Magnetic Resonance Imaging Acquisition Research Articles

Neuroimaging mimics of anoxic brain injury: A review.

Diffuse cortical diffusion changes on magnetic resonance imaging (MRI) are characteristically ascribed to global cerebral anoxia, typically after cardiac arrest. Far from being pathognomonic, however, this neuroimaging finding is relatively nonspecific, and can manifest in a myriad of disease states including hypoxia, metabolic derangements, infections, seizure, toxic exposures, and neuroinflammation. While these various conditions can all produce a neuroimaging pattern of widespread cortical diffusion restriction, many of these underlying causes do have subtly unique imaging features that are appreciable on MRI and can be of clinical and diagnostic utility. Specific populations of neurons are variably sensitive to certain types of injury, whether due to differences in perfusion, receptor type density, or the unique tropisms of infectious organisms. In this narrative review, we discuss a number of distinct etiologies of diffuse cortical diffusion restriction on MRI, the unique pathophysiologies responsible for tissue injury, and the resulting neuroimaging characteristics that can be of assistance in differentiating them. As widespread cortical injury from any cause often presents with altered mental status or coma, the differential diagnosis can be enhanced with rapid acquisition of MRI when clinical history or detailed physical examination is limited. In such settings, the distinct imaging features discussed in this article are of interest to both the clinician and the radiologist.

Assessment of Magnetic Resonance Imaging Artefacts Caused by Equine Anaesthesia Equipment: A Cadaver Study

Acquisition of magnetic resonance images of the equine limb is still sometimes conducted under general anaesthesia. Despite low-field systems allow the use of standard anaesthetic equipment, possible interferences of the extensive electronic componentry of advanced anaesthetic machines on image quality is unknown. This prospective, blinded, cadaver study investigated the effects of seven standardised conditions (Tafonius positioned as in clinical cases, Tafonius on the boundaries of the controlled area, anaesthetic monitoring only, Mallard anaesthetic machine, Bird ventilator, complete electronic silence in the room (negative control), source of electronic interference [positive control]) on image quality through the acquisition of 78 sequences using a 0.31T equine MRI scanner. Images were graded with a 4-point scoring system, where 1 denoted absence of artefacts and 4 major artefacts requiring repetition in a clinical setting. A lack of STIR fat suppression was commonly reported (16/26). Ordinal logistic regression showed no statistically significant differences in image quality between the negative control and either the non-Tafonius or the Tafonius groups (P = 0.535 and P = 0.881, respectively), and with the use of Tafonius compared to the other anaesthetic machines (P = 0.578). The only statistically significant differences in scores were observed between the positive control and the non-Tafonius (P = 0.006) and the Tafonius groups (P = 0.017). Our findings suggest that anaesthetic machines and monitoring do not appear to affect MRI scan quality and support the use of Tafonius during acquisition of images with a 0.31T MRI system in a clinical context.

Accuracy of abdominal organ motion estimation in radiotherapy using the right hemidiaphragm top as a surrogate during prolonged breath-holds quantified with MRI.

Respiratory motion presents a challenge in radiotherapy of thoracic and upper abdominal tumors. Techniques to account for respiratory motion include tracking. Using magnetic resonance imaging (MRI) guided radiotherapy systems, tumors can be tracked continuously. Using conventional linear accelerators, tracking of lung tumors is possible by determining tumor motion on kilo voltage (kV) imaging. But tracking of abdominal tumors with kV imaging is hampered by limited contrast. Therefore, surrogates for the tumor are used. One of the possible surrogates is the diaphragm. However, there is no universal method for establishing the error when using a surrogate and there are particular challenges in establishing such errors during free breathing (FB). Prolonged breath-holding might address these challenges. The aim of this study was to quantify the error when using the right hemidiaphragm top (RHT) as surrogate for abdominal organ motion during prolonged breath-holds (PBH) for possible application in radiation treatments. Fifteen healthy volunteers were trained to perform PBHs in two subsequent magnetic resonance imaging (MRI) sessions (PBH-MRI1 and PBH-MRI2). From each MRI acquisition, we selected seven images (dynamics) to determine organ displacement during PBH by using deformable image registration (DIR). On the first dynamic, the RHT, right and left hemidiaphragm, liver, spleen and right and left kidney were segmented. We used the deformation vector fields (DVF), generated by DIR, to determine the displacement of each organ between two dynamics in inferior-superior (IS), anterior-posterior (AP), left-right (LR) direction and we calculated the 3D vector magnitude (|d|). The displacements of the RHT, both hemidiaphragms and the abdominal organs were compared using a linear fit to determine the correlation (R2 of the fit) and the displacement ratio (DR, slope of the fit) between displacements of the RHT and each organ. We quantified the median difference between the DRs of PBH-MRI1 and PBH-MRI2 for each organ. Additionally, we estimated organ displacement in the second PBH by applying the DR from the first PBH to the displacement of the RHT measured during the second PBH. We compared the estimated organ displacement to the measured organ displacement during the second PBH. The difference between the two values was defined as the estimation error of using the RHT as a surrogate and assuming a constant DR over MRI sessions. The linear relationships were confirmed by the high R2 values of the linear fit between the displacements of the RHT and the abdominal organs (R2 > 0.96) in the IS and AP direction and |d|, and high to moderate correlations in the LR direction (0.93 > R2 > 0.64). The median DR difference between PBH-MRI1 and PBH-MRI2 varied between 0.13 to 0.31 for all organs. The median estimation error of the RHT as a surrogate varied between 0.4 and 0.8 mm/min for all organs. The RHT could serve as an accurate surrogate for abdominal organ motion during radiation treatments, e.g. in tracking, provided the error of the RHT as motion surrogate is taken into account in the margins. The study was registered in the Netherlands Trial Register (NL7603). This article is protected by copyright. All rights reserved.

Neural responses to emotional stimuli across the dissociative spectrum: Common and specific mechanisms.

Departing from existing neurobiological models of dissociation, the current study aims at conducting a quantitative meta-analytic review of neural responses to emotional stimuli among individuals ascribed to the dissociative spectrum (DS). Accordingly, the study explored common and specific brain mechanisms across borderline personality disorder, conversion/somatoform disorders, posttraumatic stress disorder, posttraumatic stress disorder related to repeated interpersonal traumatic experiences, and dissociative disorders. The meta-analysis included studies that administered emotional stimuli during functional magnetic resonance imaging acquisition among individuals included in the DS. There were two conducted meta-analytic procedures: (i) a Bayesian network meta-analysis for a region-of-interest-based approach; and (ii) robust voxel-based approach. Forty-four independent studies were included for a total of 1384 individuals (DS = 741 patients). The network meta-analysis showed specific patterns of neural activity considering an extended brain network involved in emotion regulation for each condition ascribed to the DS. The voxel-based meta-analysis highlighted an increased activity of dorsal anterior cingulate cortex as a common neurological signature of the DS. The common neural feature of the DS captures an implicit appraisal of emotion-eliciting stimuli as threatening and/or noxious for mental and physical integrity of the individual together with painful subjective experiences associated with physiological emotional reactions. Specific brain responses across the DS suggested the engagement in different mechanisms to address emotional stimuli, including implicit avoidance reactions and attempts to overcontrol of affective states together with a disruption of integrative processes of emotional mind-body features.

Optimizing data acquisition in undersampled magnetic resonance imaging (MRI) using two alternative forced choice (2-AFC) and search tasks.

Undersampling in the frequency domain (k-space) in MRI accelerates the data acquisition. Typically, a fraction of the low frequencies is fully collected and the rest are equally undersampled. We used a fixed 1D undersampling factor of 5x where 20% of the k-space lines are collected but varied the fraction of the low k-space frequencies that are fully sampled. We used a range of fully acquired low k-space frequencies from 0% where the primary artifact is aliasing to 20% where the primary artifact is blurring in the undersampling direction. Small lesions were placed in the coil k-space data for fluid-attenuated inversion recovery (FLAIR) brain images from the fastMRI database. The images were reconstructed using a multi-coil SENSE reconstruction with no regularization. We conducted a human observer two-alternative forced choice (2-AFC) study with a signal known exactly and a search task with variable backgrounds for each of the acquisitions. We found that for the 2-AFC task, the average human observer did better with more of the low frequencies being fully sampled. For the search task, we found that after an initial improvement from having none of the low frequencies fully sampled to just 2.5%, the performance remained fairly constant. We found that the performance in the two tasks had a different relationship to the acquired data. We also found that the search task was more consistent with common practice in MRI where a range of frequencies between 5% and 10% of the low frequencies are fully sampled.

Application of diffusion kurtosis imaging in neonatal brain development.

Deviations from the regular pattern of growth and development could lead to early childhood diseases, suggesting the importance of evaluating early brain development. Through this study, we aimed to explore the changing patterns of white matter and gray matter during neonatal brain development using diffusion kurtosis imaging (DKI). In total, 42 full-term neonates (within 28 days of birth) underwent conventional brain magnetic resonance imaging (MRI) and DKI. The DKI metrics (including kurtosis parameters and diffusion parameters) of white matter and deep gray matter were measured. DKI metrics from the different regions of interest (ROIs) were evaluated using the Kruskal-Wallis test and Bonferroni method. Spearman rank correlation analysis of the DKI metrics was conducted, and the age at the time of brain MRI acquisition was calculated. The subjects were divided into three groups according to their age at the time of brain MRI acquisition: the first group, neonates aged ≤7 days; the second group, neonates aged 8-14 days; and the third group, neonates aged 15-28 days. The rate of change in DKI metrics relative to the first group was computed. The mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), and fractional anisotropy (FA) values showed positive correlations, whereas mean diffusion (MD), axial diffusion (Da), and radial diffusion (Dr) values showed negative correlations with the age at the time of brain MRI acquisition. The absolute correlation coefficients between MK values of almost all ROIs (except genu of the corpus callosum and frontal white matter) and the age at the time of brain MRI acquisition were greater than other metrics. The kurtosis parameters and FA values of central white matter were significantly higher than that of peripheral white matter, whereas the MD and Dr values were significantly lower than that of peripheral white matter. The MK value of the posterior limb of the internal capsule was the highest among the white matter areas. The FA value of the splenium of the corpus callosum was significantly higher than that of the other white matter areas. The kurtosis parameters and FA values of globus pallidus and thalamus were significantly higher than those of the caudate nucleus and putamen, whereas the Da and Dr values of globus pallidus and thalamus were significantly lower than those of the caudate nucleus and putamen. The relative change rates of kurtosis parameters and FA values of all ROIs were greater than those of MD, Da, and Dr values. The amplitude of MK values of almost all ROIs (except for the genu of the corpus callosum and central white matter of the centrum semiovale level) was greater than that of other metrics. The relative change rates of the Kr values of most ROIs were greater than those of the Ka value, and the relative change rates of the Dr values of most ROIs were greater than those of the Da value. DKI parameters showed potential advantages in detecting the changes in brain microstructure during neonatal brain development.

A consensus protocol for functional connectivity analysis in the rat brain.

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

Physics-Driven Deep Learning Methods for Fast Quantitative Magnetic Resonance Imaging: Performance improvements through integration with deep neural networks

Quantitative magnetic resonance imaging (qMRI) aims to obtain quantitative biophysical parameters based on physical models derived from MR spin magnetization evolution. This requires the acquisition of multiple MR images, resulting in very long scan times. Recently, deep learning (DL) has shown significant potential for reconstructing undersampled MR data. When applying DL to fast qMRI, physical priors can be integrated into the DL framework to improve the performance of qMRI further. This article introduces the physical models of qMRI and four ways to integrate these models into a deep neural network (NN), namely, training sample generation, contrast image prediction, loss function design, and network architecture design. The core concepts and methods for each category are presented. The associated signal processing issues regarding the generalization and reliability of physics-driven DL-based fast qMRI methods are also discussed.

Focality in Febrile Seizures: A Retrospective Assessment Using Arterial Spin Labeling MRI.

Defining focality of febrile seizures (FS) in clinical practice remains controversial. We investigated focality issues in FS with a postictal arterial spin labeling (ASL) sequence. We retrospectively reviewed 77 children (median: 19.0 months, range: 15.0-33.0 months) who consecutively visited our emergency room for FS and underwent brain magnetic resonance imaging (MRI), including the ASL sequence, within 24 hours of seizure onset. ASL data were visually analyzed to assess perfusion changes. Factors related to the perfusion changes were investigated. The mean time to ASL acquisition was 7.0 (interquartile range: 4.0-11.0) hours. The most common seizure classification was unknown-onset seizures (n = 37, 48%), followed by focal-onset (n = 26, 34%) and generalized-onset seizures (n = 14, 18%). Perfusion changes were observed in 43 (57%) patients: most were hypoperfusion (n = 35, 83%). The temporal regions were the most common location of perfusion changes (n = 26, 60%); the majority of these were distributed in the unilateral hemisphere. Perfusion changes were independently associated with seizure classification (focal-onset seizures, adjusted odds ratio [aOR]: 9.6, p = 0.01; unknown-onset seizures aOR: 10.4, p < 0.01), and prolonged seizures (aOR: 3.1, p = 0.04), but not with other factors (age, sex, time to MRI acquisition, previous FS, repeated FS within 24 hour, family history of FS, structural abnormality on MRI, and developmental delay). The focality scale of seizure semiology positively correlated with perfusion changes (R = 0.334, p < 0.01). Focality in FS may be common, and its primary origin might be the temporal regions. ASL can be useful for assessing focality in FS, particularly when seizure onset is unknown.

Behavioral-play familiarization for non-sedated magnetic resonance imaging in young children with mild traumatic brain injury.

Mild traumatic brain injury (mTBI) sustained in early childhood affects the brain at a peak developmental period and may disrupt sensitive stages of skill acquisition, thereby compromising child functioning. However, due to the challenges of collecting non-sedated neuroimaging data in young children, the consequences of mTBI on young children's brains have not been systematically studied. In typically developing preschool children (of age 3-5years), a brief behavioral-play familiarization provides an effective alternative to sedation for acquiring awake magnetic resonance imaging (MRI) in a time- and resource-efficient manner. To date, no study has applied such an approach for acquiring non-sedated MRI in preschool children with mTBI who may present with additional MRI acquisition challenges such as agitation or anxiety. The present study aimed to compare the effectiveness of a brief behavioral-play familiarization for acquiring non-sedated MRI for research purposes between young children with and without mTBI, and to identify factors associated with successful MRI acquisition. Preschool children with mTBI (n=13) and typically developing children (n=24) underwent a 15-minutes behavioral-play MRI familiarization followed by a 35-minutes non-sedated MRI protocol. Success rate was compared between groups, MRI quality was assessed quantitatively, and factors predicting success were documented. Among the 37 participants, 15 typically developing children (63%) and 10 mTBI (77%) reached the MRI acquisition success criteria (i.e., completing the two first sequences). The success rate was not significantly different between groups (p=.48; 95% CI [-0.36 14.08]; Cramer's V=.15). The images acquired were of high-quality in 100% (for both groups) of the structural images, and 60% (for both groups) of the diffusion images. Factors associated with success included older child age (Β=0.73, p=.007, exp(B)=3.11, 95% CI [1.36 7.08]) and fewer parental concerns (Β=-1.56, p=.02, exp(Β)=0.21, 95% CI [0.05 0.82]) about the MRI procedure. Using brief behavioral-play familiarization allows acquisition of high-quality non-sedated MRI in young children with mTBI with success rates comparable to those of non-injured peers.

Diffusion Measures of Subcortical Structures Using High-Field MRI

The pathology of Parkinson's disease (PD) involves the death of dopaminergic neurons in the substantia nigra (SN), which slowly influences downstream basal ganglia pathways as dopamine transport diminishes. Diffusion magnetic resonance imaging (MRI) has been used to diagnose PD by assessing white matter connectivity in some brain areas. For this study, we applied Lead-DBS to human connectome project data to automatically segment 11 subcortical structures of 49 human connectome project subjects, reducing the reliance on manual segmentation for more consistency. The Lead-connectome pipeline, which utilizes DSI Studio to generate structural connectomes from each 3T and 7T diffusion image, was applied to 3T and 7T data to investigate possible differences in diffusion measures due to different acquisition protocols. Significantly higher fractional anisotropy (FA) values were found in the 3T left SN; significantly higher MD values were found in the 3T left SN and the right amygdala, SN, and subthalamic nucleus (STN); significantly higher AD values were found in the right RN and STN; and significantly higher RD values were found in the left RN and right amygdala. We illustrate a methodology for obtaining diffusion measures of basal ganglia and basal ganglia connectivity using diffusion images, as well as show possible differences in diffusion measures that can arise due to the differences in MRI acquisitions.

Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs): an MRI-based radiomic model to determine the malignant degeneration potential.

Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs) are the most common pancreatic cystic tumors and have a low risk of malignant transformation. Features able to early identify high-risk BD-IPMNs are lacking, and guidelines currently rely on the occurrence of worrisome features (WF) and high-risk stigmata (HRS). In our study, we aimed to use a magnetic resonance imaging (MRI) radiomic model to identify features linked to a higher risk of malignant degeneration, and whether these appear before the occurrence of WF and HRS. We retrospectively evaluated adult patients with a known BD-IPMN who had had at least two contrast-enhanced MRI studies at our center and a 24-month minimum follow-up time. MRI acquisition protocol for the two examinations included pre- and post-contrast phases and diffusion-weighted imaging (DWI)/apparent diffusion coefficient (ADC) map. Patients were divided into two groups according to the development of WF or HRS at the end of the follow-up (Group 0 = no WF or HRS; Group 1 = WF or HRS). We segmented the MRI images and quantitative features were extracted and compared between the two groups. Features that showed significant differences (SF) were then included in a LASSO regression method to build a radiomic-based predictive model. We included 50 patients: 31 in Group 0 and 19 in Group 1. No patients in this cohort developed HRS. At baseline, 47, 67, 38, and 68 SF were identified for pre-contrast T1-weighted (T1-W) sequence, post-contrast T1-W sequence, T2-weighted (T2- W) sequence, and ADC map, respectively. At the end of follow-up, we found 69, 78, 53, and 91 SF, respectively. The radiomic-based predictive model identified 16 SF: more particularly, 5 SF for pre-contrast T1-W sequence, 6 for post-contrast T1-W sequence, 3 for T2-W sequence, and 2 for ADC. We identified radiomic features that correlate significantly with WF in patients with BD-IPMNs undergoing contrast-enhanced MRI. Our MRI-based radiomic model can predict the occurrence of WF.

K-space and image domain collaborative energy-based model for parallel MRI reconstruction

Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible. Prior arts including the deep learning models have been devoted to solving the problem of long MRI imaging time. Recently, deep generative models have exhibited great potentials in algorithm robustness and usage flexibility. Nevertheless, none of existing schemes can be learned from or employed to the k-space measurement directly. Furthermore, how do the deep generative models work well in hybrid domain is also worth being investigated. In this work, by taking advantage of the deep energy-based models, we propose a k-space and image domain collaborative generative model to comprehensively estimate the MR data from under-sampled measurement. Equipped with parallel and sequential orders, experimental comparisons with the state-of-the-arts demonstrated that they involve less error in reconstruction accuracy and are more stable under different acceleration factors.

Sex-specific relationships between obesity, physical activity, and gray and white matter volume in cognitively unimpaired older adults.

Independently, obesity and physical activity (PA) influence cerebral structure in aging, yet their interaction has not been investigated. We examined sex differences in the relationships among PA, obesity, and cerebral structure in aging with 340 participants who completed magnetic resonance imaging (MRI) acquisition to quantify grey matter volume (GMV) and white matter volume (WMV). Height and weight were measured to calculate body mass index (BMI). A PA questionnaire was used to estimate weekly Metabolic Equivalents. The relationships between BMI, PA, and their interaction on GMV Regions of Interest (ROIs) and WMV ROIs were examined. Increased BMI was associated with higher GMV in females, an inverse U relationship was found between PA and GMV in females, and the interaction indicated that regardless of BMI greater PA was associated with enhanced GMV. Males demonstrated an inverse U shape between BMI and GMV, and in males with high PA and had normal weight demonstrated greater GMV than normal weight low PA revealed by the interaction. WMV ROIs had a linear relationship with moderate PA in females, whereas in males, increased BMI was associated with lower WMV as well as a positive relationship with moderate PA and WMV. Males and females have unique relationships among GMV, PA and BMI, suggesting sex-aggregated analyses may lead to biased or non-significant results. These results suggest higher BMI, and PA are associated with increased GMV in females, uniquely different from males, highlighting the importance of sex-disaggregated models. Future work should include other imaging parameters, such as perfusion, to identify if these differences co-occur in the same regions as GMV.

Beyond BMI: cardiometabolic measures as predictors of impulsivity and white matter changes in adolescents

Obesity is characterized by cardiometabolic and neurocognitive changes. However, how these two factors relate to each other in this population is unknown. We tested the association that cardiometabolic measures may have with impulse behaviors and white matter microstructure in adolescents with and without an excess weight. One hundred and eight adolescents (43 normal-weight and 65 overweight/obesity; 11–19 years old) were medically and psychologically (Temperament Character Inventory Revised, Three-Factor Eating Questionnaire-R18, Conners’ Continuous Performance Test-II, Stroop Color and Word Test, Wisconsin Card Sorting Test, Kirby Delay Discounting Task) evaluated. A subsample of participants (n = 56) underwent a brain magnetic resonance imaging acquisition. In adolescents, higher triglycerides and having a body mass index indicative of overweight/obesity predicted a more impulsive performance in Conners’ Continuous Performance Test-II (higher commission errors). In addition, higher glucose and diastolic blood pressure values predicted increments in the Three-Factor Eating Questionnaire-R18 emotional eating scale. Neuroanatomically, cingulum fractional anisotropy showed a negative relationship with glycated hemoglobin. The evaluation of the neurocognitive differences associated with obesity, usually based on body mass index, should be complemented with cardiometabolic measures.

Reproducibility of 2D versus 3D radiomics for quantitative assessment of fetal lung development: a retrospective fetal MRI study

PurposeTo investigate the reproducibility of radiomics features extracted from two-dimensional regions of interest (2D ROIs) versus whole lung (3D) ROIs in repeated in-vivo fetal magnetic resonance imaging (MRI) acquisitions.MethodsThirty fetal MRI scans including two axial T2-weighted acquisitions of the lungs were analysed. 2D (lung at the level of the carina) and 3D (whole lung) ROIs were manually segmented using ITK-Snap. Ninety-five radiomics features were extracted from 2 and 3D ROIs in initial and repeat acquisitions using Pyradiomics. Radiomics feature intra-class correlation coefficients (ICC) were calculated between 2 and 3D ROIs in the initial acquisition, and between 2 and 3D ROIs in repeated acquisitions, respectively.ResultsMRI data of 11 (36.7%) female and 19 (63.3%) male fetuses acquired at a median 25 + 0 gestational weeks plus days (GW) (interquartile range [IQR] 23 + 4 − 27 + 0 GW) were assessed. Median radiomics feature ICC between 2 and 3D ROIs in the initial MRI acquisition was 0.733 (IQR 0.313–0.814, range 0.018–0.970). ICCs between radiomics features extracted using 3D ROIs in initial and repeat acquisitions (median 0.908 [IQR 0.824–0.929, range 0.335–0.996]) were significantly higher compared to 2D ROIs (0.771 [0.699–0.835, 0.048–0.965]) (p < 0.001).ConclusionFetal MRI radiomics features extracted from 3D whole lung segmentation masks showed significantly higher reproducibility across repeat acquisitions compared to 2D ROIs. Therefore, fetal MRI whole lung radiomics features are robust diagnostic and potentially prognostic tools in the image-based in-vivo quantitative assessment of lung development.

A pentavalent approach for the evaluation of traumatic brachial plexopathy on MRI: correlation of macropattern and micropattern.

Macropattern analysis of traumatic brachial plexopathy (TBP) by Magnetic Resonance Imaging (MRI) encompasses localization of injured segments and determination of the severity of injury. The micropattern analysis implies the correlation of the MRI features of TBP with Sunderland's grading of the nerve injury, thereby guiding the management protocol. This review article presents a simplified novel pentavalent approach for the radiological anatomy of brachial plexus, MRI acquisition protocol for the evaluation of brachial plexus, cardinal imaging signs of TBP, and their correlation with Sunderland's microanatomical grading.

Clinical implementation of magnetic resonance imaging simulation for radiation oncology planning: 5 year experience

PurposeIntegrating magnetic resonance (MR) into radiotherapy planning has several advantages. This report details the clinical implementation of an MR simulation (MR-planning) program for external beam radiotherapy (EBRT) in one of North America's largest radiotherapy programs.Methods and materialsAn MR radiotherapy planning program was developed and implemented at Sunnybrook Health Sciences Center in 2016 with two dedicated wide-bore MR platforms (1.5 and 3.0 Tesla). Planning MR was sequentially implemented every 3 months for separate treatment sites, including the central nervous system (CNS), gynecologic (GYN), head and neck (HN), genitourinary (GU), gastrointestinal (GI), breast, and brachial plexus. Essential protocols and processes were detailed in this report, including clinical workflow, optimized MR-image acquisition protocols, MR-adapted patient setup, strategies to overcome risks and challenges, and an MR-planning quality assurance program. This study retrospectively reviewed simulation site data for all MR-planning sessions performed for EBRT over the past 5 years.ResultsFrom July 2016 to December 2021, 8798 MR-planning sessions were carried out, which corresponds to 25% of all computer tomography (CT) simulations (CT-planning) performed during the same period at our institution. There was a progressive rise from 80 MR-planning sessions in 2016 to 1126 in 2017, 1492 in 2018, 1824 in 2019, 2040 in 2020, and 2236 in 2021. As a result, the relative number of planning MR/CT increased from 3% of all planning sessions in 2016 to 36% in 2021. The most common site of MR-planning was CNS (49%), HN (13%), GYN (12%), GU (12%), and others (8%).ConclusionDetailed clinical processes and protocols of our MR-planning program were presented, which have been improved over more than 5 years of robust experience. Strategies to overcome risks and challenges in the implementation process are highlighted. Our work provides details that can be used by institutions interested in implementing an MR-planning program.

Development of an ultrafast brain MR neuronavigation protocol for ventricular shunt placement.

Advancements in MRI technology have provided improved ways to acquire imaging data and to more seamlessly incorporate MRI into modern pediatric surgical practice. One such situation is image-guided navigation for pediatric neurosurgical procedures, including intracranial catheter placement. Image-guided surgery (IGS) requires acquisition of CT or MR images, but the former carries the risk of ionizing radiation and the latter is associated with long scan times and often requires pediatric patients to be sedated. The objective of this project was to circumvent the use of CT and standard-sequence MRI in ventricular neuronavigation by investigating the use of fast MR sequences on the basis of 3 criteria: scan duration comparable to that of CT acquisition, visualization of ventricular morphology, and image registration with surface renderings comparable to standard of care. The aim of this work was to report image development, implementation, and results of registration accuracy testing in healthy subjects. The authors formulated 11 candidate MR sequences on the basis of the standard IGS protocol, and various scan parameters were modified, such as k-space readout direction, partial k-space acquisition, sparse sampling of k-space (i.e., compressed sensing), in-plane spatial resolution, and slice thickness. To evaluate registration accuracy, the authors calculated target registration error (TRE). A candidate sequence was selected for further evaluation in 10 healthy subjects. The authors identified a candidate imaging protocol, termed presurgical imaging with compressed sensing for time optimization (PICO). Acquisition of the PICO protocol takes 25 seconds. The authors demonstrated noninferior TRE for PICO (3.00 ± 0.19 mm) in comparison with the default MRI neuronavigation protocol (3.35 ± 0.20 mm, p = 0.20). The developed and tested sequence of this work allowed accurate intraoperative image registration and provided sufficient parenchymal contrast for visualization of ventricular anatomy. Further investigations will evaluate use of the PICO protocol as a substitute for CT and conventional MRI protocols in ventricular neuronavigation.

Parallel non-Cartesian spatial-temporal dictionary learning neural networks (stDLNN) for accelerating 4D-MRI.

Dynamic magnetic resonance imaging (MRI) acquisitions are relatively slow due to physical and physiological limitations. The spatial-temporal dictionary learning (DL) approach accelerates dynamic MRI by learning spatial-temporal correlations, but the regularization parameters need to be manually adjusted, the performance at high acceleration rate is limited, and the reconstruction can be time-consuming. Deep learning techniques have shown good performance in accelerating MRI due to the powerful representational capabilities of neural networks. In this work, we propose a parallel non-Cartesian spatial-temporal dictionary learning neural networks (stDLNN) framework that combines dictionary learning with deep learning algorithms and utilizes the spatial-temporal prior information of dynamic MRI data to achieve better reconstruction quality and efficiency. The coefficient estimation modules (CEM) are designed in the framework to adaptively adjust the regularization coefficients. Experimental results show that combining dictionary learning with deep neural networks and using spatial-temporal dictionaries can obviously improve the image quality and computational efficiency compared with the state-of-the-art non-Cartesian imaging methods for accelerating the 4D-MRI especially at high acceleration rate.