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Purpose Absorbed radiation dose-response relationships are not clear in molecular radiotherapy (MRT). Here, we propose a voxel-based dose calculation system for multicellular dosimetry in MRT. We applied confocal microscope images of a spherical cell aggregate i.e. a spheroid, to examine the computation of dose distribution within a tissue from the distribution of radiopharmaceuticals. Methods A confocal microscope Z-stack of a human hepatocellular carcinoma HepG2 spheroid was segmented using a support-vector machine algorithm and a watershed function. Heterogeneity in activity uptake was simulated by selecting a varying amount of the cell nuclei to contain 111 In, 125 I, or 177 Lu. Absorbed dose simulations were carried out using vxlPen, a software application based on the Monte Carlo code PENELOPE. Results We developed a schema for radiopharmaceutical dosimetry. The schema utilizes a partially supervised segmentation method for cell-level image data together with a novel main program for voxel-based radiation dose simulations. We observed that for 177 Lu, radiation cross-fire enabled full dose coverage even if the radiopharmaceutical had accumulated to only 60% of the spheroid cells. This effect was not found with 111 In and 125 I. Using these Auger/internal conversion electron emitters seemed to guarantee that only the cells with a high enough activity uptake will accumulate a lethal amount of dose, while neighboring cells are spared. Conclusions We computed absorbed radiation dose distributions in a 3D-cultured cell spheroid with a novel multicellular dosimetric chain. Combined with pharmacological studies in different tissue models, our cell-level dosimetric calculation method can clarify dose-response relationships for radiopharmaceuticals used in MRT. [ABSTRACT FROM AUTHOR]

Objectives: To assess radiographic brain abnormalities and investigate volumetric differences in adults born preterm at very low birth weight (<1500 g), using siblings as controls.Study Design: We recruited 79 adult same-sex sibling pairs with one born preterm at very low birth weight and the sibling at term. We acquired 3-T brain magnetic resonance imaging from 78 preterm participants and 72 siblings. A neuroradiologist, masked to participants' prematurity status, reviewed the images for parenchymal and structural abnormalities, and FreeSurfer software 6.0 was used to conduct volumetric analyses. Data were analyzed by linear mixed models.Results: We found more structural abnormalities in very low birth weight participants than in siblings (37% vs 13%). The most common finding was periventricular leukomalacia, present in 15% of very low birth weight participants and in 3% of siblings. The very low birth weight group had smaller absolute brain volumes (-0.4 SD) and, after adjusting for estimated intracranial volume, less gray matter (-0.2 SD), larger ventricles (1.5 SD), smaller thalami (-0.6 SD), caudate nuclei (-0.4 SD), right hippocampus (-0.4 SD), and left pallidum (-0.3 SD). We saw no volume differences in total white matter (-0.04 SD; 95% CI, -0.13 to 0.09).Conclusions: Preterm very low birth weight adults had a higher prevalence of brain abnormalities than their term-born siblings. They also had smaller absolute brain volumes, less gray but not white matter, and smaller volumes in several gray matter structures. [ABSTRACT FROM AUTHOR]

Abstract: Boron Neutron Capture Therapy (BNCT) is a binary radiotherapy method developed to treat patients with certain malignant tumours. To date, over 300 treatments have been carried out at the Finnish BNCT facility in various on-going and past clinical trials. In this technical review, we discuss our research work in the field of medical physics to form the groundwork for the Finnish BNCT patient treatments, as well as the possibilities to further develop and optimize the method in the future. Accordingly, the following aspects are described: neutron sources, beam dosimetry, treatment planning, boron imaging and determination, and finally the possibilities to detect the efficacy and effects of BNCT on patients. [Copyright &y& Elsevier]

Purpose The aim of this study was to investigate if 3D convolutional deep neural network implementation DeepMedic [1] could be applied to computed tomography angiography (CTA) images in acute stroke lesion detection. DeepMedic has previously been successful in lesion segmentation from magnetic resonance images [2] . Methods Preprocessing steps included robust intracranial space segmentation and intensity normalization. Hypoattenuated regions visible in the CTAs were manually delineated by two neuroradiologists and considered as ground truths. Data augmentation was performed with left–right flipped images. Training was done with scans from five infarct patients and subsequent testing with five separate cases. 35 epochs with 15 subepochs were used in the training. Results The initial results were promising as the infarcts in all the test cases could be correctly lateralized. The achieved voxel-wise segmentation performance was moderate (considering small training set) with Sørensen-Dice similarity coefficient 0.52 for the test data. Conclusions The significance of open science and freely available scientific software will only increase in the future. Convolutional neural networks demonstrate promising prospects for medical imaging. DeepMedic can be applied to CT images with proper data preprocessing. In a future study the findings will be verified with a larger cohort. Improving the performance with additional input features will also be investigated. [ABSTRACT FROM AUTHOR]