was 1.77 ± 0.49 mm between the full-skull binary masks of the optimized UTE and CT in the testing dataset. An optimized MRI acquisition, reconstruction, and segmentation workflow for craniofacial imaging was developed.

Multicompartment Imaging of the Brain Using a Comprehensive MR Imaging Protocol

In this study, we describe a comprehensive 3D magnetic resonance imaging (MRI) protocol designed to assess major tissue and fluid components in the brain. The protocol comprises four different sequences: 1) magnetization transfer prepared Cones (MT-Cones) for two-pool MT modeling to quantify macromolecular content; 2) short-TR adiabatic inversion-recovery prepared Cones (STAIR-Cones) for myelin water imaging; 3) proton-density weighted Cones (PDw-Cones) for total water imaging; and 4) highly T2 weighted Cones (T2w-Cones) for free water imaging. By integrating these techniques, we successfully mapped key brain components—namely macromolecules, myelin water, intra/extracellular water, and free water—in ten healthy volunteers and five patients with multiple sclerosis (MS) using a 3T clinical scanner. Brain macromolecular proton fraction (MMPF), myelin water proton fraction (MWPF), intra/extracellular water proton fraction (IEWPF), and free water proton fraction (FWPF) values were generated in white matter (WM), grey matter (GM), and MS lesions. Excellent repeatability of the protocol was demonstrated with high intra-class correlation coefficient (ICC) values. In MS patients, the MMPF and MWPF values of the lesions and normal-appearing WM (NAWM) were significantly lower than those in normal WM (NWM) in healthy volunteers. Moreover, we observed significantly higher FWPF values in MS lesions compared to those in NWM and NAWM regions. This study demonstrates the capability of our technique to volumetrically map major brain components. The technique may have particular value in providing a comprehensive assessment of neuroinflammatory and neurodegenerative diseases of the brain.

Could ultrasound alone substitute MR imaging in evaluation of Crohn’s disease complications?

Could ultrasound alone substitute MR imaging in evaluation of Crohn’s disease complications?

Improving 18F-FDG PET Quantification Through a Spatial Normalization Method.

Quantification of 18F-FDG PET images is useful for accurate diagnosis and evaluation of various brain diseases, including brain tumors, epilepsy, dementia, and Parkinson disease. However, accurate quantification of 18F-FDG PET images requires matched 3-dimensional T1 MRI scans of the same individuals to provide detailed information on brain anatomy. In this paper, we propose a transfer learning approach to adapt a pretrained deep neural network model from amyloid PET to spatially normalize 18F-FDG PET images without the need for 3-dimensional MRI. Methods: The proposed method is based on a deep learning model for automatic spatial normalization of 18F-FDG brain PET images, which was developed by fine-tuning a pretrained model for amyloid PET using only 103 18F-FDG PET and MR images. After training, the algorithm was tested on 65 internal and 78 external test sets. All T1 MR images with a 1-mm isotropic voxel size were processed with FreeSurfer software to provide cortical segmentation maps used to extract a ground-truth regional SUV ratio using cerebellar gray matter as a reference region. These values were compared with those from spatial normalization-based quantification methods using the proposed method and statistical parametric mapping software. Results: The proposed method showed superior spatial normalization compared with statistical parametric mapping, as evidenced by increased normalized mutual information and better size and shape matching in PET images. Quantitative evaluation revealed a consistently higher SUV ratio correlation and intraclass correlation coefficients for the proposed method across various brain regions in both internal and external datasets. The remarkably good correlation and intraclass correlation coefficient values of the proposed method for the external dataset are noteworthy, considering the dataset's different ethnic distribution and the use of different PET scanners and image reconstruction algorithms. Conclusion: This study successfully applied transfer learning to a deep neural network for 18F-FDG PET spatial normalization, demonstrating its resource efficiency and improved performance. This highlights the efficacy of transfer learning, which requires a smaller number of datasets than does the original network training, thus increasing the potential for broader use of deep learning-based brain PET spatial normalization techniques for various clinical and research radiotracers.

Hyperhomocysteinemia and its effect on ageing and language functions - HEAL study.

Hyperhomocysteinemia or high levels (> 15µmol/L) of homocysteine (Hcy)in the blood has been suggested to affect the brain through vascular and neurodegenerative pathways and potentially impact cognition. The current study aims to explore the association of high homocysteine with cognition and brain volume changes in a cohort of middle and old agedr adults. The study recruited 1296 participants aged ≥ 45 years from Tata Longitudinal Study of Ageing (TLSA), an ongoing cohort study. The participants underwent detailed cognitive assessments using Addenbrooke's Cognitive Examination-III (ACE-III) and Computerized Assessment of Adult Information Processing (COGNITO) neuropsychological battery and MR imaging using a 3T scanner. The participants were classified based on the median homocysteine level (16.89µmol/L) into low Hcy (≤ median) and high Hcy (> median) groups. When adjusted for age, gender, years of education, vitamin B12, folate and dyslipidaemia, Generalised Linear Model (GLM) found a significant association of high Hcy with vocabulary task [β (95% CI) - 1.354 (- 2.655, - 0.052); p = 0.041]. Significant associations was also obtained between cerebral white matter volume and high Hcy [β (95% CI) - 5617.182 (- 11062.762, - 173.602); p = 0.043]. The results suggest that people with high Hcy levels performed poorer in cognitive tasks related to language domain and had lesser cerebral white matter volume. This indicates that homocysteine might have a profound impact on brain structure as well as function.

A deep learning approach for cervical cord injury severity determination through axial and sagittal magnetic resonance imaging segmentation and classification.

Cross-sectional Database Study. While the American Spinal Injury Association (ASIA) Impairment Scale is the standard for assessing spinal cord injuries (SCI), it has limitations due to subjectivity and impracticality. Advances in machine learning (ML) and image recognition have spurred research into their use for outcome prediction. This study aims to analyze deep learning techniques for identifying and classifying cervical SCI severity from MRI scans. The study included patients with traumatic and nontraumatic cervical SCI admitted from 2019 to 2022. MRI images were labeled by two senior resident physicians. A deep convolutional neural network was trained using axial and sagittal cervical MRI images from the dataset. Model performance was assessed using Dice Score and IoU to measure segmentation accuracy by comparing predicted and ground truth masks. Classification accuracy was evaluated with the F1 Score, balancing false positives and negatives. In the axial spinal cord segmentation, we achieved a Dice score of 0.94 for and IoU score of 0.89. In the sagittal spinal cord segmentation, we obtained Dice score up to 0.9201 and IoU scores up to 0.8541. The model for axial image score classification gave a satisfactory result with an F1 score of 0.72 and AUC of 0.79. Our models successfully identified cervical SCI on T2-weighted MR images with satisfactory performance. Further research is needed to develop more advanced models for predicting patient outcomes in SCI cases.

The clinical value of applying diffusion-weighted imaging combined with T2-weighted imaging to assess diagnostic performance of muscularis propria invasion in mid-to-high rectal cancer.

This study aimed to evaluate the diagnostic performance of combining diffusion-weighted imaging (DWI) with T2-weighted imaging (T2WI) for detecting muscularis propria invasion in rectal cancer. We conducted a retrospective analysis of MR images from 76 patients with pathologically confirmed rectal cancer between January 2018 and June 2022. Patients were categorized into invasion and non-invasion groups. the present of muscularis propria invasion. The examination regimen included T2WI, dynamic enhanced scanning and DWI.The apparent diffusion coefficient (ADC) values from DWI were compared between the groups, and the diagnostic performance of combining ADC with T2WI was assessed. There were differences in ADCmean, ADCmsi, and ADCmin values between the non-invasion group and theinvasion group, and the t values were 3.949, 2.221 and 2.978, respectively. The P values were 0.000, 0.029 and 0.004, respectively. Using an ADCmean threshold of 1.07 × 10- 3 mm²/s, the sensitivity and specificity for detecting muscularis propria invasion were 82.22% and 61.29%, respectively, with an overall diagnostic accuracy of 89.5%. For ADCmsi, with a threshold of 0.996 × 10- 3 mm²/s, the sensitivity and specificity were 62.75% and 73.33%, respectively, yielding a diagnostic accuracy of 82.9%. The ADCmin, with a threshold of 0.854 × 10- 3 mm²/s, demonstrated sensitivity and specificity of 78.43% and 66.67%, respectively, with a diagnostic accuracy of 85.5%. T2WI combined ADC value has a good predictive value for judging the degree of musculature invasion of medium-to-high rectal cancer, while ADC mean has a higher comprehensive diagnostic efficiency. Our finding may provide reference for increasing accuracy in mid-to-high rectal cancer diagnosis.

A mixed Mamba U-net for prostate segmentation in MR images.

The diagnosis of early prostate cancer depends on the accurate segmentation of prostate regions in magnetic resonance imaging (MRI). However, this segmentation task is challenging due to the particularities of prostate MR images themselves and the limitations of existing methods. To address these issues, we propose a U-shaped encoder-decoder network MM-UNet based on Mamba and CNN for prostate segmentation in MR images. Specifically, we first proposed an adaptive feature fusion module based on channel attention guidance to achieve effective fusion between adjacent hierarchical features and suppress the interference of background noise. Secondly, we propose a global context-aware module based on Mamba, which has strong long-range modeling capabilities and linear complexity, to capture global context information in images. Finally, we propose a multi-scale anisotropic convolution module based on the principle of parallel multi-scale anisotropic convolution blocks and 3D convolution decomposition. Experimental results on two public prostate MR image segmentation datasets demonstrate that the proposed method outperforms competing models in terms of prostate segmentation performance and achieves state-of-the-art performance. In future work, we intend to enhance the model's robustness and extend its applicability to additional medical image segmentation tasks.

Quantifying extent of meningioma preoperative embolization through volumetric analysis: A retrospective case series.

Endovascular embolization is an adjunct to meningioma resection. Isolating the effectiveness of embolization is difficult as MR imaging is typically performed before embolization and after resection, and volumetric assessment of embolization on 2D angiographic imaging is challenging. We investigated the correlation between 2D angiographic and 3D MR measurements of meningioma devascularization following embolization. We implemented a protocol for postembolization, preresection MRI. Angiographic devascularization was graded according to reduction of tumor blush from 1 (partial embolization) to 4 (complete embolization with no residual circulation supply). Volumetric extent of embolization was quantified as the percent of tumor contrast enhancement lost following embolization. Tumor embolization was analyzed according to tumor location and vascular supply. Thirty consecutive patients met inclusionary criteria. Grade 1 devascularization was achieved in 7% of patients, grade 2 in 43%, grade 3 in 20%, and grade 4 in 30%. Average extent of embolization was 37 ± 6%. Extent of tumor embolization was low (<25%) in 40%, moderate (25%-75%) in 40%, and high (>75%) in 20% of patients. Convexity, parasagittal/falcine and sphenoid wing tumors were found to have distinct vascular supply patterns and extent of embolization. Angiographic devascularization grade was significantly correlated with volumetric extent of tumor embolization (p < 0.001, r = 0.758). This is the first study to implement postembolization, preoperative MRI to assess extent of embolization prior to meningioma resection. The study demonstrates that volumetric assessment of contrast reduction following embolization provides a quantitative and spatially resolved framework for assessing extent of tumor embolization.

A Fuzzy inference anisotropic diffusion based high boost filter for denoising and enhancement of brain MR image

A Fuzzy inference anisotropic diffusion based high boost filter for denoising and enhancement of brain MR image

Preoperative prediction of hepatocellular carcinoma microvascular invasion based on magnetic resonance imaging feature extraction artificial neural network.

Hepatocellular carcinoma (HCC) recurrence is highly correlated with increased mortality. Microvascular invasion (MVI) is indicative of aggressive tumor biology in HCC. To construct an artificial neural network (ANN) capable of accurately predicting MVI presence in HCC using magnetic resonance imaging. This study included 255 patients with HCC with tumors < 3 cm. Radiologists annotated the tumors on the T1-weighted plain MR images. Subsequently, a three-layer ANN was constructed using image features as inputs to predict MVI status in patients with HCC. Postoperative pathological examination is considered the gold standard for determining MVI. Receiver operating characteristic analysis was used to evaluate the effectiveness of the algorithm. Using the bagging strategy to vote for 50 classifier classification results, a prediction model yielded an area under the curve (AUC) of 0.79. Moreover, correlation analysis revealed that alpha-fetoprotein values and tumor volume were not significantly correlated with the occurrence of MVI, whereas tumor sphericity was significantly correlated with MVI (P < 0.01). Analysis of variable correlations regarding MVI in tumors with diameters < 3 cm should prioritize tumor sphericity. The ANN model demonstrated strong predictive MVI for patients with HCC (AUC = 0.79).

Ulnar variance in MRI: Relations with triangular fibrocartilage complex thickness and angle in Saudi population

BackgroundEvaluating ulnar variance (UV) and triangular fibrocartilage complex (TFCC) is crucial since they might have dual effects on the wrist structure and function. This study aims to assess some anthropometric measurements of the wrists of the Saudi population using MRI. Materials and methodsA cross-sectional retrospective study was conducted on 64 wrist normal MR images. Sociodemographic characteristics were retrieved and kept confidential. Image analysis was performed on Digital Imaging and Communications in Medicine files. UV and TFCC thickness and angle were measured in the same slice. Statistical analysis was conducted, and a significance <0.05 was considered. ResultsThe ages of the selected sample ranged from 18 to 67 years; 45 were males, and 19 were females with 32 right and 32 left wrists. The cases showed 59.4 % negative UV and 40.6 % positive UV (mean ± SD of −2.24 ± 0.55 and 1.59 ± 0.48). TFCC thickness mean ± SD of 2.34 ± 0.61, and its angle mean ± SD of 32.39 ± 4.68. Significant differences were elaborated between the positive and negative UV population in the means of UV, TFCC thickness, and angle (p < 0.001). Also, age-dependent significant differences were detected in the UV value and TFCC thickness (p = 0.042 and 0.013). Correlation and linear regression showed substantial relations between UV and TFCC thickness and angle. Also, TFCC thickness with the angle. ConclusionThis study provided a valuable guide for evaluating and managing wrist problems in Saudis and primary data for further investigations.

Two-fractionated stereotactic magnetic resonance-guided adaptive radiation therapy for patients with prostate cancer (SMART PRO trial): protocol for a confirmatory clinical trial

IntroductionIn an MRI-guided linear accelerator (MR-LINAC) system, the planned doses for organs at risk and for tumours are assessed by MR imaging and re-contouring at every treatment. This allows treatment...