Radiomics Based Differentiation of Glioblastoma and Metastatic Disease: Impact of Different T1-Contrast Enhanced Sequences on Radiomic Features and Model Performance.

Background And Purpose: To evaluate the radiomics-based model performance for differentiation between glioblastoma (GB) and brain metastases (BM) using magnetization prepared rapid gradient echo (MPRAGE) and volumetric interpolated breath-hold examination (VIBE) T1-contrast enhanced sequences.

Materials And Methods: T1-CE MPRAGE and VIBE sequences acquired in 108 patients (31 GBs and 77 BM) during the same MRI session were retrospectively evaluated. Post standardized image pre-processing and segmentation, radiomics features were extracted from necrotic and enhancing tumor components.

Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve.

Accurate segmentation of retinal layers in optical coherence tomography (OCT) images is critical for assessing diseases that affect the optic nerve, but existing automated algorithms often fail when pathology causes irregular layer topology, such as extreme thinning of the ganglion cell-inner plexiform layer (GCIPL). Deep LOGISMOS, a hybrid approach that combines the strengths of deep learning and 3D graph search to overcome their limitations, was developed to improve the accuracy, robustness and generalizability of retinal layer segmentation. The method was trained on 124 OCT volumes from both eyes of 31 non-arteritic anterior ischemic optic neuropathy (NAION) patients and tested on three cross-sectional datasets with available reference tracings: Test-NAION (40 volumes from both eyes of 20 NAION subjects), Test-G (29 volumes from 29 glaucoma subjects/eyes), and Test-JHU (35 volumes from 21 multiple sclerosis and 14 control subjects/eyes) and one longitudinal dataset without reference tracings: Test-G-L (155 volumes from 15 glaucoma patients/eyes).

Acetylsalicylic acid use and development of cardiac allograft vasculopathy: A national prospective study using highly automated 3-D optical coherence tomography analysis.

Background: There is conflicting evidence on the role of acetylsalicylic acid (ASA) use in the development of cardiac allograft vasculopathy (CAV).

Methods: A nationwide prospective two-center study investigated changes in the coronary artery vasculature by highly automated 3-D optical coherence tomography (OCT) analysis at 1 month and 12 months after heart transplant (HTx). The influence of ASA use on coronary artery microvascular changes was analyzed in the overall study cohort and after propensity score matching for selected clinical CAV risk factors.

The accuracy of detailed analysis of optical coherence tomography in detection of plaque lipid content: dual-imaging study with optical coherence tomography and near-infrared spectroscopy.

Background: Lipid-rich plaque covered by a thin fibrous cap (FC) has been identified as a frequent morphological substrate for the development of acute coronary syndrome. Optical coherence tomography (OCT) permits the identification and measurement of the FC. Near-infrared spectroscopy (NIRS) has been approved for detection of coronary lipids.

A Technique to Enable Efficient Adaptive Radiation Therapy: Automated Contouring of Prostate and Adjacent Organs.

Purpose: The purpose of this work was to investigate the use of a segmentation approach that could potentially improve the speed and reproducibility of contouring during magnetic resonance-guided adaptive radiation therapy.

Methods And Materials: The segmentation algorithm was based on a hybrid deep neural network and graph optimization approach that also allows rapid user intervention (Deep layered optimal graph image segmentation of multiple objects and surfaces [LOGISMOS] + just enough interaction [JEI]). A total of 115 magnetic resonance-data sets were used for training and quantitative assessment.