Automatic generation of subject-specific finite element models of the spine from magnetic resonance images

The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echogradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deeplearning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93–0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1 mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models

Holmium Nanoparticles: Preparation and In Vitro Characterization of a New Device for Radioablation of Solid Malignancies

# The Author(s) 2010. This article is published with open access at Springerlink.com Purpose The present study introduces the preparation and in vitro characterization of a nanoparticle device comprising holmium acetylacetonate for radioablation of unresectable solid malignancies. Methods HoAcAc nanoparticles were prepared by dissolving holmium acetylacetonate in chloroform, followed by emulsification in an aqueous solution of a surfactant and evaporation of W. Bult: R. Varkevisser: P. R. Luijten: A. D. van het Schip

Automatic generation of subject-specific finite element models of the spine from magnetic resonance images

The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echo-gradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deep-learning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93–0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1 mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models

Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours

Purpose: To assess the feasibility of magnetic resonance imaging (MRI)-only treatment planning for photon and proton radiotherapy in children with abdominal tumours. Materials and methods: The study was conducted on 66 paediatric patients with Wilms' tumour or neuroblastoma (age 4 +/- 2 years) who underwent MR and computed tomography (CT) acquisition on the same day as part of the clinical protocol. MRI intensities were converted to CT Hounsfield units (HU) by means of a UNet-like neural network trained to generate synthetic CT (sCT) from T1- and T2-weighted MR images. The CT-to-sCT image similarity was evaluated by computing the mean error (ME), mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and Dice similarity coefficient (DSC). Synthetic CT dosimetric accuracy was verified against CT-based dose distributions for volumetric-modulated arc therapy (VMAT) and intensity-modulated pencil-beam scanning (PBS). Relative dose differences (D-diff) in the internal target volume and organs-at-risk were computed and a three-dimensional gamma analysis (2 mm, 2%) was performed. Results: The average +/- standard deviation ME was -5 +/- 12 HU, MAE was 57 +/- 12 HU, PSNR was 30.3 +/- 1. 6 dB and DSC was 76 +/- 8% for bones and 92 +/- 9% for lungs. Average D-diff were 99% (range [85; 100]%) for VMAT and >96% (range [87; 100]%) for PBS. Conclusion: The deep learning-based model generated accurate sCT from planning T1w- and T2w-MR images. Most dosimetric differences were within clinically acceptable criteria for photon and proton radiotherapy, demonstrating the feasibility of an MRI-only workflow for paediatric patients with abdominal tumours. (C) 2020 The Authors. Published by Elsevier B.V

Detecting genuine multipartite continuous-variable entanglement

We derive necessary conditions in terms of the variances of position and momentum linear combinations for all kinds of separability of a multi-party multi-mode continuous-variable state. Their violations can be sufficient for genuine multipartite entanglement, provided the combinations contain both conjugate variables of all modes. Hence a complete state determination, for example by detecting the entire correlation matrix of a Gaussian state, is not needed.Comment: 13 pages, 3 figure

Magnetic resonance imaging of brain angiogenesis after stroke

Stroke is a major cause of mortality and long-term disability worldwide. The initial changes in local perfusion and tissue status underlying loss of brain function are increasingly investigated with noninvasive imaging methods. In addition, there is a growing interest in imaging of processes that contribute to post-stroke recovery. In this review, we discuss the application of magnetic resonance imaging (MRI) to assess the formation of new vessels by angiogenesis, which is hypothesized to participate in brain plasticity and functional recovery after stroke. The excellent soft tissue contrast, high spatial and temporal resolution, and versatility render MRI particularly suitable to monitor the dynamic processes involved in vascular remodeling after stroke. Here we review recent advances in the field of MR imaging that are aimed at assessment of tissue perfusion and microvascular characteristics, including cerebral blood flow and volume, vascular density, size and integrity. The potential of MRI to noninvasively monitor the evolution of post-ischemic angiogenic processes is demonstrated from a variety of in vivo studies in experimental stroke models. Finally, we discuss some pitfalls and limitations that may critically affect the accuracy and interpretation of MRI-based measures of (neo)vascularization after stroke

SMART tracking: Simultaneous anatomical imaging and real-time passive device tracking for MR-guided interventions

Purpose This study demonstrates a proof of concept of a method for simultaneous anatomical imaging and real-time (SMART) passive device tracking for MR-guided interventions. Methods Phase Correlation template matching was combined with a fast undersampled radial multi-echo acquisition using the white marker phenomenon after the first echo. In this way, the first echo provides anatomical contrast, whereas the other echoes provide white marker contrast to allow accurate device localization using fast simulations and template matching. This approach was tested on tracking of five 0.5 mm steel markers in an agarose phantom and on insertion of an MRI-compatible 20 Gauge titanium needle in ex vivo porcine tissue. The locations of the steel markers were quantitatively compared to the marker locations as found on a CT scan of the same phantom. Results The average pairwise error between the MRI and CT locations was 0.30 mm for tracking of stationary steel spheres and 0.29 mm during motion. Qualitative evaluation of the tracking of needle insertions showed that tracked positions were stable throughout needle insertion and retraction. Conclusion The proposed SMART tracking method provided accurate passive tracking of devices at high framerates, inclusion of real-time anatomical scanning, and the capability of automatic slice positioning. Furthermore, the method does not require specialized hardware and could therefore be applied to track any rigid metal device that causes appreciable magnetic field distortions