Liver resection causes marked perfusion alterations in the liver remnant both on the organ scale (vascular anatomy) and on the microscale (sinusoidal blood flow on tissue level). These changes in ...perfusion affect hepatic functions via direct alterations in blood supply and drainage, followed by indirect changes of biomechanical tissue properties and cellular function. Changes in blood flow impose compression, tension and shear forces on the liver tissue. These forces are perceived by mechanosensors on parenchymal and non-parenchymal cells of the liver and regulate cell-cell and cell-matrix interactions as well as cellular signaling and metabolism. These interactions are key players in tissue growth and remodeling, a prerequisite to restore tissue function after PHx. Their dysregulation is associated with metabolic impairment of the liver eventually leading to liver failure, a serious post-hepatectomy complication with high morbidity and mortality. Though certain links are known, the overall functional change after liver surgery is not understood due to complex feedback loops, non-linearities, spatial heterogeneities and different time-scales of events. Computational modeling is a unique approach to gain a better understanding of complex biomedical systems. This approach allows (i) integration of heterogeneous data and knowledge on multiple scales into a consistent view of how perfusion is related to hepatic function; (ii) testing and generating hypotheses based on predictive models, which must be validated experimentally and clinically. In the long term, computational modeling will (iii) support surgical planning by predicting surgery-induced perfusion perturbations and their functional (metabolic) consequences; and thereby (iv) allow minimizing surgical risks for the individual patient. Here, we review the alterations of hepatic perfusion, biomechanical properties and function associated with hepatectomy. Specifically, we provide an overview over the clinical problem, preoperative diagnostics, functional imaging approaches, experimental approaches in animal models, mechanoperception in the liver and impact on cellular metabolism, omics approaches with a focus on transcriptomics, data integration and uncertainty analysis, and computational modeling on multiple scales. Finally, we provide a perspective on how multi-scale computational models, which couple perfusion changes to hepatic function, could become part of clinical workflows to predict and optimize patient outcome after complex liver surgery.
Abstract Accurate and robust segmentation of subcortical gray matter (SGM) nuclei is required in many neuroimaging applications. FMRIB's Integrated Registration and Segmentation Tool (FIRST) is one ...of the most popular software tools for automated subcortical segmentation based on T1 -weighted (T1w) images. In this work, we demonstrate that FIRST tends to produce inaccurate SGM segmentation results in the case of abnormal brain anatomy, such as present in atrophied brains, due to a poor spatial match of the subcortical structures with the training data in the MNI space as well as due to insufficient contrast of SGM structures on T1w images. Consequently, such deviations from the average brain anatomy may introduce analysis bias in clinical studies, which may not always be obvious and potentially remain unidentified. To improve the segmentation of subcortical nuclei, we propose to use FIRST in combination with a special Hybrid image Contrast (HC) and Non-Linear (nl) registration module (HC-nlFIRST), where the hybrid image contrast is derived from T1w images and magnetic susceptibility maps to create subcortical contrast that is similar to that in the Montreal Neurological Institute (MNI) template. In our approach, a nonlinear registration replaces FIRST's default linear registration, yielding a more accurate alignment of the input data to the MNI template. We evaluated our method on 82 subjects with particularly abnormal brain anatomy, selected from a database of > 2000 clinical cases. Qualitative and quantitative analyses revealed that HC-nlFIRST provides improved segmentation compared to the default FIRST method.
MRI-based biomechanical studies can provide a deep understanding of the mechanisms governing liver function, its mechanical performance but also liver diseases. In addition, comprehensive modeling of ...the liver can help improve liver disease treatment. Furthermore, such studies demonstrate the beginning of an engineering-level approach to how the liver disease affects material properties and liver function. Aimed at researchers in the field of MRI-based liver simulation, research articles pertinent to MRI-based liver modeling were identified, reviewed, and summarized systematically. Various MRI applications for liver biomechanics are highlighted, and the limitations of different viscoelastic models used in magnetic resonance elastography are addressed. The clinical application of the simulations and the diseases studied are also discussed. Based on the developed questionnaire, the papers' quality was assessed, and of the 46 reviewed papers, 32 papers were determined to be of high-quality. Due to the lack of the suitable material models for different liver diseases studied by magnetic resonance elastography, researchers may consider the effect of liver diseases on constitutive models. In the future, research groups may incorporate various aspects of machine learning (ML) into constitutive models and MRI data extraction to further refine the study methodology. Moreover, researchers should strive for further reproducibility and rigorous model validation and verification.
Background
Preventing sepsis‐associated acute kidney injury (S‐AKI) can be challenging because it develops rapidly and is often asymptomatic. Probability assessment of disease progression for ...therapeutic follow‐up and outcome are important to intervene and prevent further damage.
Purpose
To establish a noninvasive multiparametric MRI (mpMRI) tool, including T1, T2, and perfusion mapping, for probability assessment of the outcome of S‐AKI.
Study Type
Preclinical randomized prospective study.
Animal Model
One hundred and forty adult female SD rats (65 control and 75 sepsis).
Field Strength/Sequence
9.4T; T1 and perfusion map (FAIR‐EPI) and T2 map (multiecho RARE).
Assessment
Experiment 1: To identify renal injury in relation to sepsis severity, serum creatinine levels were determined (31 control and 35 sepsis). Experiment 2: Animals underwent mpMRI (T1, T2, perfusion) 18 hours postsepsis. A subgroup of animals was immediately sacrificed for histology examination (nine control and seven sepsis). Result of mpMRI in follow‐up subgroup (25 control and 33 sepsis) was used to predict survival outcomes at 96 hours.
Statistical Tests
Mann–Whitney U test, Spearman/Pearson correlation (r), P < 0.05 was considered statistically significant.
Results
Severely ill septic animals exhibited significantly increased serum creatinine levels compared to controls (70 ± 30 vs. 34 ± 9 μmol/L, P < 0.0001). Cortical perfusion (480 ± 80 vs. 330 ± 140 mL/100 g tissue/min, P < 0.005), and cortical and medullary T2 relaxation time constants were significantly reduced compared to controls (41 ± 4 vs. 37 ± 5 msec in cortex, P < 0.05, 52 ± 7 vs. 45 ± 6 msec in medulla, P < 0.05). The combination of cortical T2 relaxation time constants and perfusion results at 18 hours could predict survival outcomes at 96 hours with high sensitivity (80%) and specificity (73%) (area under curve of ROC = 0.8, Jmax = 0.52).
Data Conclusion
This preclinical study suggests combined T2 relaxation time and perfusion mapping as first line diagnostic tool for treatment planning.
Level of Evidence
2
Technical Efficacy Stage
2
Abstract Alterations in brain function in schizophrenia and other neuropsychiatric disorders are evident not only during specific cognitive challenges, but also from functional MRI data obtained ...during a resting state. Here we apply probabilistic independent component analysis (pICA) to resting state fMRI series in 25 schizophrenia patients and 25 matched healthy controls. We use an automated algorithm to extract the ICA component representing the default mode network (DMN) as defined by a DMN-specific set of 14 brain regions, resulting in z-scores for each voxel of the (whole-brain) statistical map. While goodness of fit was found to be similar between the groups, the region of interest (ROI) as well as voxel-wise analysis of the DMN showed significant differences between groups. Healthy controls revealed stronger effects of pICA-derived connectivity measures in right and left dorsolateral prefrontal cortices, bilateral medial frontal cortex, left precuneus and left posterior lateral parietal cortex, while stronger effects in schizophrenia patients were found in the right amygdala, left orbitofrontal cortex, right anterior cingulate and bilateral inferior temporal cortices. In patients, we also found an inverse correlation of negative symptoms with right anterior prefrontal cortex activity at rest and negative symptoms. These findings suggest that aberrant default mode network connectivity contributes to regional functional pathology in schizophrenia and bears significance for core symptoms.
Abstract
Size-specific dose estimate ($\mathbf{SSDE}$) index appears to be more suitable than the commonly used volume computed tomography dose index ($\mathbf{C}{\mathbf{TDI}}_{\mathbf{vol}}$) to ...estimate the dose delivered to the patient during a computed tomography (CT) scan. We evaluated whether an ${\mathbf{SSDE}}_{\mathbf{BMI}}$ can be determined from the patient’s body mass index ($\mathbf{BMI}$) with sufficient reliability in the case that a $\mathbf{SSDE}$ is not given by the CT scanner. For each of the three most used examination types, CT examinations of 50 female and 50 male patients were analyzed. The $\mathbf{SSDE}$ values automatically provided by the scanner were compared with ${\mathbf{SSDE}}_{\mathbf{BMI}}$ determined from $\mathbf{C}{\mathbf{TDI}}_{\mathbf{vol}}$ and $\mathbf{BMI}$. A good accordance of ${\mathbf{SSDE}}_{\mathbf{BMI}}$ and $\mathbf{SSDE}$ was found for the chest and abdominal regions. A low correlation was observed for the head region. The presented method is a simple and practically useful surrogate approach for the chest and abdominal regions but not for the head.
Abstract Early intervention research in schizophrenia has suggested that brain structural alterations might be present in subjects at high risk of developing psychosis. The heterogeneity of regional ...effects of these changes, which is established in schizophrenia, however, has not been explored in prodromal or high-risk populations. We used high-resolution MRI and voxel-based morphometry (VBM8) to analyze grey matter differences in 43 ultra high-risk subjects for psychosis (meeting ARMS criteria, identified through CAARMS interviews), 24 antipsychotic–naïve first-episode schizophrenia patients and 49 healthy controls (groups matched for age and gender). Compared to healthy controls, resp., first-episode schizophrenia patients had reduced regional grey matter in left prefrontal, insula, right parietal and left temporal cortices, while the high-risk group showed reductions in right middle temporal and left anterior frontal cortices. When dividing the ultra-high-risk group in those with a genetic risk vs. those with attenuated psychotic symptoms, the former showed left anterior frontal, right caudate, as well as a smaller right hippocampus, and amygdala reduction, while the latter subgroup showed right middle temporal cortical reductions (each compared to healthy controls). Our findings in a clinical psychosis high-risk cohort demonstrate variability of brain structural changes according to subgroup and background of elevated risk, suggesting frontal and possibly also hippocampal/amygdala changes in individuals with genetic susceptibility. Heterogeneity of structural brain changes (as seen in schizophrenia) appears evident even at high-risk stage, prior to potential onset of psychosis.
Abstract Objective Previous morphometric studies are suggesting altered cortical thickness mainly in prefronto-temporal regions in first episode schizophrenia. In an extension of these earlier ...studies, we used an entire cortex vertex-wise approach and an automated clustering for the detection and exact quantification of cortical thickness alterations in first episode schizophrenia. Methods A group of 54 patients with first episode schizophrenia according to DSM-IV and 54 age and gender matched healthy control subjects were included. All participants underwent high-resolution T1-weighted MRI scans on a 1.5 T scanner. Cortical thickness was estimated as the distance between the gray–white matter border and the pial surface using an automated computerized algorithm (Freesurfer Software). Statistical cortical maps were created to estimate differences of cortical thickness between groups based on this entire cortex analysis. Results Significant cortical thinning was observed in first episode schizophrenia patients relative to controls in a number of cortical areas including the dorsolateral and frontopolar cortices, the anterior cingulate cortex, a ventrolateral–orbitofrontal cluster, as well as the superior temporal cortices and superior parietal lobe. Cortical thinning within these regions was on average 4.4–5.7% with strongest reductions in orbitofrontal regions (7.1%). Conclusions The present findings suggest widespread reduction of cortical thickness, mostly in heteromodal cortices of fronto-temporal networks to be present at an early stage of schizophrenia. Taken together, the present morphometric data in first episode schizophrenia provide further evidence for potential neurodevelopmental deficits and disruption of cortical maturation in this disorder.
PDF
