Non-invasive Blood-Brain Barrier Disruption using Acoustic Holography with a Clinical Focused Ultrasound System.

OBJECTIVE: Holographic methods can be used with phased array transducers to shape an ultrasound field. We tested a simple method to create holograms with a hemispherical 1024-element phased array transducer and explored how it could benefit ultrasound-mediated blood-brain barrier (BBB) disruption. METHODS: With this method, individual acoustic simulations for each element of the transducer were simultaneously loaded into computer memory. Each element's phase was systematically modulated until the combined field matched a desired pattern. The method was evaluated with a 220 kHz transducer being tested clinically to enhance drug delivery via BBB disruption. The holograms were evaluated in a tissue-mimicking phantom and in vivo in experiments disrupting the BBB in rats and in a macaque. We also explored whether this approach could mitigate secondary reflections from the skull using simulations of transcranial focusing in clinical treatments of transcranial sonication for BBB disruption. RESULTS: This approach can enlarge the focal volume in a patient-specific manner and could reduce the number of sonication targets needed to disrupt large volumes, improve the homogeneity of the disruption, and improve our ability to detect microbubble activity in tissues with low vascular density. Simulations suggest that the method could also mitigate secondary reflections during transcranial sonication.

ReMIND: The Brain Resection Multimodal Imaging Database.

The standard of care for brain tumors is maximal safe surgical resection. Neuronavigation augments the surgeon's ability to achieve this but loses validity as surgery progresses due to brain shift. Moreover, gliomas are often indistinguishable from surrounding healthy brain tissue. Intraoperative magnetic resonance imaging (iMRI) and ultrasound (iUS) help visualize the tumor and brain shift. iUS is faster and easier to incorporate into surgical workflows but offers a lower contrast between tumorous and healthy tissues than iMRI. With the success of data-hungry Artificial Intelligence algorithms in medical image analysis, the benefits of sharing well-curated data cannot be overstated. To this end, we provide the largest publicly available MRI and iUS database of surgically treated brain tumors, including gliomas (n = 92), metastases (n = 11), and others (n = 11). This collection contains 369 preoperative MRI series, 320 3D iUS series, 301 iMRI series, and 356 segmentations collected from 114 consecutive patients at a single institution. This database is expected to help brain shift and image analysis research and neurosurgical training in interpreting iUS and iMRI.

Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction.

Shape plays an important role in computer graphics, offering informative features to convey an object's morphology and functionality. Shape analysis in brain imaging can help interpret structural and functionality correlations of the human brain. In this work, we investigate the shape of the brain's 3D white matter connections and its potential predictive relationship to human cognitive function. We reconstruct brain connections as sequences of 3D points using diffusion magnetic resonance imaging (dMRI) tractography. To describe each connection, we extract 12 shape descriptors in addition to traditional dMRI connectivity and tissue microstructure features. We introduce a novel framework, Shape--fused Fiber Cluster Transformer (SFFormer), that leverages a multi-head cross-attention feature fusion module to predict subject-specific language performance based on dMRI tractography. We assess the performance of the method on a large dataset including 1065 healthy young adults. The results demonstrate that both the transformer-based SFFormer model and its inter/intra feature fusion with shape, microstructure, and connectivity are informative, and together, they improve the prediction of subject-specific language performance scores. Overall, our results indicate that the shape of the brain's connections is predictive of human language function.

Pilot trial of perampanel on peritumoral hyperexcitability and clinical outcomes in newly diagnosed high-grade glioma.

Background: Glutamatergic neuron-glioma synaptogenesis and peritumoral hyperexcitability promote glioma growth in a positive feedback loop. The objective of this study was to evaluate the feasibility and estimated effect sizes of the AMPA-R antagonist, perampanel, on intraoperative electrophysiologic hyperexcitability and clinical outcomes. Methods: An open-label trial was performed comparing perampanel to standard of care (SOC) in patients undergoing resection of newly-diagnosed radiologic high-grade glioma. Perampanel was administered as a pre-operative loading dose followed by maintenance therapy until progressive disease or up to 12-months. SOC treatment involved levetiracetam for 7-days or as clinically indicated. The primary outcome of hyperexcitability was defined by intra-operative electrocorticography high frequency oscillation (HFO) rates. Seizure-freedom and overall survival (OS) were estimated by the Kaplan-Meier method. Tissue concentrations of perampanel, levetiracetam, and metabolites were measured by mass spectrometry. Results: HFO rates were similar between perampanel-treated and SOC cohorts. The trial was terminated early after interim analysis for futility, and outcomes assessed in 11 patients (7 perampanel-treated, 4 SOC). Over a median 281 days of post-enrollment follow-up, 27% of patients had seizures, including 14% treated with perampanel and 50% treated with SOC. OS in perampanel-treated patients was similar to a glioblastoma reference cohort (p=0.81). Glutamate concentrations in surface biopsies were positively correlated with HFO rates in adjacent electrode contacts and were not significantly associated with treatment assignment or drug concentrations. Conclusions: A peri-operative loading regimen of perampanel was safe and well-tolerated, with similar peritumoral hyperexcitability as in levetiracetam-treated patients. Maintenance anti-glutamatergic therapy was not observed to impact survival outcomes.

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance.

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, to gain insight into the brain regions that contribute most strongly to the prediction results, we propose a Critical Region Localization algorithm. This algorithm identifies highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project Young Adult dataset. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models that have been applied to predict individual cognitive performance based on neuroimaging features. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. Within each tract, we localize critical regions whose microstructure and point information are highly and consistently predictive of language performance across different subjects and across multiple independently trained models. These critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.

Tractography-based automated identification of the retinogeniculate visual pathway with novel microstructure-informed supervised contrastive learning.

The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP , to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a simple and successful streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. We perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation, and we show superior RGVP identification results using DeepRGVP. In addition, we demonstrate a good generalizability of DeepRGVP to dMRI tractography data from neurosurgical patients with pituitary tumors and we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP.

ReMIND: The Brain Resection Multimodal Imaging Database.

The standard of care for brain tumors is maximal safe surgical resection. Neuronavigation augments the surgeon's ability to achieve this but loses validity as surgery progresses due to brain shift. Moreover, gliomas are often indistinguishable from surrounding healthy brain tissue. Intraoperative magnetic resonance imaging (iMRI) and ultrasound (iUS) help visualize the tumor and brain shift. iUS is faster and easier to incorporate into surgical workflows but offers a lower contrast between tumorous and healthy tissues than iMRI. With the success of data-hungry Artificial Intelligence algorithms in medical image analysis, the benefits of sharing well-curated data cannot be overstated. To this end, we provide the largest publicly available MRI and iUS database of surgically treated brain tumors, including gliomas (n=92), metastases (n=11), and others (n=11). This collection contains 369 preoperative MRI series, 320 3D iUS series, 301 iMRI series, and 356 segmentations collected from 114 consecutive patients at a single institution. This database is expected to help brain shift and image analysis research and neurosurgical training in interpreting iUS and iMRI.

Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods.

The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.

A Tool-free Neuronavigation Method based on Single-view Hand Tracking.

This work presents a novel tool-free neuronavigation method that can be used with a single RGB commodity camera. Compared with freehand craniotomy placement methods, the proposed system is more intuitive and less error prone. The proposed method also has several advantages over standard neuronavigation platforms. First, it has a much lower cost, since it doesn't require the use of an optical tracking camera or electromagnetic field generator, which are typically the most expensive parts of a neuronavigation system, making it much more accessible. Second, it requires minimal setup, meaning that it can be performed at the bedside and in circumstances where using a standard neuronavigation system is impractical. Our system relies on machine-learning-based hand pose estimation that acts as a proxy for optical tool tracking, enabling a 3D-3D pre-operative to intra-operative registration. Qualitative assessment from clinical users showed that the concept is clinically relevant. Quantitative assessment showed that on average a target registration error (TRE) of 1.3cm can be achieved. Furthermore, the system is framework-agnostic, meaning that future improvements to hand-tracking frameworks would directly translate to a higher accuracy.

Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound.

Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising 1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines. Based on this dataset, we present a benchmark of established deep learning methods applied to the task of B-line detection. To pave the way for interpretable quantification of B-lines, we propose a novel "single-point" approach to B-line localization using only the point of origin. Our results show that (a) the area under the receiver operating characteristic curve ranges from 0.864 to 0.955 for the benchmarked detection methods, (b) within this range, the best performance is achieved by models that leverage multiple successive frames as input, and (c) the proposed single-point approach for B-line localization reaches an F 1-score of 0.65, performing on par with the inter-observer agreement. The dataset and developed methods can facilitate further biomedical research on automated interpretation of lung ultrasound with the potential to expand the clinical utility.

Glasgow coma scale pupil score (GCS-P) and the hospital mortality in severe traumatic brain injury: analysis of 1,066 Brazilian patients.

BACKGROUND: Pupil reactivity and the Glasgow Coma Scale (GCS) score are the most clinically relevant information to predict the survival of traumatic brain injury (TBI) patients. OBJECTIVE: We evaluated the accuracy of the GCS-Pupil score (GCS-P) as a prognostic index to predict hospital mortality in Brazilian patients with severe TBI and compare it with a model combining GCS and pupil response with additional clinical and radiological prognostic factors. METHODS: Data from 1,066 patients with severe TBI from 5 prospective studies were analyzed. We determined the association between hospital mortality and the combination of GCS, pupil reactivity, age, glucose levels, cranial computed tomography (CT), or the GCS-P score by multivariate binary logistic regression. RESULTS: Eighty-five percent (n = 908) of patients were men. The mean age was 35 years old, and the overall hospital mortality was 32.8%. The area under the receiver operating characteristic curve (AUROC) was 0.73 (0.70-0.77) for the model using the GCS-P score and 0.80 (0.77-0.83) for the model including clinical and radiological variables. The GCS-P score showed similar accuracy in predicting the mortality reported for the patients with severe TBI derived from the International Mission for Prognosis and Clinical Trials in TBI (IMPACT) and the Corticosteroid Randomization After Significant Head Injury (CRASH) studies. CONCLUSION: Our results support the external validation of the GCS-P to predict hospital mortality following a severe TBI. The predictive value of the GCS-P for long-term mortality, functional, and neuropsychiatric outcomes in Brazilian patients with mild, moderate, and severe TBI deserves further investigation.

ANTECEDENTES: A reatividade pupilar e o escore da Escala de Coma de Glasgow (ECG) representam as informações clínicas mais relevantes para predizer a sobrevivência de pacientes com traumatismo cranioencefálico (TCE). OBJETIVO: Avaliar a acurácia da ECG com resposta pupilar (ECG-P) como índice prognóstico para predizer mortalidade hospitalar em pacientes brasileiros acometidos por TCE grave e compará-lo com um modelo combinando ECG e resposta pupilar com fatores prognósticos radiológicos. MéTODOS: Foram analisados dados de 1.066 pacientes com TCE grave de 5 estudos prospectivos. Foi determinada a associação entre mortalidade hospitalar e a combinação de ECG, reatividade pupilar, idade, níveis glicêmicos, tomografia computadorizada (TC) de crânio ou o escore ECG-P por regressão logística binária multivariada. RESULTADOS: Oitenta e cinco por cento (n = 908) dos pacientes eram homens. A média de idade foi de 35 anos e a mortalidade hospitalar geral foi de 32,8%. A AUROC (em português, Curva Característica de Operação do Receptor) foi de 0,73 (0,70­0,77) para o modelo utilizando o escore ECG-P e de 0,80 (0,77­0,83) para o modelo incluindo variáveis clínicas e radiológicas. O escore ECG-P mostrou acurácia semelhante na previsão da mortalidade relatada para pacientes com TCE grave derivados dos estudos International Mission for Prognosis and Clinical Trials in TBI (IMPACT, na sigla em inglês) e Corticosteroid Randomization After Significant Head Injury (CRASH, na sigla em inglês). CONCLUSãO: Nossos resultados apoiam a validação externa da ECG-P para prever a mortalidade hospitalar após um TCE grave. O valor preditivo da ECG-P para mortalidade a longo prazo, resultados funcionais e neuropsiquiátricos em pacientes brasileiros com TCE leve, moderado e grave precisam ser investigados.

Glasgow coma scale pupil score (GCS-P) and the hospital mortality in severe traumatic brain injury: analysis of 1,066 Brazilian patients / Escala de coma de Glasgow com resposta pupilar (ECC-P) e mortalidade hospitalar em traumatismo cranioencefálico grave: análise de 1.066 pacientes brasileiros

Abstract Background Pupil reactivity and the Glasgow Coma Scale (CCS) score are the most clinically relevant information to predict the survival of traumatic brain injury (TBI) patients. Objective We evaluated the accuracy of the CCS-Pupil score (CCS-P) as a prognostic index to predict hospital mortality in Brazilian patients with severe TBI and compare it with a model combining CCS and pupil response with additional clinical and radiological prognostic factors. Methods Data from 1,066 patients with severe TBI from 5 prospective studies were analyzed. We determined the association between hospital mortality and the combination of CCS, pupil reactivity, age, glucose levels, cranial computed tomography (CT), or the CCS-P score by multivariate binary logistic regression. Results Eighty-five percent (n = 908) of patients were men. The mean age was 35 years old, and the overall hospital mortality was 32.8%. The area under the receiver operating characteristic curve (AUROC) was 0.73 (0.70-0.77) for the model using the CCS-P score and 0.80 (0.77-0.83) for the model including clinical and radiological variables. The CCS-P score showed similar accuracy in predicting the mortality reported for the patients with severe TBI derived from the International Mission for Prognosis and Clinical Trials in TBI (IMPACT) and the Corticosteroid Randomization After Significant Head Injury (CRASH) studies. Conclusion Our results support the external validation of the CCS-P to predict hospital mortality following a severe TBI. The predictive value of the CCS-P for long-term mortality, functional, and neuropsychiatric outcomes in Brazilian patients with mild, moderate, and severe TBI deserves further investigation.

Resumo Antecedentes A reatividade pupilar e o escore da Escala de Coma de Glasgow (ECC) representam as informações clínicas mais relevantes para predizer a sobrevivência de pacientes com traumatismo cranioencefálico (TCE). Objetivo Avaliar a acurácia da ECC com resposta pupilar (ECC-P) como índice prognóstico para predizer mortalidade hospitalar em pacientes brasileiros acometidos por TCE grave e compará-lo com um modelo combinando ECC e resposta pupilar com fatores prognósticos radiológicos. Métodos Foram analisados dados de 1.066 pacientes com TCE grave de 5 estudos prospectivos. Foi determinada a associação entre mortalidade hospitalar e a combinação de ECC, reatividade pupilar, idade, níveis glicêmicos, tomografia computadorizada (TC) de crânio ou o escore ECC-P por regressão logística binária multivariada. Resultados Oitenta e cinco por cento (n = 908) dos pacientes eram homens. A média de idade foi de 35 anos e a mortalidade hospitalar geral foi de 32,8%. A AUROC (em português, Curva Característica de Operação do Receptor) foi de 0,73 (0,70-0,77) para o modelo utilizando o escore ECC-P e de 0,80 (0,77-0,83) para o modelo incluindo variáveis clínicas e radiológicas. O escore ECC-P mostrou acurácia semelhante na previsão da mortalidade relatada para pacientes com TCE grave derivados dos estudos International Mission for Prognosis and Clinical Trials in TBI (IMPACT, na sigla em inglês) e Corticosteroid Randomization After Significant Head Injury (CRASH, na sigla em inglês). Conclusão Nossos resultados apoiam a validação externa da ECC-P para prever a mortalidade hospitalar após um TCE grave. O valor preditivo da ECC-P para mortalidade a longo prazo, resultados funcionais e neuropsiquiátricos em pacientes brasileiros com TCE leve, moderado e grave precisam ser investigados.

Directionally encoded color track density imaging in brain tumor patients: A potential application to neuro-oncology surgical planning.

BACKGROUND: Diffusion magnetic resonance imaging white matter tractography, an increasingly popular preoperative planning modality used for pre-surgical planning in brain tumor patients, is employed with the goal of maximizing tumor resection while sparing postoperative neurological function. Clinical translation of white matter tractography has been limited by several shortcomings of standard diffusion tensor imaging (DTI), including poor modeling of fibers crossing through regions of peritumoral edema and low spatial resolution for typical clinical diffusion MRI (dMRI) sequences. Track density imaging (TDI) is a post-tractography technique that uses the number of tractography streamlines and their long-range continuity to map the white matter connections of the brain with enhanced image resolution relative to the acquired dMRI data, potentially offering improved white matter visualization in patients with brain tumors. The aim of this study was to assess the utility of TDI-based white matter maps in a neurosurgical planning context compared to the current clinical standard of DTI-based white matter maps. METHODS: Fourteen consecutive brain tumor patients from a single institution were retrospectively selected for the study. Each patient underwent 3-Tesla dMRI scanning with 30 gradient directions and a b-value of 1000 s/mm2. For each patient, two directionally encoded color (DEC) maps were produced as follows. DTI-based DEC-fractional anisotropy maps (DEC-FA) were generated on the scanner, while DEC-track density images (DEC-TDI) were generated using constrained spherical deconvolution based tractography. The potential clinical utility of each map was assessed by five practicing neurosurgeons, who rated the maps according to four clinical utility statements regarding different clinical aspects of pre-surgical planning. The neurosurgeons rated each map according to their agreement with four clinical utility statements regarding if the map 1 identified clinically relevant tracts, (2) helped establish a goal resection margin, (3) influenced a planned surgical route, and (4) was useful overall. Cumulative link mixed effect modeling and analysis of variance were performed to test the primary effect of map type (DEC-TDI vs. DEC-FA) on rater score. Pairwise comparisons using estimated marginal means were then calculated to determine the magnitude and directionality of differences in rater scores by map type. RESULTS: A majority of rater responses agreed with the four clinical utility statements, indicating that neurosurgeons found both DEC maps to be useful. Across all four investigated clinical utility statements, the DEC map type significantly influenced rater score. Rater scores were significantly higher for DEC-TDI maps compared to DEC-FA maps. The largest effect size in rater scores in favor of DEC-TDI maps was observed for clinical utility statement 2, which assessed establishing a goal resection margin. CONCLUSION: We observed a significant neurosurgeon preference for DEC-TDI maps, indicating their potential utility for neurosurgical planning.

Surgical approach to the treatment of pituicytoma. Report of five cases and a literature review.

Background: Pituicytoma (PTs) is a rare tumor of the sella and suprasellar region, derived from the pituicytes of the neurohypophysis, having distinct histological characteristics of glial neoplasms. We reported, the clinical data, neuroimaging studies, surgical approaches and pathology in five patients with PTs and also, we reviewed the literature. Methods: Retrospective chart from five consecutive patients with PTs treated at one University Hospital from 2016 to 2021 were reviewed. In addition, we conducted a search in PubMed/Medline databases using the term "Pituicytoma". Data regarding age, gender, pathological findings, and treatment modality applied were extracted. Results: All patients were female, aged 29-63, complaining of headaches, visual loss and field defects, dizziness and normal or abnormal levels of circulating pituitary hormones. Magnetic Resonance Imaging (MRI) showed in all patients a sellar and suprasellar mass, which was removed through an endoscopic transsphenoidal approach. Our third patient had a subtotal resection followed by close observation. Histopathology showed a glial non-infiltrative tumors with spindle cells, and a final diagnosis of pituicytoma was made. After surgery, visual field defects in all patients were normalized, and in two patients normal levels of plasma hormones were restored. After a mean of three years follow-up, the patients were managed post-operatively through close clinical observation and serial MRI. None of the patients had recurrence of the disease. Conclusion: PTs is a rare glial tumor of the sellar and suprasellar region that arises from neurohypophyseal pituicytes. Disease control may be achieved by total excision.

SlicerCBM: automatic framework for biomechanical analysis of the brain.

PURPOSE: Brain shift that occurs during neurosurgery disturbs the brain's anatomy. Prediction of the brain shift is essential for accurate localisation of the surgical target. Biomechanical models have been envisaged as a possible tool for such predictions. In this study, we created a framework to automate the workflow for predicting intra-operative brain deformations. METHODS: We created our framework by uniquely combining our meshless total Lagrangian explicit dynamics (MTLED) algorithm for computing soft tissue deformations, open-source software libraries and built-in functions within 3D Slicer, an open-source software package widely used for medical research. Our framework generates the biomechanical brain model from the pre-operative MRI, computes brain deformation using MTLED and outputs results in the form of predicted warped intra-operative MRI. RESULTS: Our framework is used to solve three different neurosurgical brain shift scenarios: craniotomy, tumour resection and electrode placement. We evaluated our framework using nine patients. The average time to construct a patient-specific brain biomechanical model was 3 min, and that to compute deformations ranged from 13 to 23 min. We performed a qualitative evaluation by comparing our predicted intra-operative MRI with the actual intra-operative MRI. For quantitative evaluation, we computed Hausdorff distances between predicted and actual intra-operative ventricle surfaces. For patients with craniotomy and tumour resection, approximately 95% of the nodes on the ventricle surfaces are within two times the original in-plane resolution of the actual surface determined from the intra-operative MRI. CONCLUSION: Our framework provides a broader application of existing solution methods not only in research but also in clinics. We successfully demonstrated the application of our framework by predicting intra-operative deformations in nine patients undergoing neurosurgical procedures.

Mapping Resection Progress by Tool-Tip Tracking during Brain Tumor Surgery for Real-Time Estimation of Residual Tumor.

Surgical resection continues to be the primary initial therapeutic strategy in the treatment of patients with brain tumors. Computerized cranial neuronavigation based on preoperative imaging offers precision guidance during craniotomy and early tumor resection but progressively loses validity with brain shift. Intraoperative MRI (iMRI) and intraoperative ultrasound (iUS) can update the imaging used for guidance and navigation but are limited in terms of temporal and spatial resolution, respectively. We present a system that uses time-stamped tool-tip positions of surgical instruments to generate a map of resection progress with high spatial and temporal accuracy. We evaluate this system and present results from 80 cranial tumor resections. Regions of the preoperative tumor segmentation that are covered by the resection map (True Positive Tracking) and regions of the preoperative tumor segmentation not covered by the resection map (True Negative Tracking) are determined for each case. We compare True Negative Tracking, which estimates the residual tumor, with the actual residual tumor identified using iMRI. We discuss factors that can cause False Positive Tracking and False Negative Tracking, which underestimate and overestimate the residual tumor, respectively. Our method provides good estimates of the residual tumor when there is minimal brain shift, and line-of-sight is maintained. When these conditions are not met, surgeons report that it is still useful for identifying regions of potential residual.

Global cancer surgery in low-resource settings: A strengths, weaknesses, opportunities, and threats analysis.

Global cancer surgery is an essential and complex component of oncologic care. This study aims to describe global cancer surgery literature since the 2015 Lancet Commission on Global Surgery and Cancer Surgery and perform a strengths, weaknesses, opportunities, and threats (SWOT) analysis. A systematic search was performed in PubMed of global cancer surgery articles. Themes were extracted from the included studies based on the following criteria: (1) performed in low- or low-middle-income countries, (2) published during or after 2015, (3) published in peer-reviewed journals, (4) written in the English language, and (5) accessible to the authors. Themes were further grouped into strengths, weaknesses, opportunities, and threats (SWOT analysis). The search strategy identified 154 articles published from 1992 to 2022. Forty-six articles were included in the qualitative synthesis and SWOT analysis. Recurring themes included local epidemiologic studies, local innovations and feasibility studies, prioritizing quality of life outcomes, multidisciplinary team approaches, limited resources, health system gaps, lack of economic analyses, diverse cancer management strategies and priorities, inter-setting collaboration, research expansion, the coronavirus disease 2019 pandemic, and unchecked technological advancements. These strengths, weaknesses, opportunities, and threats were described and related to the themes of research, surgical systems strengthening, economics and financing, and political framing of the 2015 Lancet Commission on Global Cancer Surgery. SWOT analyses of global cancer surgery may be helpful in suggesting future strategies for this expanding field. PLAIN LANGUAGE SUMMARY: Cancer surgery is a resource-intensive yet essential component of cancer care. In the face of projected growth of cancer burden, the present gap in cancer surgery care in low-resource settings with stressed health care and surgical infrastructure risks further exacerbation. We present a strengths, weaknesses, opportunities, and threats analysis of recent global cancer surgery literature pertaining to low-resource settings.

Superficial white matter analysis: An efficient point-cloud-based deep learning framework with supervised contrastive learning for consistent tractography parcellation across populations and dMRI acquisitions.

Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is adapted to our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers for SWM. We train our model on a large-scale tractography dataset including streamline samples from labeled long- and medium-range (over 40 mm) SWM clusters and anatomically implausible streamline samples, and we perform testing on six independently acquired datasets of different ages and health conditions (including neonates and patients with space-occupying brain tumors). Compared to several state-of-the-art methods, SupWMA obtains highly consistent and accurate SWM parcellation results on all datasets, showing good generalization across the lifespan in health and disease. In addition, the computational speed of SupWMA is much faster than other methods.