Tomography Magnetic Resonance Imaging Research Articles

Magnetic resonance imaging diagnosis of ankle joint athletic injury based on machine learning algorithms

The diagnosis of ankle joint athletic injuries using traditional magnetic resonance imaging (MRI) relies on the subjective judgment and experience of doctors, and small structural changes in athletic injuries are difficult to accurately detect and diagnose. By using machine learning (ML) algorithms and image processing techniques to obtain objective and consistent diagnostic results, the accuracy of diagnosing ankle joint athletic injuries can be improved. This article collected a large number of MRI images of ankle joint athletic injuries, and preprocessed the collected images to extract morphological and texture features, and perform feature fusion. The Residual Network (ResNet) was improved, and the Leaky linear rectification function (ReLU, Corrected linear unit) activation function was introduced. The transfer learning was utilized to increase the convergence speed of the model, and the global maximum pooling layer and softmax classifier were used to construct the fully connected layer. After sufficient training on the training set, the findings on the test set indicated that the average accuracy of the improved ResNet model for ankle joint injury classification was 98.3%. The use of an improved ResNet model can effectively improve the diagnostic effectiveness of ankle joint athletic injuries, providing a new method for medical diagnosis of MRI.

Analysis of ultrasound and magnetic resonance imaging characteristics of kaposiform hemangioen dothelioma

ObjectiveThe present study aims to investigate the ultrasound and magnetic resonance imaging (MRI) characteristics of kaposiform hemangioen dothelioma (KHE).MethodsA retrospective analysis was conducted on the clinical data of children diagnosed with KHE through postoperative pathology. Patients were divided into two groups: the KHE group and the KHE with Kasabach-Merritt Phenomenon (KMP) group (KMP group). Laboratory indicators, ultrasound, and MRI data were collected and analyzed statistically to summarize the imaging characteristics of the disease.ResultsThe levels of platelets and fibrinogen in the KHE group were significantly higher than those in the KMP group, while D-dimer levels, prothrombin time, and activated partial thromboplastin time were lower (P < 0.05). Ultrasound characteristics comparison revealed that lesions extending to the fat layer (42.47% vs. 54.24%) and invading the muscle layer (38.36% vs. 69.49%) were less common in the KHE group compared to the KMP group, with the lesion diameter being smaller in the KHE group (P < 0.05). The Adler grading predominantly showed Grade II (45.21%) in the KHE group, whereas Grade III (93.22%) was more prevalent in the KMP group (P < 0.05). MRI analysis indicated that the incidence of lesions invading the muscle layer and the presence of flow voids were lower in the KHE group compared to the KMP group (P < 0.05).ConclusionKHE patients with KMP exhibit lesions that are more prone to extending into the fat layer and invading the muscle layer, with larger diameters and abundant blood flow. Additionally, the MRI images of the lesions may exhibit flow voids.

Artificial intelligence-assisted magnetic resonance imaging technology in the differential diagnosis and prognosis prediction of endometrial cancer

It aimed to analyze the value of deep learning algorithm combined with magnetic resonance imaging (MRI) in the risk diagnosis and prognosis of endometrial cancer (EC). Based on the deep learning convolutional neural network (CNN) architecture residual network with 101 layers (ResNet-101), spatial attention and channel attention modules were introduced to optimize the model. A retrospective collection of MRI image data from 210 EC patients was used for model segmentation and reconstruction, with 140 cases as the test set and 70 cases as the validation set. The performance was compared with traditional ResNet-101 model, ResNet-101 model based on spatial attention mechanism (SA-ResNet-101), and ResNet-101 model based on channel attention mechanism (CA-ResNet-101), using accuracy (AC), precision (PR), recall (RE), and F1 score as evaluation metrics. Among the 70 cases in the validation set, there were 45 cases of low-risk EC and 25 cases of high-risk EC. Using ROC curve analysis, it was found that the area under the curve (AUC) for the diagnosis of high-risk EC of the proposed model in this article (0.918) was visibly larger as against traditional ResNet-101 (0.613), SA-ResNet-101 (0.760), and CA-ResNet-101 models (0.758). The AC, PR, RE, and F1 values of the proposed model for the diagnosis of EC risk were visibly higher (P < 0.05). In the validation set, postoperative recurrence occurred in 13 cases and did not occur in 57 cases. Using ROC curve analysis, it was found that the AUC for postoperative recurrence prediction of the patients by the proposed model (0.926) was visibly larger as against traditional ResNet-101 (0.620), SA-ResNet-101 (0.729), and CA-ResNet-101 models (0.767). The AC, PR, RE, and F1 values of the proposed model for postoperative recurrence prediction were visibly higher (P < 0.05). The proposed model in this article, assisted by MRI, presented superior performance in diagnosing high-risk EC patients, with higher sensitivity (Sen) and specificity (Spe), and also demonstrated excellent predictive AC in postoperative recurrence prediction.

Compare three deep learning-based artificial intelligence models for classification of calcified lumbar disc herniation: a multicenter diagnostic study.

To develop and validate an artificial intelligence diagnostic model for identifying calcified lumbar disc herniation based on lateral lumbar magnetic resonance imaging(MRI). During the period from January 2019 to March 2024, patients meeting the inclusion criteria were collected. All patients had undergone both lumbar spine MRI and computed tomography(CT) examinations, with regions of interest (ROI) clearly marked on the lumbar sagittal MRI images. The participants were then divided into separate sets for training, testing, and external validation. Ultimately, we developed a deep learning model using the ResNet-34 algorithm model and evaluated its diagnostic efficacy. A total of 1,224 eligible patients were included in this study, consisting of 610 males and 614 females, with an average age of 53.34 ± 10.61 years. Notably, the test datasets displayed an impressive classification accuracy rate of 91.67%, whereas the external validation datasets achieved a classification accuracy rate of 88.76%. Among the test datasets, the ResNet34 model outperformed other models, yielding the highest area under the curve (AUC) of 0.96 (95% CI: 0.93, 0.99). Additionally, the ResNet34 model also exhibited superior performance in the external validation datasets, exhibiting an AUC of 0.88 (95% CI: 0.80, 0.93). In this study, we established a deep learning model with excellent performance in identifying calcified intervertebral discs, thereby offering a valuable and efficient diagnostic tool for clinical surgeons.

High-quality multimodal MRI with simultaneous EEG using conductive ink and polymer-thick film nets

Objective. Combining magnetic resonance imaging (MRI) and electroencephalography (EEG) provides a powerful tool for investigating brain function at varying spatial and temporal scales. Simultaneous acquisition of both modalities can provide unique information that a single modality alone cannot reveal. However, current simultaneous EEG-fMRI studies are limited to a small set of MRI sequences due to the image quality and safety limitations of commercially available MR-conditional EEG nets. We tested whether the Inknet2, a high-resistance polymer thick film based EEG net that uses conductive ink, could enable the acquisition of a variety of MR image modalities with minimal artifacts by reducing the radiofrequency-shielding caused by traditional MR-conditional nets.Approach. We first performed simulations to model the effect of the EEG nets on the magnetic field and image quality. We then performed phantom scans to test image quality with a conventional copper EEG net, with the new Inknet2, and without any EEG net. Finally, we scanned five human subjects at 3 Tesla (3 T) and three human subjects at 7 Tesla (7 T) with and without the Inknet2 to assess structural and functional MRI image quality.Main results. Across these simulations, phantom scans, and human studies, the Inknet2 induced fewer artifacts than the conventional net and produced image quality similar to scans with no net present.Significance. Our results demonstrate that high-quality structural and functional multimodal imaging across a variety of MRI pulse sequences at both 3 T and 7 T is achievable with an EEG net made with conductive ink and polymer thick film technology.

SDR2Tr‐GAN: A Novel Medical Image Fusion Pipeline Based on GAN With SDR2 Module and Transformer Optimization Strategy

ABSTRACTIn clinical practice, radiologists diagnose brain tumors with the help of different magnetic resonance imaging (MRI) sequences and judge the type and grade of brain tumors. It is hard to realize the brain tumor computer‐aided diagnosis system only with a single MRI sequence. However, the existing multiple MRI sequence fusion methods have limitations in the enhancement of tumor details. To improve fusion details of multi‐modality MRI images, a novel conditional generative adversarial fusion network based on three discriminators and a Staggered Dense Residual2 (SDR2) module, named SDR2Tr‐GAN, was proposed in this paper. In the SDR2Tr‐GAN network pipeline, the generator consists of an encoder, decoder, and fusion strategy that can enhance the feature representation. SDR2 module is developed with Res2Net into the encoder to extract multi‐scale features. In addition, a Multi‐Head Spatial/Channel Attention Transformer, as a fusion strategy to strengthen the long‐range dependencies of global context information, is integrated into our pipeline. A Mask‐based constraint as a novel fusion optimization mechanism was designed, focusing on enhancing salient feature details. The Mask‐based constraint utilizes the segmentation mask obtained by the pre‐trained Unet and Ground Truth to optimize the training process. Meanwhile, MI and SSIM loss jointly improve the visual perception of images. Extensive experiments were conducted on the public BraTS2021 dataset. The visual and quantitative results demonstrate that the proposed method can simultaneously enhance both global image quality and local texture details in multi‐modality MRI images. Besides, our SDR2Tr‐GAN outperforms the other state‐of‐the‐art fusion methods regarding subjective and objective evaluation.

Magnetic Resonance Imaging Guidance for Percutaneous Needle Intervention.

Magnetic resonance imaging (MRI) is one of the guiding modalities used for percutaneous needle insertion during interventional procedures. MRI guidance has several advantages, including multiplanar imaging capability, superior soft tissue contrast resolution, and the absence of ionizing radiation. When performing MRI-guided procedures, it is important to understand the suitable MRI systems, instruments, and imaging sequences for intervention. Furthermore, needle artifact characteristics must be fully understood to ensure safe and accurate needle insertion. In this article, we present the fundamental knowledge as regards the use of MRI guidance for percutaneous needle insertion and review its usefulness in representative interventional procedures, such as biopsy and tumor ablation.

Use of Magnetic Resonance Imaging for Postoperative Radiation Therapy Planning in Patients with Carbon Fiber-Reinforced Polyetheretherketone Instrumentation

Carbon fiber-reinforced polyetheretherketone (CFR-PEEK) instrumentation is being used more frequently in the spinal oncology landscape. Better visualization with this material allows for more precise postoperative stereotactic body radiation therapy (SBRT) planning using either computed tomography (CT)-myelography or magnetic resonance imaging (MRI) studies. We compared the dosimetric planning equivalencies and outcomes. Thirty-six consecutively treated patients were reviewed who underwent spinal fusion using CFR-PEEK instrumentation for spine metastases followed by postoperative SBRT between January 1, 2022, and April 3, 2023. Patients were divided into 2 cohorts based on the imaging modality, MRI versus CT-myelogram, used for postoperative SBRT planning. Surgical, demographic, postoperative radiation dosimetry, complication, and survival data were collected. Statistical analysis was performed in SPSS (v29.0.1.0). Eleven patients underwent CT-myelograms, and 25 patients underwent MR-spine imaging for SBRT planning. The median follow-up was 145.5 days (13-530). There were no significant differences between baseline demographic, surgical characteristics, or SBRT dosimetry between the MRI spine and CT-myelogram groups. There was no significant difference between the cohorts for survival (P = .402). MR scans are an effective choice for postoperative SBRT contouring patients using CFR-PEEK instrumentation for oncologic spinal fusions. Avoidance of CT-myelography reduces the need for an invasive procedure and potential risks including cerebrospinal fluid (CSF) leak, nerve root injury, and increased procedural burden.

Evaluation of Brain and Spinal Cord Lesions and Cerebrospinal Fluid Analysis in Detecting Demyelinating Diseases in Patients with Optic Neuritis

ABSTRACT Optic neuritis can be an early sign of demyelinating diseases like multiple sclerosis (MS), neuromyelitis optica spectrum disorders (NMOSD), and myelin oligodendrocyte glycoprotein (MOG) antibody-associated diseases (MOGAD). We investigated the presence or absence of head and spinal cord lesions on magnetic resonance imaging (MRI) and assessed whether cerebrospinal fluid (CSF) tests are useful in detecting demyelinating disease in patients with first diagnosed optic neuritis. We conducted a retrospective study of 111 patients (47 idiopathic, 19 NMOSD, 16 MOGAD, 16 MS, 6 optic neuritis with cerebral lesions but that does not meet the McDonald’s criteria for MS (ON+)), and 7 chronic relapsing inflammatory optic neuropathy) diagnosed with optic neuritis without cerebral or spinal symptoms. Patients underwent evaluations including orbital, head, and spine MRI, along with CSF analysis. Among the 111 patients, 20 (35.1%: 4 NMOSD, 4 MOGAD, 7 MS, and 6 ON+) exhibited intracerebral or spinal cord lesions. Twelve patients showed findings on both orbital and head MRI, while six had no orbital MRI findings except for optic neuritis but exhibited lesions on head MRI. Five patients had spinal lesions without intracerebral lesions. CSF analysis revealed positive oligoclonal bands and elevated myelin basic protein levels indicate the high likelihood with systemic inflammatory demyelinating diseases. Even in the absence of concomitant encephalitis or myelitis symptoms or a history of these conditions, MRI images of patients with optic neuritis sometimes reveal lesions in the brain or spinal cord. CSF abnormalities were indicative of systemic demyelinating disease presence, extending beyond MS to NMOSD and MOGAD.

Forensic bone age assessment of hand and wrist joint MRI images in Chinese han male adolescents based on deep convolutional neural networks.

In Chinese criminal law, the ages of 12, 14, 16, and 18 years old play a significant role in the determination of criminal responsibility. In this study, we developed an epiphyseal grading system based on magnetic resonance image (MRI) of the hand and wrist for the Chinese Han population and explored the feasibility of employing deep learning techniques for bone age assessment based on MRI of the hand and wrist. This study selected 282 Chinese Han Chinese males aged 6.0-21.0 years old. In the course of our study, we proposed a novel deep learning model for extracting and enhancing MRI hand and wrist bone features to enhance the prediction of target MRI hand and wrist bone age and achieve precise classification of the target MRI and regression of bone age. The evaluation metric for the classification model including precision, specificity, sensitivity, and accuracy, while the evaluation metrics chosen for the regression model are MAE. The epiphyseal grading was used as a supervised method, which effectively solved the problem of unbalanced sample distribution, and the two experts showed strong consistency in the epiphyseal plate grading process. In the classification results, the accuracy in distinguishing between adults and minors was 91.1%, and the lowest accuracy in the three minor classifications (12, 14, and 16 years of age) was 94.6%, 91.1% and 96.4%, respectively. The MAE of the regression results was 1.24 years. In conclusion, the deep learning model proposed enabled the age assessment of hand and wrist bones based on MRI.

Surgical outcomes of drug-refractory infantile epileptic spasms syndrome and related prognostic factors: a retrospective study

BackgroundIn this study, we aimed to assess the efficacy of surgical treatment in children with drug-refractory infantile epileptic spasms syndrome (IESS) and examine the factors influencing the post-surgical outcomes.MethodsThe clinical data of 30 children (18 males and 12 females) with epileptic spasms (ES) who underwent surgery at the Epilepsy Center of Shenzhen Children’s Hospital between June 2018 and June 2020 were retrospectively analyzed. Post-surgical outcomes were evaluated using the Engel Epilepsy Surgery Outcome Scale. Scalp electroencephalography and developmental quotient were assessed preoperatively and postoperatively. Univariate analysis and exact logistic regression analyses were used to identify the factors affecting the postoperative efficacy.ResultsOf the 30 patients who underwent surgical resection, 22 (73.3%) achieved Engel’s class I–II outcomes. Additionally, motor and cognitive functions improved in 14 patients (46.7%). The development of 12 (40%) patients remained at the preoperative development level. The median number of antiseizure medications taken preoperatively was 5.27 (range 2–10), which decreased to 1.90 (range 0–4) at the last follow-up. Seizure duration, etiology, positive positron emission tomography-magnetic resonance imaging (PET-MRI), surgery type, and lesion location were significantly correlated with the postoperative efficacy (P < 0.05). Positive PET/MRI findings and lesion location predicted independently the postoperative outcomes. Permanent impairments of motor or language function were rare, with only two cases reporting hydrocephalus and one reporting hemiplegia.ConclusionsSurgery is an effective treatment option for children with IESS. Early referral and comprehensive preoperative evaluation are essential for identification of surgically treatable structural lesions. The primary surgically treatable cause is cortical malformation, followed by perinatal brain injury. Hemispheric disconnection is a preferred surgical approach. Positive PET/MRI findings and lesion location predicted the postoperative outcomes.

Mapping the evolution of PET/MR research: a bibliometric analysis of publication trends, leading contributors, and conceptual frameworks (2011–2023)

BackgroundPositron emission tomography-magnetic resonance imaging (PET/MR) is a cutting-edge hybrid imaging technology with the potential to revolutionize medical diagnosis. This bibliometric study aims to map the research landscape of PET/MR by analyzing a curated set of Web of Science Core Collection documents from 2011 to 2023.MethodsWe conducted a bibliometric analysis to map the research landscape of PET/MR, leveraging a curated dataset of 3,600 documents retrieved from the Web of Science Core Collection spanning the period from 2011 to 2023. We employed quantitative methods to assess the quantity and distribution of PET/MR studies, including patterns of change, research status, and directions. Additionally, we utilized VOSviewer software to conduct keyword co-occurrence analysis, explore collaborative networks among authors and institutions, and identify influential journals in the field.ResultsResults: The analysis reveals several key insights: (1) a significant increase in the number of PET/MR publications over the past 12 years, highlighting the growing interest and activity in this field; (2) the United States and Germany as the leading countries in terms of research output and collaboration, with a growing presence of other countries such as China; (3) the Journal of Nuclear Medicine and the European Journal of Nuclear Medicine and Molecular Imaging as the most influential journals in the field; (4) a shift in research focus from imaging techniques to clinical applications, with an increasing emphasis on prostate cancer, PSMA, and FDG imaging.ConclusionsThis study provides a comprehensive overview of PET/MR research, identifying prominent trends, key researchers, and influential works. Based on these findings, we propose recommendations for future research directions in this rapidly evolving field.

MRI Image Enhancement Using Multilevel Image Thresholds Based on Contrast-limited Adaptive Histogram Equalization

Magnetic resonance imaging (MRI) creates detailed images by combining a powerful magnetic field with high-frequency radio waves. MRI images need to increase brightness and contrast to facilitate distinguishing diseases. This study aims to improve the MRI images depending on the suggested algorithm, including three main stages. The first is to divide the image into three main areas using Multilevel image thresholds and then improve using CLAHE for each area separately. Finally, these images combine to form one image and improve it again. By analyzing the results and calculating non-referenced quality measures, the proposed method obtained the best quality results compared to the rest of the methods with quality rates of EN(6.14), AG(6.79), and CEM(0.84), which indicates excellent success in increasing clarity in those images.

Diffusion network with spatial channel attention infusion and frequency spatial attention for brain tumor segmentation.

Accurate segmentation of gliomas is crucial for diagnosis, treatment planning, and evaluating therapeutic efficacy. Physicians typically analyze and delineate tumor regions in brain magnetic resonance imaging (MRI) images based on personal experience, which is often time-consuming and subject to individual interpretation. Despite advancements in deep learning technology for image segmentation, current techniques still face challenges in clearly defining tumor boundary contours and enhancing segmentationaccuracy. To address these issues, this paper proposes a conditional diffusion network (SF-Diff) with a spatial channel attention infusion (SCAI) module and a frequency spatial attention (FSA) mechanism to achieve accurate segmentation of the whole tumor (WT) region in braintumors. SF-Diff initially extracts multiscale information from multimodal MRI images and subsequently employs a diffusion model to restore boundaries and details, thereby enabling accurate brain tumor segmentation (BraTS). Specifically, a SCAI module is developed to capture multiscale information within and between encoder layers. A dual-channel upsampling block (DUB) is designed to assist in detail recovery during upsampling. A FSA mechanism is introduced to better match the conditional features with the diffusion probability distribution information. Furthermore, a cross-model loss function has been implemented to supervise the feature extraction of the conditional model and the noise distribution of the diffusionmodel. The dataset used in this paper is publicly available and includes 369 patient cases from the Multimodal BraTS Challenge 2020 (BraTS2020). The conducted experiments on BraTS2020 demonstrate that SF-Diff performs better than other state-of-the-art models. The method achieved a Dice score of 91.87%, a Hausdorff 95 of 5.47 mm, an IoU of 84.96%, a sensitivity of 92.29%, and a specificity of 99.95% onBraTS2020. The proposed SF-Diff performs well in identifying the WT region of the brain tumors compared to other state-of-the-art models, especially in terms of boundary contours and non-contiguous lesion regions, which is clinically significant. In the future, we will further develop this method for brain tumor three-class segmentation task.

Cerebellar Structural and N-Acetylaspartate, Choline, and Creatine Metabolic Profiles in Parkinson's Disease and Essential Tremor.

The role of the cerebellum in Parkinson's disease (PD), particularly in tremor-dominant subtypes, is increasingly recognized. Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) provide anatomical and metabolic insights, suggesting compensatory hyperactivity or degenerative changes in the cerebellum in PD. Volumetric analysis of cerebellar structures in MRI images, combined with metabolic profiles from MRS, offers possibilities for differentiating PD from essential tremor (ET). The cerebellum may be a potential therapeutic target due to its role in neurocircuitry of PD and ET. Brain structural data were obtained using MRI, and cerebellar metabolic profiles, focusing on the quantification of N-acetylaspartate (NAA), choline, and creatine peaks were obtained using MRS. This study enrolled patients with ET and PD, both with and without tremor, as well as disease controls with cerebellar atrophy (including spinocerebellar ataxia and multiple system atrophy). Volumetric analysis of cerebellar structures was performed. Differences in MRI and MRS parameters were analyzed using one-way analysis of covariance with a significance threshold of p < 0.05. From November 2018 to March 2023, 111 patients were enrolled, including 29 ET, 29 cerebellar atrophy, 12 PD without tremor, and 41 PD with tremor. No significant differences in cerebellar volume and N-acetylaspartate/creatine and choline/creatine ratios were found between ET and PD with tremor. This preliminary retrospective study suggests similarities in cerebellar structures and metabolic profiles between ET and PD, highlighting the need for advanced imaging techniques to better differentiate between these conditions. Future research should integrate clinical data, such as tremor severity and cognitive assessments, to explore the relationships with cerebellar MRI parameters.

Prevalence of Clinically Relevant Findings on Magnetic Resonance Imaging in Middle-Aged Adults With Knee Pain and Suspected Meniscal Tear: A Follow-Up.

Radiographs are frequently obtained for patients with knee osteoarthritis (KOA), with magnetic resonance imaging (MRI) reserved for those with complex KOA. There are few data on how often subsequent MRI reveals clinically actionable but unanticipated findings. The purpose of this study is to estimate the prevalence of these findings on MRI for patients managed nonoperatively for suspected meniscal tears. The Treatment of Meniscal Problems and Osteoarthritis (TeMPO) study enrolled patients aged 45 to 85 years with knee pain, osteoarthritis (Kellgren-Lawrence [KL] grades 0-3), and suspected meniscal tear. We reviewed baseline MRI and recorded notable findings, including subchondral insufficiency fractures of the knee (SIFKs), avascular necrosis (AVN), tumors, and nonsubchondral fractures. Other baseline data included demographic characteristics, Knee Injury and Osteoarthritis Outcome Score, duration of knee symptoms, and KL grade. Study-ordered MRI was performed on 760 patients, with 61 concerning findings identified (8.03%, 95% confidence interval 6.09%-9.96%). A total of 25 participants had SIFKs, 10 had nonsubchondral fractures, 4 had AVN, 8 had benign tumors, and 14 had other clinically relevant findings. We estimated the prevalence of clinically relevant incidental findings on MRI to be 8.03% in middle-aged adults with mild to moderate KOA and suspected meniscal tear. These data may prompt clinicians to be more aware of the range of findings that can underlie knee symptoms, some of which could change management but may require different modalities of imaging to detect. Future research is needed to pinpoint factors associated with these concerning findings so that patients who are at risk can be identified and referred for advanced imaging.

Large sample size MRI measurements to assess the relation between cardiac function and structure and white matter hyperintensity volumes

Abstract Background People with established cardiovascular disease (CVD) are at risk of early cognitive decline, and neurological diseases such as dementia. CVD and neurological diseases share common risk factors, such as blood pressure, sedentary lifestyle, as well as genomic risk factors such as mutations in APOE4 [1]. It's unclear to what extent the co-occurrence of CVD and neurological diseases is explained by these risk factors, or whether there is a direct association where CVD is a first step towards poorer neurological health. White matter hyperintensities (WMH) of presumed vascular origin are damaged areas in the brain observed through magnetic resonance imaging (MRI). These lesions are associated with progressive neurological disease such as vascular dementia, as well with risk factors for CVD [2]. Purpose We set out to determine to what extent CMR measurements associated with WMH independent of known cerebrovascular risk factors. Methods Cardiac and brain MRI images were analysed for 23,963 UK biobank (UKB) participants. WMH analysis included 5 brain regions (Frontal[F], Parietal[P], Temporal[T], Occipital[O], Basal Ganglia and Thalami [BGT]) and total brain WMH volume. Cardiac traits included stroke volumes, atrial volumes, ejection fractions of right and left chambers, left ventricular (LV) strain, LV wall thickness and aortic areas. Multivariable regression analysis was carried out, where each cardiac trait was regressed on each brain region and adjusted for: age, difference between age at initial visit and imaging visit, sex, systolic and diastolic blood pressure, Hba1c, household income, educational status, Townsend score and family history of disease (Alzheimer’s, Parkinson's, high blood pressure, stroke, heart disease) . Results were evaluated against a multiple testing correct p-value threshold of 0.05, reflecting the number of principle components(n=10) necessary to explain 90% of the cardiac trait variability. Results For 5 cardiac traits (right ventricular stroke volume, left atrial stroke volume, left ventricular stroke volume, left ventricular ejection fraction and right ventricular end diastolic volume) higher values were associated with lower WMH volumes in all brain regions. Additionally, some cardiac traits such as LV cardiac output associated with specific brain regions (T, O, BGT). Additionally, APOE- ε4 stratified analyses indicated, homozygous carriers show a greater association between higher minimum LA and RA volumes and higher WMH volumes in the occipital lobe. Conclusions This study gives an insight into the WMH burden in a large population of adults. It highlights the associations of cardiac traits with white matter hyperintensity volumes in different brain regions independent of known risk factors. Using cardiac traits as surrogate markers of different cardiac disease could explain how cardiac functionality defines WMH volume distribution and subsequently the wider relationship between CVD and cerebrovascular disease.Figure 1Graphical Abstract

MRI and CT imaging characteristics in parotid tumors with false-negative fine-needle aspirations

BackgroundsPreoperative imaging, particularly with magnetic resonance imaging (MRI) and computed tomography (CT) scans, plays a crucial role in distinguishing between benign and malignant parotid gland tumors, while the reliability of Ultrasound-Guided Fine Needle Aspiration (FNA) in diagnosing these masses remains a topic of debate.MethodsThis two-center retrospective analysis was conducted on 347 patients with parotid gland tumors who had FNA and preoperative imaging (CT or MRI). All patients underwent surgery and final histopathological examination was available, along with complete medical records between January 2008 and May 2023.ResultsAmong the 347 patients, 318 (92%) had benign and 10 (3%) had malignant tumors based on FNA, with 19 (5%) unsatisfactory specimens. Final histological diagnosis revealed 303 (87%) benign and 44 (13%) malignant lesions, with a false-negative rate of 10.6% for FNA. Multivariate analysis identified irregular shape and invasion as independent predictors of malignancy in patient with benign or unsatisfactory FNA results. The odds ratio for irregular shape was 3.06 and for invasion was 12.73.ConclusionImaging characteristics, such as irregular shape and invasion may indicate towards malignant parotid tumors, even in patients with false-negative benign findings in FNA.

NOISE REMOVAL IN MAGNETIC RESONANCE IMAGING USING 3D DEEP LEARNING MODEL

Magnetic Resonance Imaging (MRI) is a widely used imaging technique for examining brain tissues and diagnosing various conditions. However, MRI images often contain noise caused by factors such as equipment limitations, environmental conditions, patient movement, and magnetic field interference. This noise can obscure critical details, making accurate diagnosis and treatment planning challenging. In this study, the focus is on the removal of Rician noise from MRI images. To address this challenge, two 3D autoencoder models, named M-UNet+ResNet and M-UNet+DenseNet, were developed. These models are based on an enhanced UNet architecture that integrates dense and residual connections, aimed at improving noise reduction capabilities. The models were trained using T1 and T2-weighted MRI images from the IXI dataset, incorporating noise levels varying from 3% to 15%. Their performance was evaluated using metrics such as peak signal-to-noise ratio, structural similarity index measure, and mean absolute error. The results demonstrated that both models effectively reduced noise across various levels, with M-UNet+ResNet generally outperforming M-UNet+DenseNet. Notably, M-UNet+ResNet achieved PSNR values of 38.72 dB and 37.04 dB, and SSIM values of 0.82 and 0.81 in the IXI-HH-T2 and IXI-Guys-T2 datasets, respectively, indicating its strong capability in preserving image quality. This study concludes that incorporating residual connections in DL models enhances their ability to remove noise from MRI images, offering a solution for maintaining the integrity of medical images in clinical settings.

Using U-Net models in deep learning for brain tumor detection from MRI scans

Tumor diseases in the nervous system are both dangerous and complex. Magnetic Resonance Imaging (MRI) is crucial for detecting brain disease; however, identifying the presence of tumors from these is time-consuming and requires a professional doctor. Utilizing deep learning for tumor detection in MRI images can reduce waiting times and enhance detection accuracy. We propose a method employing two U-Net models: ResNeXt-50 and EfficientNet architectures, integrated with a Feature Pyramid Network (FPN) for segmenting brain tumor. The models were trained on the BraTS 2021 dataset, consisting of 3,929 MRI scan images with 3,929 corresponding masks, divided into training, testing, and evaluation sets in a 70:15:15 ratio. The results indicate that the hybrid model, which combines EfficientNet and FPN, delivers superior performance, with an average Intersection over Union (IoU) accuracy of 0.90 on the test set compared to 0.50 for ResNeXt-50, and Dice accuracy of 0.92 compared to 0.66 for ResNeXt-50. Furthermore, we developed a web application that implements the EfficientNet with FPN model, facilitating convenient tumor detection from uploaded MRI images for doctors.