Analysis Of Magnetic Resonance Imaging Data Research Articles

#908 The effect of obesity and fat distribution on estimated glomerular filtration rate (eGFR): A Mendelian randomization and image data analysis study

Abstract Background and Aims Obesity is intimately linked to the initiation and progression of chronic kidney disease, but questions on causality still remain. The diagnosis of chronic kidney disease is often based solely on creatinine- and/or cystatin C-based estimated glomerular filtration rate (eGFR), but both these endogenous biomarkers are also known to be related to body composition. This study aims to further investigate how obesity causally affects eGFR, derived from both biomarkers. Method Methods used were: Traditional cross-sectional analyses and magnetic resonance imaging data analyses performed in the POEM study (502 subjects, all aged 50 years), and two-sample Mendelian randomization using already published summary data. Outcome variables for all analyses were eGFR indexed to body surface area, based on creatinine and cystatin C, respectively. In both the traditional cross-sectional and the Mendelian randomization analyses body mass index and waist circumference were used as exposure variables. In the image data analyses, eGFR was regressed nonparametically on fat content and tissue volume for each 3D voxel. Results were visualized as a correlation “imiomics” map. Results Results from the POEM study showed in an adjusted model a negative correlation between cystatin C calculated eGFR and both body mass index [beta = −0.190 (95% CI: −0.280 to −0.100)] and waist circumference [beta = −0.160 (95% CI: −0.250 to −0.060)]. In contrast, a positive association was found for eGFR when using creatinine levels [beta = 0.120 (95% CI: 0.030 to 0.210) for body mass index; beta = 0.160 (95% CI: 0.070 to 0.260) for waist circumference]. This pattern was repeated in the image data analysis, which was visualized in the imiomics map: both visceral and subcutaneous adipose tissue were negatively correlated to eGFR based on cystatin C but positively correlated to eGFR based on creatinine (Fig. 1). Mendelian randomization implied a negative causal effect of obesity-related measures on cystatin C-based eGFR, with statistically significant estimates for both body mass index [beta = −0.031 (95% CI: −0.037 to −0.026)] and waist circumference [beta = −0.038 (95% CI: −0.045 to −0.031)]. No statistically significant effects was found on creatinine-based eGFR (Fig. 2). Conclusion This study indicates a causal negative effect of obesity on cystatin C-based eGFR, but not on eGFR calculated from creatinine. This discrepancy is further highlighted by analyses of adipose tissue from magnetic resonance imaging. These findings thus warrant further research on the impact of using the endogenous biomarkers creatinine and cystatin C to estimate renal function when assessing the link between obesity and chronic kidney disease.

In situ temperature determination using magnetic resonance spectroscopy thermometry for noninvasive postmortem examinations.

Magnetic resonance spectroscopy (MRS) thermometry offers a noninvasive, localized method for estimating temperature by leveraging the temperature-dependent chemical shift of water relative to a temperature-stable reference metabolite under suitable calibration. Consequentially, this technique has significant potential as a tool for postmortem MR examinations in forensic medicine and pathology. In these examinations, the deceased are examined at a wide range of body temperatures, and MRS thermometry may be used for the temperature adjustment of magnetic resonance imaging (MRI) protocols or for corrections in the analysis of MRI or MRS data. However, it is not yet clear to what extent postmortem changes may influence temperature estimation with MRS thermometry. In addition, N-acetylaspartate, which is commonly used as an in vivo reference metabolite, is known to decrease with increasing postmortem interval (PMI). This study shows that lactate, which is not only present in significant amounts postmortem but also has a temperature-stable chemical shift, can serve as a suitable reference metabolite for postmortem MRS thermometry. Using lactate, temperature estimation in postmortem brain tissue of severed sheep heads was accurate up to 60 h after death, with a mean absolute error of less than 0.5°C. For this purpose, published calibrations intended for in vivo measurements were used. Although postmortem decomposition resulted in severe metabolic changes, no consistent deviations were observed between measurements with an MR-compatible temperature probe and MRS thermometry with lactate as a reference metabolite. In addition, MRS thermometry was applied to 84 deceased who underwent a MR examination as part of the legal examination. MRS thermometry provided plausible results of brain temperature in comparison with rectal temperature. Even for deceased with a PMI well above 60 h, MRS thermometry still provided reliable readings. The results show a good suitability of MRS thermometry for postmortem examinations in forensic medicine.

MRI Data Analysis in Malformations of Cortical Development.

Brain magnetic resonance imaging (MRI) is a noninvasive imaging modality that utilizes powerful magnets and radio waves to generate detailed images of the brain, making it a valuable tool for investigating malformations of cortical development (MCD). Various MRI techniques, including 3D T1-weighted, multiplanar thin-sliced T2-weighted, and 3D fluid-attenuated inversion recovery (FLAIR) sequences, can provide high-resolution images with excellent spatial and contrast resolution, allowing for a detailed visualization of cortical anatomy and abnormalities. Almost all MCD can be detected and characterized using MRI. Advanced techniques, such as arterial spin labeling MR perfusion, diffusion tensor imaging (DTI), and functional MRI (fMRI), may be used to improve the detection rate of these malformations and to plan surgery in case of drug-resistant epilepsy. However, there are also limitations related to high cost, relatively low availability, need for sedation or anesthesia, and limited sensitivity for detecting subtle focal cortical malformations. Despite these limitations, brain MRI plays a crucial role in the investigation of MCD, providing valuable information for diagnosis, treatment planning, and patient management.

Association of cognitive reserve indicator with cognitive decline and structural brain differences in mid‐ and late life: a large population‐based cohort study

AbstractBackgroundCognitive reserve (CR) contributes to preserving cognition when facing brain aging and damage. CR has been linked to reduced dementia risk in late life. However, the association between CR and brain imaging measures, especially in mid‐life, is unclear. We aimed to explore the association of CR with cognitive decline and structural brain differences in mid‐ and late life.MethodWithin the UK Biobank, 42,301 dementia‐free participants aged 40‐70 were followed for a median of 9 (interquartile range: 8 to 10) years. Latent class analysis was used to generate an indicator of CR (categorized as high, moderate, and low) based on education, occupation, and multiple cognitively stimulating activities at baseline. Cognitive tests for global and domain‐specific cognitive function were administrated at baseline and follow‐up. A subsample (n = 34,041) underwent brain magnetic resonance imaging (MRI) scans at the follow‐up examination. Volumes of total brain, white matter, grey matter, hippocampus, and white matter hyperintensities (TBV, WMV, GMV, HV, and WMHV) were assessed. Data were analyzed using mixed‐effects models and linear regression.ResultAt baseline, 16,032 (37.90%), 10,709 (25.32%), and 15,560 (36.78%) participants had low, moderate, and high levels of CR, respectively. In multi‐adjusted mixed‐effects models, compared to low CR, high CR was associated with a slower decline in global cognitive function (β [95% confidence interval, CI]: 0.10 [0.08, 0.11]), prospective memory (0.10 [0.06, 0.15]), fluid intelligence (0.07 [0.04, 0.10]), and reaction time (0.04 [0.02, 0.06]). In MRI data analysis, high CR was related to lesser TBV (‐0.03 [‐0.04, ‐0.02]), WMV (‐0.03 [‐0.04, ‐0.02], GMV (‐0.02 [‐0.04, ‐0.01]), and WMHV (‐0.03 [‐0.05, ‐0.002]), but greater HV (0.04 [0.02, 0.06]) compared to low CR. In analyses stratified by baseline age, the significant associations between high CR and global cognition and TBV were present among both mid‐life (<60 years) and late‐life (≥60 years) participants. The CR‐cognitive decline association remained significant after additional adjustment for MRI measures.ConclusionHigher CR is associated with slower cognitive decline, lesser TBV, greater HV, and lower microvascular burden, especially in mid‐life. The protective role of high CR on cognition is independent of structural brain differences.

Association of resting heart rate with cognitive decline and brain age in older adults: a large‐scale cohort study in the UK Biobank

AbstractBackgroundThe relationship of resting heart rate (RHR), a common indicator of cardiovascular health, with cognitive decline is unclear. We aimed to investigate the association of RHR with cognitive trajectory and brain age assessed by brain Magnetic Resonance Imaging (MRI) in the UK Biobank.MethodWithin the UK Biobank 18,210 chronic brain disorders‐free participants aged 60+ were followed for up to 16 years. RHR was assessed by blood pressure monitor and categorized into <60, 60‐69 (reference), 70‐79, and ≥80 beats per minute (bpm). Cognitive function including global cognition, numeric memory, prospective memory, pairs matching, fluid intelligence, and reaction time was collected at baseline and each follow‐up wave. A subsample of 10,458 participants underwent brain MRI (T1‐weighted MRI, T2‐FLAIR, T2∗, diffusion‐MRI, task fMRI, and resting‐state fMRI) to detect brain age and brain predicted age difference (PAD) using Lasso regression. Data were analyzed using mixed‐effects model and linear regression model.ResultCompared to reference group, RHR≥80 bpm was associated with faster decline in global cognition (β: ‐0.03, 95% confidence interval [CI]: ‐0.05, ‐0.01) and reaction time (β: ‐0.03, 95% CI: ‐0.07, ‐0.01). In the MRI data analysis, RHR≥80 bpm was associated with older brain age (β [95% CI]: 0.50 [0.25, 0.76]) and bigger brain PAD (β [95% CI]: 0.50 [0.26, 0.74]).ConclusionHigher RHR is associated with faster cognitive decline and older brain age. Monitoring and controlling elevated RHR could be a promising interventional target in preserving cognitive function.

The Sporadic Early‐Onset Alzheimer’s Disease Signature of Regional Atrophy

AbstractBackgroundIn EOAD, the localization of neurodegeneration is often more prominent in posterior lateral temporal, lateral and medial parietal, frontal, or occipital cortex relative to the medial and ventral temporal cortical localization of typical LOAD. Convergently, neuropathological investigations have identified a “hippocampal‐sparing” form of AD that is often present in younger patients. For these reasons, imaging biomarkers of neurodegeneration in EOAD, such as Magnetic Resonance Imaging (MRI) measures of atrophy, likely need to be different than those of neurodegeneration in typical LOAD. Although prior neuroimaging work has advanced our understanding of anatomical abnormalities in EOAD, these studies have included small samples, most composed of amnestic EOAD dementia and none focused on developing an MRI biomarker specific to EOAD. Such a disease‐signature MRI biomarker has been established and validated in LOAD dementia and has proven to be powerful in predicting progressive decline in people with MCI or who were cognitively unimpaired and in predicting molecular biomarker status. An a priori‐defined regional atrophy signature may also be helpful in constraining the analysis of MRI data in studies of putative disease‐modifying therapies.MethodLEADS is an observational study which includes annual MRI. In this analysis, we used FreeSurfer to define the sporadic EOAD atrophy signature in a small sample of MGH EOAD patients, and investigated its reproducibility in LEADS, as well as the magnitude of atrophy and its relationship to clinical measures.ResultThe EOAD signature is robustly reproducible across the two independent patient cohorts, with prominent atrophy in caudal lateral temporal cortex, inferior parietal lobule, and posterior cingulate and precuneus cortex, with relative sparing of medial temporal lobe structures. Comparing patients to controls, Cohen’s d effect sizes within various regions ranged from 0.99 to 1.98.ConclusionThis study demonstrates the specific cortical regions with most prominent atrophy in patients with sporadic EOAD; this signature of regional atrophy will be used to examine longitudinal change.

Detection of brown adipose tissue in rats with acute cold stimulation using quantitative susceptibility mapping.

Detection of brown adipose tissue in rats with acute cold stimulation using quantitative susceptibility mapping.

Magnetic Resonance Imaging of the Gastrointestinal Tract: Current Role, Recent Advancements and Future Prospectives.

This review focuses on the role of magnetic resonance imaging (MRI) in the evaluation of the gastrointestinal tract (GI MRI), analyzing the major technical advances achieved in this field, such as diffusion-weighted imaging, molecular imaging, motility studies, and artificial intelligence. Today, MRI performed with the more advanced imaging techniques allows accurate assessment of many bowel diseases, particularly inflammatory bowel disease and rectal cancer; in most of these diseases, MRI is invaluable for diagnosis, staging, and disease monitoring under treatment. Several MRI parameters are currently considered activity biomarkers for inflammation and neoplastic disease. Furthermore, in younger patients with acute or chronic GI disease, MRI can be safely used for short-term follow-up studies in many critical clinical situations because it is radiation-free. MRI assessment of functional gastro-esophageal and small bowel disorders is still in its infancy but very promising, while it is well established and widely used for dynamic assessment of anorectal and pelvic floor dysfunction; MRI motility biomarkers have also been described. There are still some limitations to GI MRI related to high cost and limited accessibility. However, technical advances are expected, such as faster sequences, more specific intestinal contrast agents, AI analysis of MRI data, and possibly increased accessibility to GI MRI studies. Clinical interest in the evaluation of bowel disease using MRI is already very high, but is expected to increase significantly in the coming years.

Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging.

Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses. However, most of the DL models are considered as "black boxes". There is an unmet need to demystify DL models so domain experts can trust these high-performance DL models. This has resulted in a sub-domain of AI research called explainable artificial intelligence (XAI). In the last decade, many experts have dedicated their efforts to developing novel XAI methods that are competent at visualizing and explaining the logic behind data-driven DL models. However, XAI techniques are still in their infancy for medical MRI image analysis. This study aims to outline the XAI applications that are able to interpret DL models for MRI data analysis. We first introduce several common MRI data modalities. Then, a brief history of DL models is discussed. Next, we highlight XAI frameworks and elaborate on the principles of multiple popular XAI methods. Moreover, studies on XAI applications in MRI image analysis are reviewed across the tissues/organs of the human body. A quantitative analysis is conducted to reveal the insights of MRI researchers on these XAI techniques. Finally, evaluations of XAI methods are discussed. This survey presents recent advances in the XAI domain for explaining the DL models that have been utilized in MRI applications.

Pathophysiologic mechanism for MYOcarditis in COVID-19 VAccinations ("MYOVAx" Study)

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NMRC COVID-19 Research Fund Objective This is the first prospective cohort study in Singapore to investigate the COVID-19 vaccine-associated myocarditis to understand its pathophysiology. Introduction Acute myocarditis and other cardiovascular symptoms have been observed to be associated with the two mRNA-based coronavirus disease 2019 (COVID-19) vaccines—namely Pfizer-BioNTech BNT162b2 and Moderna mRNA-1273)—currently in-use in Singapore. The mechanisms through which myocarditis occurs are unknown, hence our study aims to understand the pathophysiology of myocarditis associated with COVID-19 vaccines. Methods Patients with onset of cardiac manifestations were recruited from multiple hospital outpatient clinics between November 2021 and September 2022. Clinical history and physical examination data was collected with blood sample collection, echocardiography, 12-lead electrocardiogram (ECG), coronary angiography and magnetic resonance imaging (MRI) at recruitment and 6-month follow-up. Analysis of biomarkers, genetic, serological and MRI data was conducted. Results As of 6 September 2022, a total of 5 patients have been enrolled (4 males, 1 female). The most commonly reported symptoms across all patients were chest pain/discomfort (80%), followed by palpitations (40%). MRI evidence of myocarditis has been detected in 2 (50%) of the male patients, of which both reported two or more symptoms occurring 1-2 days post-vaccination. Both patients have each received at least two doses of either the Pfizer-BioNTech BNT162b2 vaccine or Moderna mRNA-1273 vaccine. Their MRI findings were consistent with myocarditis. On late gadolinium enhancement (LGE) imaging, epicardial enhancement at the basal inferolateral segment and mid-wall enhancement at the apical anterior, lateral and inferior walls were observed in one patient. Patchy, mid-wall LGE in the basal inferior/inferolateral wall was observed in the other patient. No MRI evidence of myocarditis was available for the sole female patient. Conclusion While more data is needed to definitively prove the association of the two mRNA-based Pfizer-BioNTech BNT162b2 and Moderna mRNA-1273 COVID-19 vaccines with post-vaccination myocarditis, we believe our findings may support further investigations to enable risk stratification for vaccine-associated myocarditis and identify potential preventative strategies accordingly.

Time-Synchronized MRI-Assessment of Respiratory Apparatus Subsystems—A Feasibility Study

The thorax (TH), the thoracic diaphragm (TD), and the abdominal wall (AW) are three sub-systems of the respiratory apparatus whose displacement motion has been well studied with the use of magnetic resonance imaging (MRI). Another sub-system, which has however received less research attention with respect to breathing, is the pelvic floor (PF). In particular, there is no study that has investigated the displacement of all four sub-systems simultaneously. Addressing this issue, it was the purpose of this feasibility study to establish a data acquisition paradigm for time-synchronous quantitative analysis of dynamic MRI data from these four major contributors to respiration and phonation (TH, TD, AW, and PF). Three healthy females were asked to breathe in and out forcefully while being recorded in a 1.5-Tesla whole body MR-scanner. Spanning a sequence of 15.12 seconds, 40 MRI data frames were acquired. Each data frame contained two slices, simultaneously documenting the mid-sagittal (TH, TD, PF) and transversal (AW) planes. The displacement motion of the four anatomical structures of interest was documented using kymographic analysis, resulting in time-varying calibrated structure displacement data. After computing the fundamental frequency of the cyclical breathing motion, the phase offsets of the TH, PF, and AW with respect to the TD were computed. Data analysis revealed three fundamentally different displacement patterns. Total structure displacement was in the range of 0.94 cm (TH) to 4.27cm (TD). Phase delays of up to 90∘ (i.e., a quarter of a breathing cycle) between different structures were found. Motion offsets in the range of -28.30∘ to 14.90∘ were computed for the PF with respect to the TD. The diversity of results in only three investigated participants suggests a variety of possible breathing strategies, warranting further research.

Better Together: Data Harmonization and Cross-Study Analysis of Abdominal MRI Data From UK Biobank and the German National Cohort.

The UK Biobank (UKBB) and German National Cohort (NAKO) are among the largest cohort studies, capturing a wide range of health-related data from the general population, including comprehensive magnetic resonance imaging (MRI) examinations. The purpose of this study was to demonstrate how MRI data from these large-scale studies can be jointly analyzed and to derive comprehensive quantitative image-based phenotypes across the general adult population. Image-derived features of abdominal organs (volumes of liver, spleen, kidneys, and pancreas; volumes of kidney hilum adipose tissue; and fat fractions of liver and pancreas) were extracted from T1-weighted Dixon MRI data of 17,996 participants of UKBB and NAKO based on quality-controlled deep learning generated organ segmentations. To enable valid cross-study analysis, we first analyzed the data generating process using methods of causal discovery. We subsequently harmonized data from UKBB and NAKO using the ComBat approach for batch effect correction. We finally performed quantile regression on harmonized data across studies providing quantitative models for the variation of image-derived features stratified for sex and dependent on age, height, and weight. Data from 8791 UKBB participants (49.9% female; age, 63 ± 7.5 years) and 9205 NAKO participants (49.1% female, age: 51.8 ± 11.4 years) were analyzed. Analysis of the data generating process revealed direct effects of age, sex, height, weight, and the data source (UKBB vs NAKO) on image-derived features. Correction of data source-related effects resulted in markedly improved alignment of image-derived features between UKBB and NAKO. Cross-study analysis on harmonized data revealed comprehensive quantitative models for the phenotypic variation of abdominal organs across the general adult population. Cross-study analysis of MRI data from UKBB and NAKO as proposed in this work can be helpful for future joint data analyses across cohorts linking genetic, environmental, and behavioral risk factors to MRI-derived phenotypes and provide reference values for clinical diagnostics.

Joint connectivity matrix independent component analysis: Auto-linking of structural and functional connectivities.

The study of human brain connectivity, including structural connectivity (SC) and functional connectivity (FC), provides insights into the neurophysiological mechanism of brain function and its relationship to human behavior and cognition. Both types of connectivity measurements provide crucial yet complementary information. However, integrating these two modalities into a single framework remains a challenge, because of the differences in their quantitative interdependencies as well as their anatomical representations due to distinctive imaging mechanisms. In this study, we introduced a new method, joint connectivity matrix independent component analysis (cmICA), which provides a data-driven parcellation and automated-linking of SC and FC information simultaneously using a joint analysis of functional magnetic resonance imaging (MRI) and diffusion-weighted MRI data. We showed that these two connectivity modalities produce common cortical segregation, though with various degrees of (dis)similarity. Moreover, we show conjoint FC networks and structural white matter tracts that directly link these cortical parcellations/sources, within one analysis. Overall, data-driven joint cmICA provides a new approach for integrating or fusing structural connectivity and FC systematically and conveniently, and provides an effective tool for connectivity-based multimodal data fusion in brain.

Method of Analysis of the Temporomandibular Joint Parameters According to Magnetic Resonance Imaging

Background. The existing variety of approaches to the treatment of temporomandibular joint (TMJ) dysfunction corresponds to the variety of methods for its diagnosing. The absence of a universal technique of updating a particular diagnostic method data makes it impossible to evaluate the effectiveness of treatment.Objective: to evaluate the universality of magnetic resonance imaging (MRI) in the diagnosis and determination of tactics for the treatment of TMJ pathologies.Material and methods. One hundred patients of different age groups and genders with and without TMJ pathology were divided into groups according to Angle’s classification: Class 1 – 27 persons, Class 2 – 41, Class 3 – 32. The studies were carried out using Green 18 computer tomograph (Vatech, South Korea), Optima MR450W magnetic resonance tomograph (General Electric, USA), Dentograph digital Roshchin axiograph (Prosystom, Russia), SAM 2PX articulator (SAM Praezisionstechnik GmbH, Germany). The RadiAnt Viewer software was used.Results. We conducted a comparative analysis of the indicators of articular structures mutual disposition obtained using various methods of TMJ diagnosis. A technique for estimating TMJ parameters using the analysis of MRI data was proposed. A correlation was revealed between the indications obtained using various diagnostic methods.Conclusion. The developed method of TMJ structures analysis has high accuracy and is applicable in practice.

Whole-body Magnetic Resonance Imaging in the German National Cohort (NAKO): Design & Current Status

Abstract Background Whole-body magnetic resonance imaging (MRI) permits non-invasive, non-ionizing phenotyping of the human body and ideally complements the epidemiological assessment of the NAKO participants. As such, it allows for the detection of morphologic or functional predisposition, early disease stages prior to overt clinical events as well as evident pathological changes. The assessment of progression and regression of imaging phenotypes over time will provide the basis to identify and understand the relevance of imaging-based risk factor profiles for disease development. Methods Integrated in the general NAKO study program and managed by a central Imaging Core, study participants underwent whole-body imaging at five dedicated MR imaging sites. Imaging was performed on five identical 3 Tesla scanners (Magnetom Skyra, Siemens Healthineers, Erlangen, Germany) applying a one hour protocol, including sequences for the brain, the cardiovascular and musculoskeletal system as well as for the thorax and abdomen. Comprehensive measures to assure high image quality and management of incidental findings were established. Results As part of the baseline examination program, a total of 30,861 participants successfully underwent the MR imaging program. All measures of quality assurance and incidental findings management were successfully employed throughout the study period and obtained image quality and completeness of all MR sequences was excellent (>94.2% completeness). While MR imaging as part of the first re-examination is ongoing, baseline MRI data is currently accessible for scientific analyses. Conclusions The MRI-Study of the NAKO will provide a comprehensive imaging phenotypic biobank covering different organ systems with highest morphological and functional detail. MRI data analysis will gain novel insights into the natural history of disease development, the role of subclinical disease burden, and revolutionize our understanding of imaging biomarkers of risk.

Vestibular paroxysmia entails vestibular nerve function, microstructure and endolymphatic space changes linked to root-entry zone neurovascular compression

Combining magnetic resonance imaging (MRI) sequences that permit the determination of vestibular nerve angulation (NA = change of nerve caliber or direction), structural nerve integrity via diffusion tensor imaging (DTI), and exclusion of endolymphatic hydrops (ELH) via delayed gadolinium-enhanced MRI of the inner ear (iMRI) could increase the diagnostic accuracy in patients with vestibular paroxysmia (VP). Thirty-six participants were examined, 18 with VP (52.6 ± 18.1 years) and 18 age-matched with normal vestibulocochlear testing (NP 50.3 ± 16.5 years). This study investigated whether (i) NA, (ii) DTI changes, or (iii) ELH occur in VP, and (iv) to what extent said parameters relate. Methods included vestibulocochlear testing and MRI data analyses for neurovascular compression (NVC) and NA verification, DTI and ELS quantification. As a result, (i) NA increased NVC specificity. (ii) DTI structural integrity was reduced on the side affected by VP (p < 0.05). (iii) 61.1% VP showed mild ELH and higher asymmetry indices than NP (p > 0.05). (iv) “Disease duration” and “total number of attacks” correlated with the decreased structural integrity of the affected nerve in DTI (p < 0.001). NVC distance within the nerve’s root-entry zone correlated with nerve function (Roh = 0.72, p < 0.001), nerve integrity loss (Roh = − 0.638, p < 0.001), and ELS volume (Roh = − 0.604, p < 0.001) in VP. In conclusion, this study is the first to link eighth cranial nerve function, microstructure, and ELS changes in VP to clinical features and increased vulnerability of NVC in the root-entry zone. Combined MRI with NVC or NA verification, DTI and ELS quantification increased the diagnostic accuracy at group-level but did not suffice to diagnose VP on a single-subject level due to individual variability and lack of diagnostic specificity.

Bayesian tensor logistic regression with applications to neuroimaging data analysis of Alzheimer's disease.

Alzheimer's disease (AD) can be diagnosed by utilizing traditional logistic regression models to fit magnetic resonance imaging (MRI) data of brain, which is regarded as a vector of covariates. But its parameter estimation is inefficient and computationally extensive due to ultrahigh dimensionality and complicated structure of MRI data. To overcome this deficiency, this paper proposes a tensor logistic regression model (TLRM) for AD's MRI data by regarding MRI tensor as covariates. Under this framework, a tensor candecomp/parafac (CP) decomposition tool is employed to reduce ultrahigh dimensional tensor to a high dimensional level, a novel Bayesian adaptive Lasso method is developed to simultaneously select important components of tensor and estimate model parameters by incorporating the Plya-Gamma method leading a closed-form likelihood and avoiding the usage of the Metropolis-Hastings algorithm, and Gibbs sampler technique in Markov chain Monte Carlo (MCMC). A tensor's product technique is utilized to optimize the calculation program and speed up the calculation of MCMC. Bayes factor together with the path sampling approach is presented to select tensor rank in CP decomposition. Effectiveness of the proposed method is illustrated on simulation studies and an MRI data analysis.

MRI Diagnosis of the Brachial Plexus Lesions

Summary. Damage to the brachial plexus (brachial plexopathy) is considered one of the most severe pathologies of the upper limb, which can lead to gross impairment of function and permanent disability of the patient. Today, MRI diagnostics is the first-line method for visualizing normal anatomy and pathological conditions of the brachial plexus (BP).&#x0D; Objective: to optimize the diagnosis of BP pathology based on the study of diagnostic capabilities of magnetic resonance imaging (MRI).&#x0D; Materials and Methods. A retrospective analysis of MRI data of 62 patients with traumatic injury of the BP (group 1) and 23 patients with lesions of non-traumatic genesis (group 2) was performed. The MRI examination was performed on a PHILIPS Achieva magnetic resonance tomograph with a magnetic field strength of 1.5 T using sequences of T1 and T2 weighted images (33), a 3DT2 DRIVE sequence with a high degree of resolution, and STIR sequences in axial, sagittal and coronal projections.&#x0D; Results. The MRI picture of brachial plexopathies was quite diverse and depended on the etiology of the lesion, the level and severity of damage to neural structures. When analyzing the MRI studies of patients of group 1, preganglionic lesion was detected in 39 patients (62.9%); 8 patients (12.9%) had trunks lesion and 15 patients (24.2%) had cords lesion. In group 2, BP ​​dysfunction associated with detected MRI signs of a tumor of nerve structures or infiltration and/or compression of the brachial plexus by a tumor of other organs or a metastasis was detected in 21 patients (84%); BP ​​dysfunction resulted from radiation therapy in 2 patients (8.7%) and from the disease – neuralgic amytrophy – in 2 patients (8.7%). The use of MRI made it possible to carry out a differential diagnosis of pathology and to determine the nature, extent and degree of severity of damage to nervous structures.&#x0D; Conclusions. MRI examination is an effective method of diagnosing the brachial plexus pathology, which makes it possible to determine the level, extent and severity of the damage, and to justify the further treatment of this category of patients at the early stages.

Quantitative classification of invasive and noninvasive breast cancer using dynamic magnetic resonance imaging of the mammary gland.

ObjectivesBreast cancers are classified as invasive or noninvasive based on histopathological findings. Although time-intensity curve (TIC) analysis using magnetic resonance imaging (MRI) can differentiate benign from malignant disease, its diagnostic ability to quantitatively distinguish between invasive and noninvasive breast cancers has not been determined. In this study, we evaluated the ability of TIC analysis of dynamic MRI data (MRI-TIC) to distinguish between invasive and noninvasive breast cancers.Material and MethodsWe collected and analyzed data for 429 cases of epithelial invasive and noninvasive breast carcinomas. TIC features were extracted in washout areas suggestive of malignancy.ResultsThe graph determining the positive diagnosis rate for invasive and noninvasive cases revealed that the cut-off θi/ni value was 21.6° (invasive: θw > 21.6°, noninvasive: θw ≤ 21.6°). Tissues were classified as invasive or noninvasive using this cut-off value, and each result was compared with the histopathological diagnosis. Using this method, the accuracy of tissue classification by MRI-TIC was 88.6% (380/429), which was higher than that using ultrasound (73.4%, 315/429).ConclusionMRI-TIC is effective for the classification of invasive vs. noninvasive breast cancer.

Prediction of Alzheimer\'s Disease Using CNN

Abstract: Alzheimer’s disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer’s disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited by researchers for Alzheimer’s disease diagnosis. Analysing magnetic resonance imaging (MRI) is a common practice for Alzheimer’s disease diagnosis in clinical research. Detection of Alzheimer’s disease is exacting due to the similarity in Alzheimer’s disease MRI data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a deep convolutional neural network for Alzheimer’s disease diagnosis using brain MRI data analysis. While most of the existing approaches perform binary classification, our model can identify different stages of Alzheimer’s disease and obtains superior performance for early-stage diagnosis.