Investigating Neural Plasticity and Cortical Reorganization via fMRI Following Tumor Resection

The brain is known to dynamically repair and reorganize itself after sustaining damage. Patients undergoing tumor resection display cortical reorganization, but the specific processes remain relatively unknown. Our longitudinal study investigates the effectiveness of task-based functional magnetic resonance imaging (fMRI) in detecting recovery of eloquent function in brain tumor patients. We assessed the changes in brain activity as the brain recovers from tumor growth and surgery through the correlations between neuropsychological analysis and changes in fMRI activation.https://digitalcommons.unmc.edu/surp2021/1026/thumbnail.jp

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Post-operative perfusion and diffusion MR imaging and tumor progression in high-grade gliomas.

PURPOSE:Perfusion and diffusion magnetic resonance imaging (MRI) provide important biomarkers for brain tumor analysis. Our aim was to investigate if regions of increased perfusion or tumor with restricted diffusion on the immediate post-operative MRI examination would be predictive of time to tumor progression in patients with high-grade gliomas. MATERIALS AND METHODS:Twenty-three patients with high-grade gliomas were retrospectively analyzed. We measured the perfusion at the resection area and evaluated the presence or absence of the restricted diffusion in residual tumor masses. The associations of the perfusion, diffusion and contrast enhancement (delayed static enhancement (DSE)) characteristics with time to tumor progression were statistically calculated. We also evaluated if the location of the tumor progression was concordant to the areas of the elevated perfusion, tumor type restricted diffusion and enhancement. RESULTS:Patients with >200 days to progression are more likely to have no elevated relative cerebral blood volume (rCBV) ratio (p = 0.0004), no tumor restriction (p = 0.024), and no DSE (p = 0.052). The elevated mean rCBV ratio (p1.5 progressed in 275 days or earlier. Tumors tended to progress at the area where patients with post-operative MRIs showed elevated perfusion (p = 0.006), tumor-type restricted diffusion (p = 0.005) and DSE (p = 0.008). CONCLUSIONS:Post-operative analysis of rCBV, tumor type restricted diffusion and enhancement characteristics are predictive of time to progression, risk of progression and where tumor progression is likely to occur

Convection perfusion of glucocerebrosidase for neuronopathic Gaucher's disease

Systemic enzyme replacement for Gaucher's disease has not prevented premature death or severe morbidity in patients with a neuronopathic phenotype, because the enzyme does not cross the blood-brain barrier. We used convection-enhanced delivery for regional distribution of glucocerebrosidase in rat and primate brains and examined its safety and feasibility for neuronopathic Gaucher's disease. Rats underwent intrastriatal infusion and were observed and then sacrificed at 14 hours, 4 days, or 6 weeks. Primates underwent serial magnetic resonance imaging during enzyme perfusion of the right frontal lobe or brainstem, were observed and then sacrificed after infusion completion. Animals underwent histologic and enzymatic tissue analyses. Magnetic resonance imaging revealed perfusion of the primate right frontal lobe or Pons with infitsate. Enzyme activity was substantially and significantly (p <0.05) increased in cortex and white matter of the infused frontal lobe and pons compared to control. Immunohistochemistry demonstrated intraneuronal glucocerebrosidase. There was no toxicity. Convection-enhanced delivery can be used to safely perfuse large regions of the brain and brainstem with therapeutic levels of glucocerebrosidase. Patients with neuronopathic Gaucher's disease and similar central nervous system disorders may benefit from this treatmen

AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi

AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi

AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use.Fil: Li, Jianning. Technische Universitat Graz; AustriaFil: Pimentel, Pedro. No especifíca;Fil: Szengel, Angelika. No especifíca;Fil: Ehlke, Moritz. No especifíca;Fil: Lamecker, Hans. No especifíca;Fil: Zachow, Stefan. No especifíca;Fil: Estacio, Laura. Universidad Católica San Pablo; PerúFil: Doenitz, Christian. No especifíca;Fil: Ramm, Heiko. No especifíca;Fil: Shi, Haochen. Shanghai Jiao Tong University; ChinaFil: Chen, Xiaojun. Shanghai Jiao Tong University; ChinaFil: Matzkin, Victor Franco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Newcombe, Virginia. University of Cambridge; Estados UnidosFil: Ferrante, Enzo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Jin, Yuan. Technische Universitat Graz; AustriaFil: Ellis, David G.. No especifíca;Fil: Aizenberg, Michele R.. University of Nebraska; Estados UnidosFil: Kodym, Oldrich. No especifíca;Fil: Spanel, Michal. No especifíca;Fil: Herout, Adam. No especifíca;Fil: Mainprize, James G.. Sunnybrook Health Sciences Centre; CanadáFil: Fishman, Zachary. Sunnybrook Health Sciences Centre; CanadáFil: Hardisty, Michael R.. Sunnybrook Health Sciences Centre; CanadáFil: Bayat, Amirhossein. No especifíca;Fil: Shit, Suprosanna. No especifíca;Fil: Wang, Bomin. Shandong University; ChinaFil: Liu, Zhi. Shandong University; ChinaFil: Eder, Matthias. Technische Universitat Graz; AustriaFil: Pepe, Antonio. Technische Universitat Graz; AustriaFil: Gsaxner, Christina. Technische Universitat Graz; AustriaFil: Alves, Victor. Universidade do Minho; PortugalFil: Zefferer, Ulrike. Medizinische Universität Graz; AustriaFil: Von Campe, Gord. Medizinische Universität Graz; AustriaFil: Pistracher, Karin. Medizinische Universität Graz; AustriaFil: Schafer, Ute. Medizinische Universität Graz; AustriaFil: Schmalstieg, Dieter. Technische Universitat Graz; AustriaFil: Menze, Bjoern H.. No especifíca;Fil: Glocker, Ben. Imperial College London; Reino UnidoFil: Egger, Jan. Computer Algorithms For Medicine Laboratory; Austri

AutoImplant 2020 - First MICCAI Challenge on Automatic Cranial Implant Design

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi.FWF - Austrian Science Fund(KLI 678-B31

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac