APOE4 leads to blood-brain barrier dysfunction predicting cognitive decline.

Vascular contributions to dementia and Alzheimer's disease are increasingly recognized1-6. Recent studies have suggested that breakdown of the blood-brain barrier (BBB) is an early biomarker of human cognitive dysfunction7, including the early clinical stages of Alzheimer's disease5,8-10. The E4 variant of apolipoprotein E (APOE4), the main susceptibility gene for Alzheimer's disease11-14, leads to accelerated breakdown of the BBB and degeneration of brain capillary pericytes15-19, which maintain BBB integrity20-22. It is unclear, however, whether the cerebrovascular effects of APOE4 contribute to cognitive impairment. Here we show that individuals bearing APOE4 (with the ε3/ε4 or ε4/ε4 alleles) are distinguished from those without APOE4 (ε3/ε3) by breakdown of the BBB in the hippocampus and medial temporal lobe. This finding is apparent in cognitively unimpaired APOE4 carriers and more severe in those with cognitive impairment, but is not related to amyloid-ß or tau pathology measured in cerebrospinal fluid or by positron emission tomography23. High baseline levels of the BBB pericyte injury biomarker soluble PDGFRß7,8 in the cerebrospinal fluid predicted future cognitive decline in APOE4 carriers but not in non-carriers, even after controlling for amyloid-ß and tau status, and were correlated with increased activity of the BBB-degrading cyclophilin A-matrix metalloproteinase-9 pathway19 in cerebrospinal fluid. Our findings suggest that breakdown of the BBB contributes to APOE4-associated cognitive decline independently of Alzheimer's disease pathology, and might be a therapeutic target in APOE4 carriers.

The effect of prolonged spaceflight on cerebrospinal fluid and perivascular spaces of astronauts and cosmonauts.

Long-duration spaceflight induces changes to the brain and cerebrospinal fluid compartments and visual acuity problems known as spaceflight-associated neuro-ocular syndrome (SANS). The clinical relevance of these changes and whether they equally affect crews of different space agencies remain unknown. We used MRI to analyze the alterations occurring in the perivascular spaces (PVS) in NASA and European Space Agency astronauts and Roscosmos cosmonauts after a 6-mo spaceflight on the International Space Station (ISS). We found increased volume of basal ganglia PVS and white matter PVS (WM-PVS) after spaceflight, which was more prominent in the NASA crew than the Roscosmos crew. Moreover, both crews demonstrated a similar degree of lateral ventricle enlargement and decreased subarachnoid space at the vertex, which was correlated with WM-PVS enlargement. As all crews experienced the same environment aboard the ISS, the differences in WM-PVS enlargement may have been due to, among other factors, differences in the use of countermeasures and high-resistive exercise regimes, which can influence brain fluid redistribution. Moreover, NASA astronauts who developed SANS had greater pre- and postflight WM-PVS volumes than those unaffected. These results provide evidence for a potential link between WM-PVS fluid and SANS.

Automated segmentation of epilepsy surgical resection cavities: Comparison of four methods to manual segmentation.

Accurate resection cavity segmentation on MRI is important for neuroimaging research involving epilepsy surgical outcomes. Manual segmentation, the gold standard, is highly labour intensive. Automated pipelines are an efficient potential solution; however, most have been developed for use following temporal epilepsy surgery. Our aim was to compare the accuracy of four automated segmentation pipelines following surgical resection in a mixed cohort of subjects following temporal or extra temporal epilepsy surgery. We identified 4 open-source automated segmentation pipelines. Epic-CHOP and ResectVol utilise SPM-12 within MATLAB, while Resseg and Deep Resection utilise 3D U-net convolutional neural networks. We manually segmented the resection cavity of 50 consecutive subjects who underwent epilepsy surgery (30 temporal, 20 extratemporal). We calculated Dice similarity coefficient (DSC) for each algorithm compared to the manual segmentation. No algorithm identified all resection cavities. ResectVol (n = 44, 88 %) and Epic-CHOP (n = 42, 84 %) were able to detect more resection cavities than Resseg (n = 22, 44 %, P < 0.001) and Deep Resection (n = 23, 46 %, P < 0.001). The SPM-based pipelines (Epic-CHOP and ResectVol) performed better than the deep learning-based pipelines in the overall and extratemporal surgery cohorts. In the temporal cohort, the SPM-based pipelines had higher detection rates, however there was no difference in the accuracy between methods. These pipelines could be applied to machine learning studies of outcome prediction to improve efficiency in pre-processing data, however human quality control is still required.

Automated Methods for Detecting and Quantitation of Enlarged Perivascular spaces on MRI.

The brain's glymphatic system is a network of intracerebral vessels that function to remove "waste products" such as degraded proteins from the brain. It comprises of the vasculature, perivascular spaces (PVS), and astrocytes. Poor glymphatic function has been implicated in numerous diseases; however, its contribution is still unknown. Efforts have been made to image the glymphatic system to further assess its role in the pathogenesis of different diseases. Numerous imaging modalities have been utilized including two-photon microscopy and contrast-enhanced magnetic resonance imaging (MRI). However, these are associated with limitations for clinical use. PVS form a part of the glymphatic system and can be visualized on standard MRI sequences when enlarged. It is thought that PVS become enlarged secondary to poor glymphatic drainage of metabolites. Thus, quantitating PVS could be a good surrogate marker for glymphatic function. Numerous manual rating scales have been developed to measure the PVS number and size on MRI scans; however, these are associated with many limitations. Instead, automated methods have been created to measure PVS more accurately in different diseases. In this review, we discuss the imaging techniques currently available to visualize the glymphatic system as well as the automated methods currently available to measure PVS, and the strengths and limitations associated with each technique. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

Topographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy.

Temporal lobe epilepsy, a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter structural alterations in temporal lobe epilepsy relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry; or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multisite ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 temporal lobe epilepsy patients and 1418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in temporal lobe epilepsy, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity calculated using 207 healthy controls obtained from Human Connectome Project and an independent dataset containing 23 temporal lobe epilepsy patients and 53 healthy controls and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of temporal lobe epilepsy-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of temporal lobe epilepsy and may inform future discovery and validation of complementary MRI biomarkers in temporal lobe epilepsy.

Machine learning approaches for imaging-based prognostication of the outcome of surgery for mesial temporal lobe epilepsy.

OBJECTIVES: Around 30% of patients undergoing surgical resection for drug-resistant mesial temporal lobe epilepsy (MTLE) do not obtain seizure freedom. Success of anterior temporal lobe resection (ATLR) critically depends on the careful selection of surgical candidates, aiming at optimizing seizure freedom while minimizing postoperative morbidity. Structural MRI and FDG-PET neuroimaging are routinely used in presurgical assessment and guide the decision to proceed to surgery. In this study, we evaluate the potential of machine learning techniques applied to standard presurgical MRI and PET imaging features to provide enhanced prognostic value relative to current practice. METHODS: Eighty two patients with drug resistant MTLE were scanned with FDG-PET pre-surgery and T1-weighted MRI pre- and postsurgery. From these images the following features of interest were derived: volume of temporal lobe (TL) hypometabolism, % of extratemporal hypometabolism, presence of contralateral TL hypometabolism, presence of hippocampal sclerosis, laterality of seizure onset volume of tissue resected and % of temporal lobe hypometabolism resected. These measures were used as predictor variables in logistic regression, support vector machines, random forests and artificial neural networks. RESULTS: In the study cohort, 24 of 82 (28.3%) who underwent an ATLR for drug-resistant MTLE did not achieve Engel Class I (i.e., free of disabling seizures) outcome at a minimum of 2 years of postoperative follow-up. We found that machine learning approaches were able to predict up to 73% of the 24 ATLR surgical patients who did not achieve a Class I outcome, at the expense of incorrect prediction for up to 31% of patients who did achieve a Class I outcome. Overall accuracies ranged from 70% to 80%, with an area under the receiver operating characteristic curve (AUC) of .75-.81. We additionally found that information regarding overall extent of both total and significantly hypometabolic tissue resected was crucial to predictive performance, with AUC dropping to .59-.62 using presurgical information alone. Incorporating the laterality of seizure onset and the choice of machine learning algorithm did not significantly change predictive performance. SIGNIFICANCE: Collectively, these results indicate that "acceptable" to "good" patient-specific prognostication for drug-resistant MTLE surgery is feasible with machine learning approaches utilizing commonly collected imaging modalities, but that information on the surgical resection region is critical for optimal prognostication.

Diffusion Imaging Reveals Sex Differences in the White Matter Following Sports-Related Concussion.

Sports-related concussion (SRC) is a serious health concern. However, the temporal profile of neuropathophysiological changes after SRC and how these relate to biological sex are still poorly understood. This preliminary study investigated whether diffusion-weighted magnetic resonance imaging (dMRI) was sensitive to neuropathophysiological changes following SRC; whether these changes were sex-specific; and whether they persisted beyond the resolution of self-reported symptoms. Recently concussed athletes (n = 14), and age- and education-matched nonconcussed control athletes (n = 16), underwent MRI 24-48-h postinjury and again at 2-week postinjury (i.e., when cleared to return-to-play). Male athletes reported more symptoms and greater symptom severity compared with females. dMRI revealed white matter differences between athletes with SRC and their nonconcussed counterparts at 48-h postinjury. These differences were still present at 2-week postinjury, despite SRC athletes being cleared to return to play and may indicate increased cerebral vulnerability beyond the resolution of subjective symptoms. Furthermore, we identified sex-specific differences, with male SRC athletes having significantly greater white matter disruption compared with female SRC athletes. These results have important implications for the management of concussion, including guiding return-to-play decisions, and further improve our understanding regarding the role of sex in SRC outcomes.

White and Gray Matter Abnormalities in Australian Footballers With a History of Sports-Related Concussion: An MRI Study.

Sports-related concussion (SRC) is a form of mild traumatic brain injury that has been linked to long-term neurological abnormalities. Australian rules football is a collision sport with wide national participation and is growing in popularity worldwide. However, the chronic neurological consequences of SRC in Australian footballers remain poorly understood. This study investigated the presence of brain abnormalities in Australian footballers with a history of sports-related concussion (HoC) using multimodal MRI. Male Australian footballers with HoC (n = 26), as well as noncollision sport athletes with no HoC (n = 27), were recruited to the study. None of the footballers had sustained a concussion in the preceding 6 months, and all players were asymptomatic. Data were acquired using a 3T MRI scanner. White matter integrity was assessed using diffusion tensor imaging. Cortical thickness, subcortical volumes, and cavum septum pellucidum (CSP) were analyzed using structural MRI. Australian footballers had evidence of widespread microstructural white matter damage and cortical thinning. No significant differences were found regarding subcortical volumes or CSP. These novel findings provide evidence of persisting white and gray matter abnormalities in Australian footballers with HoC, and raise concerns related to the long-term neurological health of these athletes.

Microstructural correlates of 23Na relaxation in human brain at 7 Tesla.

23Na provides the second strongest MR-observable signal in biological tissue and exhibits bi-exponential T2∗ relaxation in micro-environments such as the brain. There is significant interest in developing 23Na biomarkers for neurological diseases that are associated with sodium channel dysfunction such as multiple sclerosis and epilepsy. We have previously reported methods for acquisition of multi-echo sodium MRI and continuous distribution modelling of sodium relaxation properties as surrogate markers of brain microstructure. This study aimed to compare 23Na T2∗ relaxation times to more established measures of tissue microstructure derived from advanced diffusion MRI at 7 â€‹T. Six healthy volunteers were scanned using a 3D multi-echo radial ultra-short TE sequence using a dual-tuned 1H/23Na birdcage coil, and a high-resolution multi-shell, high angular resolution diffusion imaging sequence using a 32-channel 1H receive coil. 23Na T2∗ relaxation parameters [mean T2∗ (T2∗mean) and fast relaxation fraction (T2∗ff)] were calculated from a voxel-wise continuous gamma distribution signal model. White matter (restricted anisotropic diffusion) and grey matter (restricted isotropic diffusion) density were calculated from multi-shell multi-tissue constrained spherical deconvolution. Sodium parameters were compared with white and grey matter diffusion properties. Sodium T2∗mean and T2∗ff showed little variation across a range of white matter axonal fibre and grey matter densities. We conclude that sodium T2∗ relaxation parameters are not greatly influenced by relative differences in intra- and extracellular partial volumes. We suggest that care be taken when interpreting sodium relaxation changes in terms of tissue microstructure in healthy tissue.

Treatment of symptomatic fibroid disease using uterine fibroid embolisation: An Australian perspective.

Uterine leiomyomata (fibroids) are symptomatic in up to 35% of women and treatment can be a costly burden to the individual and society. Options for treatment range from non-hormonal, hormonal, minimally invasive, to surgery. While symptoms from smaller fibroids may respond to simple treatment, those with larger fibroids or with a large volume of disease require a more definitive option. Surgery (hysterectomy or myomectomy) are both well-established treatment modalities with good clinical outcomes. Since the 1990s, uterine fibroid embolisation has emerged as a less invasive option for women than for surgical techniques, while level 1 evidence shows that in the short to mid-term, there is a similar improvement in symptom-related quality of life outcomes to surgery, but with reduced hospital stay and reduced cost. However, in the longer term there may be a need for further treatment or retreatment in some patients compared with surgery. Since its introduction, uptake of this procedure in Australia has been low relative to surgical options. This manuscript reviews the current literature surrounding treatment, along with the trends in uptake of embolisation by Australian women, places this in context of current guidelines from major societies, and encourages gynaecologists and interventional radiologists to be aware of the advantages and limitations of embolisation.

Perivascular space fluid contributes to diffusion tensor imaging changes in white matter.

Diffusion tensor imaging (DTI) has been extensively used to map changes in brain tissue related to neurological disorders. Among the most widespread DTI findings are increased mean diffusivity and decreased fractional anisotropy of white matter tissue in neurodegenerative diseases. Here we utilize multi-shell diffusion imaging to separate diffusion signal of the brain parenchyma from non-parenchymal fluid within the white matter. We show that unincorporated anisotropic water in perivascular space (PVS) significantly, and systematically, biases DTI measures, casting new light on the biological validity of many previously reported findings. Despite the challenge this poses for interpreting these past findings, our results suggest that multi-shell diffusion MRI provides a new opportunity for incorporating the PVS contribution, ultimately strengthening the clinical and scientific value of diffusion MRI.

Undetectable gadolinium brain retention in individuals with an age-dependent blood-brain barrier breakdown in the hippocampus and mild cognitive impairment.

INTRODUCTION: Blood-brain barrier (BBB) breakdown is an early independent biomarker of human cognitive dysfunction, as found using gadolinium (Gd) as a contrast agent. Whether Gd accumulates in brains of individuals with an age-dependent BBB breakdown and/or mild cognitive impairment remains unclear. METHODS: We analyzed T1-weighted magnetic resonance imaging (MRI) scans from 52 older participants with BBB breakdown in the hippocampus 19-28 months after either cyclic or linear Gd agent. RESULTS: There was no change in T1-weighted signal intensity between the baseline contrast MRI and unenhanced MRI on re-examination in any of the studied 10 brain regions with either Gd agent suggesting undetectable Gd brain retention. DISCUSSION: Gd does not accumulate in brains of older individuals with a BBB breakdown in the hippocampus. Thus, Gd agents can be used without risk of brain retention within a ∼2-year follow-up to study BBB in the aging human brain in relation to cognition and/or other pathologies.

Vascular dysfunction-The disregarded partner of Alzheimer's disease.

Increasing evidence recognizes Alzheimer's disease (AD) as a multifactorial and heterogeneous disease with multiple contributors to its pathophysiology, including vascular dysfunction. The recently updated AD Research Framework put forth by the National Institute on Aging-Alzheimer's Association describes a biomarker-based pathologic definition of AD focused on amyloid, tau, and neuronal injury. In response to this article, here we first discussed evidence that vascular dysfunction is an important early event in AD pathophysiology. Next, we examined various imaging sequences that could be easily implemented to evaluate different types of vascular dysfunction associated with, and/or contributing to, AD pathophysiology, including changes in blood-brain barrier integrity and cerebral blood flow. Vascular imaging biomarkers of small vessel disease of the brain, which is responsible for >50% of dementia worldwide, including AD, are already established, well characterized, and easy to recognize. We suggest that these vascular biomarkers should be incorporated into the AD Research Framework to gain a better understanding of AD pathophysiology and aid in treatment efforts.

Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis.

Diffusion MRI tractography provides a non-invasive modality to examine the human retinofugal projection, which consists of the optic nerves, optic chiasm, optic tracts, the lateral geniculate nuclei (LGN) and the optic radiations. However, the pathway has several anatomic features that make it particularly challenging to study with tractography, including its location near blood vessels and bone-air interface at the base of the cerebrum, crossing fibers at the chiasm, somewhat-tortuous course around the temporal horn via Meyer's Loop, and multiple closely neighboring fiber bundles. To date, these unique complexities of the visual pathway have impeded the development of a robust and automated reconstruction method using tractography. To overcome these challenges, we develop a novel, fully automated system to reconstruct the retinofugal visual pathway from high-resolution diffusion imaging data. Using multi-shell, high angular resolution diffusion imaging (HARDI) data, we reconstruct precise fiber orientation distributions (FODs) with high order spherical harmonics (SPHARM) to resolve fiber crossings, which allows the tractography algorithm to successfully navigate the complicated anatomy surrounding the retinofugal pathway. We also develop automated algorithms for the identification of ROIs used for fiber bundle reconstruction. In particular, we develop a novel approach to extract the LGN region of interest (ROI) based on intrinsic shape analysis of a fiber bundle computed from a seed region at the optic chiasm to a target at the primary visual cortex. By combining automatically identified ROIs and FOD-based tractography, we obtain a fully automated system to compute the main components of the retinofugal pathway, including the optic tract and the optic radiation. We apply our method to the multi-shell HARDI data of 215 subjects from the Human Connectome Project (HCP). Through comparisons with post-mortem dissection measurements, we demonstrate the retinotopic organization of the optic radiation including a successful reconstruction of Meyer's loop. Then, using the reconstructed optic radiation bundle from the HCP cohort, we construct a probabilistic atlas and demonstrate its consistency with a post-mortem atlas. Finally, we generate a shape-based representation of the optic radiation for morphometry analysis.

Optimal acquisition and modeling parameters for accurate assessment of low Ktrans blood-brain barrier permeability using dynamic contrast-enhanced MRI.

PURPOSE: To determine optimal parameters for acquisition and processing of dynamic contrast-enhanced MRI (DCE-MRI) to detect small changes in near normal low blood-brain barrier (BBB) permeability. METHODS: Using a contrast-to-noise ratio metric (K-CNR) for Ktrans precision and accuracy, the effects of kinetic model selection, scan duration, temporal resolution, signal drift, and length of baseline on the estimation of low permeability values was evaluated with simulations. RESULTS: The Patlak model was shown to give the highest K-CNR at low Ktrans . The Ktrans transition point, above which other models yielded superior results, was highly dependent on scan duration and tissue extravascular extracellular volume fraction (ve ). The highest K-CNR for low Ktrans was obtained when Patlak model analysis was combined with long scan times (10-30 min), modest temporal resolution (<60 s/image), and long baseline scans (1-4 min). Signal drift as low as 3% was shown to affect the accuracy of Ktrans estimation with Patlak analysis. CONCLUSION: DCE acquisition and modeling parameters are interdependent and should be optimized together for the tissue being imaged. Appropriately optimized protocols can detect even the subtlest changes in BBB integrity and may be used to probe the earliest changes in neurodegenerative diseases such as Alzheimer's disease and multiple sclerosis.

Fiber estimation and tractography in diffusion MRI: development of simulated brain images and comparison of multi-fiber analysis methods at clinical b-values.

Advances in diffusion-weighted magnetic resonance imaging (DW-MRI) have led to many alternative diffusion sampling strategies and analysis methodologies. A common objective among methods is estimation of white matter fiber orientations within each voxel, as doing so permits in-vivo fiber-tracking and the ability to study brain connectivity and networks. Knowledge of how DW-MRI sampling schemes affect fiber estimation accuracy, tractography and the ability to recover complex white-matter pathways, differences between results due to choice of analysis method, and which method(s) perform optimally for specific data sets, all remain important problems, especially as tractography-based studies become common. In this work, we begin to address these concerns by developing sets of simulated diffusion-weighted brain images which we then use to quantitatively evaluate the performance of six DW-MRI analysis methods in terms of estimated fiber orientation accuracy, false-positive (spurious) and false-negative (missing) fiber rates, and fiber-tracking. The analysis methods studied are: 1) a two-compartment "ball and stick" model (BSM) (Behrens et al., 2003); 2) a non-negativity constrained spherical deconvolution (CSD) approach (Tournier et al., 2007); 3) analytical q-ball imaging (QBI) (Descoteaux et al., 2007); 4) q-ball imaging with Funk-Radon and Cosine Transform (FRACT) (Haldar and Leahy, 2013); 5) q-ball imaging within constant solid angle (CSA) (Aganj et al., 2010); and 6) a generalized Fourier transform approach known as generalized q-sampling imaging (GQI) (Yeh et al., 2010). We investigate these methods using 20, 30, 40, 60, 90 and 120 evenly distributed q-space samples of a single shell, and focus on a signal-to-noise ratio (SNR = 18) and diffusion-weighting (b = 1000 s/mm(2)) common to clinical studies. We found that the BSM and CSD methods consistently yielded the least fiber orientation error and simultaneously greatest detection rate of fibers. Fiber detection rate was found to be the most distinguishing characteristic between the methods, and a significant factor for complete recovery of tractography through complex white-matter pathways. For example, while all methods recovered similar tractography of prominent white matter pathways of limited fiber crossing, CSD (which had the highest fiber detection rate, especially for voxels containing three fibers) recovered the greatest number of fibers and largest fraction of correct tractography for complex three-fiber crossing regions. The synthetic data sets, ground-truth, and tools for quantitative evaluation are publically available on the NITRC website as the project "Simulated DW-MRI Brain Data Sets for Quantitative Evaluation of Estimated Fiber Orientations" at http://www.nitrc.org/projects/sim_dwi_brain.

Principles of T2 *-weighted dynamic susceptibility contrast MRI technique in brain tumor imaging.

Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is used to track the first pass of an exogenous, paramagnetic, nondiffusible contrast agent through brain tissue, and has emerged as a powerful tool in the characterization of brain tumor hemodynamics. DSC-MRI parameters can be helpful in many aspects, including tumor grading, prediction of treatment response, likelihood of malignant transformation, discrimination between tumor recurrence and radiation necrosis, and differentiation between true early progression and pseudoprogression. This review aims to provide a conceptual overview of the underlying principles of DSC-MRI of the brain for clinical neuroradiologists, scientists, or students wishing to improve their understanding of the technical aspects, pitfalls, and controversies of DSC perfusion MRI of the brain. Future consensus on image acquisition parameters and postprocessing of DSC-MRI will most likely allow this technique to be evaluated and used in high-quality multicenter studies and ultimately help guide clinical care.

Highly accelerated dynamic contrast enhanced imaging.

PURPOSE: Dynamic contrast-enhanced imaging provides unique physiological information, notably the endothelial permeability (K(trans)), and may improve the diagnosis and management of multiple pathologies. Current acquisition methods provide limited spatial-temporal resolution and field-of-view, often preventing characterization of the entire pathology and precluding measurement of the arterial input function. We present a method for highly accelerated dynamic imaging and demonstrate its utility for dynamic contrast-enhanced modeling. METHODS: We propose a novel Poisson ellipsoid sampling scheme and enforce multiple spatial and temporal l1-norm constraints during image reconstruction. Retrospective and prospective analyses were performed to validate the approach. RESULTS: Retrospectively, no mean bias or diverging trend was observed as the acceleration rate was increased from 3× to 18×; less than 10% error was measured in K(trans) at any individual rates in this range. Prospectively accelerated images at a rate of 36× enabled full brain coverage with 0.94 × 0.94 × 1.9 mm(3) spatial and 4.1 s temporal resolutions. Images showed no visible degradation and provided accurate K(trans) values when compared to a clinical population. CONCLUSION: Highly accelerated dynamic MRI using compressed sensing and parallel imaging provides accurate permeability modeling and enables full brain, high resolution acquisitions.

Image coregistration: quantitative processing framework for the assessment of brain lesions.

The quantitative, multiparametric assessment of brain lesions requires coregistering different parameters derived from MRI sequences. This will be followed by analysis of the voxel values of the ROI within the sequences and calculated parametric maps, and deriving multiparametric models to classify imaging data. There is a need for an intuitive, automated quantitative processing framework that is generalized and adaptable to different clinical and research questions. As such flexible frameworks have not been previously described, we proceeded to construct a quantitative post-processing framework with commonly available software components. Matlab was chosen as the programming/integration environment, and SPM was chosen as the coregistration component. Matlab routines were created to extract and concatenate the coregistration transforms, take the coregistered MRI sequences as inputs to the process, allow specification of the ROI, and store the voxel values to the database for statistical analysis. The functionality of the framework was validated using brain tumor MRI cases. The implementation of this quantitative post-processing framework enables intuitive creation of multiple parameters for each voxel, facilitating near real-time in-depth voxel-wise analysis. Our initial empirical evaluation of the framework is an increased usage of analysis requiring post-processing and increased number of simultaneous research activities by clinicians and researchers with non-technical backgrounds. We show that common software components can be utilized to implement an intuitive real-time quantitative post-processing framework, resulting in improved scalability and increased adoption of post-processing needed to answer important diagnostic questions.