MR Data Research Articles

EPCO-03. METABOLIC INSIGHT INTO GLIOMA HETEROGENEITY: MAPPING WHOLE EXOME SEQUENCING TOIN VIVO IMAGING WITH STEREOTACTIC LOCALIZATION AND DEEP LEARNING

Abstract Intra-tumoral heterogeneity (ITH) complicates the diagnosis and treatment of glioma, partly due to the diverse metabolic profiles driven by genomic alterations. While multiparametric imaging captures spatial and functional variations enabling ITH characterization, it falls short in assessing the metabolic activities underpinning phenotypic differences. This gap stems from the challenge of integrating easily accessible, co-located pathology and genomic data with metabolic insights. This study presents a multi-faceted approach combining stereotactic biopsy with clinical open-craniotomy for sample collection, voxel-wise analysis of MR images, regression-based GAM, and whole-exome sequencing. The aim is to demonstrate the potential of machine learning algorithms to predict variations in cellular and molecular tumor characteristics. This retrospective study enrolled ten treatment-naïve patients with radiologically confirmed glioma who underwent multiparametric MR scans (T1W, T1W-CE, T2W, T2W-FLAIR, DWI) prior to surgery. During craniotomy, at least one stereotactic biopsy was collected from each patient, with the screenshots of sample locations saved for spatial registration to pre-surgical MR data. Whole-exome sequencing was performed on flash-frozen tumor samples, prioritizing five glioma-related genes: IDH1, TP53, EGFR, PIK3CA, and NF1. Regression was implemented with a GAM using a univariate shape function for each predictor. Standard ROC analyses were used to evaluate detection, with AUC calculated for each gene target and MR contrast combination. Mean AUC for five gene targets and 31 MR contrast combinations was 0.75±0.11, with individual AUCs as high as 0.96 for IDH1 and TP53 with T2W-FLAIR and ADC, and 0.99 for EGFR with T2W and ADC. An average AUC of 0.85 across the five mutations was achieved using the combination of T1W, T2W-FLAIR, and ADC. These results suggest the possibility of predicting exome-wide mutation events from non-invasive, in vivo imaging by combining stereotactic localization of glioma samples with a semi-parametric deep learning method. This approach holds potential for refining targeted therapy by better addressing the genomic heterogeneity of glioma tumors.

129Xe Image Processing Pipeline: An open-source, graphical user interface application for the analysis of hyperpolarized 129Xe MRI.

Hyperpolarized 129Xe MRI presents opportunities to assess regional pulmonary microstructure and function. Ongoing advancements in hardware, sequences, and image processing have helped it become increasingly adopted for both research and clinical use. As the number of applications and users increase, standardization becomes crucial. To that end, this study developed an executable, open-source 129Xe image processing pipeline (XIPline) to provide a user-friendly, graphical user interface-based analysis pipeline to analyze and visualize 129Xe MR data, including scanner calibration, ventilation, diffusion-weighted, and gas exchange images. The customizable XIPline is designed in MATLAB to analyze data from all three major scanner platforms. Calibration data is processed to calculate optimal flip angle and determine129Xe frequency offset. Data processing includes loading, reconstructing, registering, segmenting, and post-processing images. Ventilation analysis incorporates three common algorithms to calculate ventilation defect percentage and novel techniques to assess defect distribution and ventilation texture. Diffusion analysis features ADC mapping, modified linear binning to account for ADC age-dependence, and common diffusion morphometry methods. Gas exchange processing uses a generalized linear binning for data acquired using 1-point Dixon imaging. The XIPline workflow is demonstrated using analysis from representative calibration, ventilation, diffusion, and gas exchange data. The application will reduce redundant effort when implementing new techniques across research sites by providing an open-source framework for developers. In its current form, it offers a robust and adaptable platform for 129Xe MRI analysis to ensure methodological consistency, transparency, and support for collaborative research across multiple sites and MRI manufacturers.

Finite element study of stress distribution in medial UKA under varied lower limb alignment

Knee osteoarthritis (KOA) has a high incidence among the elderly, significantly impacting their quality of life and overall health. Medial unicompartmental knee arthroplasty (UKA) is an excellent choice for treating knee single-compartment lesions, and lower limb alignment has a profound impact on medial UKA. To explore the influence of different lower limb alignments on medial UKA. In this study, we selected MR and CT data of healthy adult male knee joints to establish a complete finite element analysis (FEA) model of the knee joint. After validation, we established a finite element model of medial UKA. Subsequently, we created 60 sets of FEA models with different lower limb alignments to analyze the impact of different lower limb alignments on medial UKA. A vertical load of 1000 N was applied to the FEA models with different lower limb alignments. During the process of shifting the Mikulicz line from the midpoint of the knee joint towards the medial side, the lower limb load was primarily concentrated on the medial compartment. The stress values of the lateral meniscus, tibial cartilage, and femoral cartilage gradually decreased. ROI (region of interest) 1 and ROI 2 showed the maximum principal strain changes, while ROI 3 and ROI 4 exhibited less pronounced fluctuations, with the maximum principal strain roughly proportionally increased. During the process of shifting the Mikulicz line towards the lateral side from the midpoint of the knee joint, the stress on the lateral compartment increased observably. ROI 1, ROI 2, ROI 3, and ROI 4 showed decreased maximum principal strains, approximately inversely proportional changes, but the overall reduction was relatively small. Different lower limb alignments have a profound impact on the short- and long-term joint function after UKA. When the Mikulicz line is 10 mm inside the midpoint of the knee joint or slightly outside, there is a relatively lower risk of tibial component fractures, lower stress on the lateral compartment, and lower load on the prosthesis. During medial UKA, measures such as bone resection and prosthesis selection should be taken to ensure that the Mikulicz line is in the ideal position.

Cross-modality image translation of 3 Tesla Magnetic Resonance Imaging to 7 Tesla using Generative Adversarial Networks.

The rapid advancements in magnetic resonance imaging (MRI) technology have precipitated a new paradigm wherein cross-modality data translation across diverse imaging platforms, field strengths, and different sites is increasingly challenging. This issue is particularly accentuated when transitioning from 3 Tesla (3T) to 7 Tesla (7T) MRI systems. This study proposes a novel solution to these challenges using generative adversarial networks (GANs)-specifically, the CycleGAN architecture- to create synthetic 7T images from 3T data. Employing a dataset of 1112 and 490 unpaired 3T and 7T MR images, respectively, we trained a 2-dimensional (2D) CycleGAN model, evaluating its performance on a paired dataset of 22 participants scanned at 3T and 7T. Independent testing on 22 distinct participants affirmed the model's proficiency in accurately predicting various tissue types, encompassing cerebral spinal fluid, gray matter, and white matter. Our approach provides a reliable and efficient methodology for synthesizing 7T images, achieving a median Dice of 6.82%,7,63%, and 4.85% for Cerebral Spinal Fluid (CSF), Gray Matter (GM), and White Matter (WM), respectively, in the testing dataset, thereby significantly aiding in harmonizing heterogeneous datasets. Furthermore, it delineates the potential of GANs in amplifying the contrast-to-noise ratio (CNR) from 3T, potentially enhancing the diagnostic capability of the images. While acknowledging the risk of model overfitting, our research underscores a promising progression towards harnessing the benefits of 7T MR systems in research investigations while preserving compatibility with existent 3T MR data. This work was previously presented at the ISMRM 2021 conference (Diniz, Helmet, Santini, Aizenstein, & Ibrahim, 2021).

FCSSL: fusion enhanced contrastive self-supervised learning method for parallel MRI reconstruction

Objective. The implementation of deep learning in magnetic resonance imaging (MRI) has significantly advanced the reduction of data acquisition times. However, these techniques face substantial limitations in scenarios where acquiring fully sampled datasets is unfeasible or costly.Approach. To tackle this problem, we propose a fusion enhanced contrastive self-supervised learning (FCSSL) method for parallel MRI reconstruction, eliminating the need for fully sampledk-space training dataset and coil sensitivity maps. First, we introduce a strategy based on two pairs of re-undersampling masks within a contrastive learning framework, aimed at enhancing the representational capacity to achieve higher quality reconstruction. Subsequently, a novel adaptive fusion network, trained in a self-supervised learning manner, is designed to integrate the reconstruction results of the framework.Results. Experimental results on knee datasets under different sampling masks demonstrate that the proposed FCSSL achieves superior reconstruction performance compared to other self-supervised learning methods. Moreover,the performance of FCSSL approaches that of the supervised methods, especially under the 2DRU and RADU masks, but no need for fully sample data. The proposed FCSSL, trained under the 3× 1DRU and 2DRU masks, can effectively generalize to unseen 1D and 2D undersampling masks, respectively. For target domain data that exhibit significant differences from source domain data, the proposed model, fine-tuned with just a few dozen instances of undersampled data in the target domain, achieves reconstruction performance comparable to that achieved by the model trained with the entire set of undersampled data.Significance. The novel FCSSL model offers a viable solution for reconstructing high-quality MR images without needing fully sampled datasets, thereby overcoming a major hurdle in scenarios where acquiring fully sampled MR data is difficult.

Estimation and Removal of Residual Motion Artifact in Retrospectively Motion-Corrected fMRI Data: A Comparison of Intervolume and Intravolume Motion Using Gold Standard Simulated Motion Data

Residual head motion artifact in motion-corrected resting-state (rs-) functional MRI (fMRI) and fMRI datasets reduces the temporal signal-to-noise ratio and leaves non-neuronal signal components in the data, which can induce false findings in these studies. While various residual motion nuisance regressors have been proposed to regress out residual motion artifact after motion correction, these validations have typically been conducted empirically in in vivo data, since realistic head motion–corrupted MR data are not available. Here, we generated motion-corrupted MR data by altering imaging plane coordinates before each volume and slice acquisition from an ex vivo brain phantom using the simulated prospective acquisition correction (SIMPACE) sequence. Testing SIMPACE motion-corrupted data with various intervolume motion patterns, we first investigated the mechanism of the residual motion signal after motion correction and also proposed a voxel-wise motion nuisance regressor, called the partial volume (PV) regressor. We also modified the slice-oriented motion-correction method (SLOMOCO) pipeline with 6 volume-wise rigid intervolume motion parameters (Vol-mopa), 6 slice-wise rigid intravolume motion parameters (Sli-mopa), and the proposed PV motion nuisance regressor. We then compared the residual signal after application of the modified SLOMOCO (mSLOMOCO) pipeline with two other methods: intervolume motion-correction method (VOLMOCO), and the original SLOMOCO (oSLOMOCO). We found that mSLOMOCO with 12 Vol-/Sli-mopa and PV regressors outperformed both VOLMOCO with 6 Vol-mopa and PV regressors and oSLOMOCO with 14 voxel-wise regressors. In tests of the 10 different motion patterns of SIMPACE datasets with 1× and 2× amplified intravolume motion, mSLOMOCO with 12 Vol-/Sli-mopa and PV regressors pipeline produced the average standard deviation (SD) of the residual time series signals in the gray matter (GM) smaller by 29% (1× amplified intravolume motion) and 45% (2× amplified intravolume motion) than VOLMOCO with 6 Vol-mopa and PV regressors pipeline. Also, mSLOMOCO with 12 Vol-/Sli-mopa and PV regressors pipeline outperformed oSLOMOCO with 14 voxelwise regressors pipeline, generating the average SD in GM smaller by 28% (1× amplified intravolume motion) and 31% (2× amplified intravolume motion) than oSLOMOCO with 14 voxel-wise regressors pipeline. The novel PV regressor also effectively reduced residual motion artifact as a motion nuisance regressor after both VOLMOCO and mSLOMOCO.

Dynamic MRI reconstruction via multi-directional low-rank tensor regularization

Compressed sensing significantly accelerates dynamic magnetic resonance imaging (MRI) by allowing the exact reconstruction of images from a small number of measurements in (k, t)-space. The similarity in three different directions of dynamic MRI makes it have a multi-directional low-rank prior. In this paper, we propose a tensor-based multi-dimensional low-rank regularization for dynamic MRI reconstruction model, termed as MDLRT, and introduce the Schatten capped p-norm to enforce the low-rank prior of multi-directional slices. We take dynamic MRI tensor as processing unit to protect its multi-channel structure and avoid losing internal information, which promises the reconstruction performance. The utilization of multi-directional low-rank characteristics adequately exploits the spatial and temporal redundancies. In addition, using Schatten capped p-norm to regularize the low-rank property realizes a better approximation to the rank than widely used surrogate functions. The proposed model is efficiently solved by the alternating direction multiplier method (ADMM), in which the fast algorithm for each sub-problem is separately developed. In comparison with state-of-the-art methods, extensive experiments on dynamic MR data demonstrate the superior performance and robustness to noise of the proposed method in terms of reconstruction accuracy.

CT and MR imaging characteristics of primary hepatic lymphoma

BackgroundPrimary hepatic lymphoma (PHL) is a rare primary liver tumor. The purpose of this study was to investigate the crucial imaging characteristics of PHL.MethodsCT/MR data and clinical features of 16 patients with pathologically proven PHL were retrospectively reviewed. The assessed imaging characteristics included lesion distribution, growth appearance, density or signal characteristics, and typical signs during the dynamic enhancement.ResultsA total of sixteen patients were included in this study, ranging in age from 27 to 86 years (nine men and seven women; mean age, 62 years). Right upper abdominal pain, malaise, or first detection during physical examination were the chief complaint. Dynamic contrast-enhanced CT and MRI demonstrated focal masses in thirteen patients (13/16, 81%), and multiple nodules in three patients (3/16, 19%). The lesions showed vascular floating sign in 10 patients (10/16, 63%) and biliary pass-through sign in 6 patients (6/16, 37%). Necrosis in PHLs were observed in 10 patients (10/16, 63%) and presented target sign on T2WI in 5 out of 11 patients (5/11, 45%) undergoing MRI. After enhancement, PHLs had multinodular sign in the arterial phase in 10 patients (10/16, 63%), and presented washout of contrast medium in the portal-venous or delayed phase in 12 patients (12/16, 75%), pseudocapsule in the delayed phase in 15 patients (15/16, 94%), and double-ring sign in the delayed phase in 8 patients (8/16, 50%).ConclusionPHL is generally seen in elderly patients with elevated beta-2-microglobulin levels. Its distinctive imaging features including obviously low ADC value, biliary/vascular floating sign, multinodular sign and double-ring sign in the delayed phase, may help to diagnose and differentiate PHL.

Development of a medication review intervention by seconding a hospital pharmacist to primary care

BackgroundMedication reviews (MRs) are a well-described initiative that improves health outcomes for polypharmacy patients. However, there is limited knowledge about the performance of medication reviews carried out in general practice especially under the leadership of hospital clinical pharmacists. When developing complex interventions, such as MRs, it is essential to describe the development process to ensure transparency and avoid research waste. ObjectiveThus, this study aimed to describe the steps of developing a new MR intervention targeting general practice to ensure transparency and transferability. MethodsA stepwise approach inspired by the Medical Research Council framework was utilised in the process, covering two of the phases, i.e., development and feasibility, divided into four steps: 1) intervention drafting by a literature search, 2) expert opinion, 3) pilot testing in general practice clinics, and 4) evaluation of quantitative MR data. ResultsBased on the results from the first three steps, four main themes which influenced the success of the MR intervention were identified: general practitioner resources, patient involvement, implementation difficulties and interdisciplinarity. These themes guided the pilot evaluation in step four. ConclusionA new feasible, complex MR intervention utilising clinical pharmacists in general practice involving hospital clinical pharmacists in a real-life setting was developed.

The Diffusion Exchange Ratio (DEXR): A minimal sampling of diffusion exchange spectroscopy to probe exchange, restriction, and time-dependence.

Water exchange is increasingly recognized as an important biological process that can affect the study of biological tissue using diffusion MR. Methods to measure exchange, however, remain immature as opposed to those used to characterize restriction, with no consensus on the optimal pulse sequence(s) or signal model(s). In general, the trend has been towards data-intensive fitting of highly parameterized models. We take the opposite approach and show that a judicious sub-sample of diffusion exchange spectroscopy (DEXSY) data can be used to robustly quantify exchange, as well as restriction, in a data-efficient manner. This sampling produces a ratio of two points per mixing time: (i) one point with equal diffusion weighting in both encoding periods, which gives maximal exchange contrast, and (ii) one point with the same total diffusion weighting in just the first encoding period, for normalization. We call this quotient the Diffusion EXchange Ratio (DEXR). Furthermore, we show that it can be used to probe time-dependent diffusion by estimating the velocity autocorrelation function (VACF) over intermediate to long times (~ 2-500 ms). We provide a comprehensive theoretical framework for the design of DEXR experiments in the case of static or constant gradients. Data from Monte Carlo simulations and experiments acquired in fixed and viable ex vivo neonatal mouse spinal cord using a permanent magnet system are presented to test and validate this approach. In viable spinal cord, we report the following apparent parameters from just 6 data points: , , , and , which correspond to the exchange time, restricted or non-Gaussian signal fraction, an effective spherical radius, and permeability, respectively. For the VACF, we report a long-time, power-law scaling with , which is approximately consistent with disordered domains in 3-D. Overall, the DEXR method is shown to be highly efficient, capable of providing valuable quantitative diffusion metrics using minimal MR data.

Assessing the causal effects of Eubacterium and Rumphococcus on constipation: a Mendelian randomized study.

Constipation is affected by a number of risk variables, including cardiovascular disease and growth factors. However, the impacts of gut flora on constipation incidence has not been shown. This work, Single-Variable Mendelian Randomization (SVMR) was utilized to estimate the causal relationship between the Eubacterium genus or Rumphococcus, and constipation. Data for constipation, Eubacterium genus and Rumphococcus were taken from the Integrated Epidemiology Unit (IEU) open GWAS database. Including 218,792 constipation samples, and there were 16,380,466 Single Nucleotide Polymorphisms (SNPs) for constipation. The ids of Eubacterium genus and Rumphococcus were sourced from MiBioGen database. The sample count for the Eubacterium genus was 17,380, with 656 SNPs. In addition, the sample size for Rumphococcus was 15,339, with 545 SNPs. The SVMR was performed to assess the risk of Eubacterium genus and Rumphococcus in constipation using weighted median, MR Egger, simple mode, inverse variance weighted (IVW), and weighted mode. Finally, we did a sensitivity analysis that included a heterogeneity, horizontal pleiotropy, and Leave-One-Out (LOO) test to examine the viability of the MR data. The SVMR revealed that the Eubacterium genus and Rumphococcus were causally connected to constipation, with Rumphococcus (P = 0.042, OR = 1.074) as a hazardous factor and Eubacterium genus (P = 0.004, OR = 0.909) as a safety factor. Sensitivity tests then revealed the absence of variability between the constipation and the exposure factors (Eubacterium genus and Rumphococcus). Additionally, there were no other confounding factors and the examined SNPs could only influence constipation through the aforementioned exposure factors, respectively. As a result, the MR results were fairly robust. Our investigation verified the causal links between the Eubacterium genus or Rumphococcus, and constipation, with greater Rumphococcus expression increasing the likelihood of constipation and the opposite being true for the Eubacterium genus.

Synthetic MRI and MR Fingerprinting-Derived Relaxometry of Antenatal Human Brainstem Myelination: A Postmortem-Based Quantitative Imaging Study.

The radiologic evaluation of ongoing myelination is currently limited prenatally. Novel quantitative MR imaging modalities provide relaxometric properties that are linked to myelinogenesis. In this retrospective postmortem imaging study, the capability of Synthetic MR imaging and MR fingerprinting-derived relaxometry for tracking fetal myelin development was investigated. Moreover, the consistency of results for both MR approaches was analyzed. In 26 cases, quantitative postmortem fetal brain MR data were available (gestational age range, 15 + 1 to 32 + 1; female/male ratio, 14/12). Relaxometric measurements (T1-/T2-relexation times) were determined in the medulla oblongata and the midbrain using Synthetic MR imaging/MR fingerprinting-specific postprocessing procedures (Synthetic MR imaging and MR Robust Quantitative Tool for MR fingerprinting). The Pearson correlations were applied to detect relationships between T1-relaxation times/T2-relaxation times metrics and gestational age at MR imaging. Intraclass correlation coefficients were calculated to assess the consistency of the results provided by both modalities. Both modalities provided quantitative data that revealed negative correlations with gestational age at MR imaging: Synthetic MR imaging-derived relaxation times (medulla oblongata [r = -0.459; P = .021]; midbrain [r = -0.413; P = .040]), T2-relaxation times (medulla oblongata [r = -0.625; P < .001]; midbrain [r = -0.571; P = .003]), and MR fingerprinting-derived T1-relaxation times (medulla oblongata [r = -0.433; P = .035]; midbrain [r = -0.386; P = .062]), and T2-relaxation times (medulla oblongata [r =-0.883; P < .001]; midbrain [r = -0.890; P < .001]).The intraclass correlation coefficient analysis for result consistency between both MR approaches ranged between 0.661 (95% CI, 0.351-0.841) (T2-relaxation times: medulla oblongata) and 0.920 (95% CI, 0.82-0.965) (T1-relaxation times: midbrain). There is a good-to-excellent consistency between postmortem Synthetic MR imaging and MR fingerprinting myelin quantifications in fetal brains older than 15 + 1 gestational age. The strong correlations between quantitative myelin metrics and gestational age indicate the potential of quantitative MR imaging to identify delayed or abnormal states of myelination at prenatal stages of cerebral development.

Microsurgical anatomy and approaches to thalamic gliomas. Part 1: A cartography guide for navigating to the thalamus. Integrating 3D model rendering with anatomical dissections.

The selection of appropriate microsurgical approaches to treat thalamic pathologies is currently largely subjective. The objective of this study was to provide a structured cartography map for surgical navigation to treat gliomas involving different surfaces of the thalamus. Fifteen formalin-fixed, silicone-injected cadavers (30 sides) were dissected, and 10 adult brain specimens (20 sides) were used to illustrate thalamic microsurgical anatomy using the Klingler fiber dissection technique. Exposures and trajectories for the six most common microsurgical approaches were depicted using MR data from healthy subjects converted into surface-rendered 3D virtual brain models. Additionally, thalamic surfaces exposed with all six approaches were color mapped on the virtual 3D model and compared side-by-side in 360° views with previously reported microsurgical approaches. These 3D models were then used in conjunction with topographic data to guide cadaveric dissection steps. There are two general surgical routes to thalamic lesions: the subarachnoid transcisternal and transcortical routes. The transcisternal route consists of the following three approaches: 1) anterior interhemispheric transcallosal approach, which exposes the anterior and superior thalamus; 2) posterior interhemispheric transcallosal approach, which exposes the posterosuperior thalamus; and 3) supracerebellar infratentorial approach, which exposes the posteromedial cisternal thalamus and can be extended laterally to approach the posterolateral thalamus by cutting the tentorium. The three transcortical approaches are the 1) superior parietal lobule approach, which exposes the posterosuperior thalamus and is particularly advantageous in the setting of hydrocephalus; 2) transtemporal gyrus approach, which exposes the inferolateral thalamus; and 3) transsylvian transinsular approach, which exposes the lateral thalamus (slightly more superiorly and posteriorly) and is advantageous for pathologies extending laterally into the peduncle, lenticular nucleus, or insula. Microsurgical approaches to thalamic gliomas continue to be challenging. Nonetheless, safe and effective cisternal, ventricular, and cortical corridors can be developed with thoughtful planning, anatomical understanding, and knowledge of the advantages, risks, and limitations of each approach. In some cases, it is wise to combine these approaches with staged procedures, as the authors demonstrate in Part 2. In Part 1 of this two-part series, they discuss thalamic microsurgical anatomy and illustrate the trajectory and exposures of all six approaches to guide decision-making. Part 2 discusses their thalamic glioma microsurgical case series, which utilizes these microsurgical approaches.

Training deep learning based dynamic MR image reconstruction using open-source natural videos

To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K). Learning was performed for a range of DL architectures (VarNet, 3D UNet, FastDVDNet) and corresponding sampling patterns (Cartesian, radial, spiral) either from true multi-coil cardiac MR data (N = 692) or from synthetic MR data simulated from Inter4K natural videos (N = 588). Real-time undersampled dynamic MR images were reconstructed using DL networks trained with cardiac data and natural videos, and compressed sensing (CS). Differences were assessed in simulations (N = 104 datasets) in terms of MSE, PSNR, and SSIM and prospectively for cardiac cine (short axis, four chambers, N = 20) and speech cine (N = 10) data in terms of subjective image quality ranking, SNR and Edge sharpness. Friedman Chi Square tests with post-hoc Nemenyi analysis were performed to assess statistical significance. In simulated data, DL networks trained with cardiac data outperformed DL networks trained with natural videos, both of which outperformed CS (p < 0.05). However, in prospective experiments DL reconstructions using both training datasets were ranked similarly (and higher than CS) and presented no statistical differences in SNR and Edge Sharpness for most conditions.The developed pipeline enabled learning dynamic MR reconstruction from natural videos preserving DL reconstruction advantages such as high quality fast and ultra-fast reconstructions while overcoming some limitations (data scarcity or sharing). The natural video dataset, code and pre-trained networks are made readily available on github.

MR defecography in assessing stress urinary incontinence with or without symptomatic pelvic organ prolapse.

Utilize magnetic resonance defecography (MRD) to analyze the primary pelvic floor dysfunctions in patients with stress urinary incontinence (SUI) associated with pelvic organ prolapse (POP), and in SUI patients with asymptomatic POP. We performed MRD in both SUI and POP subjects. As a primary analysis, the functional MR parameters were compared between the isolated POP and POP combined SUI groups. As a secondary analysis, the functional MR data were compared between the POP combined SUI and the SUI with asymptomatic POP (isolated SUI) groups. MRD noted the main characteristics of SUI combined moderate or severe POP, including the shorter closed urethra length (1.87cm vs. 2.50cm, p < 0.001), more prevalent urethral hypermobility (112.31° vs. 85.67°, p = 0.003), bladder neck funneling (48.28% vs. 20.51%, p = 0.020), lower position of vesicourethral junction (2.11cm vs. 1.67cm, p = 0.030), and more severe prolapse of the posterior bladder wall (6.26cm vs. 4.35cm, p = 0.008). The isolated SUI patients showed the shortest length of the closed urethra (1.56cm vs. 1.87cm, p = 0.029), a larger vesicourethral angle (153.80° vs. 107.58°, p < 0.001), the more positive bladder funneling (84.85% vs. 48.28%, p = 0.002) and a special urethral opening sign (45.45% vs. 3.45%, p < 0.001). Patients with SUI accompanying POP primarily exhibit excessive urethral mobility and a shortened urethral closure. SUI patients with asymptomatic POP mainly show dysfunction of the urethra and bladder neck, characterized by the opening of the urethra and bladder neck and a shortened urethral closure.

Characterization of pulsations in the brain and cerebrospinal fluid using ultra-high field magnetic resonance imaging.

The development of innovative non-invasive neuroimaging methods and biomarkers is critical for studying brain disease. Imaging of cerebrospinal fluid (CSF) pulsatility may inform the brain fluid dynamics involved in clearance of cerebral metabolic waste. In this work, we developed a methodology to characterize the frequency and spatial localization of whole brain CSF pulsations in humans. Using 7 Tesla (T) human magnetic resonance imaging (MRI) and ultrafast echo-planar imaging (EPI), in-vivo images were obtained to capture pulsations of the CSF signal. Physiological data were simultaneously collected and compared with the 7 T MR data. The primary components of signal pulsations were identified using spectral analysis, with the most evident frequency bands identified around 0.3, 1.2, and 2.4 Hz. These pulsations were mapped spatially and temporally onto the MR image domain and temporally onto the physiological measures of electrocardiogram and respiration. We identified peaks in CSF pulsations that were distinct from peaks in grey matter and white matter regions. This methodology may provide novel in vivo biomarkers of disrupted brain fluid dynamics.

3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR).

Objective. 3D cine-magnetic resonance imaging (cine-MRI) can capture images of the human body volume with high spatial and temporal resolutions to study anatomical dynamics. However, the reconstruction of 3D cine-MRI is challenged by highly under-sampled k-space data in each dynamic (cine) frame, due to the slow speed of MR signal acquisition. We proposed a machine learning-based framework, spatial and temporal implicit neural representation learning (STINR-MR), for accurate 3D cine-MRI reconstruction from highly under-sampled data.Approach. STINR-MR used a joint reconstruction and deformable registration approach to achieve a high acceleration factor for cine volumetric imaging. It addressed the ill-posed spatiotemporal reconstruction problem by solving a reference-frame 3D MR image and a corresponding motion model that deforms the reference frame to each cine frame. The reference-frame 3D MR image was reconstructed as a spatial implicit neural representation (INR) network, which learns the mapping from input 3D spatial coordinates to corresponding MR values. The dynamic motion model was constructed via a temporal INR, as well as basis deformation vector fields (DVFs) extracted from prior/onboard 4D-MRIs using principal component analysis. The learned temporal INR encodes input time points and outputs corresponding weighting factors to combine the basis DVFs into time-resolved motion fields that represent cine-frame-specific dynamics. STINR-MR was evaluated using MR data simulated from the 4D extended cardiac-torso (XCAT) digital phantom, as well as two MR datasets acquired clinically from human subjects. Its reconstruction accuracy was also compared with that of the model-based non-rigid motion estimation method (MR-MOTUS) and a deep learning-based method (TEMPEST).Main results. STINR-MR can reconstruct 3D cine-MR images with high temporal (<100 ms) and spatial (3 mm) resolutions. Compared with MR-MOTUS and TEMPEST, STINR-MR consistently reconstructed images with better image quality and fewer artifacts and achieved superior tumor localization accuracy via the solved dynamic DVFs. For the XCAT study, STINR reconstructed the tumors to a mean ± SD center-of-mass error of 0.9 ± 0.4 mm, compared to 3.4 ± 1.0 mm of the MR-MOTUS method. The high-frame-rate reconstruction capability of STINR-MR allows different irregular motion patterns to be accurately captured.Significance. STINR-MR provides a lightweight and efficient framework for accurate 3D cine-MRI reconstruction. It is a 'one-shot' method that does not require external data for pre-training, allowing it to avoid generalizability issues typically encountered in deep learning-based methods.

A central vein sign in the magnetic resonance diagnosing of multiple sclerosis

Background. Multiple sclerosis (MS) is a chronic autoimmune demyelinating disease, which is characterized by the inevitable disability of patients in the long term, which determines the relevance of this problem. Currently, active improvements are being made in the methods of diagnosing multiple sclerosis, which include the use of the central vein sign in magnetic resonance imaging (MRI) as a neuroimaging biomarker of MS with high sensitivity and specificity.Aim of study. Determination of the possibility of assessing the central vein sign (CVS) according to MRI data as a potential diagnostic biomarker of MS.Object and methods. An open single-center prospective study of brain MR data was conducted in 55 patients with a verified diagnosis of MS (EDSS 1.0-6.5) aged 19 to 72 years. MR-images were obtained on a tomograph with a magnetic field induction force of 3.0 T. Patients underwent MRI of the brain according to the standard protocol: T2-VI, FLAIR, T1-VI (before and after administration of contrast agent), SWI. A comprehensive statistical analysis and evaluation of the obtained MRI data was performed using the Statistica 12 program.Result. During the evaluation of MR-tomograms, all patients with a verified diagnosis in the foci of MS were found to have CVS. In 14.5 % of patients, CVS was detected in 10-30 % of foci, in 61.8 % of patients in 30-60 %, in 23.6 % of patients, from 60 to 95 % was detected. Accordingly, 52.7 % of patients overcame the threshold value of 45 % required for the differential diagnosis of MS from other conditions.Conclusion. The use of CVS in MRI helps to solve the problem of differential diagnosis of MS from other demyelinating diseases when using its threshold criterion – the percentage of foci containing central veins.

Micromagnetic simulations of Nd-Fe-B hot-deformed magnets subjected to eutectic grain boundary diffusion process

The grain boundary diffusion process (GBDP) is one of the most efficient treatments for increasing the coercivity (Hc) of Nd-Fe-B magnets. However, this enhancement typically occurs at the expense of remanence (Mr). In this study, micromagnetic simulations were performed to quantify this tradeoff in hot-deformed Nd-Fe-B magnets subjected to the GBDP using a Nd-based eutectic alloy. The GBDP was imitated in a series of models with a gradually increasing volume fraction of the infiltrated Nd-rich nonmagnetic phase. This infiltration reduced the remanence and grain connectivity via the remaining thin magnetic intergranular phase (IGP), which in turn increased the coercivity. We distinguished between the roles of exchange and magnetostatic interactions in this coercivity enhancement. Furthermore, the simulated Mr vs. Hc curves defined realistic limits for coercivity that depended on the IGP magnetization, which was estimated to be 0.9 ± 0.1 T by reproducing experimental Mr vs. Hc data from the literature.

CT, MR, and isotope data of a mummified child from Zagreb Cathedral, Croatia: giving voice to the past through imaging.

CT, MR, and isotope data of a mummified child from Zagreb Cathedral, Croatia: giving voice to the past through imaging.