Explore the vast range of titles available.

Peritumoral edema impedes the full delineation of fiber tracts due to partial volume effects in image voxels that contain a mixture of cerebral parenchyma and extracellular water. The purpose of this study is to investigate the effect of incorporating a free water (FW) model of edema for white matter tractography in the presence of edema. We retrospectively evaluated 26 consecutive brain tumor patients with diffusion MRI and T2-weighted images acquired presurgically. Tractography of the arcuate fasciculus (AF) was performed using the two-tensor unscented Kalman filter tractography (UKFt) method, the UKFt method with a reduced fiber tracking stopping fractional anisotropy (FA) threshold (UKFt+rFA), and the UKFt method with the addition of a FW compartment (UKFt+FW). An automated white matter fiber tract identification approach was applied to delineate the AF. Quantitative measurements included tract volume, edema volume, and mean FW fraction. Visual comparisons were performed by three experts to evaluate the quality of the detected AF tracts. The AF volume in edematous brain hemispheres was significantly larger using the UKFt+FW method (p<0.0001) compared to UKFt, but not significantly larger (p = 0.0996) in hemispheres without edema. The AF size increase depended on the volume of edema: a significant correlation was found between AF volume affected by (intersecting) edema and AF volume change with the FW model (Pearson r = 0.806, p<0.0001). The mean FW fraction was significantly larger in tracts intersecting edema (p = 0.0271). Compared to the UKFt+rFA method, there was a significant increase of the volume of the AF tract that intersected the edema using the UKFt+FW method, while the whole AF volumes were similar. Expert judgment results, based on the five patients with the smallest AF volumes, indicated that the expert readers generally preferred the AF tract obtained by using the FW model, according to their anatomical knowledge and considering the potential influence of the final results on the surgical route. Our results indicate that incorporating biophysical models of edema can increase the sensitivity of tractography in regions of peritumoral edema, allowing better tract visualization in patients with high grade gliomas and metastases.

Major Depressive Disorder continues to remain one of the most prevalent psychiatric diseases globally. Despite multiple trials of conventional therapies, a subset of patients fail to have adequate benefit to treatment. Deep brain stimulation (DBS) is a promising treatment in this difficult to treat population and has shown strong antidepressant effects across multiple cohorts. Nearly two decades of work have provided insights into the potential for chronic focal stimulation in precise brain targets to modulate pathological brain circuits that are implicated in the pathogenesis of depression. In this paper we review the rationale that prompted the selection of various brain targets for DBS, their subsequent clinical outcomes and common adverse events reported. We additionally discuss some of the pitfalls and challenges that have prevented more widespread adoption of this technology as well as future directions that have shown promise in improving therapeutic efficacy of DBS in the treatment of depression.

Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (iUS) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy iUS. We present a method that builds on previous work to address the need for accuracy and generality of MR-iUS registration algorithms in multi-site clinical data. High-dimensional texture attributes were used instead of image intensities for image registration and the standard difference-based attribute matching was replaced with correlation-based attribute matching. A strategy that deals explicitly with the large field-of-view mismatch between MR and iUS images was proposed. Key parameters were optimized across independent MR-iUS brain tumor datasets acquired at 3 institutions, with a total of 43 tumor patients and 758 reference landmarks for evaluating the accuracy of the proposed algorithm. Despite differences in imaging protocols, patient demographics and landmark distributions, the algorithm is able to reduce landmark errors prior to registration in three data sets (5.37±4.27, 4.18±1.97 and 6.18±3.38 mm, respectively) to a consistently low level (2.28±0.71, 2.08±0.37 and 2.24±0.78 mm, respectively). This algorithm was tested against 15 other algorithms and it is competitive with the state-of-the-art on multiple datasets. We show that the algorithm has one of the lowest errors in all datasets (accuracy), and this is achieved while sticking to a fixed set of parameters for multi-site data (generality). In contrast, other algorithms/tools of similar performance need per-dataset parameter tuning (high accuracy but lower generality), and those that stick to fixed parameters have larger errors or inconsistent performance (generality but not the top accuracy). Landmark errors were further characterized according to brain regions and tumor types, a topic so far missing in the literature. •We developed correlation-based attribute matching for MR-US registration.•We optimize the algorithm in so far the most comprehensive multi-site data.•We validated ours with 15 state-of-the-art MR-US registration algorithms.•We show high accuracy and generality in multi-site data, with fixed parameters.•We present, for the first time, MR-US registration errors by regions and tumor types.

PurposeThe brain undergoes significant structural change over the course of neurosurgery, including highly nonlinear deformation and resection. It can be informative to recover the spatial mapping between structures identified in preoperative surgical planning and the intraoperative state of the brain. We present a novel feature-based method for achieving robust, fully automatic deformable registration of intraoperative neurosurgical ultrasound images.MethodsA sparse set of local image feature correspondences is first estimated between ultrasound image pairs, after which rigid, affine and thin-plate spline models are used to estimate dense mappings throughout the image. Correspondences are derived from 3D features, distinctive generic image patterns that are automatically extracted from 3D ultrasound images and characterized in terms of their geometry (i.e., location, scale, and orientation) and a descriptor of local image appearance. Feature correspondences between ultrasound images are achieved based on a nearest-neighbor descriptor matching and probabilistic voting model similar to the Hough transform.ResultsExperiments demonstrate our method on intraoperative ultrasound images acquired before and after opening of the dura mater, during resection and after resection in nine clinical cases. A total of 1620 automatically extracted 3D feature correspondences were manually validated by eleven experts and used to guide the registration. Then, using manually labeled corresponding landmarks in the pre- and post-resection ultrasound images, we show that our feature-based registration reduces the mean target registration error from an initial value of 3.3 to 1.5 mm.ConclusionsThis result demonstrates that the 3D features promise to offer a robust and accurate solution for 3D ultrasound registration and to correct for brain shift in image-guided neurosurgery.

Abstract BACKGROUND Intraoperative magnetic resonance imaging (IO-MRI) provides real-time assessment of extent of resection of brain tumor. Development of new enhancement during IO-MRI can confound interpretation of residual enhancing tumor, although the incidence of this finding is unknown. OBJECTIVE To determine the frequency of new enhancement during brain tumor resection on intraoperative 3 Tesla (3T) MRI. To optimize the postoperative imaging window after brain tumor resection using 1.5 and 3T MRI. METHODS We retrospectively evaluated 64 IO-MRI performed for patients with enhancing brain lesions referred for biopsy or resection as well as a subset with an early postoperative MRI (EP-MRI) within 72 h of surgery (N = 42), and a subset with a late postoperative MRI (LP-MRI) performed between 120 h and 8 wk postsurgery (N = 34). Three radiologists assessed for new enhancement on IO-MRI, and change in enhancement on available EP-MRI and LP-MRI. Consensus was determined by majority response. Inter-rater agreement was assessed using percentage agreement. RESULTS A total of 10 out of 64 (16%) of the IO-MRI demonstrated new enhancement. Seven of 10 patients with available EP-MRI demonstrated decreased/resolved enhancement. One out of 42 (2%) of the EP-MRI demonstrated new enhancement, which decreased on LP-MRI. Agreement was 74% for the assessment of new enhancement on IO-MRI and 81% for the assessment of new enhancement on the EP-MRI. CONCLUSION New enhancement occurs in intraoperative 3T MRI in 16% of patients after brain tumor resection, which decreases or resolves on subsequent MRI within 72 h of surgery. Our findings indicate the opportunity for further study to optimize the postoperative imaging window.

Despite the presentation of similar psychological symptoms, psychological dysfunction secondary to brain injury exhibits markedly lower treatment efficacy compared to injury-independent psychological dysfunction. This gap remains evident, despite extensive research efforts. This review integrates clinical and preclinical evidence to provide a comprehensive overview of the neurobiological mechanisms underlying neuropsychological disorders, focusing on the role of key brain regions in emotional regulation across various forms of brain injuries. It examines therapeutic interventions and mechanistic targets, with the primary goal of identifying pathways for targeted treatments. The review highlights promising therapeutic avenues for addressing injury-associated psychological dysfunction, emphasizing Nrf2, neuropeptides, and nonpharmacological therapies as multi-mechanistic interventions capable of modulating upstream mediators to address the complex interplay of factors underlying psychological dysfunction in brain injury. Additionally, it identifies sexually dimorphic factors as potential areas for further exploration and advocates for detailed investigations into sex-specific patterns to uncover additional contributors to these disorders. Furthermore, it underscores significant gaps, particularly the inadequate consideration of interactions among causal factors, environmental influences, and individual susceptibilities. By addressing these gaps, this review provides new insights and calls for a paradigm shift toward a more context-specific and integrative approach to developing targeted therapies for psychological dysfunction following brain injuries.

PurposeBrain shift during tumor resection can progressively invalidate the accuracy of neuronavigation systems and affect neurosurgeons’ ability to achieve optimal resections. This paper compares two methods that have been presented in the literature to compensate for brain shift: a thin-plate spline deformation model and a finite element method (FEM). For this comparison, both methods are driven by identical sparse data. Specifically, both methods are driven by displacements between automatically detected and matched feature points from intraoperative 3D ultrasound (iUS). Both methods have been shown to be fast enough for intraoperative brain shift correction (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525–1538, 2018; Luo et al. in J Med Imaging (Bellingham) 4(3):035003, 2017). However, the spline method requires no preprocessing and ignores physical properties of the brain while the FEM method requires significant preprocessing and incorporates patient-specific physical and geometric constraints. The goal of this work was to explore the relative merits of these methods on recent clinical data.MethodsData acquired during 19 sequential tumor resections in Brigham and Women’s Hospital’s Advanced Multi-modal Image-Guided Operating Suite between December 2017 and October 2018 were considered for this retrospective study. Of these, 15 cases and a total of 24 iUS to iUS image pairs met inclusion requirements. Automatic feature detection (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525–1538, 2018) was used to detect and match features in each pair of iUS images. Displacements between matched features were then used to drive both the spline model and the FEM method to compensate for brain shift between image acquisitions. The accuracies of the resultant deformation models were measured by comparing the displacements of manually identified landmarks before and after deformation.ResultsThe mean initial subcortical registration error between preoperative MRI and the first iUS image averaged 5.3 ± 0.75 mm. The mean subcortical brain shift, measured using displacements between manually identified landmarks in pairs of iUS images, was 2.5 ± 1.3 mm. Our results showed that FEM was able to reduce subcortical registration error by a small but statistically significant amount (from 2.46 to 2.02 mm). A large variability in the results of the spline method prevented us from demonstrating either a statistically significant reduction in subcortical registration error after applying the spline method or a statistically significant difference between the results of the two methods.ConclusionsIn this study, we observed less subcortical brain shift than has previously been reported in the literature (Frisken et al., in: Miller (ed) Biomechanics of the brain, Springer, Cham, 2019). This may be due to the fact that we separated out the initial misregistration between preoperative MRI and the first iUS image from our brain shift measurements or it may be due to modern neurosurgical practices designed to reduce brain shift, including reduced craniotomy sizes and better control of intracranial pressure with the use of mannitol and other medications. It appears that the FEM method and its use of geometric and biomechanical constraints provided more consistent brain shift correction and better correction farther from the driving feature displacements than the simple spline model. The spline-based method was simpler and tended to give better results for small deformations. However, large variability in the spline results and relatively small brain shift prevented this study from demonstrating a statistically significant difference between the results of the two methods.

PurposeMatching points that are derived from features or landmarks in image data is a key step in some medical imaging applications. Since most robust point matching algorithms claim to be able to deal with outliers, users may place high confidence in the matching result and use it without further examination. However, for tasks such as feature-based registration in image-guided neurosurgery, even a few mismatches, in the form of invalid displacement vectors, could cause serious consequences. As a result, having an effective tool by which operators can manually screen all matches for outliers could substantially benefit the outcome of those applications.MethodsWe introduce a novel variogram-based outlier screening method for vectors. The variogram is a powerful geostatistical tool for characterizing the spatial dependence of stochastic processes. Since the spatial correlation of invalid displacement vectors, which are considered as vector outliers, tends to behave differently than normal displacement vectors, they can be efficiently identified on the variogram.ResultsWe validate the proposed method on 9 sets of clinically acquired ultrasound data. In the experiment, potential outliers are flagged on the variogram by one operator and further evaluated by 8 experienced medical imaging researchers. The matching quality of those potential outliers is approximately 1.5 lower, on a scale from 1 (bad) to 5 (good), than valid displacement vectors.ConclusionThe variogram is a simple yet informative tool. While being used extensively in geostatistical analysis, it has not received enough attention in the medical imaging field. We believe there is a good deal of potential for clinically applying the proposed outlier screening method. By way of this paper, we also expect researchers to find variogram useful in other medical applications that involve motion vectors analyses.