Light-sheet microscopy using MEMS and active optics for 3D image acquisition control

A miniaturized version of a light-sheet microscopy (LSM) system, with 3D imaging enabled through active optical control, is presented. Even though the field of LSM technology has advanced significantly in recent years, it is still not considered an easily available technique. This is mainly due to its cost compared to epifluorescence setups and the requirement for specific sample mounting techniques in most cases, as well as stringent optical alignment and difficulty to reduce motion artifacts when the sample is moved through the light path to create the imaging slices. In our research, we demonstrate a miniaturized version of an LSM that can reduce size and cost, and is able to achieve 3D imaging through control of multiple active optical elements and MEMS micromirrors used in both the illumination and imaging path instead of moving the sample. The laser excitation is controlled and shaped via multiple MEMS elements for 3D beam position control and multilens beam shaping to generate a 2.85 μm wide light-sheet with controllable height of up to 550 μm, and orthogonal positioning over a 200 μm range. Additionally, the focal point of the excitation can be shifted along the laser propagation direction by 200 μm. The orthogonally positioned imaging path incorporates a x20, NA = 0.4 objective and a tunable lens for imaging selected focal planes synchronized with the excitation positioning. The imaging results show sub-micron resolution with a field-of-view of 400 μm x 300 μm. The synchronization of the two active elements allows for fast imaging of different slices of a sample and promises convenient 3D reconstruction and representation of cell tissue

Advanced Magnetic Resonance Imaging in Glioblastoma: A Review

INTRODUCTION In 2017, it is estimated that 26,070 patients will be diagnosed with a malignant primary brain tumor in the United States, with more than half having the diagnosis of glioblas- toma (GBM).1 Magnetic resonance imaging (MRI) is a widely utilized examination in the diagnosis and post-treatment management of patients with glioblastoma; standard modalities available from any clinical MRI scanner, including T1, T2, T2-FLAIR, and T1-contrast-enhanced (T1CE) sequences, provide critical clinical information. In the last decade, advanced imaging modalities are increasingly utilized to further charac- terize glioblastomas. These include multi-parametric MRI sequences, such as dynamic contrast enhancement (DCE), dynamic susceptibility contrast (DSC), diffusion tensor imaging (DTI), functional imaging, and spectroscopy (MRS), to further characterize glioblastomas, and significant efforts are ongoing to implement these advanced imaging modalities into improved clinical workflows and personalized therapy approaches. A contemporary review of standard and advanced MR imaging in clinical neuro-oncologic practice is presented

The University of Pennsylvania Glioblastoma (UPenn-GBM) cohort: Advanced MRI, clinical, genomics, & radiomics

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks (CNNs) and classical regression methods with hand-crafted features for survival time regression of patients with high grade brain tumors. The tested CNNs for regression showed promising but unstable results. The best performing deep learning approach reached an accuracy of 51.5% on held-out samples of the training set. All tested deep learning experiments were outperformed by a Support Vector Classifier (SVC) using 30 radiomic features. The investigated features included intensity, shape, location and deep features. The submitted method to the BraTS 2018 survival prediction challenge is an ensemble of SVCs, which reached a cross-validated accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set, and 42.9% on the testing set. The results suggest that more training data is necessary for a stable performance of a CNN model for direct regression from magnetic resonance images, and that non-imaging clinical patient information is crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation (BraTS) Challenge 2018, survival prediction tas

A miniaturised light-sheet microscopy system using MEMS micromirror control

We present the optical characterization of a MEMS enabled light-sheet microscopy system which combines 2-axis MEMS control over the light delivery for a scanned and freely positioned light-sheet as well as MEMS focal control of the image collection. The system performance is evaluated using standard imaging targets and fluorescence bead samples for 3D image generation

MEMS enabled control of light-sheet microscopy optical beam paths

The excitation and imaging optical beam paths for the design of a digitally controllable light-sheet microscopy system using a piezoelectric MEMS scanner and a thermal bimorph varifocal MEMS mirror, respectively, are presented