Quantitative Magnetic Resonance Imaging Data Research Articles

Effect of sirolimus on muscle in inclusion body myositis observed with magnetic resonance imaging and spectroscopy.

Finding sensitive clinical outcome measures has become crucial in natural history studies and therapeutic trials of neuromuscular disorders. Here, we focus on 1-year longitudinal data from quantitative magnetic resonance imaging (MRI) and phosphorus magnetic resonance spectroscopy (31P MRS) in a placebo-controlled study of sirolimus for inclusion body myositis (IBM), also examining their links to functional, strength, and clinical parameters in lower limb muscles. Quantitative MRI and 31P MRS data were collected at 3T from a single site, involving 44 patients (22 on placebo, 22 on sirolimus) at baseline and year-1, and 21 healthy controls. Assessments included fat fraction (FF), contractile cross-sectional area (cCSA), and water T2 in global leg and thigh segments, muscle groups, individual muscles, as well as 31P MRS indices in quadriceps or triceps surae. Analyses covered patient-control comparisons, annual change assessments via standard t-tests and linear mixed models, calculation of standardized response means (SRM), and exploration of correlations between MRI, 31P MRS, functional, strength, and clinical parameters. The quadriceps and gastrocnemius medialis muscles had the highest FF values, displaying notable heterogeneity and asymmetry, particularly in the quadriceps. In the placebo group, the median 1-year FF increase in the quadriceps was 3.2% (P<0.001), whereas in the sirolimus group, it was 0.7% (P=0.033). Both groups experienced a significant decrease in cCSA in the quadriceps after 1year (P<0.001), with median changes of 12.6% for the placebo group and 5.5% for the sirolimus group. Differences in FF and cCSA changes between the two groups were significant (P<0.001). SRM values for FF and cCSA were 1.3 and 1.4 in the placebo group and 0.5 and 0.8 in the sirolimus group, respectively. Water T2 values were highest in the quadriceps muscles of both groups, significantly exceeding control values in both groups (P<0.001) and were higher in the placebo group than in the sirolimus group. After treatment, water T2 increased significantly only in the sirolimus group's quadriceps (P<0.01). Multiple 31P MRS indices were abnormal in patients compared to controls and remained unchanged after treatment. Significant correlations were identified between baseline water T2 and FF at baseline and the change in FF (P<0.001). Additionally, significant correlations were observed between FF, cCSA, water T2, and functional and strength outcome measures. This study has demonstrated that quantitative MRI/31P MRS can discern measurable differences between placebo and sirolimus-treated IBM patients, offering promise for future therapeutic trials in idiopathic inflammatory myopathies such as IBM.

129 Neuroimaging Analysis of Bilateral Thalamic Delivery for Gene Therapy

INTRODUCTION: Axonal transport from the thalamus (an integrative hub for the entire cortex) has been shown to facilitate widespread gene/protein delivery. Here, we report our neuroimaging experience with bilateral thalamic convection-enhanced delivery of adeno-associated virus (AAV) vectors for GM2 gangliosidoses. METHODS: Magnetic resonance imaging (MRI) of eight pediatric patients who underwent bilateral thalamic HEXA/HEXB delivery were retrospectively reviewed. Quantitative three-dimensional volumetric analyses of MRI thalamic signals were used to establish volume of distribution (Vd) versus volume of infusion (Vi). The distance of cannula placement within the thalamus was calculated and validated using the numbers used in the intraoperative ROSA robotic software. Backflow and leakage into surrounding structures were also analyzed. RESULTS: Fourteen thalamic injection volumes were considered in the final analysis. Calculated volume of thalamic infusate signal was up to 43.8% (mean = 21.3±13.3) of the thalamic volume. The Vd/Vi ratio ranged from 0.4-2.7 (mean = 1.9±0.7), depending on infusate volume. Diffusate shape was round to slightly ellipsoid in all patients. Backflow and leakage were encountered in 14.3% and 12.5%, respectively, of thalamic injections. CONCLUSIONS: This study provides the first in-human quantitative MRI data for intrathalamic infusion. The ideal location for the cannula tip was determined to be between the mediodorsal and lateral dorsal nuclei and the ideal trajectory keeps all of the cannula steps within the thalamic boundaries. We also compare these data to published NHP and our own NHP and sheep data. Overall, the Vd/Vi ratios are lower in human subjects (∼2) than in animal subjects (∼3). We hypothesize that this may be due in part to species specific differences in displacement of extracellular fluid versus infusion volume.

More severe supraspinatus tendon degeneration on the contralateral shoulders in patients treated for symptomatic rotator cuff tears compared to healthy controls: a quantitative MRI-based study.

Patients treated for symptomatic rotator cuff tear (RCT) on one shoulder seem to have a higher prevalence of RCT on the contralateral shoulder. To compare the supraspinatus (SSP) tendon and RC muscle properties on the contralateral shoulder in patients after repair surgery to those healthy individuals using quantitative magnetic resonance imaging (MRI). A total of 23 patients treated for RCT (group A) and 23 healthy controls (group B) were recruited. Constant score, visual analog scale score (VAS), and MRI examinations were conducted. The SSP tendon structural status was graded based on the Zlatkin classification and quantified on ultrashort echo time (UTE)-T2* mapping images. Fatty degeneration of RC muscles was classified according to the Goutallier classification and quantified on T2 mapping. The Constant and VAS scores were comparable between groups A and B (all P >0.05). No significant differences were observed in tendon structural status between the two groups (P >0.05). However, significant differences were established in UTE-T2* values of the SSP tendon on the distal subregion between groups A and B (16.4 ± 2.4 ms vs. 14.8 ± 1.2 ms; P = 0.01). Regarding muscle degeneration, no significant differences were displayed in T2 values and Goutallier classification of RC muscles (all P >0.05). Patients with a treated RCT demonstrated inferior SSP tendon in the distal subregion on the contralateral shoulders one year postoperatively compared to that of healthy controls based on quantitative MRI data.

Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth.

Reliably predicting the future spread of brain tumors using imaging data and on a subject-specific basis requires quantifying uncertainties in data, biophysical models of tumor growth, and spatial heterogeneity of tumor and host tissue. This work introduces a Bayesian framework to calibrate the two-/three-dimensional spatial distribution of the parameters within a tumor growth model to quantitative magnetic resonance imaging (MRI) data and demonstrates its implementation in a pre-clinical model of glioma. The framework leverages an atlas-based brain segmentation of grey and white matter to establish subject-specific priors and tunable spatial dependencies of the model parameters in each region. Using this framework, the tumor-specific parameters are calibrated from quantitative MRI measurements early in the course of tumor development in four rats and used to predict the spatial development of the tumor at later times. The results suggest that the tumor model, calibrated by animal-specific imaging data at one time point, can accurately predict tumor shapes with a Dice coefficient 0.89. However, the reliability of the predicted volume and shape of tumors strongly relies on the number of earlier imaging time points used for calibrating the model. This study demonstrates, for the first time, the ability to determine the uncertainty in the inferred tissue heterogeneity and the model-predicted tumor shape.

A Novel Nomogram Based on Quantitative MRI and Clinical Features for the Prediction of Neonatal Intracranial Hypertension.

Intracranial hypertension (ICH) is a serious threat to the health of neonates. However, early and accurate diagnosis of neonatal intracranial hypertension remains a major challenge in clinical practice. In this study, a predictive model based on quantitative magnetic resonance imaging (MRI) data and clinical parameters was developed to identify neonates with a high risk of ICH. Newborns who were suspected of having intracranial lesions were included in our study. We utilized quantitative MRI to obtain the volumetric data of gray matter, white matter, and cerebrospinal fluid. After the MRI examination, a lumbar puncture was performed. The nomogram was constructed by incorporating the volumetric data and clinical features by multivariable logistic regression. The performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve. Clinical parameters and volumetric quantitative MRI data, including postmenstrual age (p = 0.06), weight (p = 0.02), mode of delivery (p = 0.01), and gray matter volume (p = 0.003), were included in and significantly associated with neonatal intracranial hypertension risk. The nomogram showed satisfactory discrimination, with an area under the curve of 0.761. Our results demonstrated that decision curve analysis had promising clinical utility of the nomogram. The nomogram, incorporating clinical and quantitative MRI features, provided an individualized prediction of neonatal intracranial hypertension risk and facilitated decision making guidance for the early diagnosis and treatment for neonatal ICH. External validation from studies using a larger sample size before implementation in the clinical decision-making process is needed.

Determining the Relationship between Mechanical Properties and Quantitative Magnetic Resonance Imaging of Joint Soft Tissues Using Patient-Specific Templates.

To determine whether the mechanical properties of joint soft tissues such as cartilage can be calculated from quantitative magnetic resonance imaging (MRI) data, we investigated whether the mechanical properties of articular cartilage and meniscus scheduled to be resected during arthroplasty are correlated with the T2 relaxation time on quantitative MRI at the same location. Six patients who had undergone knee arthroplasty and seven who had undergone hip arthroplasty were examined. For the knee joint, the articular cartilage and lateral meniscus of the distal lateral condyle of the femur and proximal lateral tibia were examined, while for the hip joint, the articular cartilage above the femoral head was studied. We investigated the relationship between T2 relaxation time by quantitative MRI and stiffness using a hand-made compression tester at 235 locations. The patient-individualized template technique was used to align the two measurement sites. The results showed a negative correlation (from -0.30 to -0.35) in the less severely damaged articular cartilage and meniscus. This indicates that tissue mechanical properties can be calculated from T2 relaxation time, suggesting that quantitative MRI is useful in determining when to start loading after interventional surgery on cartilage tissue and in managing the health of elderly patients.

The differential value of radiomics based on traditional T1-weighted sequences in newborns with hyperbilirubinemia

BackgroundOn the basis of visual-dependent reading method, radiological recognition and assessment of neonatal hyperbilirubinemia (NH) or acute bilirubin encephalopathy (ABE) on conventional magnetic resonance imaging (MRI) sequences are challenging. Prior studies had shown that radiomics was possible to characterize ABE-induced intensity and morphological changes on MRI sequences, and it has emerged as a desirable and promising future in quantitative and objective MRI data extraction. To investigate the utility of radiomics based on T1-weighted sequences for identifying neonatal ABE in patients with hyperbilirubinemia and differentiating between those with NH and the normal controls.MethodsA total of 88 patients with NH were enrolled, including 50 patients with ABE and 38 ABE-negative individuals, and 70 age-matched normal neonates were included as controls. All participants were divided into training and validation cohorts in a 7:3 ratio. Radiomics features extracted from the basal ganglia of T1-weighted sequences on magnetic resonance imaging were evaluated and selected to set up the prediction model using the K-nearest neighbour-based bagging algorithm. A receiver operating characteristic curve was plotted to assess the differentiating performance of the radiomics-based model.ResultsFour of 744 radiomics features were selected for the diagnostic model of ABE. The radiomics model yielded an area under the curve (AUC) of 0.81 and 0.82 in the training and test cohorts, with accuracy, precision, sensitivity, and specificity of 0.82, 0.80, 0.91, and 0.69 and 0.78, 0.8, 0.8, and 0.75, respectively. Six radiomics features were selected in this model to distinguish those with NH from the normal controls. The AUC for the training cohort was 0.97, with an accuracy of 0.92, a precision of 0.92, a sensitivity of 0.93, and a specificity of 0.90. The performance of the radiomics model was confirmed by testing the test cohort, and the AUC, accuracy, precision, sensitivity, and specificity were 0.97, 0.92, 0.96, 0.89, and 0.95, respectively.ConclusionsThe proposed radiomics model based on traditional TI-weighted sequences may be used effectively for identifying ABE and even differentiating patients with NH from the normal controls, which can provide microcosmic information beyond experience-dependent vision and potentially assist in clinical diagnosis and treatment.

A Cellular Ground Truth to Develop MRI Signatures in Glioma Models by Correlative Light Sheet Microscopy and Atlas-Based Coregistration.

Glioblastoma is the most common malignant primary brain tumor with poor overall survival. Magnetic resonance imaging (MRI) is the main imaging modality for glioblastoma but has inherent shortcomings. The molecular and cellular basis of MR signals is incompletely understood. We established a ground truth-based image analysis platform to coregister MRI and light sheet microscopy (LSM) data to each other and to an anatomic reference atlas for quantification of 20 predefined anatomic subregions. Our pipeline also includes a segmentation and quantification approach for single myeloid cells in entire LSM datasets. This method was applied to three preclinical glioma models in male and female mice (GL261, U87MG, and S24), which exhibit different key features of the human glioma. Multiparametric MR data including T2-weighted sequences, diffusion tensor imaging, T2 and T2* relaxometry were acquired. Following tissue clearing, LSM focused on the analysis of tumor cell density, microvasculature, and innate immune cell infiltration. Correlated analysis revealed differences in quantitative MRI metrics between the tumor-bearing and the contralateral hemisphere. LSM identified tumor subregions that differed in their MRI characteristics, indicating tumor heterogeneity. Interestingly, MRI signatures, defined as unique combinations of different MRI parameters, differed greatly between the models. The direct correlation of MRI and LSM allows an in-depth characterization of preclinical glioma and can be used to decipher the structural, cellular, and, likely, molecular basis of tumoral MRI biomarkers. Our approach may be applied in other preclinical brain tumor or neurologic disease models, and the derived MRI signatures could ultimately inform image interpretation in a clinical setting.SIGNIFICANCE STATEMENT We established a histologic ground truth-based approach for MR image analyses and tested this method in three preclinical glioma models exhibiting different features of glioblastoma. Coregistration of light sheet microscopy to MRI allowed for an evaluation of quantitative MRI data in histologically distinct tumor subregions. Coregistration to a mouse brain atlas enabled a regional comparison of MRI parameters with a histologically informed interpretation of the results. Our approach is transferable to other preclinical models of brain tumors and further neurologic disorders. The method can be used to decipher the structural, cellular, and molecular basis of MRI signal characteristics. Ultimately, information derived from such analyses could strengthen the neuroradiological evaluation of glioblastoma as they enhance the interpretation of MRI data.

Clustering patients with vascular cognitive impairment using quantitative magnetic resonance imaging data and a finite mixture model

AbstractBackgroundBlood‐brain barrier (BBB) leakage is a marker of vascular cognitive impairment and dementia (VCID). The advance of in vivo imaging enabled us to quantify the degree of BBB transfer rates in VCID patients. However, a consensus has not been reached on the role of BBB in the differential diagnosis of VCI subtypes. The objective of this work was to use neuroinflammatory and in vivo imaging data on BBB health to identify clusters of VCID patients with distinct underlying pathologies.MethodPatients included were on average 61 (SD = 15.94, M/F = 27/34) years old. The MRI data were collected by using a 1.5 Tesla Siemens Sonata scanner. Pixel‐by‐pixel BBB transfer rate (Ki) was calculated using Patlak compartmental analysis on dynamic contrast‐enhanced T1 maps. The mean BBB transfer rate was calculated for the brain section above the ventricular zone (sampled by six consecutive slices). MR Spectroscopy was used to investigate N‐Acetylaspartate (NAA), Creatine (Cr), and Choline (Cho) levels. Moreover, Active MMP3 data were used in patients clustering. Thirty‐eight patients enrolled in the study have all data for three entities; NAA, mean Ki, and active MMP3 used for clustering. A multi‐parametric model‐based classification method was used to blindly classify VCID data based on Ki, NAA, and active MMPs.ResultBBB leakage increased by age, which is more pronounced in patients diagnosed with cognitive impairment when compared with 17 controls, r2 = 0.060, P = 0.021. Statistical classifiers identified three clusters in VCID patients. Identified VCID clusters are similar to the distinction that was made on this VCID cohort based on the clinical scores and psychological entities that we have published earlier.ConclusionThe results suggest that a statistical combination of imaging and inflammatory attributes renders a tool to blindly separate VCID patients into distinct groups that may have different underlying pathology.

Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma

Tumors exhibit high molecular, phenotypic, and physiological heterogeneity. In this effort, we employ quantitative magnetic resonance imaging (MRI) data to capture this heterogeneity through imaging-based subregions or “habitats” in a murine model of glioma. We then demonstrate the ability to model and predict the growth of the habitats using coupled ordinary differential equations (ODEs) in the presence and absence of radiotherapy. Female Wistar rats (N = 21) were inoculated intracranially with 106 C6 glioma cells, a subset of which received 20 Gy (N = 5) or 40 Gy (N = 8) of radiation. All rats underwent diffusion-weighted and dynamic contrast-enhanced MRI at up to seven time points. All MRI data at each visit were subsequently clustered using k-means to identify physiological tumor habitats. A family of four models consisting of three coupled ODEs were developed and calibrated to the habitat time series of control and treated rats and evaluated for predictive capability. The Akaike Information Criterion was used for model selection, and the normalized sum-of-square-error (SSE) was used to evaluate goodness-of-fit in model calibration and prediction. Three tumor habitats with significantly different imaging data characteristics (p < 0.05) were identified: high-vascularity high-cellularity, low-vascularity high-cellularity, and low-vascularity low-cellularity. Model selection resulted in a five-parameter model whose predictions of habitat dynamics yielded SSEs that were similar to the SSEs from the calibrated model. It is thus feasible to mathematically describe habitat dynamics in a preclinical model of glioma using biology-based ODEs, showing promise for forecasting heterogeneous tumor behavior.

Diagnostic efficacy of diffusion-weighted imaging and semiquantitative and quantitative dynamic contrast-enhanced magnetic resonance imaging in salivary gland tumors.

Increased use of functional magnetic resonance imaging (MRI) methods such as diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI consisting of sequential contrast series, allows us to obtain more information on the microstructure, cellularity, interstitial distance, and vascularity of tumors, which has increased the discrimination power for benign and malignant salivary gland tumors (SGTs). In the last few years, quantitative DCE MRI data containing T1 perfusion parameters (Ktrans, Kep and Ve), were reported to contribute to the differentiation of benign or malignant subtypes in SGTs. To evaluate the diagnostic efficacy of DWI and semiquantitative and quantitative perfusion MRI parameters in SGTs. Diffusion MRI [apparent diffusion coefficient (ADC) value] with a 1.5 T MR machine, semiquantitative perfusion MRI [time intensity curve (TIC) pattern], and quantitative perfusion MRI examinations (Ktrans, Kep and Ve) of 73 tumors in 67 patients with histopathological diagnosis performed from 2017 to 2021 were retrospectively evaluated. In the ADC value and semiquantitative perfusion MRI measurements, cystic components of the tumors were not considered, and the region of interest (ROI) was manually placed through the widest axial section of the tumor. TIC patterns were divided into four groups: Type A = Tpeak > 120 s; type B = Tpeak ≤ 120 s, washout ratio (WR) ≥ 30%; type C = Tpeak ≤ 120 s, WR < 30%; and type D = flat TIC. For the quantitative perfusion MRI analysis, a 3D ROI was placed in the largest solid component of the tumor, and the Ktrans, Kep and Ve values were automatically generated. The majority of SGTs were located in the parotid glands (86.3%). Of all the SGTs, 68.5% were benign and 31.5% were malignant. Significant differences were found for ADC values among pleomorphic adenomas (PMAs), Warthin's tumors (WTs), and malignant tumors (MTs) (P < 0.001). PMAs had type A and WTs had type B TIC pattern while the vast majority of MTs and other benign tumors (OBTs) (54.5% and 45.5%, respectively) displayed type C TIC pattern. PMAs showed no washout, while the highest mean WR was observed in WTs (59% ± 11%). Ktrans values of PMAs, WTs, OBTs, and MTs were not significantly different. Kep values of PMAs and WTs were significantly different from those of OBTs and MTs. Mean Ve value of WTs was significantly different from those of PMAs, OBTs, and MTs (P < 0.001). The use of quantitative DCE parameters along with diffusion MRI and semiquantitative contrast-enhanced MRI in SGTs could improve the diagnostic accuracy.

QMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data

The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI through multicenter dissemination of interoperable datasets. This way, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. In conclusion, this BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the entrance barrier for qMRI in the field of neuroimaging.

Abstract 2736: Forecasting treatment response to neoadjuvant therapy in triple-negative breast cancer via an image-guided digital twin

Abstract Introduction: Patients with locally advanced, triple-negative breast cancer (TNBC) typically receive neoadjuvant therapy (NAT) to downstage the tumor and to improve the outcome of subsequent breast conservation surgery. There are currently no methods to accurately predict how a TNBC patient will respond to NAT before surgery. In this work, we applied a digital twin framework to address this unmet clinical need, by integrating quantitative magnetic resonance imaging (MRI) data with mechanism-based mathematical modeling. Methods: Multiparametric MRI was acquired in patients (N = 50) before, after 2 and 4 cycles of Adriamycin/Cyclophosphamide (A/C), and again after 12 cycles of Paclitaxel as part of the ARTEMIS (NCT02276433) trial. Within each imaging session, dynamic contrast-enhanced (DCE) MRI, diffusion-weighted imaging (DWI), and a pre-contrast T1-map were acquired. The images were processed by a pipeline consisting of motion correction, multiparametric image alignment, inter-visit image registration to align the tumor and surrounding breast tissue, tissue segmentation, and estimation of tumor cellularity from DWI. A mechanism-based mathematical model, a reaction-diffusion equation, is used to characterize the mobility of tumor cells via diffusion damped by mechanical tissue properties, tumor proliferation via logistic growth, and treatment-induced cell death via the delivery and decay of therapies. For each patient, pre-treatment images were used for model initialization. The model calibration and prediction were implemented with two strategies: 1) using images acquired during the A/C for calibration and predicting up to the end of A/C, and 2) using images acquired during and after the A/C for calibration and predicting up to the end of NAT. For strategy 1), we evaluated the model by comparing its predicted tumor volume and total tumor cellularity to the imaging measurements at the end of A/C. For strategy 2), we evaluated the model by comparing its predicted final response to the post-surgical pathological findings. Results: For strategy 1), our framework predicted the change of tumor volume and total tumor cellularity with Pearson correlation coefficients of 0.91 and 0.89, respectively. Regarding strategy 2), our framework achieved an area under the receiver operator characteristic curve of 0.88 for distinguishing pCR from non-pCR. As a comparison, imaging measurement of tumor volume at the end of A/C achieved an AUC of 0.79. Conclusion: Our approach successfully captures the patient-specific dynamics of TNBC response to NAT and provides an improved prediction of final response, which demonstrates the potential of a digital twin framework to be a powerful tool for predicting response to NAT. Once validated, the method will provide a unique opportunity for optimizing treatment plans on a patient-specific basis. Citation Format: Chengyue Wu, Angela M. Jarrett, Zijian Zhou, Nabil Elshafeey, Beatriz E. Adrada, Rosalind P. Candelaria, Rania Mohamed, Medine Boge, Lei Huo, Jason White, Debu Tripathy, Vicente Valero, Jennifer Litton, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane M. Rauch, Thomas E. Yankeelov. Forecasting treatment response to neoadjuvant therapy in triple-negative breast cancer via an image-guided digital twin [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2736.

Restoring statistical validity in group analyses of motion-corrupted MRI data.

Motion during the acquisition of magnetic resonance imaging (MRI) data degrades image quality, hindering our capacity to characterise disease in patient populations. Quality control procedures allow the exclusion of the most affected images from analysis. However, the criterion for exclusion is difficult to determine objectively and exclusion can lead to a suboptimal compromise between image quality and sample size. We provide an alternative, data‐driven solution that assigns weights to each image, computed from an index of image quality using restricted maximum likelihood. We illustrate this method through the analysis of quantitative MRI data. The proposed method restores the validity of statistical tests, and performs near optimally in all brain regions, despite local effects of head motion. This method is amenable to the analysis of a broad type of MRI data and can accommodate any measure of image quality.

Clinical phenotype modulates brain\u2019s myelin and iron content in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is associated with brain pathology extending beyond temporal lobe structures. We sought to look for informative patterns of brain tissue properties in TLE that go beyond the established morphometry differences. We hypothesised that volume differences, particularly in hippocampus, will be paralleled by changes in brain microstructure. The cross-sectional study included TLE patients (n = 25) from a primary care center and sex-/age-matched healthy controls (n = 55). We acquired quantitative relaxometry-based magnetic resonance imaging (MRI) data yielding whole-brain maps of grey matter volume, magnetization transfer (MT) saturation, and effective transverse relaxation rate R2* indicative for brain tissue myelin and iron content. For statistical analysis, we used the computational anatomy framework of voxel-based morphometry and voxel-based quantification. There was a positive correlation between seizure activity and MT saturation measures in the ipsilateral hippocampus, paralleled by volume differences bilaterally. Disease duration correlated positively with iron content in the mesial temporal lobe, while seizure freedom was associated with a decrease of iron in the very same region. Our findings demonstrate the link between TLE clinical phenotype and brain anatomy beyond morphometry differences to show the impact of disease burden on specific tissue properties. We provide direct evidence for the differential effect of clinical phenotype characteristics on processes involving tissue myelin and iron in mesial temporal lobe structures. This study offers a proof-of-concept for the investigation of novel imaging biomarkers in focal epilepsy.

Brain volumetric MRI study in healthy adolescent and young person’s using automated method

Objective: Adolescence is a critical period for the maturation of neurobiological processes that underlie higher cognitive functions and social and emotional behaviour. However, there are limited studies that investigated brain volumes in healthy adolescents and young persons. The aim of this study was to compare the Grey Matter (GM), White Matter (WM) and some specific brain subcortical volumes such as hippocampus and amygdala between healthy adolescents and young groups by using VolBrain.&#x0D; Material and Methods: Magnetic resonance imaging brain scans were retrospectively obtained from 20 healthy adolescent and young subjects. The mean ages of the adolescent and young persons were 13±1 and 24±2, respectively. Brain parenchyma (BP), GM, WM and asymmetry features were calculated using VolBrain, and the GM and WM volumes of each subjects were compared with those of the both groups. The current study to examine whether regional gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), some brain subcortical structures volumes differed between healthy adolescent and young groups. Also, of the whole brain, hemispheres, and hippocampus, amigdala of adolescent and young subject volumes were measured with an automated method.&#x0D; Results: We have observed that the young group was found to have a 4 % less in volume of GM, when compared with adolescent groups.&#x0D; Conclusion: Our data indicate that quantitative structural Magnetic Resonance Imaging (MRI) data of the adolescent brain is important in understanding the age-related human morphological changes.

Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy.

Simple SummaryUsing medical imaging data and computational models, we develop a modeling framework to provide personalized treatment response forecasts to fractionated radiation therapy for individual tumors. We evaluate this approach in an animal model of brain cancer and forecast changes in tumor cellularity and vasculature.Fractionated radiation therapy is central to the treatment of numerous malignancies, including high-grade gliomas where complete surgical resection is often impractical due to its highly invasive nature. Development of approaches to forecast response to fractionated radiation therapy may provide the ability to optimize or adapt treatment plans for radiotherapy. Towards this end, we have developed a family of 18 biologically-based mathematical models describing the response of both tumor and vasculature to fractionated radiation therapy. Importantly, these models can be personalized for individual tumors via quantitative imaging measurements. To evaluate this family of models, rats (n = 7) with U-87 glioblastomas were imaged with magnetic resonance imaging (MRI) before, during, and after treatment with fractionated radiotherapy (with doses of either 2 Gy/day or 4 Gy/day for up to 10 days). Estimates of tumor and blood volume fractions, provided by diffusion-weighted MRI and dynamic contrast-enhanced MRI, respectively, were used to calibrate tumor-specific model parameters. The Akaike Information Criterion was employed to select the most parsimonious model and determine an ensemble averaged model, and the resulting forecasts were evaluated at the global and local level. At the global level, the selected model’s forecast resulted in less than 16.2% error in tumor volume estimates. At the local (voxel) level, the median Pearson correlation coefficient across all prediction time points ranged from 0.57 to 0.87 for all animals. While the ensemble average forecast resulted in increased error (ranging from 4.0% to 1063%) in tumor volume predictions over the selected model, it increased the voxel wise correlation (by greater than 12.3%) for three of the animals. This study demonstrates the feasibility of calibrating a model of response by serial quantitative MRI data collected during fractionated radiotherapy to predict response at the conclusion of treatment.

Impact of Distortion on Local Radiomic Analysis of Quadriceps Based on Quantitative Magnetic Resonance Imaging Data

Impact of Distortion on Local Radiomic Analysis of Quadriceps Based on Quantitative Magnetic Resonance Imaging Data

Automated analysis of cortical volume loss predicts seizure outcomes after frontal lobectomy.

Patients undergoing frontal lobectomy demonstrate lower seizure-freedom rates than patients undergoing temporal lobectomy and several other resective interventions. We attempted to utilize automated preoperative quantitative analysis of focal and global cortical volume loss to develop predictive volumetric indicators of seizure outcome after frontal lobectomy. Ninety patients who underwent frontal lobectomy were stratified based on seizure freedom at a mean follow-up time of 3.5 (standard deviation [SD] 2.5) years. Automated quantitative analysis of cortical volume loss organized by distinct brain region and laterality was performed on preoperative T1-weighted magnetic resonance imaging (MRI) studies. Univariate statistical analysis was used to select potential predictors of seizure freedom. Backward variable selection and multivariate logistical regression were used to develop models to predict seizure freedom. Forty-eight of 90 (53.3%) patients were seizure-free at the last follow-up. Several frontal and extrafrontal brain regions demonstrated statistically significant differences in both volumetric cortical volume loss and volumetric asymmetry between the left and right sides in the seizure-free and non-seizure-free cohorts. A final multivariate logistic model utilizing only preoperative quantitative MRI data to predict seizure outcome was developed with a c-statistic of 0.846. Using both preoperative quantitative MRI data and previously validated clinical predictors of seizure outcomes, we developed a model with a c-statistic of 0.897. This study demonstrates that preoperative cortical volume loss in both frontal and extrafrontal regions can be predictive of seizure outcome after frontal lobectomy, and models can be developed with excellent predictive capabilities using preoperative MRI data. Automated quantitative MRI analysis can be quickly and reliably performed in patients with frontal lobe epilepsy, and further studies may be developed for integration into preoperative risk stratification.

Lean regional muscle volume estimates using explanatory bioelectrical models in healthy subjects and patients with muscle wasting.

BackgroundThe availability of non‐invasive, accessible, and reliable methods for estimating regional skeletal muscle volume is paramount in conditions involving primary and/or secondary muscle wasting. This work aimed at (i) optimizing serial bioelectrical impedance analysis (SBIA) by computing a conductivity constant based on quantitative magnetic resonance imaging (MRI) data and (ii) investigating the potential of SBIA for estimating lean regional thigh muscle volume in patients with severe muscle disorders.MethodsTwenty healthy participants with variable body mass index and 20 patients with idiopathic inflammatory myopathies underwent quantitative MRI. Anatomical images and fat fraction maps were acquired in thighs. After manual muscle segmentation, lean thigh muscle volume (lVMRI) was computed. Subsequently, multifrequency (50 to 350 kHz) serial resistance profiles were acquired between current skin electrodes (i.e. ankle and hand) and voltage electrodes placed on the anterior thigh. In vivo values of the muscle electrical conductivity constant were computed using data from SBIA and MRI gathered in the right thigh of 10 healthy participants. Lean muscle volume (lVBIA) was derived from SBIA measurements using this newly computed constant. Between‐day reproducibility of lVBIA was studied in six healthy participants.ResultsElectrical conductivity constant values ranged from 0.82 S/m at 50 kHz to 1.16 S/m at 350 kHz. The absolute percentage difference between lVBIA and lVMRI was greater at frequencies >270 kHz (P < 0.0001). The standard error of measurement and the intra‐class correlation coefficient for lVBIA computed from measurements performed at 155 kHz (i.e. frequency with minimal difference) against lVMRI were 6.1% and 0.95 in healthy participants and 9.4% and 0.93 in patients, respectively. Between‐day reproducibility of lVBIA was as follows: standard error of measurement = 4.6% (95% confidence interval [3.2, 7.8] %), intra‐class correlation coefficient = 0.98 (95% confidence interval [0.95, 0.99]).ConclusionsThese findings demonstrate a strong agreement of lean muscle volume estimated using SBIA against quantitative MRI in humans, including in patients with severe muscle wasting and fatty degeneration. SBIA shows promises for non‐invasive, fast, and accessible estimation and follow‐up of lean regional skeletal muscle volume for transversal and longitudinal studies.