Individual Magnetic Resonance Imaging Research Articles

Open access EEG dataset of repeated measurements from a single subject for microstate analysis

Electroencephalography (EEG) microstate analysis is a neuroimaging analytical method that has received considerable attention in recent years and is widely used for analysing EEG signals. EEG is easily influenced by internal and external factors, which can affect the repeatability and stability of EEG microstate analysis. However, there have been few reports and publicly available datasets on the repeatability of EEG microstate analysis. In the current study, a 39-year-old healthy male underwent a total of 60 simultaneous electroencephalography and electrocardiogram measurements over a period of three months. After the EEG recording was completed, magnetic resonance imaging (MRI) was also conducted. To date, this EEG dataset has the highest number of repeated measurements for one individual. The dataset can be used to assess the stability and repeatability of EEG microstates and other analytical methods, to decode resting EEG states among subjects with open eyes, and to explore the stability and repeatability of cortical spatiotemporal dynamics through source analysis with individual MRI.

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

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

Measurement of TT-TG can change with sequential MRIs due to variations in tibiofemoral rotation in patellofemoral instability patients.

There are various anatomic risk factors for patellofemoral instability (PFI) that help guide surgical treatment, including the tibial tubercle to trochlear groove (TT-TG) distance. However, no study has analysed the temporal changes in TT-TG prior to surgical intervention. This study sought to understand the variations in TT-TG over time for pediatric patients suffering from PFI prior to surgical intervention. The authors hypothesised that the TT-TG would substantially change between time points. Patients undergoing medial patellofemoral ligament (MPFL) reconstruction between 2014 and 2019 by one of two fellowship-trained orthopaedic surgeons were identified. Patients were included if they had two preoperative magnetic resonance imaging (MRI) performed on the same knee within 7.5 months of each other prior to any surgical intervention and had an initial TT-TG greater than 10 mm. After considering 251 patients for inclusion, 21 patients met the final inclusion criteria. The mean age was 14.5 ± 2.5 years and 61.9% were female. TT-TG was initially noted to be 15.1 ± 1.8 mm. At mean time aftersequential MRIs of 5.0 ± 1.9 months, TT-TG was noted to be 16.7 ± 3.2 mm. The differences between initial and subsequent TT-TG ranged from a 21.2% decrease to a 61.1% increase, with a mean difference of an 11.3% increase. Comparison between initial and subsequent TT-TG values demonstrated a significant difference (p = 0.017). Change in tibiofemoral rotation ranged from -9.2° to 7.5°. When comparing the change in TT-TG to change in tibiofemoral rotation, a significant correlation was found (p = 0.019). Despite only a mean time between MRIs of 5 months, variations in TT-TG ranged from a decrease of 21.2% to an increase of 61.1%. The significant relationship between the changes in TT-TG and changes in tibiofemoral rotation between MRIs suggest that TT-TG measurements may vary due to variations in tibiofemoral rotation at the time of individual MRIs. Level IV.

Quality of life in lumbar spinal stenosis: Does it correlate with magnetic resonance imaging and spinopelvic parameters?

PurposeDegenerative lumbar spinal stenosis (LSS) is a common degenerative spinal disorder with debilitating symptoms that can impact quality of life (QoL). However, the anatomical basis for typical complaints has been poorly quantified. This study aims to correlate QoL assessments of patients with LSS with radiographic spinopelvic parameters and magnetic resonance imaging (MRI) measurements. MethodsWe screened 371 patients hospitalized for LSS and excluded those with a history of spine surgery. Ultimately, we analyzed the data of 34 patients retrospectively. Two independent members of our research group evaluated the alignment parameters on preoperative spinal radiographs, MRI, and classified the images according to the Pfirrmann grading. The spinopelvic alignment was then compared with the clinical QoL scores Core Outcome Measures Index (COMI) and the Oswestry Disability Index (ODI) as collected by the Spine Tango registry. In addition, the interobserver reliability was analyzed. ResultsThe individual MRI measurements correlated significantly. This correlation could not be found when compared with the spinopelvic parameters on radiographs. Neither the COMI nor the ODI scores showed a significant correlation with the MRI or radiographic imaging. ConclusionsThe severity of LSS related disability according to QoL questionnaires could not be quantified by any MRI or spinopelvic parameter that was measured. There was also no correlation of the MRI and spinopelvic parameters among themselves. Consequently, treatment recommendations for symptomatic LSS should never be based on radiological data only.

Determination of optimal injection current pattern for multichannel transcranial electrical stimulation without individual MRI using multiple head models

Background and ObjectiveMultichannel transcranial electrical stimulation (tES) is widely used to achieve improved stimulation focality. In the multichannel tES, the injection current pattern is generally determined through an optimization process with a finite element (FE) head model extracted from individual magnetic resonance images (MRIs). Although using an individual head model ensures the best outcome, acquiring MRIs of individual subjects in many practical applications is often difficult. Alternatively, a standard head model can be used to determine the optimal injection current pattern to stimulate a specific target; however, this may result in a relatively inaccurate delivery of stimulation current owing to the difference in individual anatomical structures. To address this issue, we propose a new approach for determining the injection current pattern using multiple head models, which can improve the stimulation focality compared to that achieved with a single standard head model. MethodsTwenty FE head models were used to optimize the injection current patterns to stimulate three cortical regions that are widely considered targets for tES. The individual injection current patterns were then averaged to obtain each target's mean injection current pattern. The stimulation focality for each target was then calculated by applying different current patterns (the mean current, individual current, and current from a standard model). ResultsOur results showed that the stimulation focality obtained using the mean injection current pattern was significantly higher than that obtained using the injection current pattern from a standard head model. Additionally, our results demonstrated that a minimum of 13 head models are required to determine mean current pattern, allowing for a higher stimulation focality than when using the current from a standard head model. ConclusionsHence, using multiple head models can provide a viable solution for improving the stimulation efficacy of multichannel tES when individual MRIs are not available.

Outcomes of Activity-Related Lower Extremity Muscle Tears After Application of the British Athletics Muscle Injury Classification: A Systematic Review.

Muscle injury classification and grading systems have been reported for >100 years; yet it offer limited evidence relating the clinical or radiological qualities of a muscle injury to the pathology or clinical outcome. The British Athletics Muscle Injury Classification (BAMIC) incorporates recent predictive features of muscle injuries and provides a precise radiographic framework for clinical prediction and management. To investigate clinical outcomes, particularly time to return to play (RTP), reinjury rate (RIR), and prognostic value of specific magnetic resonance imaging (MRI) findings, of activity-related muscle injuries (tears) in athletes after application of the BAMIC. A search of PubMed (NLM), EMBASE (Ovid), Web of Science (Clarivate), Cochrane Library (Wiley), and ClinicalTrials.gov from the inception date of each database through August 31, 2022, was conducted. Keywords included the BAMIC. All English language studies evaluating clinical outcomes of RTP and RIR after activity-related muscle injuries and where BAMIC was applied were included. A total of 136 articles were identified, and 11 studies met inclusion criteria. Systematic review (PROSPERO: CRD42022353801). Level 2. Two reviewers independently screened studies for eligibility and extracted data. Methodological quality of included study was assessed independently by 2 reviewers with the Newcastle-Ottawa Quality Scale (NOS); 11 good quality studies (4 prospective cohort studies, 7 retrospective cohort studies) with 468 athletes (57 female) and 574 muscle injuries were included. All studies reported a statistically significant relationship between BAMIC grade, BAMIC injury site, and/or combined BAMIC grade and injury site with RTP. A statistically significant increased RIR was reported by BAMIC grade and BAMIC injury site in 2 of 4 and 3 of 4 studies, respectively. The prognostic value of individual MRI criteria was limited. Consistent evidence suggests that BAMIC offers prognostic and therapeutic guidance for clinical outcomes, particularly RTP and RIR, after activity-related muscle injuries in athletes that may be superior to previous muscle injury classification and grading systems.

Optimizing TMS Coil Placement Approaches for Targeting the Dorsolateral Prefrontal Cortex in Depressed Adolescents: An Electric Field Modeling Study.

High-frequency repetitive transcranial magnetic stimulation (rTMS) to the left dorsolateral prefrontal cortex (L-DLPFC) shows promise as a treatment for treatment-resistant depression in adolescents. Conventional rTMS coil placement strategies include the 5 cm, the Beam F3, and the magnetic resonance imaging (MRI) neuronavigation methods. The purpose of this study was to use electric field (E-field) models to compare the three targeting approaches to a computational E-field optimization coil placement method in depressed adolescents. Ten depressed adolescents (4 females, age: 15.9±1.1) participated in an open-label rTMS treatment study and were offered MRI-guided rTMS five times per week over 6-8 weeks. Head models were generated based on individual MRI images, and E-fields were simulated for the four targeting approaches. Results showed a significant difference in the induced E-fields at the L-DLPFC between the four targeting methods (χ2=24.7, p<0.001). Post hoc pairwise comparisons showed that there was a significant difference between any two of the targeting methods (Holm adjusted p<0.05), with the 5 cm rule producing the weakest E-field (46.0±17.4V/m), followed by the F3 method (87.4±35.4V/m), followed by MRI-guided (112.1±14.6V/m), and followed by the computational approach (130.1±18.1V/m). Variance analysis showed that there was a significant difference in sample variance between the groups (K2=8.0, p<0.05), with F3 having the largest variance. Participants who completed the full course of treatment had median E-fields correlated with depression symptom improvement (r=-0.77, p<0.05). E-field models revealed limitations of scalp-based methods compared to MRI guidance, suggesting computational optimization could enhance dose delivery to the target.

Application of Spectral Algorithm Applied to Spatially Registered Bi-Parametric MRI to Predict Prostate Tumor Aggressiveness: A Pilot Study

Background: Current prostate cancer evaluation can be inaccurate and burdensome. Quantitative evaluation of Magnetic Resonance Imaging (MRI) sequences non-invasively helps prostate tumor assessment. However, including Dynamic Contrast Enhancement (DCE) in the examined MRI sequence set can add complications, inducing possible side effects from the IV placement or injected contrast material and prolonging scanning time. More accurate quantitative MRI without DCE and artificial intelligence approaches are needed. Purpose: Predict the risk of developing Clinically Significant (Insignificant) prostate cancer CsPCa (CiPCa) and correlate with the International Society of Urologic Pathology (ISUP) grade using processed Signal to Clutter Ratio (SCR) derived from spatially registered bi-parametric MRI (SRBP-MRI) and thereby enhance non-invasive management of prostate cancer. Methods: This pilot study retrospectively analyzed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRI (Apparent Diffusion Coefficient, High B-value, T2) were resized, translated, cropped, and stitched to form spatially registered SRBP-MRI. Efficacy of noise reduction was tested by regularizing, eliminating principal components (PC), and minimizing elliptical volume from the covariance matrix to optimize the SCR. MRI guided biopsy (MRBx), Systematic Biopsy (SysBx), combination (MRBx + SysBx), or radical prostatectomy determined the ISUP grade for each patient. ISUP grade ≥ 2 (&lt;2) was judged as CsPCa (CiPCa). Linear and logistic regression were fitted to ISUP grade and CsPCa/CiPCa SCR. Correlation Coefficients (R) and Area Under the Curves (AUC) for Receiver Operator Curves (ROC) evaluated the performance. Results: High correlation coefficients (R) (&gt;0.55) and high AUC (=1.0) for linear and/or logistic fit from processed SCR and z-score for SRBP-MRI greatly exceed fits using prostate serum antigen, prostate volume, and patient age (R ~ 0.17). Patients assessed with combined MRBx + SysBx and from individual MRI scanners achieved higher R (DR = 0.207+/−0.118) than all patients used in the fits. Conclusions: In the first study, to date, spectral approaches for assessing tumor aggressiveness on SRBP-MRI have been applied and tested and achieved high values of R and exceptional AUC to fit the ISUP grade and CsPCA/CiPCA, respectively.

Cardiac Magnetic Resonance Imaging in Individuals With Prenatal Alcohol Exposure.

Prenatal alcohol exposure (PAE) has teratogenic effects on numerous body systems including the heart. However, research magnetic resonance imaging (MRI) studies in humans with PAE have thus far been limited to the brain. This study aims to use MRI to examine heart structure and function, brain volumes, and body composition in children and adolescents with PAE. Heart, brain, and abdominal 3T MRI of 17 children, adolescents, and young adults with PAE and 53 unexposed controls was acquired to measure: (1) left ventricular ejection fraction, end-diastolic volume, end-systolic volume, stroke volume, cardiac output, longitudinal strain, circumferential strain, and heart mass; (2) total brain, cerebellum, white matter, grey matter, caudate, thalamus, putamen, and globus pallidus volumes; and (3) subcutaneous fat, visceral fat, muscle fat, and muscle (body composition). Cardiac MRI revealed no abnormalities in the PAE group on evaluation by a paediatric cardiologist and by statistical comparison with a control group. Cardiac parameters in both groups were in line with previous reports, including expected sex- and age-related differences. Cerebellum, caudate, and globus pallidus volumes were all smaller. Body mass index and subcutaneous fat percent were higher in females with PAE relative to control females, but lower in males with PAE relative to control males. Children with PAE did not have abnormalities in MRI-derived measures of cardiac structure or function despite smaller brain volumes and sex-specific differences in body composition relative to healthy controls.

Diagnostic Value of MRI in Placental Adhesive Disorders in Pregnancy

Abstract Background The spectrum of placental adhesive disorders (PAD) forms an important cause for emergency cesarean hysterectomy, requiring an accurate prenatal diagnosis for optimal obstetric management. Purpose The aim of this study was to assess the utility of magnetic resonance imaging (MRI) and to identify the individual MRI features that are most useful in the evaluation of PAD. Materials and Methods This was a retrospective review of the MRI of 24 women with abnormal placentation, confirmed using histopathology/intraoperative findings as the reference standard. Patients were categorized as negative or positive for PAD (placenta accreta, increta, and percreta) on MRI and compared with the reference standard. We assessed the diagnostic performance of MRI and the features that best correlated with the presence of PAD. Results Among the 24 women (mean age: 29.8 years) with risk factors, 16 had PAD (6 accreta, 7 increta, and 3 percreta). There was a history of previous lower segment cesarean section and placenta previa in 14 (87.5%). MRI could identify the presence of PAD in all (100% sensitivity) and its absence in three out of eight patients (37.5% specificity). The features with highest sensitivity were intraplacental dark bands (100%), myometrial thinning/loss of interface with myometrium (100%), placental heterogeneity (75%), and uterine contour abnormality (75%). Conclusion MRI is an important modality for the investigation of PAD in suspected cases, with excellent sensitivity and good accuracy. Identifying the presence of risk factors, low-signal-intensity bands, and thinning/loss of placental–myometrial interface will aid in its diagnosis.

Feasibility of 18F-florzolotau quantification in patients with Alzheimer's disease based on an MRI-free tau PET template.

Quantification of tau accumulation using positron emission tomography (PET) is critical for the diagnosis of Alzheimer's disease (AD). This study aimed to evaluate the feasibility of 18F-florzolotau quantification in patients with AD using a magnetic resonance imaging (MRI)-free tau PET template, since individual high-resolution MRI is costly and not always available in practice. 18F-florzolotau PET and MRI scans were obtained in a discovery cohort including (1) patients within the AD continuum (n = 87), (2) cognitively impaired patients with non-AD (n = 32), and (3) cognitively unimpaired subjects (n = 26). The validation cohort comprised 24 patients with AD. Following MRI-dependent spatial normalization (standard approach) in randomly selected subjects (n = 40) to cover the entire spectrum of cognitive function, selected PET images were averaged to create the 18F-florzolotau-specific template. Standardized uptake value ratios (SUVRs) were calculated in five predefined regions of interest (ROIs). MRI-free and MRI-dependent methods were compared in terms of continuous and dichotomous agreement, diagnostic performances, and associations with specific cognitive domains. MRI-free SUVRs had a high continuous and dichotomous agreement with MRI-dependent measures for all ROIs (intraclass correlation coefficient ≥ 0.980; agreement ≥ 94.5%). Similar findings were observed for AD-related effect sizes, diagnostic performances with respect to categorization across the cognitive spectrum, and associations with cognitive domains. The robustness of the MRI-free approach was confirmed in the validation cohort. The use of an 18F-florzolotau-specific template is a valid alternative to MRI-dependent spatial normalization, improving the clinical generalizability of this second-generation tau tracer. • Regional 18F-florzolotau SUVRs reflecting tau accumulation in the living brains are reliable biomarkers for the diagnosis, differential diagnosis, and assessment of disease severity in patients with AD. • The 18F-florzolotau-specific template is a valid alternative to MRI-dependent spatial normalization, improving the clinical generalizability of this second-generation tau tracer.

Direct localization and delineation of human pedunculopontine nucleus based on a self-supervised magnetic resonance image super-resolution method.

The pedunculopontine nucleus (PPN) is a small brainstem structure and has attracted attention as a potentially effective deep brain stimulation (DBS) target for the treatment of Parkinson's disease (PD). However, the in vivo location of PPN remains poorly described and barely visible on conventional structural magnetic resonance (MR) images due to a lack of high spatial resolution and tissue contrast. This study aims to delineate the PPN on a high-resolution (HR) atlas and investigate the visibility of the PPN in individual quantitative susceptibility mapping (QSM) images. We combine a recently constructed Montreal Neurological Institute (MNI) space unbiased QSM atlas (MuSus-100), with an implicit representation-based self-supervised image super-resolution (SR) technique to achieve an atlas with improved spatial resolution. Then guided by a myelin staining histology human brain atlas, we localize and delineate PPN on the atlas with improved resolution. Furthermore, we examine the feasibility of directly identifying the approximate PPN location on the 3.0-T individual QSM MR images. The proposed SR network produces atlas images with four times the higher spatial resolution (from 1 to 0.25 mm isotropic) without a training dataset. The SR process also reduces artifacts and keeps superb image contrast for further delineating small deep brain nuclei, such as PPN. Using the myelin staining histological atlas as guidance, we first identify and annotate the location of PPN on the T1-weighted (T1w)-QSM hybrid MR atlas with improved resolution in the MNI space. Then, we relocate and validate that the optimal targeting site for PPN-DBS is at the middle-to-caudal part of PPN on our atlas. Furthermore, we confirm that the PPN region can be identified in a set of individual QSM images of 10 patients with PD and 10 healthy young adults. The contrast ratios of the PPN to its adjacent structure, namely the medial lemniscus, on images of different modalities indicate that QSM substantially improves the visibility of the PPN both in the atlas and individual images. Our findings indicate that the proposed SR network is an efficient tool for small-size brain nucleus identification. HR QSM is promising for improving the visibility of the PPN. The PPN can be directly identified on the individual QSM images acquired at the 3.0-T MR scanners, facilitating a direct targeting of PPN for DBS surgery.

A Comparison of Magnetic Resonance Imaging and SPECT-CT Imaging in Complex Spine Pathology: Does SPECT-CT Provide Additional Diagnostic Information Over Magnetic Resonance Imaging?

Retrospective cohort study. Magnetic Resonance Imaging (MRI) is often regarded as the gold standard for spinal pathology, as it provides good structural visualisation. SPECT-CT, however, provides combined structural and functional information. There is a paucity of literature comparing SPECT-CT with MRI in the spine. Our aim was to determine whether SPECT-CT provides additional information to MRI in individuals with complex spinal pathology, including deformity, which altered management. We conducted a retrospective review of all individuals seen at our tertiary spinal unit that were investigated with both MRI and SPECT-CT of the spine between 2007-2020. We reviewed imaging reports, and collated diagnoses, surgical treatment and the relative contributions of MRI and SPECT-CT to management decisions. 104 individuals identified, with a mean age of 30 years (89 females and 15 males). Diagnostic categories were adolescent, adult, and congenital deformity, degenerative pathology, and miscellaneous pathology. MRI returned positive findings in 58 (55.8%), and SPECT-CT in 41 (39.4%) cases. SPECT-CT identified 10 cases of facet joint degeneration, 5 of increased uptake around metalwork suggestive of loosening, 1 pseudoarthrosis, 1 partial failure of fusion and 1 osteoid osteoma which were not reported on MRI, all in individuals who had previously undergone spinal instrumentation. Despite this, SPECT-CT only altered management for 6 individuals (5.8%). MRI is less useful in the setting of previous instrumentation due to metal artefact. Where MRI is inconclusive, particularly in individuals with previous spinal instrumentation, SPECT-CT may provide a diagnosis, but is not recommended as primary imaging.

Comparison of EEG Source Localization Using Simplified and Anatomically Accurate Head Models in Younger and Older Adults.

Accuracy of electroencephalography (EEG) source localization relies on the volume conduction head model. A previous analysis of young adults has shown that simplified head models have larger source localization errors when compared with head models based on magnetic resonance images (MRIs). As obtaining individual MRIs may not always be feasible, researchers often use generic head models based on template MRIs. It is unclear how much error would be introduced using template MRI head models in older adults that likely have differences in brain structure compared to young adults. The primary goal of this study was to determine the error caused by using simplified head models without individual-specific MRIs in both younger and older adults. We collected high-density EEG during uneven terrain walking and motor imagery for 15 younger (22±3 years) and 21 older adults (74±5 years) and obtained [Formula: see text]-weighted MRI for each individual. We performed equivalent dipole fitting after independent component analysis to obtain brain source locations using four forward modeling pipelines with increasing complexity. These pipelines included: 1) a generic head model with template electrode positions or 2) digitized electrode positions, 3) individual-specific head models with digitized electrode positions using simplified tissue segmentation, or 4) anatomically accurate segmentation. We found that when compared to the anatomically accurate individual-specific head models, performing dipole fitting with generic head models led to similar source localization discrepancies (up to 2 cm) for younger and older adults. Co-registering digitized electrode locations to the generic head models reduced source localization discrepancies by ∼ 6 mm. Additionally, we found that source depths generally increased with skull conductivity for the representative young adult but not as much for the older adult. Our results can help inform a more accurate interpretation of brain areas in EEG studies when individual MRIs are unavailable.

Fast Deformable Image Registration for Real-Time Target Tracking During Radiation Therapy Using Cine MRI and Deep Learning

We developed a deep learning (DL) model for fast deformable image registration using 2-dimensional sagittal cine magnetic resonance imaging (MRI) acquired during radiation therapy and evaluated its potential for real-time target tracking compared with conventional image registration methods. Our DL model uses a pair of cine MRI images as input and provides a motion vector field (MVF) as output. The MVF is then applied to align the input images. A retrospective study was conducted to train and evaluate our model using cine MRI data from patients undergoing treatment for abdominal and thoracic tumors. For each treatment fraction, MR-linear accelerator delivery log files, tracking videos, and cine image files were analyzed. Individual MRI frames were temporally sampled to construct a large set of image registration pairs used to evaluate multiple methods. The DL model was optimized using 5-fold cross validation, and model outputs (transformed images and MVFs) using test set images were saved for comparison with 3 conventional registration methods (affine, b-spline, and demons). Evaluation metrics were 3-fold: (1) registration error, (2) MVF stability (both spatial and temporal), and (3) average computation time. We analyzed >21 hours of cine MRI (>629,000 frames) acquired during 86 treatment fractions from 21 patients. In a test set of 10,320 image registration pairs, DL registration outperformed conventional methods in both registration error (affine, b-spline, demons, DL; root mean square error: 0.067, 0.040, 0.036, 0.032; paired t test demons vs DL: t[20]=4.2, P < .001) and computation time per frame (51, 1150, 4583, 8 ms). Among deformable methods, spatial stability of resulting MVFs was comparable; however, the DL model had significantly improved temporal consistency. DL-based image registration can leverage large-scale MR cine data sets to outperform conventional registration methods and is a promising solution for real-time deformable motion estimation in radiation therapy.

Non-functioning pituitary microadenoma in children and adolescents: Is follow-up with diagnostic imaging necessary?

PurposeNo consensus exists regarding follow-up recommendations for suspected pituitary microadenoma in children. To address this knowledge gap, we investigated the growth potential of pituitary solid and cystic lesions <10 mm in children and evaluated the accuracy of magnetic resonance imaging (MRI) measurements.MethodsThe children included were <18 years at first pituitary MRI and radiologically diagnosed with a non-functioning microadenoma or cyst <10 mm. Lesion size at first and latest MRI as well as all individual MRI examinations were re-evaluated.ResultsIn total, 74 children, median age 12 years (range 3–17), had a non-functioning microadenoma, probable microadenoma, or cyst. Of these, 55 underwent repeated MRI (median 3, range 2–7) with a median follow-up of 37 months (range 4–189). None of the pituitary lesions without hormonal disturbances increased significantly during follow-up. Two radiologists agreed that no lesion could be identified in 38/269 (14%) MRI examinations, and in 51/231 (22%) they disagreed about lesion location. In 34/460 (7%) MRI measurements size differed >2 mm, which had been considered significant progression.ConclusionNon-functioning pituitary microadenoma in children has small size variations, often below the spatial resolution of the scanners. We suggest lesions <4 mm only for clinical follow-up, lesions 4–6 mm for MRI after 2 years and ≥7 mm MRI after 1 and 3 years, with clinical follow-up in between. If no progression, further MRI should only be performed after new clinical symptoms or hormonal disturbances.

Muscle adaptations in acute SCI following overground exoskeleton + FES training: A pilot study.

ObjectiveTo evaluate the combined effects of robotic exoskeleton and functional electrical stimulation (FES) training on muscle composition during over-ground gait training in persons with acute spinal cord injury (SCI).DesignRandomized crossover pilot study.SettingInpatient-rehabilitation Hospital.ParticipantsSix individuals with acute SCI.InterventionParticipants were randomized to either receive training with the Ekso® Bionics exoskeleton combined with FES in addition to standard-of-care or standard-of-care alone.Outcome measuresThe main outcome measures for the study were quantified using magnetic resonance imaging (MRI), specifically, lower extremity muscle volume and intramuscular adipose tissue (IMAT). Static balance and fall risk were assessed using the Berg Balance Scale.ResultsSignificant improvements were observed in muscle volume in the exoskeleton intervention group when compared to only standard-of-care (p < 0.001). There was no significant difference between the groups in IMAT even though the intervention group saw a reduction in IMAT that trended towards statistical significance (p = 0.07). Static balance improved in both groups, with greater improvements seen in the intervention group.ConclusionsEarly intervention with robotic exoskeleton may contribute to improved muscle function measured using MRI in individuals with acute SCI.

New MS lesion segmentation with deep residual attention gate U-Net utilizing 2D slices of 3D MR images.

Multiple sclerosis (MS) is an autoimmune disease that causes lesions in the central nervous system of humans due to demyelinating axons. Magnetic resonance imaging (MRI) is widely used for monitoring and measuring MS lesions. Automated methods for MS lesion segmentation have usually been performed on individual MRI scans. Recently, tracking lesion activity for quantifying and monitoring MS disease progression, especially detecting new lesions, has become an important biomarker. In this study, a unique pipeline with a deep neural network that combines U-Net, attention gate, and residual learning is proposed to perform better new MS lesion segmentation using baseline and follow-up 3D FLAIR MR images. The proposed network has a similar architecture to U-Net and is formed from residual units which facilitate the training of deep networks. Networks with fewer parameters are designed with better performance through the skip connections of U-Net and residual units, which facilitate information propagation without degradation. Attention gates also learn to focus on salient features of the target structures of various sizes and shapes. The MSSEG-2 dataset was used for training and testing the proposed pipeline, and the results were compared with those of other proposed pipelines of the challenge and experts who participated in the same challenge. According to the results over the testing set, the lesion-wise F1 and dice scores were obtained as a mean of 48 and 44.30%. For the no-lesion cases, the number of tested and volume of tested lesions were obtained as a mean of 0.148 and 1.488, respectively. The proposed pipeline outperformed 22 proposed pipelines and ranked 8th in the challenge.

Integrated MRI-Immune-Genomic Features Enclose a Risk Stratification Model in Patients Affected by Glioblastoma.

Simple SummaryDespite crucial scientific advances, Glioblastoma (GB) remains a fatal disease with limited therapeutic options and a lack of suitable biomarkers. The unveiled competence of the brain immune system together with the breakthrough advent of immunotherapy has shifted the present translational research on GB towards an immune-focused perspective. Several clinical trials targeting the immunosuppressive GB background are ongoing. So far, results are inconclusive, underpinning our partial understanding of the complex cancer-immune interplay in brain tumors. High throughput Magnetic Resonance (MR) imaging has shown the potential to decipher GB heterogeneity, including pathologic and genomic clues. However, whether distinct GB immune contextures can be deciphered at an imaging scale is still elusive, leaving unattained the non-invasive achievement of prognostic and predictive biomarkers. Along these lines, we integrated genetic, immunopathologic and imaging features in a series of GB patients. Our results suggest that multiparametric approaches might offer new efficient risk stratification models, opening the possibility to intercept the critical events implicated in the dismal prognosis of GB.Background: The aim of the present study was to dissect the clinical outcome of GB patients through the integration of molecular, immunophenotypic and MR imaging features. Methods: We enrolled 57 histologically proven and molecularly tested GB patients (5.3% IDH-1 mutant). Two-Dimensional Free ROI on the Biggest Enhancing Tumoral Diameter (TDFRBETD) acquired by MRI sequences were used to perform a manual evaluation of multiple quantitative variables, among which we selected: SD Fluid Attenuated Inversion Recovery (FLAIR), SD and mean Apparent Diffusion Coefficient (ADC). Characterization of the Tumor Immune Microenvironment (TIME) involved the immunohistochemical analysis of PD-L1, and number and distribution of CD3+, CD4+, CD8+ Tumor Infiltrating Lymphocytes (TILs) and CD163+ Tumor Associated Macrophages (TAMs), focusing on immune-vascular localization. Genetic, MR imaging and TIME descriptors were correlated with overall survival (OS). Results: MGMT methylation was associated with a significantly prolonged OS (median OS = 20 months), while no impact of p53 and EGFR status was apparent. GB cases with high mean ADC at MRI, indicative of low cellularity and soft consistency, exhibited increased OS (median OS = 24 months). PD-L1 and the overall number of TILs and CD163+TAMs had a marginal impact on patient outcome. Conversely, the density of vascular-associated (V) CD4+ lymphocytes emerged as the most significant prognostic factor (median OS = 23 months in V-CD4high vs. 13 months in V-CD4low, p = 0.015). High V-CD4+TILs also characterized TIME of MGMTmeth GB, while p53mut appeared to condition a desert immune background. When individual genetic (MGMTunmeth), MR imaging (mean ADClow) and TIME (V-CD4+TILslow) negative predictors were combined, median OS was 21 months (95% CI, 0–47.37) in patients displaying 0–1 risk factor and 13 months (95% CI 7.22–19.22) in the presence of 2–3 risk factors (p = 0.010, HR = 3.39, 95% CI 1.26–9.09). Conclusion: Interlacing MRI–immune–genetic features may provide highly significant risk-stratification models in GB patients.

Multi sequence average templates for aging and neurodegenerative disease populations

Magnetic resonance image (MRI) processing pipelines use average templates to enable standardization of individual MRIs in a common space. MNI-ICBM152 is currently used as the standard template by most MRI processing tools. However, MNI-ICBM152 represents an average of 152 healthy young adult brains and is vastly different from brains of patients with neurodegenerative diseases. In those populations, extensive atrophy might cause inevitable registration errors when using an average template of young healthy individuals for standardization. Disease-specific templates that represent the anatomical characteristics of the populations can reduce such errors and improve downstream driven estimates. We present multi-sequence average templates for Alzheimer’s Dementia (AD), Fronto-temporal Dementia (FTD), Lewy Body Dementia (LBD), Mild Cognitive Impairment (MCI), cognitively intact and impaired Parkinson’s Disease patients (PD-CIE and PD-CI, respectively), individuals with Subjective Cognitive Impairment (SCI), AD with vascular contribution (V-AD), Vascular Mild Cognitive Impairment (V-MCI), Cognitively Intact Elderly (CIE) individuals, and a human phantom. We also provide separate templates for males and females to allow better representation of the diseases in each sex group.