Magnetic Resonance Imaging Sequences Research Articles

Dynamic Magnetic Resonance Imaging (MRI) in Inguinal-Related Chronic Groin Pain (CGP): Comparison With Systematic Surgical Assessment.

Objective This study aimed to assess the performance of dynamic MRI in Chronic Groin Pain (CGP) related to the inguinal region, comparing it with surgery as the gold standard. Materials and methods A cohort of 25 consecutive patients exhibiting persistent clinical inguinal-related CGP underwent a pre-surgical pelvis MRI. Imaging encompassed strictly axial Fast Spin Echo (FSE) T1 sequences, both without (static sequence) and with Valsalva Maneuver (VM, dynamic sequence), alongside axial-oblique Proton Density weighted with Fat Saturation (PDFS). Evaluation of these sequences focused on identifying Abdominal Wall (AW) injuries. A consistent surgical approach was employed by the same surgeon across all patients (34 AW injuries in 25 patients). Specificity (Sp), Sensitivity (Se), Negative Predictive Value (NPV), Positive Predictive Value (PPV), and overall accuracy of MRI sequences and their combinations for detecting AW injuries were computed by comparing them to surgical findings. Results Ninety sequences were obtained, revealing that the axial PDFS oblique sequence emerged as the most singularly reliable (Accuracy: 58.82%). The optimal sequence combination was found to be axial T1 combined with axial T1 VM, exhibiting an accuracy of 75.00% (Se: 85.71%, Sp: 70.59%, PPV: 54.55%, NPV: 92.31%, with an average duration of 4 minutes and 31 seconds). Conclusion Based on our findings, we advocate for the adoption of the axial FSE T1 combined with Valsalva Maneuver as a dependable protocol for inguinal-related CGP, characterized by a highly reasonable examination duration.

Cardiovascular Management in Asymptomatic (Silent) Cerebral Microbleeds and Suspected Cerebral Amyloid Angiopathy.

Cerebral microbleeds (CMBs) detected on blood-sensitive magnetic resonance imaging sequences are usually a sign of an underlying cerebral small vessel disease such as sporadic cerebral amyloid angiopathy or sporadic nonamyloid small vessel pathology (eg, arteriolosclerosis). Much of the enduring interest in CMBs relates to their high prevalence (partly due to the widespread use of magnetic resonance imaging) in the context of stroke, cognitive impairment and in healthy individuals, and the clinical uncertainties created about the safety of antithrombotic medications due to their association with both future hemorrhagic and ischemic stroke. Historically, the research literature overwhelmingly emphasized the future hemorrhagic risk associated with CMBs, potentially leading to unnecessary withholding of treatments proven effective at preventing thrombosis, such as anticoagulants in patients with atrial fibrillation who happened to have some microbleeds. The lack of strong guidelines in this area contributes to wide variation in clinical practice. In this article, we critically review and discuss the implications of silent CMBs and cortical superficial siderosis (ie, without symptomatic intracerebral hemorrhage) in different clinical settings: the general population, patients with ischemic stroke, and the memory clinic. Emerging evidence, albeit not from randomized controlled trials, suggests that in most patients, CMBs alone should not prevent the use of antithrombotics or anticoagulants for stroke prevention, when they are otherwise indicated. Where possible, we provide specific suggestions for clinical care grounded in both the limited available literature and our personal clinical practice.

Cerebral microbleeds: Causes, clinical relevance, and imaging approach - A narrative review.

With advances in magnetic resonance imaging (MRI) sequences, there has been increased identification of microbleed/microhemorrhage across different population ages, but more commonly in the older age group. These are defined as focal areas of signal loss on gradient echo MRI sequences (T2* and susceptibility-weighted images), which are usually <5 mm in size representing hemosiderin deposition with wide ranges of etiologies. Susceptibility-weighted imaging (SWI) has become a routine MRI sequence for practices across the globe resulting in better identification of these entities. Over the past decade, there has been a better understanding of the clinical significance of microbleeds including their prognostic value in ischemic and hemorrhagic stroke. Cerebral amyloid angiopathy and hypertension are the two most common causes of microbleeds following peripheral and central pattern, respectively. In the younger age group, microbleeds are more common due to familial conditions or a wide range of hypercoagulable states. This review outlines the pathophysiology, prevalence, and clinical implications of cerebral microhemorrhage along with a brief discussion about the technical considerations of SWI.

Peptide radionuclide radiation therapy with Lutathera in multirecurrent nonanaplastic meningiomas: antitumoral activity study by growth rate analysis.

Several retrospective studies and meta-analyses of Peptide Radionuclide Radiation Therapy in meningiomas suggest six-month progression-free survival improvement for WHO grade 1 and 2 meningiomas. In the present study, we aimed to evaluate the impact of such treatment on three-dimensional volume growth rate (3DVGR) in nonanaplastic meningiomas. The authors performed a retrospective study including eight patients treated with Lutathera®. Millimetric 3D T1-weighted with gadolinium enhancement magnetic resonance imaging sequences were requested for volume measurement. Then, tumor growth rate was classified following a previously described 3DVGR classification (Graillon et al.). Patients harbored seven WHO grade 2 meningiomas and one aggressive WHO grade 1. All patients, except one, underwent four treatment cycles. 3DVGR significantly decreased at 3, 6, and 12 months after treatment initiation analyzing each lesion separately. Mean and median 3DVGR from all patients were respectively at 29.5% and 44.5%/6 months before treatment initiation, then at 16.5% and 25%/6 months at three months post-treatment initiation, 9.5% and 4.5%/6 months after 6 months, as well as 9.5% and 10.5%/6 months after 12 months. At 3, 6, and 12 months after treatment initiation, 4/8, 6/7, and 5/6 patients were class 2 (stabilization or severe 3DVGR slowdown), respectively. No patient was class 1 at 6 and 12 months, suggesting a lack of drug response. In nonanaplastic meningiomas, Lutathera®'s antitumoral activity appeared delayed and more likely observed at six months, while no major response was observed under treatment. Moreover, its antitumoral activity persisted for 12-18 months following treatment initiation.

Diagnostic contribution of diffusion-weighted imaging in liver hemangiomas

Objective: To evaluate the contribution of diffusion-weighted imaging (DWI) and measure apparent diffusion coefficient values (ADC) in hepatic hemangiomas.&#x0D; Material and Methods: Seventy patients with liver hemangiomas were evaluated with DWI with a b value of 800 s/mm2. ADC map was created and ADC values were measured for 70 liver masses and normal liver tissue following DWI. ADC measurements of 70 normal liver parenchyma and mean ADC values of 80 hemangiomas are performed.&#x0D; Results: Eighty hemangiomas of 70 patients (50 female and 20 male) were evaluated. The mean age of the patients was 49.61±10.96 years. Hemangiomas were most frequently demonstrated at segment 7 (%28.8), segment 6 (%21.3), and least demonstrated at segment 5 (%5). While the mean ADC measurements of normal livers were 1.06±0.11 x 10-3 mm2/sn, the mean ADC values of hemangiomas were measured as 1.70±0.29 x 10-3 mm2/sn.&#x0D; Conclusion: DWI and the ADC measurements seem to be useful for the diagnosis of liver hemangiomas. DWI should be routinely added to conventional magnetic resonance imaging sequences.

Compatibility of Standard Vagus Nerve Stimulation and Investigational Microburst Vagus Nerve Stimulation Therapy with fMRI.

Vagus nerve stimulation devices are conditionally approved for MR imaging with stimulation turned off, and the requirement to modify the stimulation settings may be a barrier to scanning in some radiology practices. There is increasing interest in studying the effects of stimulation during MR imaging/fMRI. This study evaluated the safety of standard and investigational microburst vagus nerve stimulation therapies during MR imaging/fMRI. A prospective, multicenter study was conducted in patients with an investigational vagus nerve stimulation device that delivered either standard or investigational microburst vagus nerve stimulation. Thirty participants underwent sequential MR imaging and fMRI scans, encompassing 188 total hours of scan time (62.7 hours with standard vagus nerve stimulation and 125.3 hours with investigational microburst vagus nerve stimulation). No adverse events were reported with active stimulation during MR imaging or during 12 months of follow-up. Our results support the safety of standard and investigational microburst vagus nerve stimulation therapy during MR imaging and fMRI scans.

T 1 Relaxation of Methane in Mixtures with Gaseous Water.

Synthetic, ecofriendly fuels and chemicals can be produced through Power-To-X (PtX) processes. To study such catalytic processes operando and spatially resolved, magnetic resonance imaging (MRI) is a versatile tool. A main issue in the application of MRI in reactive studies is a lack of knowledge about how the gathered signals can be interpreted into reaction data like temperature or species concentration. In this work, the interaction of methane and gaseous water is studied regarding their longitudinal relaxation time T 1 and the chemical shift. To this end, defined quantities of methane-water mixtures were sealed in glass tubes and probed at temperatures between 130 and 360 °C and pressures from 6 to 20 bar. From the obtained T 1 relaxation times, the collision cross section of methane with water σ j,CH4-H2O is derived, which can be used to estimate the temperature and molar concentration of methane during the methanation reaction. The obtained T 1 relaxation times can additionally be used to improve the timing of MRI sequences involving water vapor or methane. Further, details about the measurement workflow and tube preparation are shared.

Content-aware Nakagami morphing for incremental brain MRI

Within the carcinogenesis mechanism, from the initiation of the very first tumor cell to the preneoplastic and neoplastic cancer cell groups, cancer cells omnidirectionally and unpredictably proliferate in three-dimensional (3D) space during the promotion and progression steps. Whole tumors areas, consisting of edema, necrosis, enhancing and non-enhancing tumor subareas, could easily be identified by the typical sequences of magnetic resonance imaging (MRI), mostly by FLAIR, the fluid attenuated inversion recovery sequence. Depending on the increment value of the scanner, not every scan could be contiguous though, which might create some gaps between the slices and cause some crucial loss of information. Shape-based morphing by erosion and dilation operations might be inadequate when the source or target lesions are not found on the MRI slices, which might emerge due to unpredictable shape of tumors. Furthermore, the real trajectories and genuine shapes of the tumors in spatial view might not be seen in the planar MRI slices due to approximations during the voxel-to-pixel transformations executed in MRI devices. Therefore, we propose a novel content-aware morphing procedure to generate imaginary slices by m-parametric Nakagami imaging. The mathematical and parametric design in our framework not only leads to precise segmentation of the whole tumors according to the ground truth images; but also, to realistic morphing, based on the content of tumor footprints in MRI slices. For testing purposes, a total number of 5133 morphs are created using 1831 genuine images containing at least one lesion taken from the Brats 2012 dataset. Given the average dice score coefficients (DSC), we obtained an average of 88.44% DSC with our morphing framework outperforming the conventional dilation-based morphing result which is 83.25%. Besides being a novel morphing methodology, the outcomes of this research could also be very beneficial in smoother 3D reconstruction and more realistic volumetric calculations of lesions to help the experts working on assessment of lesions.

Evaluation of glenoid morphology and bony Bankart lesion in shoulders with traumatic anterior instability using zero echo time magnetic resonance imaging

BackgroundPreoperative computed tomography (CT) evaluation of bone morphometry aids in determining treatment strategies for shoulder instability. The use of zero echo time (ZTE) sequence in magnetic resonance imaging (MRI), a new bone cortex imaging technique, may help reduce radiation exposure and medical costs. Therefore, this study aimed to evaluate the glenoid morphology and detect the presence of bony Bankart lesion using ZTE MRI in shoulders with anterior instability and compare its diagnostic accuracy with that of CT. MethodsThirty-six patients (36 shoulders) with anterior instability who underwent preoperative CT and MRI examinations between April 2019 and October 2021 were retrospectively analyzed. The percentages of glenoid bone defects on three-dimensional (3D) CT and ZTE images were determined, and the correlation between these percentages was evaluated. The number of cases with bony Bankart lesion on CT and two types of ZTE (3D and CT-like) images was determined, and the diagnostic accuracy of ZTE for detecting bony Bankart lesion was assessed, with CT as the gold standard. Patients with bony Bankart lesion on CT images were divided into two groups based on whether the lesion was detectable on 3D ZTE or CT-like images. The longer diameters of bony Bankart lesion were compared between the groups. ResultsThe median percentage of glenoid bone loss was 12.1% (range, 1.3%–45.9%) and 12.3% (range, 0%–46.6%) on 3D CT and 3D ZTE images, respectively. The Spearman’s rank correlation coefficient was 0.89. Bony Bankart lesion was detected in 18, 13, and eight shoulders of the 36 patients on CT, 3D ZTE, and CT-like images, respectively. The overall diagnostic accuracy of the CT-like and 3D ZTE images for detecting bony Bankart lesion was 86.1% and 72.2%, respectively. A significant difference was observed between the groups with and without bony Bankart lesion on CT-like images in terms of the long diameter of the bone fragments on CT (p < 0.01). ConclusionsZTE MRI demonstrated high reproducibility for the evaluation of glenoid bone defect in shoulders with anterior instability. Although no significant difference in the measurement was observed compared with that on CT, the ability of ZTE MRI to delineate bone Bankart lesion remains limited.

Evaluation of Infantile Brachial Plexopathy Using 3T MRI and High-Resolution Ultrasound: Experience From a Tertiary Care Centre.

Currently, clinical assessment is the main tool for the evaluation of brachial plexus injury, complemented by electrophysiologic studies (EPS), and imaging studies whenever available. Imaging plays an important role as it enables the differentiation of pre-ganglionic and postganglionic injuries, and adds objectivity to presurgical evaluation. The primary objective was to evaluate the utility of magnetic resonance imaging (MRI) and high-resolution ultrasonography (USG) in the localization and characterization of brachial plexus injury in infants. In this prospective study, 34 infants with signs and symptoms of brachial plexus injury were evaluated by clinical examination, EPS, MRI, and USG. Imaging findings were correlated with intraoperative findings in infants who underwent surgical management. The association between EPS and MRI findings, and USG and MRI findings were assessed using Fisher's exact test. Semi-quantitative subjective analysis of various MRI sequences was done as well. The most common findings of preganglionic injury and postganglionic injury, in our study, were pseudomeningocele and nerve thickening, respectively. MRI detection of injuries had a significant association with EPS findings. All MRI-detected injuries had a muscle power of grade 3 or less. muscle. Three-dimensional (3D) short tau inversion recovery (STIR) sequence was found to be superior for detecting postganglionic injuries (P < 0.05). Imaging studies enable localization of the site of injury, determining the extent, and nature/morphology of injury. The gamut of findings obtained from MRI is far wider compared to that from USG. USG can be used as the first-line screening investigation.

Faster acquisition of magnetic resonance imaging sequences of the knee via deep learning reconstruction: a volunteer study

To evaluate whether deep learning reconstruction (DLR) can accelerate the acquisition of magnetic resonance imaging (MRI) sequences of the knee for clinical use. Using a 1.5-T MRI scanner, sagittal fat-suppressed T2-weighted imaging (fs-T2WI), coronal proton density-weighted imaging (PDWI), and coronal T1-weighted imaging (T1WI) were performed. DLR was applied to images with a number of signal averages (NSA) of 1 to obtain 1DLR images. Then 1NSA, 1DLR, and 4NSA images were compared subjectively, and by noise (standard deviation of intra-articular water or medial meniscus) and contrast-to-noise ratio between two anatomical structures or between an anatomical structure and intra-articular water. Twenty-seven healthy volunteers (age: 40.6±11.9 years) were enrolled. Three 1DLR image sequences were obtained within 200 s (approximately 12 minutes for 4NSA image). According to objective evaluations, PDWI 1DLR images showed the smallest noise and significantly higher contrast than 1NSA and 4NSA images. For fs-T2WI, smaller noise and higher contrast were observed in the order of 4NSA, 1DLR, and 1NSA images. According to the subjective analysis, structure visibility, image noise, and overall image quality were significantly better for PDWI 1DLR than 1NSA images; moreover, the visibility of the meniscus and bone, image noise, and overall image quality were significantly better for 1DLR than 4NSA images. Fs-T2WI and T1WI 1DLR images showed no difference between 1DLR and 4NSA images. Compared to PDWI 4NSA images, PDWI 1DLR images were of higher quality, while the quality of fs-T2WI and T1WI 1DLR images was similar to that of 4NSA images.

Deep learning convolutional neural network ResNet101 and radiomic features accurately analyzes mpMRI imaging to predict MGMT promoter methylation status with transfer learning approach

AbstractAccurate brain tumor classification is crucial for enhancing the diagnosis, prognosis, and treatment of glioblastoma patients. We employed the ResNet101 deep learning method with transfer learning to analyze the 2021 Radiological Society of North America (RSNA) Brain Tumor challenge dataset. This dataset comprises four structural magnetic resonance imaging (MRI) sequences: fluid‐attenuated inversion‐recovery (FLAIR), T1‐weighted pre‐contrast (T1w), T1‐weighted post‐contrast (T1Gd), and T2‐weighted (T2). We assessed the model's performance using standard evaluation metrics. The highest performance to detect MGMT methylation status for patients suffering glioblastoma was an accuracy (85.48%), sensitivity (80.64%), specificity (90.32%). Whereas classification performance with no tumor was yielded with accuracy (85.48%), sensitivity (90.32%), specificity (80.64%). The radiomic features (74) computed with ensembled Bagged Tree and relief feature selection method (30/74) improved the validation accuracy of 84.3% and AUC of 0.9038 to detect. O6‐methylguanine‐DNA methyltransferase (MGMT) promoter methylation status in glioblastoma patients holds promise for optimizing treatment planning and prognosis. By understanding MGMT methylation status, clinicians can make informed decisions about treatment strategies, potentially leading to improved clinical outcomes.

Intracerebral hemorrhage-induced brain injury in mice: The role of peroxiredoxin 2-Toll-like receptor 4 inflammatory axis.

Peroxiredoxin 2 (Prx2), an intracellular protein that regulates redox reactions, released from red blood cells is involved in inflammatory brain injury after intracerebral hemorrhage (ICH). Toll-like receptor 4 (TLR4) may be crucial in this process. This study investigated the role of the Prx2-TLR4 inflammatory axis in brain injury following experimental ICH in mice. First, C57BL/6 mice received an intracaudate injection of autologous arterial blood or saline and their brains were harvested on day 1 to measure Prx2 levels. Second, mice received an intracaudate injection of either recombinant mouse Prx2 or saline. Third, the mice were co-injected with autologous arterial blood and conoidin A, a Prx2 inhibitor, or vehicle. Fourth, the mice received a Prx2 injection and were treated with TAK-242, a TLR4 antagonist, or saline (intraperitoneally). Behavioral tests, magnetic resonance imaging, western blot, immunohistochemistry/immunofluorescence staining, and RNA sequencing (RNA-seq) were performed. Brain Prx2 levels were elevated after autologous arterial blood injection. Intracaudate injection of Prx2 caused brain swelling, microglial activation, neutrophil infiltration, neuronal death, and neurological deficits. Co-injection of conoidin A attenuated autologous arterial blood-induced brain injury. TLR4 was expressed on the surface of microglia/macrophages and neutrophils and participated in Prx2-induced inflammation. TAK-242 treatment attenuated Prx2-induced inflammation and neurological deficits. Prx2 can cause brain injury following ICH through the TLR4 pathway, revealing the Prx2-TLR4 inflammatory axis as a potential therapeutic target.

Appropriate incorporation of susceptibility-weighted magnetic resonance imaging into routine imaging protocols for accurate diagnosis of traumatic brain injuries: a systematic review.

Traumatic brain injury (TBI) results from physical or traumatic injuries to the brain's surrounding bony structures and associated tissues, which can lead to various sequelae, including simple concussion, acute epidural hematoma, parenchymal contusions, subarachnoid hemorrhage, diffuse axonal injury, and chronic traumatic encephalopathy. Susceptibility-weighted imaging (SWI) has enhanced the accuracy of neuroimaging for these injuries. SWI is based on 3D gradient echo magnetic resonance imaging (MRI) with long echo times and flow compensation. Owing to its sensitivity to deoxyhemoglobin, hemosiderin, iron, and calcium, SWI is extremely informative and superior to conventional MRI for the diagnosis and follow-up of patients with acute, subacute, and prolonged hemorrhage. This systematic review aimed to evaluate and summarize the published articles that report SWI results for the evaluation of TBI and to determine correlations between clinical status and SWI results. Consequently, our analysis also aimed to identify the appropriate MRI sequences to use in the assessment of patients with TBI. We searched the Medline and Embase online electronic databases for relevant papers published from 2012 onwards. We found that SWI had higher sensitivity than gradient echo MRI in detecting and characterizing microbleeds in TBIs and was able to differentiate diamagnetic calcifications from paramagnetic microhemorrhages. However, it is important that future research not only continues to evaluate the utility of SWI in TBIs but also attempts to overcome the limitations of the studies described in this review, which should help validate the conclusions and recommendations from our analysis.

PETS-Nets: Joint Pose Estimation and Tissue Segmentation of Fetal Brains Using Anatomy-Guided Networks.

Fetal Magnetic Resonance Imaging (MRI) is challenged by fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion frequently occurs in the acquisition of spatially adjacent slices. Motion correction for each slice is thus critical for the reconstruction of 3D fetal brain MRI. In this paper, we propose a novel multi-task learning framework that adopts a coarse-to-fine strategy to jointly learn the pose estimation parameters for motion correction and tissue segmentation map of each slice in fetal MRI. Particularly, we design a regression-based segmentation loss as a deep supervision to learn anatomically more meaningful features for pose estimation and segmentation. In the coarse stage, a U-Net-like network learns the features shared for both tasks. In the refinement stage, to fully utilize the anatomical information, signed distance maps constructed from the coarse segmentation are introduced to guide the feature learning for both tasks. Finally, iterative incorporation of the signed distance maps further improves the performance of both regression and segmentation progressively. Experimental results of cross-validation across two different fetal datasets acquired with different scanners and imaging protocols demonstrate the effectiveness of the proposed method in reducing the pose estimation error and obtaining superior tissue segmentation results simultaneously, compared with state-of-the-art methods.

Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan

BackgroundThe choroid plexus functions as the blood-cerebrospinal fluid (CSF) barrier, plays an important role in CSF production and circulation, and has gained increased attention in light of the recent elucidation of CSF circulation dysfunction in neurodegenerative conditions. However, methods for routinely quantifying choroid plexus volume are suboptimal and require technical improvements and validation. Here, we propose three deep learning models that can segment the choroid plexus from commonly-acquired anatomical MRI data and report performance metrics and changes across the adult lifespan.MethodsFully convolutional neural networks were trained from 3D T1-weighted, 3D T2-weighted, and 2D T2-weighted FLAIR MRI using gold-standard manual segmentations in control and neurodegenerative participants across the lifespan (n = 50; age = 21–85 years). Dice coefficients, 95% Hausdorff distances, and area-under-curve (AUCs) were calculated for each model and compared to segmentations from FreeSurfer using two-tailed Wilcoxon tests (significance criteria: p < 0.05 after false discovery rate multiple comparisons correction). Metrics were regressed against lateral ventricular volume using generalized linear models to assess model performance for varying levels of atrophy. Finally, models were applied to an expanded cohort of adult controls (n = 98; age = 21–89 years) to provide an exemplar of choroid plexus volumetry values across the lifespan.ResultsDeep learning results yielded Dice coefficient = 0.72, Hausdorff distance = 1.97 mm, AUC = 0.87 for T1-weighted MRI, Dice coefficient = 0.72, Hausdorff distance = 2.22 mm, AUC = 0.87 for T2-weighted MRI, and Dice coefficient = 0.74, Hausdorff distance = 1.69 mm, AUC = 0.87 for T2-weighted FLAIR MRI; values did not differ significantly between MRI sequences and were statistically improved compared to current commercially-available algorithms (p < 0.001). The intraclass coefficients were 0.95, 0.95, and 0.96 between T1-weighted and T2-weighted FLAIR, T1-weighted and T2-weighted, and T2-weighted and T2-weighted FLAIR models, respectively. Mean lateral ventricle choroid plexus volume across all participants was 3.20 ± 1.4 cm3; a significant, positive relationship (R2 = 0.54-0.60) was observed between participant age and choroid plexus volume for all MRI sequences (p < 0.001).ConclusionsFindings support comparable performance in choroid plexus delineation between standard, clinically available, non-contrasted anatomical MRI sequences. The software embedding the evaluated models is freely available online and should provide a useful tool for the growing number of studies that desire to quantitatively evaluate choroid plexus structure and function (https://github.com/hettk/chp_seg).

GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent Advances.

The approach of using more than one processor to compute in order to overcome the complexity of different medical imaging methods that make up an overall job is known as GPU (graphic processing unit)-based parallel processing. It is extremely important for several medical imaging techniques such as image classification, object detection, image segmentation, registration, and content-based image retrieval, since the GPU-based parallel processing approach allows for time-efficient computation by a software, allowing multiple computations to be completed at once. On the other hand, a non-invasive imaging technology that may depict the shape of an anatomy and the biological advancements of the human body is known as magnetic resonance imaging (MRI). Implementing GPU-based parallel processing approaches in brain MRI analysis with medical imaging techniques might be helpful in achieving immediate and timely image capture. Therefore, this extended review (the extension of the IWBBIO2023 conference paper) offers a thorough overview of the literature with an emphasis on the expanding use of GPU-based parallel processing methods for the medical analysis of brain MRIs with the imaging techniques mentioned above, given the need for quicker computation to acquire early and real-time feedback in medicine. Between 2019 and 2023, we examined the articles in the literature matrix that include the tasks, techniques, MRI sequences, and processing results. As a result, the methods discussed in this review demonstrate the advancements achieved until now in minimizing computing runtime as well as the obstacles and problems still to be solved in the future.

Comparative evaluation of multiparametric lumbar MRI radiomic models for detecting osteoporosis

BackgroundOsteoporosis is a serious global public health issue. Currently, there are few studies that explore the use of multiparametric MRI radiomics for osteoporosis detection. The purpose of this study was to compare the performance of radiomics features from multiple MRI sequences (T1WI, T2WI and T1WI combined with T2WI) for detecting osteoporosis in patients.MethodsA retrospective analysis was performed on 160 patients who had undergone dual-energy X-ray absorptiometry(DXA) and lumbar magnetic resonance imaging (MRI) at our hospital. Among them, 86 patients were diagnosed with abnormal bone mass (osteoporosis or low bone mass), and 74 patients were diagnosed with normal bone mass based on the DXA results. Sagittal T1-and T2-weighted images of all patients were imported into the uAI Research Portal (United Imaging Intelligence) for image delineation and radiomics analysis, where a series of radiomic features were obtained. A radiomic model that included T1WI, T2WI, and T1WI+T2WI was established using features selected by LASSO regression. We used ROC curve analysis to evaluate the predictive efficacy of each model for identifying bone abnormalities and conducted decision curve analysis (DCA) to evaluate the net benefit of each model. Finally, we validated the model in a sample of 35 patients from different health care institution.ResultsThe T1WI + T2WI radiomics model showed better screening performance for patients with abnormal bone mass. In the training group, the sensitivity was 0.758, the specificity was 0.78, and the accuracy was 0.768 (AUC =0.839, 95% CI=0.757-0.901). In the validation group, the sensitivity was 0.792, the specificity was 0.875, and the accuracy was 0.833 (AUC =0.86, 95% CI=0.73-0.943).The DCA also showed that the combined model had better net benefits. In the external validation group, the sensitivity was 0.764, the specificity was 0.833, and the accuracy was 0.8 (AUC =0.824, 95% CI 0.678-0.969).ConclusionsRadiomics-based multiparametric MRI can be used for the quantitative analysis of lumbar MRI and for accurately screening patients with abnormal bone mass.

Role of Imaging in Renal Infections: A Narrative Review

Urinary tract infections (UTIs) are among the most common bacterial infections in community and hospital settings. Infections of the urinary system can range from pyelonephritis to urethritis. Acute renal infections encompass entities such as acute pyelonephritis, acute focal bacterial nephritis, renal and perinephric abscesses, emphysematous pyelonephritis, and pyonephrosis, while xanthogranulomatous pyelonephritis is a chronic granulomatous inflammatory condition that affects the kidneys.The diagnosis of UTIs predominantly relies on clinical assessment and confirmation through abnormal laboratory results. Routine medical imaging is therefore typically reserved for complicated cases, aiming to identify contributing factors such as renal calculi, determine the type of infection, and evaluate the extent of the infection and its associated complications. Furthermore, imaging has become crucial for radiologists during interventional procedures in treating pyonephrosis and renal abscesses. Traditional imaging tools, like abdominal X-rays, are vital for identifying renal calculi and gas shadows in emphysematous pyelonephritis, while ultrasonography emerges as an excellent modality for assessing urinary infections in emergency scenarios, pregnancy, and paediatric cases. Contrast-enhanced computed tomography (CECT) or non-contrast computed tomography (NCCT) is the imaging method of choice in the most complex situations, allowing for precise assessment of the disease burden. Current magnetic resonance imaging (MRI) sequences yield promising results in specific scenarios such as pregnancy and paediatric subjects where minimising radiation exposure is a concern. This review focuses on elucidating the essential radiological modalities and their distinctive features in the context of renal infections, offering valuable guidance to microbiologists and physicians.

A MRI radiomics-based model for prediction of pelvic lymph node metastasis in cervical cancer

BackgroundCervical cancer (CC) is a common malignancy of the female reproductive tract, and preoperative prediction of lymph node metastasis (LNM) is essential. This study aims to design and validate a magnetic resonance imaging (MRI) radiomics-based predictive model capable of detecting LNM in patients diagnosed with CC.MethodsThis retrospective analysis incorporated 86 and 38 CC patients into the training and testing groups, respectively. Radiomics features were extracted from MRI T2WI, T2WI-SPAIR, and axial apparent diffusion coefficient (ADC) sequences. Selected features identified in the training group were then used to construct a radiomics scoring model, with relevant LNM-related risk factors having been identified through univariate and multivariate logistic regression analyses. The resultant predictive model was then validated in the testing cohort.ResultsIn total, 16 features were selected for the construction of a radiomics scoring model. LNM-related risk factors included worse differentiation (P < 0.001), more advanced International Federation of Gynecology and Obstetrics (FIGO) stages (P = 0.03), and a higher radiomics score from the combined MRI sequences (P = 0.01). The equation for the predictive model was as follows: −0.0493–2.1410 × differentiation level + 7.7203 × radiomics score of combined sequences + 1.6752 × FIGO stage. The respective area under the curve (AUC) values for the T2WI radiomics score, T2WI-SPAIR radiomics score, ADC radiomics score, combined sequence radiomics score, and predictive model were 0.656, 0.664, 0.658, 0.835, and 0.923 in the training cohort, while these corresponding AUC values were 0.643, 0.525, 0.513, 0.826, and 0.82 in the testing cohort.ConclusionsThis MRI radiomics-based model exhibited favorable accuracy when used to predict LNM in patients with CC. Relative to the use of any individual MRI sequence-based radiomics score, this predictive model yielded superior diagnostic accuracy.