Pulmonary MRI with hyperpolarized xenon-129 demonstrates novel alterations in gas transfer across the air-blood barrier in asthma.

BACKGROUND: Individuals with asthma can vary widely in clinical presentation, severity, and pathobiology. Hyperpolarized xenon-129 (Xe129) MRI is a novel imaging method to provide 3-D mapping of both ventilation and gas exchange in the human lung. PURPOSE: To evaluate the functional changes in adults with asthma as compared to healthy controls using Xe129 MRI. METHODS: All subjects (20 controls and 20 asthmatics) underwent lung function measurements and Xe129 MRI on the same day. Outcome measures included the pulmonary ventilation defect and transfer of inspired Xe129 into two soluble compartments: tissue and blood. Ten asthmatics underwent Xe129 MRI before and after bronchodilator to test whether gas transfer measures change with bronchodilator effects. RESULTS: Initial analysis of the results revealed striking differences in gas transfer measures based on age, hence we compared outcomes in younger (n = 24, ≤ 35 years) versus older (n = 16, > 45 years) asthmatics and controls. The younger asthmatics exhibited significantly lower Xe129 gas uptake by lung tissue (Asthmatic: 0.98% ± 0.24%, Control: 1.17% ± 0.12%, P = 0.035), and higher Xe129 gas transfer from tissue to the blood (Asthmatic: 0.40 ± 0.10, Control: 0.31% ± 0.03%, P = 0.035) than the younger controls. No significant difference in Xe129 gas transfer was observed in the older group between asthmatics and controls (P > 0.05). No significant change in Xe129 transfer was observed before and after bronchodilator treatment. CONCLUSIONS: By using Xe129 MRI, we discovered heterogeneous alterations of gas transfer that have associations with age. This finding suggests a heretofore unrecognized physiological derangement in the gas/tissue/blood interface in young adults with asthma that deserves further study.

Neurological Effects of Repeated Blast Exposure in Special Operations Personnel.

Exposure to blast overpressure has been a pervasive feature of combat-related injuries. Studies exploring the neurological correlates of repeated low-level blast exposure in career "breachers" demonstrated higher levels of tumor necrosis factor alpha (TNFα) and interleukin (IL)-6 and decreases in IL-10 within brain-derived extracellular vesicles (BDEVs). The current pilot study was initiated in partnership with the U.S. Special Operations Command (USSOCOM) to explore whether neuroinflammation is seen within special operators with prior blast exposure. Data were analyzed from 18 service members (SMs), inclusive of 9 blast-exposed special operators with an extensive career history of repeated blast exposures and 9 controls matched by age and duration of service. Neuroinflammation was assessed utilizing positron emission tomography (PET) imaging with [18F]DPA-714. Serum was acquired to assess inflammatory biomarkers within whole serum and BDEVs. The Blast Exposure Threshold Survey (BETS) was acquired to determine blast history. Both self-report and neurocognitive measures were acquired to assess cognition. Similarity-driven Multi-view Linear Reconstruction (SiMLR) was used for joint analysis of acquired data. Analysis of BDEVs indicated significant positive associations with a generalized blast exposure value (GBEV) derived from the BETS. SiMLR-based analyses of neuroimaging demonstrated exposure-related relationships between GBEV, PET-neuroinflammation, cortical thickness, and volume loss within special operators. Affected brain networks included regions associated with memory retrieval and executive functioning, as well as visual and heteromodal processing. Post hoc assessments of cognitive measures failed to demonstrate significant associations with GBEV. This emerging evidence suggests neuroinflammation may be a key feature of the brain response to blast exposure over a career in operational personnel. The common thread of neuroinflammation observed in blast-exposed populations requires further study.

Lung Volume Dependence and Repeatability of Hyperpolarized 129Xe MRI Gas Uptake Metrics in Healthy Volunteers and Participants with COPD.

Purpose: To assess the effect of lung volume on measured values and repeatability of xenon 129 (129Xe) gas uptake metrics in healthy volunteers and participants with chronic obstructive pulmonary disease (COPD). Materials and Methods: This Health Insurance Portability and Accountability Act-compliant prospective study included data (March 2014-December 2015) from 49 participants (19 with COPD [mean age, 67 years ± 9 (SD)]; nine women]; 25 older healthy volunteers [mean age, 59 years ± 10; 20 women]; and five young healthy women [mean age, 23 years ± 3]). Thirty-two participants underwent repeated 129Xe and same-breath-hold proton MRI at residual volume plus one-third forced vital capacity (RV+FVC/3), with 29 also undergoing one examination at total lung capacity (TLC). The remaining 17 participants underwent imaging at TLC, RV+FVC/3, and residual volume (RV). Signal ratios between membrane, red blood cell (RBC), and gas-phase compartments were calculated using hierarchical iterative decomposition of water and fat with echo asymmetry and least-squares estimation (ie, IDEAL). Repeatability was assessed using coefficient of variation and intraclass correlation coefficient, and volume relationships were assessed using Spearman correlation and Wilcoxon rank sum tests. Results: Gas uptake metrics were repeatable at RV+FVC/3 (intraclass correlation coefficient = 0.88 for membrane/gas; 0.71 for RBC/gas, and 0.88 for RBC/membrane). Relative ratio changes were highly correlated with relative volume changes for membrane/gas (r = -0.97) and RBC/gas (r = -0.93). Membrane/gas and RBC/gas measured at RV+FVC/3 were significantly lower in the COPD group than the corresponding healthy group (P ≤ .001). However, these differences lessened upon correction for individual volume differences (P = .23 for membrane/gas; P = .09 for RBC/gas). Conclusion: Dissolved-phase 129Xe MRI-derived gas uptake metrics were repeatable but highly dependent on lung volume during measurement.Keywords: Blood-Air Barrier, MRI, Chronic Obstructive Pulmonary Disease, Pulmonary Gas Exchange, Xenon Supplemental material is available for this article © RSNA, 2023.

Image- versus histogram-based considerations in semantic segmentation of pulmonary hyperpolarized gas images.

PURPOSE: To characterize the differences between histogram-based and image-based algorithms for segmentation of hyperpolarized gas lung images. METHODS: Four previously published histogram-based segmentation algorithms (ie, linear binning, hierarchical k-means, fuzzy spatial c-means, and a Gaussian mixture model with a Markov random field prior) and an image-based convolutional neural network were used to segment 2 simulated data sets derived from a public (n = 29 subjects) and a retrospective collection (n = 51 subjects) of hyperpolarized 129Xe gas lung images transformed by common MRI artifacts (noise and nonlinear intensity distortion). The resulting ventilation-based segmentations were used to assess algorithmic performance and characterize optimization domain differences in terms of measurement bias and precision. RESULTS: Although facilitating computational processing and providing discriminating clinically relevant measures of interest, histogram-based segmentation methods discard important contextual spatial information and are consequently less robust in terms of measurement precision in the presence of common MRI artifacts relative to the image-based convolutional neural network. CONCLUSIONS: Direct optimization within the image domain using convolutional neural networks leverages spatial information, which mitigates problematic issues associated with histogram-based approaches and suggests a preferred future research direction. Further, the entire processing and evaluation framework, including the newly reported deep learning functionality, is available as open source through the well-known Advanced Normalization Tools ecosystem.

Similarity-driven multi-view embeddings from high-dimensional biomedical data.

Diverse, high-dimensional modalities collected in large cohorts present new opportunities for the formulation and testing of integrative scientific hypotheses. Similarity-driven multi-view linear reconstruction (SiMLR) is an algorithm that exploits inter-modality relationships to transform large scientific datasets into smaller, more well-powered and interpretable low-dimensional spaces. SiMLR contributes an objective function for identifying joint signal, regularization based on sparse matrices representing prior within-modality relationships and an implementation that permits application to joint reduction of large data matrices. We demonstrate that SiMLR outperforms closely related methods on supervised learning problems in simulation data, a multi-omics cancer survival prediction dataset and multiple modality neuroimaging datasets. Taken together, this collection of results shows that SiMLR may be applied to joint signal estimation from disparate modalities and may yield practically useful results in a variety of application domains.

Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign.

PURPOSE: The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. METHODS: Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × 10-3mm2/s (VADC>1.5), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. RESULTS: VADC>1.5 classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with VADC>1.5 ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign-all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited VADC>1.5 ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. CONCLUSION: Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with VADC>1.5 achieving > 90% classification specificity in both internal and validation cohorts. VADC>1.5 exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG.

Single-Session Bronchial Thermoplasty Guided by 129Xe Magnetic Resonance Imaging. A Pilot Randomized Controlled Clinical Trial.

Rationale: Adverse events have limited the use of bronchial thermoplasty (BT) in severe asthma.Objectives: We sought to evaluate the effectiveness and safety of using 129Xe magnetic resonance imaging (129Xe MRI) to prioritize the most involved airways for guided BT.Methods: Thirty subjects with severe asthma were imaged with volumetric computed tomography and 129Xe MRI to quantitate segmental ventilation defects. Subjects were randomized to treatment of the six most involved airways in the first session (guided group) or a standard three-session BT (unguided). The primary outcome was the change in Asthma Quality of Life Questionnaire score from baseline to 12 weeks after the first BT for the guided group compared with after three treatments for the unguided group.Measurements and Main Results: There was no significant difference in quality of life after one guided compared with three unguided BTs (change in Asthma Quality of Life Questionnaire guided = 0.91 [95% confidence interval, 0.28-1.53]; unguided = 1.49 [95% confidence interval, 0.84-2.14]; P = 0.201). After one BT, the guided group had a greater reduction in the percentage of poorly and nonventilated lung from baseline when compared with unguided (-17.2%; P = 0.009). Thirty-three percent experienced asthma exacerbations after one guided BT compared with 73% after three unguided BTs (P = 0.028).Conclusions: Results of this pilot study suggest that similar short-term improvements can be achieved with one BT treatment guided by 129Xe MRI when compared with standard three-treatment-session BT with fewer periprocedure adverse events.

Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features.

Accurate segmentation of different sub-regions of gliomas such as peritumoral edema, necrotic core, enhancing, and non-enhancing tumor core from multimodal MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape of these tumors, segmentation of the sub-regions is challenging. Recent developments using deep learning models has proved its effectiveness in various semantic and medical image segmentation tasks, many of which are based on the U-Net network structure with symmetric encoding and decoding paths for end-to-end segmentation due to its high efficiency and good performance. In brain tumor segmentation, the 3D nature of multimodal MRI poses challenges such as memory and computation limitations and class imbalance when directly adopting the U-Net structure. In this study we aim to develop a deep learning model using a 3D U-Net with adaptations in the training and testing strategies, network structures, and model parameters for brain tumor segmentation. Furthermore, instead of picking one best model, an ensemble of multiple models trained with different hyper-parameters are used to reduce random errors from each model and yield improved performance. Preliminary results demonstrate the effectiveness of this method and achieved the 9th place in the very competitive 2018 Multimodal Brain Tumor Segmentation (BraTS) challenge. In addition, to emphasize the clinical value of the developed segmentation method, a linear model based on the radiomics features extracted from segmentation and other clinical features are developed to predict patient overall survival. Evaluation of these innovations shows high prediction accuracy in both low-grade glioma and glioblastoma patients, which achieved the 1st place in the 2018 BraTS challenge.

In vivo imaging of canine lung deformation: effects of posture, pneumonectomy, and inhaled erythropoietin.

Mechanical stresses on the lung impose the major stimuli for developmental and compensatory lung growth and remodeling. We used computed tomography (CT) to noninvasively characterize the factors influencing lobar mechanical deformation in relation to posture, pneumonectomy (PNX), and exogenous proangiogenic factor supplementation. Post-PNX adult canines received weekly inhalations of nebulized nanoparticles loaded with recombinant human erythropoietin (EPO) or control (empty nanoparticles) for 16 wk. Supine and prone CT were performed at two transpulmonary pressures pre- and post-PNX following treatment. Lobar air and tissue volumes, fractional tissue volume (FTV), specific compliance (Cs), mechanical strains, and shear distortion were quantified. From supine to prone, lobar volume and Cs increased while strain and shear magnitudes generally decreased. From pre- to post-PNX, air volume increased less and FTV and Cs increased more in the left caudal (LCa) than in other lobes. FTV increased most in the dependent subpleural regions, and the portion of LCa lobe that expanded laterally wrapping around the mediastinum. Supine deformation was nonuniform pre- and post-PNX; strains and shear were most pronounced in LCa lobe and declined when prone. Despite nonuniform regional expansion and deformation, post-PNX lobar mechanics were well preserved compared with pre-PNX because of robust lung growth and remodeling establishing a new mechanical equilibrium. EPO treatment eliminated posture-dependent changes in FTV, accentuated the post-PNX increase in FTV, and reduced FTV heterogeneity without altering absolute air or tissue volumes, consistent with improved microvascular blood volume distribution and modestly enhanced post-PNX alveolar microvascular reserves.NEW & NOTEWORTHY Mechanical stresses on the lung impose the major stimuli for lung growth. We used computed tomography to image deformation of the lung in relation to posture, loss of lung units, and inhalational delivery of the growth promoter erythropoietin. Following loss of one lung in adult large animals, the remaining lung expanded and grew while retaining near-normal mechanical properties. Inhalation of erythropoietin promoted more uniform distribution of blood volume within the remaining lung.

Learning image-based spatial transformations via convolutional neural networks: A review.

Recent methodological innovations in deep learning and associated advancements in computational hardware have significantly impacted the various core subfields of quantitative medical image analysis. The generalizability, computational efficiency and open-source availability of deep learning algorithms and related software, particularly those utilizing convolutional neural networks, have produced paradigm shifts within the field. This impact is evident from topical prevalence in the literature, conference and workshop themes and winning methodologies in relevant competitions. In this work, we review the various state-of-the-art approaches to learning and prediction and/or optimizing image transformations using convolutional neural networks. Although of primary importance within the quantitative imaging domain, image registration algorithmic development, in the context of these deep learning strategies, has received comparatively less attention than its counterparts (e.g., image segmentation). Nevertheless, significant progress has been made in this particular subfield which has been presented in various research venues. We contextualize these contributions within the broader scope of deep learning advancements and, in so doing, attempt to facilitate the leveraging and further development of such techniques within the medical imaging research community.

Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3D images.

PURPOSE: Automatic segmentation of organs-at-risk (OARs) is a key step in radiation treatment planning to reduce human efforts and bias. Deep convolutional neural networks (DCNN) have shown great success in many medical image segmentation applications but there are still challenges in dealing with large 3D images for optimal results. The purpose of this study is to develop a novel DCNN method for thoracic OARs segmentation using cropped 3D images. METHODS: To segment the five organs (left and right lungs, heart, esophagus and spinal cord) from the thoracic CT scans, preprocessing to unify the voxel spacing and intensity was first performed, a 3D U-Net was then trained on resampled thoracic images to localize each organ, then the original images were cropped to only contain one organ and served as the input to each individual organ segmentation network. The segmentation maps were then merged to get the final results. The network structures were optimized for each step, as well as the training and testing strategies. A novel testing augmentation with multiple iterations of image cropping was used. The networks were trained on 36 thoracic CT scans with expert annotations provided by the organizers of the 2017 AAPM Thoracic Auto-segmentation Challenge and tested on the challenge testing dataset as well as a private dataset. RESULTS: The proposed method earned second place in the live phase of the challenge and first place in the subsequent ongoing phase using a newly developed testing augmentation approach. It showed superior-than-human performance on average in terms of Dice scores (spinal cord: 0.893 ± 0.044, right lung: 0.972 ± 0.021, left lung: 0.979 ± 0.008, heart: 0.925 ± 0.015, esophagus: 0.726 ± 0.094), mean surface distance (spinal cord: 0.662 ± 0.248 mm, right lung: 0.933 ± 0.574 mm, left lung: 0.586 ± 0.285 mm, heart: 2.297 ± 0.492 mm, esophagus: 2.341 ± 2.380 mm) and 95% Hausdorff distance (spinal cord: 1.893 ± 0.627 mm, right lung: 3.958 ± 2.845 mm, left lung: 2.103 ± 0.938 mm, heart: 6.570 ± 1.501 mm, esophagus: 8.714 ± 10.588 mm). It also achieved good performance in the private dataset and reduced the editing time to 7.5 min per patient following automatic segmentation. CONCLUSIONS: The proposed DCNN method demonstrated good performance in automatic OAR segmentation from thoracic CT scans. It has the potential for eventual clinical adoption of deep learning in radiation treatment planning due to improved accuracy and reduced cost for OAR segmentation.

Deep learning-based quantification of abdominal fat on magnetic resonance images.

Obesity is increasingly prevalent and associated with increased risk of developing type 2 diabetes, cardiovascular diseases, and cancer. Magnetic resonance imaging (MRI) is an accurate method for determination of body fat volume and distribution. However, quantifying body fat from numerous MRI slices is tedious and time-consuming. Here we developed a deep learning-based method for measuring visceral and subcutaneous fat in the abdominal region of mice. Congenic mice only differ from C57BL/6 (B6) Apoe knockout (Apoe-/-) mice in chromosome 9 that is replaced by C3H/HeJ genome. Male congenic mice had lighter body weight than B6-Apoe-/- mice after being fed 14 weeks of Western diet. Axial and coronal T1-weighted sequencing at 1-mm-thickness and 1-mm-gap was acquired with a 7T Bruker ClinScan scanner. A deep learning approach was developed for segmenting visceral and subcutaneous fat based on the U-net architecture made publicly available through the open-source ANTsRNet library-a growing repository of well-known neural networks. The volumes of subcutaneous and visceral fat measured through our approach were highly comparable with those from manual measurements. The Dice score, root-mean-square error (RMSE), and correlation analysis demonstrated the similarity between two methods in quantifying visceral and subcutaneous fat. Analysis with the automated method showed significant reductions in volumes of visceral and subcutaneous fat but not non-fat tissues in congenic mice compared to B6 mice. These results demonstrate the accuracy of deep learning in quantification of abdominal fat and its significance in determining body weight.

Use of hyperpolarized helium-3 MRI to assess response to ivacaftor treatment in patients with cystic fibrosis.

BACKGROUND: This pilot study evaluated the effect of short- and long-term ivacaftor treatment on hyperpolarized 3He-magnetic resonance imaging (MRI)-defined ventilation defects in patients with cystic fibrosis aged ≥12years with a G551D-CFTR mutation. METHODS: Part A (single-blind) comprised 4weeks of ivacaftor treatment; Part B (open-label) comprised 48weeks of treatment. The primary outcome was change from baseline in total ventilation defect (TVD; total defect volume:total lung volume ratio). RESULTS: Mean change in TVD ranged from -8.2% (p=0.0547) to -12.8% (p=0.0078) in Part A (n=8) and -6.3% (p=0.1953) to -9.0% (p=0.0547) in Part B (n=8) as assessed by human reader and computer algorithm, respectively. CONCLUSIONS: TVD responded to ivacaftor therapy. 3He-MRI provides an individual quantification of disease burden that may be able to detect aspects of the disease missed by population-based spirometry metrics. Assessments by human reader and computer algorithm exhibit similar trends, but the latter appears more sensitive. www.clinicaltrials.gov identifier: NCT01161537.

Semiautomatic segmentation of longitudinal computed tomography images in a rat model of lung injury by surfactant depletion.

Quantitative analysis of computed tomography (CT) is essential to the study of acute lung injury. However, quantitative CT is made difficult by poor lung aeration, which complicates the critical step of image segmentation. To overcome this obstacle, this study sought to develop and validate a semiautomated, multilandmark, registration-based scheme for lung segmentation that is effective in conditions of poor aeration. Expiratory and inspiratory CT images were obtained in rats (n = 8) with surfactant depletion of incremental severity to mimic worsening aeration. Trained operators manually delineated the images to provide a comparative landmark. Semiautomatic segmentation originated from a single, previously segmented reference image obtained at healthy baseline. Deformable registration of the target images (after surfactant depletion) was performed using the symmetric diffeomorphic transformation model with B-spline regularization. Registration used multiple landmarks (i.e., rib cage, spine, and lung parenchyma) to minimize the effect of poor aeration. Then target images were automatically segmented by applying the calculated transformation function to the reference image contour. Semiautomatically and manually segmented contours proved to be highly similar in all aeration conditions, including those characterized by more severe surfactant depletion and expiration. The Dice similarity coefficient was over 0.9 in most conditions, confirming high agreement, irrespective of poor aeration. Furthermore, CT density-based measurements of gas volume, tissue mass, and lung aeration distribution were minimally affected by the method of segmentation. Moving forward, multilandmark registration has the potential to streamline quantitative CT analysis by enabling semiautomatic image segmentation of lungs with a broad range of injury severity.

Rapid acquisition of helium-3 and proton three-dimensional image sets of the human lung in a single breath-hold using compressed sensing.

PURPOSE: To develop and validate a method for acquiring helium-3 ((3) He) and proton ((1) H) three-dimensional (3D) image sets of the human lung with isotropic spatial resolution within a 10-s breath-hold by using compressed sensing (CS) acceleration, and to assess the fidelity of undersampled images compared with fully sampled images. METHODS: The undersampling scheme for CS acceleration was optimized and tested using (3) He ventilation data. Rapid 3D acquisition of both (3) He and (1) H data during one breath-hold was then implemented, based on a balanced steady-state free-precession pulse sequence, by random undersampling of k-space with reconstruction by means of minimizing the L1 norm and total variance. CS-reconstruction fidelity was evaluated quantitatively by comparing fully sampled and retrospectively undersampled image sets. RESULTS: Helium-3 and (1) H 3D image sets of the lung with isotropic 3.9-mm resolution were acquired during a single breath-hold in 12 s and 8 s using acceleration factors of 2 and 3, respectively. Comparison of fully sampled and retrospectively undersampled (3) He and (1) H images yielded mean absolute errors <10% and structural similarity indices >0.9. CONCLUSION: By randomly undersampling k-space and using CS reconstruction, high-quality (3) He and (1) H 3D image sets with isotropic 3.9-mm resolution can be acquired within an 8-s breath-hold.

Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR.

Segmenting and quantifying gliomas from MRI is an important task for diagnosis, planning intervention, and for tracking tumor changes over time. However, this task is complicated by the lack of prior knowledge concerning tumor location, spatial extent, shape, possible displacement of normal tissue, and intensity signature. To accommodate such complications, we introduce a framework for supervised segmentation based on multiple modality intensity, geometry, and asymmetry feature sets. These features drive a supervised whole-brain and tumor segmentation approach based on random forest-derived probabilities. The asymmetry-related features (based on optimal symmetric multimodal templates) demonstrate excellent discriminative properties within this framework. We also gain performance by generating probability maps from random forest models and using these maps for a refining Markov random field regularized probabilistic segmentation. This strategy allows us to interface the supervised learning capabilities of the random forest model with regularized probabilistic segmentation using the recently developed ANTsR package--a comprehensive statistical and visualization interface between the popular Advanced Normalization Tools (ANTs) and the R statistical project. The reported algorithmic framework was the top-performing entry in the MICCAI 2013 Multimodal Brain Tumor Segmentation challenge. The challenge data were widely varying consisting of both high-grade and low-grade glioma tumor four-modality MRI from five different institutions. Average Dice overlap measures for the final algorithmic assessment were 0.87, 0.78, and 0.74 for "complete", "core", and "enhanced" tumor components, respectively.

Multimodal MR imaging model to predict tumor infiltration in patients with gliomas.

INTRODUCTION: Gliomas remain difficult to treat, in part, due to our inability to accurately delineate the margins of the tumor. The goal of our study was to evaluate if a combination of advanced MR imaging techniques and a multimodal imaging model could be used to predict tumor infiltration in patients with diffuse gliomas. METHODS: Institutional review board approval and written consent were obtained. This prospective pilot study enrolled patients undergoing stereotactic biopsy for a suspected de novo glioma. Stereotactic biopsy coordinates were coregistered with multiple standard and advanced neuroimaging sequences in 10 patients. Objective imaging values were assigned to the biopsy sites for each of the imaging sequences. A principal component analysis was performed to reduce the dimensionality of the imaging dataset without losing important information. A univariate analysis was performed to identify the statistically relevant principal components. Finally, a multivariate analysis was used to build the final model describing nuclear density. RESULTS: A univariate analysis identified three principal components as being linearly associated with the observed nuclear density (p values 0.021, 0.016, and 0.046, respectively). These three principal component composite scores are predominantly comprised of DTI (mean diffusivity or average diffusion coefficient and fractional anisotropy) and PWI data (rMTT, Ktrans). The p value of the model was <0.001. The correlation between the predicted and observed nuclear density was 0.75. CONCLUSION: A multi-input, single output imaging model may predict the extent of glioma invasion with significant correlation with histopathology.

Ventilation-based segmentation of the lungs using hyperpolarized (3)He MRI.

PURPOSE: To develop an automated segmentation method to differentiate the ventilated lung volume on (3) He magnetic resonance imaging (MRI). MATERIALS AND METHODS: Computational processing (CP) for each subject consisted of the following three essential steps: 1) inhomogeneity bias correction, 2) whole lung segmentation, and 3) subdivision of the lung segmentation into regions of similar ventilation. Evaluation consisted of two comparative analyses: i) comparison of the number of defects scored by two human readers in 43 subjects, and ii) simultaneous truth and performance level estimation (STAPLE) in 18 subjects in which the ventilation defects were manually segmented by four human readers. RESULTS: There was excellent correlation between the number of ventilation defects tabulated by CP and reader #1 (intraclass correlation coefficient [ICC] = 0.86), CP and reader #2 (ICC = 0.85), and between the two readers (ICC = 0.97). The STAPLE results from the second analysis yielded the following sensitivity/specificity numbers: CP (0.898/0.905), radiologist #1 (0.743/0.897), radiologist #2 (0.501/0.985), radiologist #3 (0.898/0.848), and the first author (0.600/0.984). CONCLUSION: We developed and evaluated an automated method for quantifying the ventilated lung volume on (3) He MRI. The findings strongly indicate that our proposed algorithmic processing may be a reliable, automatic method for quantitating ventilation defects.

Progressive adaptation in regional parenchyma mechanics following extensive lung resection assessed by functional computed tomography.

In adult canines following major lung resection, the remaining lobes expand asymmetrically, associated with alveolar tissue regrowth, remodeling, and progressive functional compensation over many months. To permit noninvasive longitudinal assessment of regional growth and function, we performed serial high-resolution computed tomography (HRCT) on six male dogs (∼9 mo old, 25.0 ± 4.5 kg, ±SD) at 15 and 30 cmH(2)O transpulmonary pressure (Ptp) before resection (PRE) and 3 and 15 mo postresection (POST3 and POST15, respectively) of 65-70% of lung units. At POST3, lobar air volume increased 83-148% and tissue (including microvascular blood) volume 120-234% above PRE values without further changes at POST15. Lobar-specific compliance (Cs) increased 52-137% from PRE to POST3 and 28-79% from POST3 to POST15. Inflation-related parenchyma strain and shear were estimated by detailed registration of corresponding anatomical features at each Ptp. Within each lobe, regional displacement was most pronounced at the caudal region, whereas strain was pronounced in the periphery. Regional three-dimensional strain magnitudes increased heterogeneously from PRE to POST3, with further medial-lateral increases from POST3 to POST15. Lobar principal strains (PSs) were unchanged or modestly elevated postresection; changes in lobar maximum PS correlated inversely with changes in lobar air and tissue volumes. Lobar shear distortion increased in coronal and transverse planes at POST3 without further changes thereafter. These results establish a novel use of functional HRCT to map heterogeneous regional deformation during compensatory lung growth and illustrate a stimulus-response feedback loop whereby postresection mechanical stress initiates differential lobar regrowth and sustained remodeling, which in turn, relieves parenchyma stress and strain, resulting in progressive increases in lobar Cs and a delayed increase in whole lung Cs.