Investigation of Deep-Learning-Driven Identification of Multiple Sclerosis Patients Based on Susceptibility-Weighted Images Using Relevance Analysis

The diagnosis of multiple sclerosis (MS) is usually based on clinical symptoms and signs of damage to the central nervous system, which is assessed using magnetic resonance imaging. The correct interpretation of these data requires excellent clinical expertise and experience. Deep neural networks aim to assist clinicians in identifying MS using imaging data. However, before such networks can be integrated into clinical workflow, it is crucial to understand their classification strategy. In this study, we propose to use a convolutional neural network to identify MS patients in combination with attribution algorithms to investigate the classification decisions. The network was trained using images acquired with susceptibility-weighted imaging (SWI), which is known to be sensitive to the presence of paramagnetic iron components and is routinely applied in imaging protocols for MS patients. Different attribution algorithms were used to the trained network resulting in heatmaps visualizing the contribution of each input voxel to the classification decision. Based on the quantitative image perturbation method, we selected DeepLIFT heatmaps for further investigation. Single-subject analysis revealed veins and adjacent voxels as signs for MS, while the population-based study revealed relevant brain areas common to most subjects in a class. This pattern was found to be stable across different echo times and also for a multi-echo trained network. Intensity analysis of the relevant voxels revealed a group difference, which was found to be primarily based on the T1w magnitude images, which are part of the SWI calculation. This difference was not observed in the phase mask data.

Experience-dependent structural plasticity in the adult brain: How the learning brain grows

Volumetric magnetic resonance imaging studies have shown that intense learning can be associated with grey matter volume increases in the adult brain. The underlying mechanisms are poorly understood. Here we used monocular deprivation in rats to analyze the mechanisms underlying use-dependent grey matter increases. Optometry for quantification of visual acuity was combined with volumetric magnetic resonance imaging and microscopic techniques in longitudinal and cross-sectional studies. We found an increased spatial vision of the open eye which was associated with a transient increase in the volumes of the contralateral visual and lateral entorhinal cortex. In these brain areas dendrites of neurons elongated, and there was a strong increase in the number of spines, the targets of synapses, which was followed by spine maturation and partial pruning. Astrocytes displayed a transient pronounced swelling and underwent a reorganization of their processes. The use-dependent increase in grey matter corresponded predominantly to the swelling of the astrocytes. Experience-dependent increase in brain grey matter volume indicates a gain of structure plasticity with both synaptic and astrocyte remodeling.

Superficial white matter imaging: Contrast mechanisms and whole-brain in vivo mapping

Superficial white matter (SWM) contains the most cortico-cortical white matter connections in the human brain encompassing the short U-shaped association fibers. Despite its importance for brain connectivity, very little is known about SWM in humans, mainly due to the lack of noninvasive imaging methods. Here, we lay the groundwork for systematic in vivo SWM mapping using ultrahigh resolution 7 T magnetic resonance imaging. Using biophysical modeling informed by quantitative ion beam microscopy on postmortem brain tissue, we demonstrate that MR contrast in SWM is driven by iron and can be linked to the microscopic iron distribution. Higher SWM iron concentrations were observed in U-fiber-rich frontal, temporal, and parietal areas, potentially reflecting high fiber density or late myelination in these areas. Our SWM mapping approach provides the foundation for systematic studies of interindividual differences, plasticity, and pathologies of this crucial structure for cortico-cortical connectivity in humans.

Energy Expenditure of Dynamic Submaximal Human Plantarflexion Movements: Model Prediction and Validation by in-vivo Magnetic Resonance Spectroscopy

To understand the organization and efficiency of biological movement, it is important to evaluate the energy requirements on the level of individual muscles. To this end, predicting energy expenditure with musculoskeletal models in forward-dynamic computer simulations is currently the most promising approach. However, it is challenging to validate muscle models in-vivo in humans, because access to the energy expenditure of single muscles is difficult. Previous approaches focused on whole body energy expenditure, e.g., oxygen consumption (VO2), or on thermal measurements of individual muscles by tracking blood flow and heat release (through measurements of the skin temperature). This study proposes to validate models of muscular energy expenditure by using functional phosphorus magnetic resonance spectroscopy (³¹P-MRS). ³¹P-MRS allows to measure phosphocreatine (PCr) concentration which changes in relation to energy expenditure. In the first 25 s of an exercise, PCr breakdown rate reflects ATP hydrolysis, and is therefore a direct measure of muscular enthalpy rate. This method was applied to the gastrocnemius medialis muscle of one healthy subject during repetitive dynamic plantarflexion movements at submaximal contraction, i.e., 20% of the maximum plantarflexion force using a MR compatible ergometer. Furthermore, muscle activity was measured by surface electromyography (EMG). A model (provided as open source) that combines previous models for muscle contraction dynamics and energy expenditure was used to reproduce the experiment in simulation. All parameters (e.g., muscle length and volume, pennation angle) in the model were determined from magnetic resonance imaging or literature (e.g., fiber composition), leaving no free parameters to fit the experimental data. Model prediction and experimental data on the energy supply rates are in good agreement with the validation phase (<25 s) of the dynamic movements. After 25 s, the experimental data differs from the model prediction as the change in PCr does not reflect all metabolic contributions to the energy expenditure anymore and therefore underestimates the energy consumption. This shows that this new approach allows to validate models of muscular energy expenditure in dynamic movements in vivo.

Immersion of Achilles tendon in phosphate-buffered saline influences T1 and T2 * relaxation times: An ex vivo study

Robust mapping of relaxation parameters in ex vivo tissues is based on hydration and therefore requires control of the tissue treatment to ensure tissue integrity and consistent measurement conditions over long periods of time. One way to maintain the hydration of ex vivo tendon tissue is to immerse the samples in a buffer solution. To this end, various buffer solutions have been proposed; however, many appear to influence the tissue relaxation times, especially with prolonged exposure. In this work, ovine Achilles tendon tissue was used as a model to investigate the effect of immersion in phosphate-buffered saline (PBS) and the effects on the T₁ and T₂^* relaxation times. Ex vivo samples were measured at 0 (baseline), 30 and 67 hours after immersion in PBS. Ultrashort echo time (UTE) imaging was performed using variable flip angle and echo train-shifted multi-echo imaging for T₁ and T₂^* estimation, respectively. Compared with baseline, both T₁ and T₂^* relaxation time constants increased significantly after 30 hours of immersion. T₂^* continued to show a significant increase between 30 and 67 hours. Both T₁ and T₂^* tended to approach saturation at 67 hours. These results exemplify the relevance of stringently controlled tissue preparation and preservation techniques, both before and during MRI experiments.

Reichenbach J, Reichstein M, Römermann C, Wittig A. A virtual “Werkstatt” for digitization in the sciences

Data is central in almost all scientific disciplines nowadays. Furthermore, intelligent systems have developed rapidly in recent years, so that in many disciplines the expectation is emerging that with the help of intelligent systems, significant challenges can be overcome and science can be done in completely new ways. In order for this to succeed, however, first, fundamental research in computer science is still required, and, second, generic tools must be developed on which specialized solutions can be built. In this paper, we introduce a recently started collaborative project funded by the Carl Zeiss Foundation, a virtual manufactory for digitization in the sciences, the “Werkstatt”, which is being established at the Michael Stifel Center Jena (MSCJ) for data-driven and simulation science to address fundamental questions in computer science and applications. The Werkstatt focuses on three key areas, which include generic tools for machine learning, knowledge generation using machine learning processes, and semantic methods for the data life cycle, as well as the application of these topics in different disciplines. Core and pilot projects address the key aspects of the topics and form the basis for sustainable work in the Werkstatt.

Characterization of Iron Accumulation in Deep Gray Matter in Myotonic Dystrophy Type 1 and 2 Using Quantitative Susceptibility Mapping and R2* Relaxometry: A Magnetic Resonance Imaging Study at 3 Tesla

Quantitative mapping of the magnetic susceptibility and the effective transverse relaxation rate (R2^*) are suitable to assess the iron content in distinct brain regions. In this prospective, explorative study the iron accumulation in deep gray matter nuclei (DGM) in myotonic dystrophy type 1 (DM1) and 2 (DM2) and its clinical and neuro-cognitive relevance using susceptibility and R2^* mapping was examined. Twelve classical DM1, four childhood-onset DM1 (DM1_c.o.), twelve DM2 patients and twenty-nine matched healthy controls underwent MRI at 3 Tesla, neurological and neuro-cognitive tests. Susceptibility, R2^* and volumes were determined for eleven DGM structures and compared between patients and controls. Twelve classical DM1, four childhood-onset DM1, and 12 DM2 patients as well as 29 matched healthy controls underwent MRI at 3 Tesla, and neurological and neuro-cognitive tests. Susceptibility, R2^* and volumes were determined for 11 DGM structures and compared between patients and controls. Iron accumulation in DGM reflected by R2^* or susceptibility was found in the putamen and accumbens of DM1 and in DM2, but was more widespread in DM1 (caudate, pallidum, hippocampus, subthalamic nucleus, thalamus, and substantia nigra). Opposed changes of R2^* or susceptibility were detected in caudate, putamen and accumbens in the childhood-onset DM1 patients compared to classical DM1. R2^* or susceptibility alterations in DGM were significantly associated with clinical symptoms including muscular weakness (DM1), daytime sleepiness (DM1), depression (DM2), and with specific cognitive deficits in DM1 and DM2.

Segmentation and visualization of the human cranial bone by T2* approximation using ultra-short echo time (UTE) magnetic resonance imaging

Segmentation of the human cranial bone from MRI data is challenging, because compact bone is characterized by very short transverse relaxation times and typically produces no signal when using conventional magnetic resonance imaging (MRI) sequences. In this work, we propose a fully automated segmentation algorithm, which uses dual-echo, ultra-short echo-time (UTE) MRI data. The segmentation was initialized by interval thresholding of approximated T₂* relaxation time maps in the range of 1ms<T₂*<3ms. This parameter range was derived from a manual region-of-interest analysis of high resolution data of the cranial layers, resulting in average T₂* relaxation times of 1.7±0.3ms in the lamina externa, 2.5±0.3ms in the diploe and 1.7±0.2ms in the lamina interna. Segmentations were performed based on data of 8 healthy volunteers that were acquired with different acquisition parameters and spatial resolutions to test the stability of the algorithm. Comparison with computed tomography data demonstrated close agreement with the segmented UTE MRI data. Visualization of the segmented cranial bone was performed by volumetric rendering and by using the approximated T₂* values for color coding, clearly visualizing the cranial sutures as well as their intersections.

A new framework for assessing subject-specific whole brain circulation and perfusion using MRI-based measurements and a multi-scale continuous flow model

A large variety of severe medical conditions involve alterations in microvascular circulation. Hence, measurements or simulation of circulation and perfusion has considerable clinical value and can be used for diagnostics, evaluation of treatment efficacy, and for surgical planning. However, the accuracy of traditional tracer kinetic one-compartment models is limited due to scale dependency. As a remedy, we propose a scale invariant mathematical framework for simulating whole brain perfusion. The suggested framework is based on a segmentation of anatomical geometry down to imaging voxel resolution. Large vessels in the arterial and venous network are identified from time-of-flight (ToF) and quantitative susceptibility mapping (QSM). Macro-scale flow in the large-vessel-network is accurately modelled using the Hagen-Poiseuille equation, whereas capillary flow is treated as two-compartment porous media flow. Macro-scale flow is coupled with micro-scale flow by a spatially distributing support function in the terminal endings. Perfusion is defined as the transition of fluid from the arterial to the venous compartment. We demonstrate a whole brain simulation of tracer propagation on a realistic geometric model of the human brain, where the model comprises distinct areas of grey and white matter, as well as large vessels in the arterial and venous vascular network. Our proposed framework is an accurate and viable alternative to traditional compartment models, with high relevance for simulation of brain perfusion and also for restoration of field parameters in clinical brain perfusion applications.

An accurate simulation of blood-flow in the human brain can be used for improved diagnostics and assignment of personalized treatment regimes. However, current algorithms are limited to simulation of blood flow within tumours only, and in terms of parameter estimation, traditional compartment models have limited accuracy due to lack of spatial connectivity within the models. As a remedy, we propose a data-driven computational fluid dynamics model where the geometric domains for simulation are defined from state-of-the art MR acquisitions enabling a segmentation of large arteries and veins. In the capillary tissue we apply a two-compartment porous media model, where the perfusion is pressure-driven and is defined as the transition of blood from arterial to venous side. In addition, we propose a model for dealing with the intermediate scale problem where the vessels are undetectable and the flow does not adhere to requirements of porous media flow. For this scale, we propose a support function distributing the fluid in a nearby region around the vessel terminals. Combining these elements, we have developed a novel full human brain blood-flow simulator.

The relationship between heart rate and functional connectivity of brain regions involved in autonomic control

The peripheral autonomic nervous system (ANS) adjusts the heart rate (HR) to intrinsic and extrinsic demands. It is controlled by a group of functionally connected brain regions assembling the so-called central autonomic network (CAN). More specifically, forebrain cortical regions, limbic and brainstem structures within the CAN have been identified as important components of circuits involved in HR regulation. The present study aimed to investigate whether functional connectivity (FC) between these regions varies in subjects with different heart rates. Thus, 84 healthy subjects were separated according to their HR in slow, medium and fast. We observed a direct association between HR and FC in CAN regions, where stronger FC was related to slower HR. This relationship, however, is non-linear, follows an exponential course and is not restricted to CAN areas only. The network-based analysis (NBS) using time series from 262 independent anatomical ROIs revealed significantly increased functional connectivity in subjects with slow HR compared to subjects with fast HR mainly in regions being part of the salience network, but also of the default-mode network. We additionally simulated the effect of aliasing on the functional connectivity using several TRs and heart rates to exclude the possibility that FC differences might be due to different aliasing effects in the data. The result of the simulation indicated that aliasing cannot explain our findings. Thus, present results imply a functionally meaningful coupling between FC and HR that need to be accounted for in future studies. Moreover, given the established link between HR and emotional, cognitive and social processes, present findings may also be considered to explain individual differences in brain activation or connectivity when using corresponding paradigms in the MR scanner to investigate such processes.

Establishment and effects of allograft and synthetic bone graft substitute treatment of a critical size metaphyseal bone defect model in the sheep femur

Assessment of bone graft material efficacy is difficult in humans, since invasive methods like staged CT scans or biopsies are ethically unjustifiable. Therefore, we developed a novel large animal model for the verification of a potential transformation of synthetic bone graft substitutes into vital bone. The model combines multiple imaging methods with corresponding histology in standardized critical sized cancellous bone defect. Cylindrical bone voids (10 ml) were created in the medial femoral condyles of both hind legs (first surgery at right hind leg, second surgery 3 months later at left hind leg) in three merino‐wool sheep and either (i) left empty, filled with (ii) cancellous allograft bone or (iii) a synthetic, gentamicin eluting bone graft substitute. All samples were analysed with radiographs, MRI, μCT, DEXA and histology after sacrifice at 6 months. Unfilled defects only showed ingrowth of fibrous tissue, whereas good integration of the cancellous graft was seen in the allograft group. The bone graft substitute showed centripetal biodegradation and new trabecular bone formation in the periphery of the void as early as 3 months. μCT gave excellent insight into the structural changes within the defects, particularly progressive allograft incorporation and the bone graft substitute biodegradation process. MRI completed the picture by clearly visualizing soft tissue ingrowth into unfilled bone voids and presence of fluid collections. Histology was essential for verification of trabecular bone and osteoid formation. Conventional radiographs and DEXA could not differentiate details of the ongoing transformation process. This model appears well suited for detailed in vivo and ex vivo evaluation of bone graft substitute behaviour within large bone defects.

Analysis of intensity normalization for optimal segmentation performance of a fully convolutional neural network

INTRODUCTION

Convolutional neural networks have begun to surpass classical statistical- and atlas based machine learning techniques in medical image segmentation in recent years, proving to be superior in performance and speed. However, a major challenge that the community faces are mismatch between variability within training and evaluation datasets and therefore a dependency on proper data pre-processing. Intensity normalization is a widely applied technique for reducing the variance of the data for which there are several methods available ranging from uniformity transformation to histogram equalization. The current study analyses the influence of intensity normalization on cerebellum segmentation performance of a convolutional neural network (CNN).

METHOD

The study included three population samples with a total number of 218 datasets, all including a T1w MRI data set acquired at 3T and a ground truth segmentation delineating the cerebellum. A 12 layer deep 3D fully convolutional neural network was trained using 150 datasets from one of the population samples. Four different intensity normalization methods were separately applied to pre-process the data, and the CNN was correspondingly trained four times with respect to the different normalization techniques. A quantitative analysis of the segmentation performance, assessed via the Sørensen-Dice similarity coefficient (DSC) of all four CNNs, was performed to investigate the intensity sensitivity of the CNNs. Additionally, the optimal network performance was determined by identifying the best parameter set for intensity normalization.

RESULTS

All four normalization methods led to excellent (mean DSC score=0.96) segmentation results when evaluated using known data; however, the segmentation performance differed depending on the applied intensity normalization method when testing with formerly unseen data, in which case the histogram equalization methods outperformed the unit distribution methods. A detailed, systematic analysis of intensity manipulations revealed, that the distribution of input intensities clearly affected the segmentation performance and that for each input dataset a linear intensity modification (shifting and scaling) existed leading to optimal segmentation results. This was further proven by an optimization analysis to find the optimal adjustment for an individual input evaluation sample within each normalization configuration.

DISCUSSION

The findings suggest that proper preparation of the evaluation data is more crucial than the exact choice of normalization method to prepare the training data. The histogram equalization methods tested in this study were found to perform this task best, although leaving room for further improvements, as shown by the optimization analysis.

The Use of Physiological Signals in Brainstem/Midbrain fMRI

Brainstem and midbrain nuclei are closely linked to cognitive performance and autonomic function. To advance the localization in this area, precise functional imaging is fundamental. In this study, we used a sophisticated fMRI technique as well as physiological recordings to investigate the involvement of brainstem/midbrain nuclei in cognitive control during a Stroop task. The temporal signal-to-noise ratio (tSNR) increased due to physiological noise correction (PNC) especially in regions adjacent to arteries and cerebrospinal fluid. Within the brainstem/cerebellum template an average tSNR of 68 ± 16 was achieved after the simultaneous application of a high-resolution fMRI, specialized co-registration, and PNC. The analysis of PNC data revealed an activation of the substantia nigra in the Stroop interference contrast whereas no significant results were obtained in the midbrain or brainstem when analyzing uncorrected data. Additionally, we found that pupil size indicated the level of cognitive effort. The Stroop interference effect on pupillary responses was correlated to the effect on reaction times (R 2 = 0.464, p < 0.05). When Stroop stimuli were modulated by pupillary responses, we observed a significant activation of the LC in the Stroop interference contrast. Thus, we demonstrated the beneficial effect of PNC on data quality and statistical results when analyzing neuronal responses to a cognitive task. Parametric modulation of task events with pupillary responses improved the model of LC BOLD activations in the Stroop interference contrast.

Disturbed glutathione antioxidative defense is associated with structural brain changes in neuroleptic-naïve first-episode psychosis patients

BACKGROUND

Oxidative stress and impaired antioxidant defense are reported in schizophrenia and are thought to be associated with disturbed neurodevelopment, brain structural alterations, glutamatergic imbalance, negative symptomatology, and cognitive impairment. To test some of these assumptions we investigated the glutathione (GSH) antioxidant defense system (AODS) and brain structural abnormalities in drug-naïve individuals with first acute episode of psychosis (FEP).

METHOD

The study involved 27 drug-naïve FEP patients and 31 healthy controls (HC). GSH AODS markers and TBARS (thiobarbituric acid reactive substances) were measured in blood plasma and erythrocytes. High-resolution T₁-weighted 3T MRI were acquired from all subjects. To investigate brain structural abnormalities and effects of illness on interactions between GSH metabolites or enzyme activities and local grey matter density, voxel-based morphometry (VBM) with the computational anatomy toolbox (CAT12) was used. Symptomatology was assessed using the Positive and Negative Syndrome Scale (PANSS) and the Symptom Checklist 1990 revised (SCL-90-R).

RESULTS

(i) In FEP patients, glutathione reductase activity (GSR) was lower than in the HC group. GSR activity in plasma was inversely correlated with SCL-90-R scores of depression and PANSS scores of the negative symptom subscale. (ii) A reduction of GM was observed in left inferior frontal, bilateral temporal, as well as parietal cortices of FEP patients. (iii) Interaction analyses revealed an influence of illness on GSR/GM associations in the left orbitofrontal cortex (BA 47).

CONCLUSION

Our findings support the notion of altered GSH antioxidative defense in untreated acute psychosis as a potential pathomechanism for localized brain structural abnormalities. This pathology relates to a key brain region of social cognition, affective motivation control and decision making, and is clinically accompanied by depressive and negative symptoms.

Comparison of metabolic adaptations between endurance- and sprint-trained athletes after an exhaustive exercise in two different calf muscles using a multi-slice 31 P-MR spectroscopic sequence

Measurements of exercise-induced metabolic changes, such as oxygen consumption, carbon dioxide exhalation or lactate concentration, are important indicators for assessing the current performance level of athletes in training science. With exercise-limiting metabolic processes occurring in loaded muscles, ³¹ P-MRS represents a particularly powerful modality to identify and analyze corresponding training-induced alterations. Against this background, the current study aimed to analyze metabolic adaptations after an exhaustive exercise in two calf muscles (m. soleus - SOL - and m. gastrocnemius medialis - GM) of sprinters and endurance athletes by using localized dynamic ³¹ P-MRS. In addition, the respiratory parameters VO₂ and VCO₂ , as well as blood lactate concentrations, were monitored simultaneously to assess the effects of local metabolic adjustments in the loaded muscles on global physiological parameters. Besides noting obvious differences between the SOL and the GM muscles, we were also able to identify distinct physiological strategies in dealing with the exhaustive exercise by recruiting two athlete groups with opposing metabolic profiles. Endurance athletes tended to use the aerobic pathway in the metabolism of glucose, whereas sprinters produced a significantly higher peak concentration of lactate. These global findings go along with locally measured differences, especially in the main performer GM, with sprinters revealing a higher degree of acidification at the end of exercise (pH 6.29 ± 0.20 vs. 6.57 ± 0.21). Endurance athletes were able to partially recover their PCr stores during the exhaustive exercise and seemed to distribute their metabolic activity more consistently over both investigated muscles. In contrast, sprinters mainly stressed Type II muscle fibers, which corresponds more to their training orientation preferring the glycolytic energy supply pathway. In conclusion, we were able to analyze the relation between specific local metabolic processes in loaded muscles and typical global adaptation parameters, conventionally used to monitor the training status of athletes, in two cohorts with different sports orientations.

Prefrontal glutamatergic emotion regulation is disturbed in cluster B and C personality disorders - A combined 1H/31P-MR spectroscopic study

BACKGROUND

Personality disorders (PD) belong to the most common and most serious mental disorders as regards social dysfunction, inability to work, occurrence of comorbidity and suicidal risk. PDs also crucially influence the incidence, clinical course and treatment response of mental disorders with high suicidal risk, such as depression or substance abuse. One key issue of PD concerns the regulation of emotions.

METHODS

Both ¹H-/³¹P-Chemical Shift Imaging (CSI) was applied in a single session to assess neurochemical markers of glutamate function (NAA, Glu) and local energy metabolism (PCr, ATP) in two patient cohorts encompassing 22 cluster B (CB) and 21 cluster C (CC) PD patients, whereby 10 patients of each group were on low-dose antidepressants, and in 60 healthy controls (HC). Non-parametric statistical tests and correlation analyses were performed to assess disease effects on the metabolites and their relation to symptomatology as assessed by SCL-90R self-ratings.

RESULTS

Overall comparison including Bonferroni correction revealed significant differences of Glu across all groups in the dorsolateral prefrontal cortex (DLPFC). The following uncorrected results of pairwise tests were obtained: (i) Glu was bilaterally increased in the DLPFC in CB patients, whereas it was - together with NAA - bilaterally decreased in the DLPFC in CC patients and accompanied by increased PCr in the left DLPFC. (ii) NAA and Glu, accompanied by increased PCr, were significantly decreased in the dorsomedial prefrontal cortex (DMPFC) in CC patients. (iii) NAA was decreased in the right anterior cingulate cortex (ACC) in CB patients, and in the left ACC in CC patients with PCr being increased bilaterally. (iv) No associations were observed between metabolites and psychopathology measures.

CONCLUSION

The observations in the DLPFC may reflect a neurobiochemical correlate of disturbed cognitive control function in CB and CC PD. While the alterations in CB patients suggest increased basal activity, the observed patterns in CC patients likely reflect decreased or inhibited activity. The alterations of NAA and Glu levels in the ACC and DMPFC indirectly support the assumption of disturbed neuronal function in regions involved in social cognition and mentalizing abilities in both CB and CC PD. Further studies should include the investigation of metabolites of neuronal inhibition (GABA) and the examination of treatment effects.

Vascular and Tissue Changes of Magnetic Susceptibility in the Mouse Brain After Transient Cerebral Ischemia

Quantitative susceptibility mapping (QSM) has been recently introduced as a novel MRI post-processing technique of gradient recalled echo (GRE) data. QSM is useful in depicting both brain anatomy and for detecting abnormalities. Its utility in the context of ischemic stroke has, however, not been extensively characterized so far. In this study, we explored the potential of QSM to characterize vascular and tissue changes in the transient middle cerebral artery occlusion (tMCAO) mouse model of cerebral ischemia. We acquired GRE data of mice brains at different time points after tMCAO, from which we computed QSM and MR frequency maps, and compared these maps with diffusion imaging and multi-slice multi-echo imaging data acquired in the same animals. Prominent vessels with increased magnetic susceptibility were visible surrounding the lesion on both frequency and magnetic susceptibility maps at all time points (mostly visible at > 12 h after reperfusion). Immunohistochemistry revealed the presence of compressed capillaries and dilated larger vessels, suggesting that the appearance of prominent vessels after reestablishment of reperfusion may serve compensatory purposes. In addition, on both contrast maps, tissue regions of decreased magnetic susceptibility were observed at 24 and 48 h after reperfusion that were distinctly different from the lesions seen on maps of the apparent diffusion coefficient and T₂ relaxation time constant. Since QSM can be extracted as an add-on from GRE data and thus requires no additional acquisition time in the course of acute stroke MRI examination, it may provide unique and complementary information during the course of acute stroke MRI examinations.

Computational Modeling in Liver Surgery

The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.

Diffusion tensor imaging of cingulum bundle and corpus callosum in schizophrenia vs. bipolar disorder

Both schizophrenia and bipolar disorder show abnormalities of white matter, as seen in diffusion tensor imaging (DTI) analyses of major brain fibre bundles. While studies in each of the two conditions have indicated possible overlap in anatomical location, there are few direct comparisons between the disorders. Also, it is unclear whether phenotypically similar subgroups (e.g. patients with bipolar disorder and psychotic features) might share white matter pathologies or be rather similar. Using region-of-interest (ROI) analysis of white matter with diffusion tensor imaging (DTI) at 3 T, we analysed fractional anisotropy (FA), radial diffusivity (RD), and apparent diffusion coefficient (ADC) of the corpus callosum and cingulum bundle in 33 schizophrenia patients, 17 euthymic (previously psychotic) bipolar disorder patients, and 36 healthy controls. ANOVA analysis showed significant main effects of group for RD and ADC (both elevated in schizophrenia). Across the corpus callosum ROIs, there was not group effect on FA, but for RD (elevated in schizophrenia, lower in bipolar disorder) and ADC (higher in schizophrenia, intermediate in bipolar disorder). Our findings show similarities and difference (some gradual) across regions of the two major fibre tracts implicated in these disorders, which would be consistent with a neurobiological overlap of similar clinical phenotypes.

GlucoCEST magnetic resonance imaging in vivo may be diagnostic of acute renal allograft rejection

Acute cellular renal allograft rejection (AR) frequently occurs after kidney transplantations. It is a sterile T-cell mediated inflammation leading to increased local glucose metabolism. Here we demonstrate in an allogeneic model of Brown Norway rat kidneys transplanted into uninephrectomized Lewis rats the successful implementation of the recently developed glucose chemical exchange saturation transfer (glucoCEST) magnetic resonance imaging. This technique is a novel method to assess and differentiate AR. Renal allografts undergoing AR showed significantly increased glucoCEST contrast ratios of cortex to medulla of 1.61 compared to healthy controls (1.02), syngeneic Lewis kidney to Lewis rat transplants without rejection (0.92), kidneys with ischemia reperfusion injury (0.99) and kidneys affected by cyclosporine A toxicity (1.10). Receiver operating characteristic curve analysis showed an area under the curve value of 0.92, and the glucoCEST contrast ratio predicted AR with a sensitivity of 100% and a specificity of 69% at a threshold level over 1.08. In defined animal models of kidney injuries, the glucoCEST contrast ratios of cortex to medulla correlated positively with mRNA expression levels of T-cell markers (CD3, CD4, CD8a/b), but did not correlate to impaired renal perfusion. Thus, the glucoCEST parameter may be valuable for the assessment and follow up treatment of AR.

Combined spiroergometry and 31 P-MRS of human calf muscle during high-intensity exercise

Simultaneous measurements of pulmonary oxygen consumption (VO₂ ), carbon dioxide exhalation (VCO₂ ) and phosphorus magnetic resonance spectroscopy (³¹ P-MRS) are valuable in physiological studies to evaluate muscle metabolism during specific loads. Therefore, the aim of this study was to adapt a commercially available spirometric device to enable measurements of VO₂ and VCO₂ whilst simultaneously performing ³¹ P-MRS at 3 T. Volunteers performed intense plantar flexion of their right calf muscle inside the MR scanner against a pneumatic MR-compatible pedal ergometer. The use of a non-magnetic pneumotachograph and extension of the sampling line from 3 m to 5 m to place the spirometric device outside the MR scanner room did not affect adversely the measurements of VO₂ and VCO₂ . Response and delay times increased, on average, by at most 0.05 s and 0.79 s, respectively. Overall, we were able to demonstrate a feasible ventilation response (VO₂ = 1.05 ± 0.31 L/min; VCO₂ = 1.11 ± 0.33 L/min) during the exercise of a single calf muscle, as well as a good correlation between local energy metabolism and muscular acidification (τ_{PCr fast} and pH; R² = 0.73, p < 0.005) and global respiration (τ_{PCr fast} and VO₂ ; R²  = 0.55, p = 0.01). This provides improved insights into aerobic and anaerobic energy supply during strong muscular performances.