Large Voxel Research Articles

Dose accumulation and 3D imaging with He+ ions

Ion implantation accumulation is modeled for the theoretical case of using scanning He+ ion microscopy to image a stack of virtual slices from Si and Cu for 3D tomography. Ion implantation during imaging reaches steady state conditions of maximum concentration at the surface when the virtual thickness of all slices exceeds the total ion range. Imaging at low dose and large voxel sizes offers the greatest likelihood of damage-free targets for the best fidelity 3D data sets. The possibility of damage accumulation increases as the voxel size decreases, which may limit the effectiveness of helium ion microscopy imaging for high resolution 3D data sets. In particular, a surface imaging dose of 2.5 × 1016 ions/cm2 distributed through the volume after each slice can create a situation where almost 2.5 × 1013 ions/cm2 may accumulate at the surface of both Si and Cu at a voxel size of 10 × 10 × 10 nm, possibly causing severe crystalline damage to these targets. This model may be used to predict voxel sizes required for high quality 3D tomography data using helium ion microscopy imaging.

Brain intracellular metabolites are freely diffusing along cell fibers in grey and white matter, as measured by diffusion-weighted MR spectroscopy in the human brain at 7 T.

Due to the specific compartmentation of brain metabolites, diffusion-weighted magnetic resonance spectroscopy opens unique insight into neuronal and astrocytic microstructures. The apparent diffusion coefficient (ADC) of brain metabolites depends on various intracellular parameters including cytosol viscosity and molecular crowding. When diffusion time (t d) is long enough, the size and geometry of the compartment in which the metabolites diffuse strongly influence metabolites ADC. In a previous study, performed in the macaque brain, we measured neuronal and astrocytic metabolites ADC at long t d (from 86 to 1,011 ms) in a large voxel enclosing an equal proportion of white and grey matter. We showed that metabolites apparently diffuse freely along the axis of dendrites, axons and astrocytic processes. To assess potential differences between these two tissue types, here we measured for the first time in the Human brain the t d-dependency of metabolites trace/3 ADC at 7 teslas using a localized diffusion-weighted STEAM sequence, in parietal and occipital voxels, respectively, containing mainly white and grey matter. We show that, in both tissues and over the observed timescale (t d varying from 92 to 712 ms) metabolite ADC reaches a non-zero plateau, suggesting that metabolites are not confined inside subcellular regions such as cell bodies, or inside subcellular compartments such as organelles, but are rather free to diffuse in the whole fiber-like structure of neurons and astrocytes. Beyond the fundamental insights into intracellular compartmentation of metabolites, this work also provides a new framework for interpreting results of neuroimaging techniques based on molecular diffusion, such as diffusion-weighted magnetic resonance spectroscopy and imaging.

A dose error evaluation study for 4D dose calculations

Previous studies have shown that respiration induced motion is not negligible for Stereotactic Body Radiation Therapy. The intrafractional breathing induced motion influences the delivered dose distribution on the underlying patient geometry such as the lung or the abdomen. If a static geometry is used, a planning process for these indications does not represent the entire dynamic process. The quality of a full 4D dose calculation approach depends on the dose coordinate transformation process between deformable geometries. This article provides an evaluation study that introduces an advanced method to verify the quality of numerical dose transformation generated by four different algorithms.The used transformation metric value is based on the deviation of the dose mass histogram (DMH) and the mean dose throughout dose transformation. The study compares the results of four algorithms. In general, two elementary approaches are used: dose mapping and energy transformation. Dose interpolation (DIM) and an advanced concept, so called divergent dose mapping model (dDMM), are used for dose mapping. The algorithms are compared to the basic energy transformation model (bETM) and the energy mass congruent mapping (EMCM). For evaluation 900 small sample regions of interest (ROI) are generated inside an exemplary lung geometry (4DCT). A homogeneous fluence distribution is assumed for dose calculation inside the ROIs. The dose transformations are performed with the four different algorithms.The study investigates the DMH-metric and the mean dose metric for different scenarios (voxel sizes: 8 mm, 4 mm, 2 mm, 1 mm; 9 different breathing phases). dDMM achieves the best transformation accuracy in all measured test cases with 3–5% lower errors than the other models. The results of dDMM are reasonable and most efficient in this study, although the model is simple and easy to implement. The EMCM model also achieved suitable results, but the approach requires a more complex programming structure. The study discloses disadvantages for the bETM and for the DIM. DIM yielded insufficient results for large voxel sizes, while bETM is prone to errors for small voxel sizes.

Initial Scale of MRI Heterogeneity of Pediatric Rhabdomyosarcoma is Prognostic for Recurrence

voxel size of the planning CT. The dose volume was then scaled to reflect dose delivered after 1 week, 2 weeks, and 3 weeks. The region of interest (ROI) was defined as the bony pelvis shrunk by a uniform 5 mm margin. The reduction in ROI volume removed a region that was most likely cortical bone and reduced potential noise due to partial volume effect from the large voxel size of the FLT PET image. The resampled FLT images were used to calculate the voxel by voxel value change in FLT activity as a surrogate for marrow activity which was then correlated to dose in 1 Gy increments. The results for each subject were plotted on the same frame of reference for inter subject comparison. Results: Voxel by voxel analysis of bone marrow activity change represented in time series FLT PET images shows an exponential decrease in FLT SUV during chemoradiation therapy when normalized by the pre-therapy image for all subjects. This relationship can be fit by the exponential equation FLTn Z 1.3 e , where FLTn Z weekly normalized FLT SUVand DoseZ radiation dose (Gy) at the time of FLTn. The average coefficient of determination (R) Z 0.84 for all 21 subjects. Out of 21 subjects, 19 had R > 0.7. This relationship appears to be independent of time or fractionation. FLT SUV voxel values decreased 50% when compared to pretherapy values for a range of radiation doses between 2.2 Gy 6.8 Gy for all subjects after 1, 2, or 3 weeks. The mean dose to reach 50% of pretherapy FLT SUV was 4.5 Gy. However, the range in FLT SUV voxel change decrease after receiving 4.5 Gy was 25 83% for all subjects after 1, 2, or 3 weeks suggesting significant patient variation that was not related to fraction size. Conclusions: Bone marrow activity changes as a function of dose can be measured using voxel by voxel analysis of time serial FLT PET images. This method allows quantitative analysis of FLT PET images at a resolution equal to the voxel size in the PET image for determining the relationship between FLT uptake change and radiation dose. Author Disclosure: S.M. McGuire: None. R. Hareendran: None. J. Xia: None. S. Bhatia: None. W. Sun: None. W.M. Rockey: None. Y. Menda: None. L. Ponto: None. B. Gross: None. J. Buatti: None.

Phosphorus Magnetic Resonance Spectroscopic Imaging at 7 T in Patients With Prostate Cancer

The aim of this study was to identify characteristics of phosphorus (P) spectra of the human prostate and to investigate changes of individual phospholipid metabolites in prostate cancer through in vivo P magnetic resonance spectroscopic imaging (MRSI) at 7 T. In this institutional review board-approved study, 15 patients with biopsy-proven prostate cancer underwent T2-weighted magnetic resonance imaging and 3-dimensional P MRSI at 7 T. Voxels were selected at the tumor location, in normal-appearing peripheral zone tissue, normal-appearing transition zone tissue, and in the base of the prostate close to the seminal vesicles. Phosphorus metabolite ratios were determined and compared between tissue types. Signals of phosphoethanolamine (PE) and phosphocholine (PC) were present and well resolved in most P spectra in the prostate. Glycerophosphocholine signals were observable in 43% of the voxels in malignant tissue, but in only 10% of the voxels in normal-appearing tissue away from the seminal vesicles. In many spectra, independent of tissue type, 2 peaks resonated in the chemical shift range of inorganic phosphate, possibly representing 2 separate pH compartments. The PC/PE ratio in the seminal vesicles was highly elevated compared with the prostate in 5 patients. A considerable overlap of P metabolite ratios was found between prostate cancer and normal-appearing prostate tissue, preventing direct discrimination of these tissues. The only 2 patients with high Gleason scores tumors (≥4+5) presented with high PC and glycerophosphocholine levels in their cancer lesions. Phosphorus MRSI at 7 T shows distinct features of phospholipid metabolites in the prostate gland and its surrounding structures. In this exploratory study, no differences in P metabolite ratios were observed between prostate cancer and normal-appearing prostate tissue possibly because of the partial volume effects of small tumor foci in large MRSI voxels.

Reconstruction of finer voxel grid transmission images in Tomographic Gamma Scanning

Tomographic Gamma Scanning (TGS) is a technique used to assay the nuclide distribution and radioactivity in nuclear waste drums. Limited to few transmission measurements, the drum is divided into large voxels and thus leads to the inhomogeneity of the voxels and negatively affects the results. A new algorithm is presented to reconstruct finer voxel grid transmission images with the same number of measurements. The small voxel size decreases the effect of the inhomogeneity and makes the result more accurate. The algorithm employs total variation minimization and precisely describes the attenuation process. The influences of the different scan modes are discussed with Monte Carlo simulations. Experiments are performed to verify the effectiveness of our method.

Isotropic non-white matter partial volume effects in constrained spherical deconvolution.

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVEs) are present in the DW signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by multiple non-parallel WM fiber populations. High angular resolution diffusion imaging (HARDI) methods have been developed to correctly characterize complex WM fiber configurations, but to date, many of the HARDI methods do not account for non-WM PVEs. In this work, we investigated the isotropic PVEs caused by non-WM tissue in WM voxels on fiber orientations extracted with constrained spherical deconvolution (CSD). Experiments were performed on simulated and real DW-MRI data. In particular, simulations were performed to demonstrate the effects of varying the diffusion weightings, signal-to-noise ratios (SNRs), fiber configurations, and tissue fractions. Our results show that the presence of non-WM tissue signal causes a decrease in the precision of the detected fiber orientations and an increase in the detection of false peaks in CSD. We estimated 35–50% of WM voxels to be affected by non-WM PVEs. For HARDI sequences, which typically have a relatively high degree of diffusion weighting, these adverse effects are most pronounced in voxels with GM PVEs. The non-WM PVEs become severe with 50% GM volume for maximum spherical harmonics orders of 8 and below, and already with 25% GM volume for higher orders. In addition, a low diffusion weighting or SNR increases the effects. The non-WM PVEs may cause problems in connectomics, where reliable fiber tracking at the WM–GM interface is especially important. We suggest acquiring data with high diffusion-weighting 2500–3000 s/mm2, reasonable SNR (~30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD.

Computing global minimizers to a constrained B‐spline image registration problem from optimal l1 perturbations to block match data

Block matching is a well-known strategy for estimating corresponding voxel locations between a pair of images according to an image similarity metric. Though robust to issues such as image noise and large magnitude voxel displacements, the estimated point matches are not guaranteed to be spatially accurate. However, the underlying optimization problem solved by the block matching procedure is similar in structure to the class of optimization problem associated with B-spline based registration methods. By exploiting this relationship, the authors derive a numerical method for computing a global minimizer to a constrained B-spline registration problem that incorporates the robustness of block matching with the global smoothness properties inherent to B-spline parameterization. The method reformulates the traditional B-spline registration problem as a basis pursuit problem describing the minimall1-perturbation to block match pairs required to produce a B-spline fitting error within a given tolerance. The sparsity pattern of the optimal perturbation then defines a voxel point cloud subset on which the B-spline fit is a global minimizer to a constrained variant of the B-spline registration problem. As opposed to traditional B-spline algorithms, the optimization step involving the actual image data is addressed by block matching. The performance of the method is measured in terms of spatial accuracy using ten inhale/exhale thoracic CT image pairs (available for download atwww.dir-lab.com) obtained from the COPDgene dataset and corresponding sets of expert-determined landmark point pairs. The results of the validation procedure demonstrate that the method can achieve a high spatial accuracy on a significantly complex image set. The proposed methodology is demonstrated to achieve a high spatial accuracy and is generalizable in that in can employ any displacement field parameterization described as a least squares fit to block match generated estimates. Thus, the framework allows for a wide range of image similarity block match metric and physical modeling combinations.

Retest imaging of [11C]NOP-1A binding to nociceptin/orphanin FQ peptide (NOP) receptors in the brain of healthy humans

[11C]NOP-1A is a novel high-affinity PET ligand for imaging nociceptin/orphanin FQ peptide (NOP) receptors. Here, we report reproducibility and reliability measures of binding parameter estimates for [11C]NOP-1A binding in the brain of healthy humans.After intravenous injection of [11C]NOP-1A, PET scans were conducted twice on eleven healthy volunteers on the same (10/11 subjects) or different (1/11 subjects) days. Subjects underwent serial sampling of radial arterial blood to measure parent radioligand concentrations. Distribution volume (VT; a measure of receptor density) was determined by compartmental (one- and two-tissue) modeling in large regions and by simpler regression methods (graphical Logan and bilinear MA1) in both large regions and voxel data. Retest variability and intraclass correlation coefficient (ICC) of VT were determined as measures of reproducibility and reliability respectively.Regional [11C]NOP-1A uptake in the brain was high, with a peak radioactivity concentration of 4–7 SUV (standardized uptake value) and a rank order of putamen>cingulate cortex>cerebellum. Brain time–activity curves fitted well in 10 of 11 subjects by unconstrained two-tissue compartmental model. The retest variability of VT was moderately good across brain regions except cerebellum, and was similar across different modeling methods, averaging 12% for large regions and 14% for voxel-based methods. The retest reliability of VT was also moderately good in most brain regions, except thalamus and cerebellum, and was similar across different modeling methods averaging 0.46 for large regions and 0.48 for voxels having gray matter probability >20%. The lowest retest variability and highest retest reliability of VT were achieved by compartmental modeling for large regions, and by the parametric Logan method for voxel-based methods.Moderately good reproducibility and reliability measures of VT for [11C]NOP-1A make it a useful PET ligand for comparing NOP receptor binding between different subject groups or under different conditions in the same subject.

Super resolution reconstruction of diffusion tensor parameters from multi-oriented diffusion weighted images

Event Abstract Back to Event Super resolution reconstruction of diffusion tensor parameters from multi-oriented diffusion weighted images Gwendolyn Van Steenkiste1*, Ben Jeurissen1, Dirk H. Poot2 and Jan Sijbers1 1 University of Antwerp, Physics, Belgium 2 Erasmus Medical Center Rotterdam, Medical informatics and Radiology, Netherlands Diffusion tensor imaging (DTI) is inherently a noisy imaging technique. Clinical time constraints prohibit the use of extensive averaging to increase the signal-to-noise ratio (SNR). To ensure that the SNR is sufficiently high, DTI acquisitions typically employ large voxels. As a result, many voxels consist of a mixture of signals from differently oriented structures, leading to partial volume effects and complicating DTI analysis. In this work, we propose a super resolution reconstruction (SRR) method to improve the trade-off between spatial resolution, SNR, and acquisition time. High resolution (HR) diffusion tensor parameters are estimated directly from a set of diffusion weighted (DW) images with thick slices and different slice-orientations. We simultaneously take into account the spatial transformations arising from the slice orientations [1], motion correction, and the DTI-model. This makes our method much more flexible in terms of image acquisition requirements than existing SRR methods for DTI [2, 3]. Including the DTI model results in a non-linear regularized least squares problem that is solved using a trust-region Newton method. Reconstruction quality of the proposed SRR methodology was evaluated using both simulations and real data. The SRR result was compared with a direct HR acquisition, obtained in the same scan time. Simulation experiments show a much lower mean squared error of fractional anisotropy and mean angular error of the first eigenvector for the proposed method compared to a direct HR acquisition. In real data, the reconstruction has a higher SNR than a direct HR acquisition, while still maintaining a good detail of the fine structures. We have developed a SRR method specifically designed for DTI that enables HR investigation of the brain in clinically feasible scan time. It enables DTI with unprecedented resolution, minimizing the partial volume effects and thereby revealing finer structures. Acknowledgements This work was financially supported by the iMinds-SuperMRI project (Interdisciplinary Institute for Technology, a research institute founded by the Flemish Government) References [1] D. H. J. Poot, V. Van Meir, and J. Sijbers, General and efficient super-resolution method for multi-slice MRI, 2010, MICCAI, 13(1), 615–22. [2] D. H. J. Poot, B. Jeurissen, Y. Bastiaensen, J. Veraart, W. Van Hecke, P. M. Parizel, and J. Sijbers, Super-resolution for multislice diffusion tensor imaging, 2013, Magnetic Resonance in Medicine, 69(1) [3] B. Scherrer, A. Gholipour, and S. K. Warfield, Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions., 2012, Medical image analysis, 16(7), 1465-1476 Keywords: MRI, diffusion MRI, Diffusion Tensor Imaging, super resolution, Motion Correction Conference: Imaging the brain at different scales: How to integrate multi-scale structural information?, Antwerp, Belgium, 2 Sep - 6 Sep, 2013. Presentation Type: Poster presentation Topic: Poster session Citation: Van Steenkiste G, Jeurissen B, Poot DH and Sijbers J (2013). Super resolution reconstruction of diffusion tensor parameters from multi-oriented diffusion weighted images. Front. Neuroinform. Conference Abstract: Imaging the brain at different scales: How to integrate multi-scale structural information?. doi: 10.3389/conf.fninf.2013.10.00031 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 31 Aug 2013; Published Online: 31 Aug 2013. * Correspondence: Miss. Gwendolyn Van Steenkiste, University of Antwerp, Physics, Antwerp, Belgium, gwendolyn.vansteenkiste@uantwerpen.be Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Gwendolyn Van Steenkiste Ben Jeurissen Dirk H Poot Jan Sijbers Google Gwendolyn Van Steenkiste Ben Jeurissen Dirk H Poot Jan Sijbers Google Scholar Gwendolyn Van Steenkiste Ben Jeurissen Dirk H Poot Jan Sijbers PubMed Gwendolyn Van Steenkiste Ben Jeurissen Dirk H Poot Jan Sijbers Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

In vivo spatially localized high resolution 1H MRS via intermolecular single‐quantum coherence of rat brain at 7 T

To compare the conventional localized point-resolved spectroscopy (PRESS) with localized 2D intermolecular single-quantum coherence (iSQC) magnetic resonance spectroscopy (MRS) and obtain in vivo MRS spectrum of rat brain using the latter technique. A brain phantom, an intact pig brain tissue, and mature Sprague-Dawley rat were studied by PRESS, Nano magic-angle spinning spectroscopy, and iSQC MRS. Using PRESS, high-resolution MRS can be obtained from the brain phantom and pig brain tissue with a small voxel in a relatively homogeneous field. When a large voxel is selected, the field homogeneity is distinctly reduced. No useful information is obtained from the PRESS spectra. However, using the iSQC MRS, high-resolution spectra can be obtained from the two samples with a relatively large voxel. In the same way, an iSQC MRS spectrum can be obtained from a relatively large voxel of in vivo rat brain with a comparable resolution to the PRESS spectrum with a small voxel. Compared to PRESS, the iSQC MRS may be more feasible and promising for detection of strongly structured tissues with relatively large voxels.

Least median of squares filtering of locally optimal point matches for compressible flow image registration

Compressible flow based image registration operates under the assumption that the mass of the imaged material is conserved from one image to the next. Depending on how the mass conservation assumption is modeled, the performance of existing compressible flow methods is limited by factors such as image quality, noise, large magnitude voxel displacements, and computational requirements. The Least Median of Squares Filtered Compressible Flow (LFC) method introduced here is based on a localized, nonlinear least squares, compressible flow model that describes the displacement of a single voxel that lends itself to a simple grid search (block matching) optimization strategy. Spatially inaccurate grid search point matches, corresponding to erroneous local minimizers of the nonlinear compressible flow model, are removed by a novel filtering approach based on least median of squares fitting and the forward search outlier detection method. The spatial accuracy of the method is measured using ten thoracic CT image sets and large samples of expert determined landmarks (available at www.dir-lab.com). The LFC method produces an average error within the intra-observer error on eight of the ten cases, indicating that the method is capable of achieving a high spatial accuracy for thoracic CT registration.

SU‐C‐211‐02: A Fast Monte Carlo Dose Algorithm for Radiotherapy Treatment Planning Based on Hybrid Adaptive Meshes

Purpose: Monte Carlo methods are considered to be the most accurate dose algorithm for radiotherapy. Variance reduction techniques such as history repetition, Russian roulette and photon splitting are employed to improve the calculation efficiency. Generally, it takes a large portion of the simulation time for two inevitable tasks, that is, voxel‐to‐voxel boundary crossing and energy deposition. The purpose of this work is to investigate the potential for additional speedup achieved by reducing the number of boundary crossing based on hybrid adaptive meshes. Method and Materials: A Monte Carlo code was developed to simulate the coupled photon‐electron transport for radiation therapy using a hybrid adaptive mesh. Photon transport was modeled in an analog fashion. The Compton scattering, photoelectric ionization and pair production were considered. For electron transport, a condensed history method was used in which the hard interactions such as inelastic collision and bremsstrahlung were simulated explicitly. The formulation by Kawrakow and Bielajew was used for electron multiple scattering. Photons and electrons were tracked on a hybrid adaptive mesh, which was generated from an initial uniform coarse mesh. The coarse uniform mesh was divided voxel‐by‐voxel into an unstructured finer mesh depending on the splitting criterion such as the density gradient. The resulting adaptive mesh contains larger voxels in smooth density areas and smaller voxels used for density regions with large gradient to retain the accuracy. Results: The speed up observed by varying the splitting level is proportional to N1/3, where N is the total number of the voxels. For the test cases, 30% of the calculation time was saved by using the adaptive meshes starting with 10mm spacing and reduced to 1.25mm voxels for high gradient regions comparing with the 1.25mm uniform meshes. Conclusions: The Monte Carlo simulations can be further accelerated based on these hybrid adaptive meshes.

Efficient voxel navigation for proton therapy dose calculation in TOPAS and Geant4

A key task within all Monte Carlo particle transport codes is ‘navigation’, the calculation to determine at each particle step what volume the particle may be leaving and what volume the particle may be entering. Navigation should be optimized to the specific geometry at hand. For patient dose calculation, this geometry generally involves voxelized computed tomography (CT) data. We investigated the efficiency of navigation algorithms on currently available voxel geometry parameterizations in the Monte Carlo simulation package Geant4: G4VPVParameterisation, G4VNestedParameterisation and G4PhantomParameterisation, the last with and without boundary skipping, a method where neighboring voxels with the same Hounsfield unit are combined into one larger voxel. A fourth parameterization approach (MGHParameterization), developed in-house before the latter two parameterizations became available in Geant4, was also included in this study. All simulations were performed using TOPAS, a tool for particle simulations layered on top of Geant4. Runtime comparisons were made on three distinct patient CT data sets: a head and neck, a liver and a prostate patient. We included an additional version of these three patients where all voxels, including the air voxels outside of the patient, were uniformly set to water in the runtime study. The G4VPVParameterisation offers two optimization options. One option has a 60–150 times slower simulation speed. The other is compatible in speed but requires 15–19 times more memory compared to the other parameterizations. We found the average CPU time used for the simulation relative to G4VNestedParameterisation to be 1.014 for G4PhantomParameterisation without boundary skipping and 1.015 for MGHParameterization. The average runtime ratio for G4PhantomParameterisation with and without boundary skipping for our heterogeneous data was equal to 0.97: 1. The calculated dose distributions agreed with the reference distribution for all but the G4PhantomParameterisation with boundary skipping for the head and neck patient. The maximum memory usage ranged from 0.8 to 1.8 GB depending on the CT volume independent of parameterizations, except for the 15–19 times greater memory usage with the G4VPVParameterisation when using the option with a higher simulation speed. The G4VNestedParameterisation was selected as the preferred choice for the patient geometries and treatment plans studied.

Angular versus spatial resolution trade-offs for diffusion imaging under time constraints

Diffusion weighted magnetic resonance imaging (DW-MRI) are now widely used to assess brain integrity in clinical populations. The growing interest in mapping brain connectivity has made it vital to consider what scanning parameters affect the accuracy, stability, and signal-to-noise of diffusion measures. Trade-offs between scan parameters can only be optimized if their effects on various commonly-derived measures are better understood. To explore angular versus spatial resolution trade-offs in standard tensor-derived measures, and in measures that use the full angular information in diffusion signal, we scanned eight subjects twice, 2 weeks apart, using three protocols that took the same amount of time (7 min). Scans with 3.0, 2.7, 2.5 mm isotropic voxels were collected using 48, 41, and 37 diffusion-sensitized gradients to equalize scan times. A specially designed DTI phantom was also scanned with the same protocols, and different b-values. We assessed how several diffusion measures including fractional anisotropy (FA), mean diffusivity (MD), and the full 3D orientation distribution function (ODF) depended on the spatial/angular resolution and the SNR. We also created maps of stability over time in the FA, MD, ODF, skeleton FA of 14 TBSS-derived ROIs, and an information uncertainty index derived from the tensor distribution function, which models the signal using a continuous mixture of tensors. In scans of the same duration, higher angular resolution and larger voxels boosted SNR and improved stability over time. The increased partial voluming in large voxels also led to bias in estimating FA, but this was partially addressed by using "beyond-tensor" models of diffusion.

VoxMorph: 3-scale freeform deformation of large voxel grids

We propose VoxMorph, a new interactive freeform deformation tool for high-resolution voxel grids. Our system exploits cages for high-level deformation control. We tackle the scalability issue by introducing a new 3-scale deformation algorithm composed of a high quality as-rigid-as possible deformation at coarse scale, a quasi-conformal space deformation at mid-scale and a new deformation-adaptive local linear technique at fine scale. The two first scales are applied interactively on a visualization envelope, while the complete full resolution deformation is computed as a post-process after the interactive session, resulting in a high-resolution voxel grid containing the deformed model. We tested our system on various real world datasets and demonstrate that our approach offers a good balance between performance and quality.

Accuracy of MRI wall shear stress estimation

Methods Three methods for WSS estimation were studied. These methods are based on 1) linear extrapolation (LE) of MRI velocity data, 2) MRI velocity data in combination with estimation of location of vessel wall, and 3) Fourier velocity encoding (FVE). Numerical velocity fields representing axisymmetric 2D velocity profiles were generated for WSS values ranging from 1-20 N/m. Based on the numerical velocity fields, phase-contrast MRI data voxels were simulated as follows: A jinc-function was used to model the 2D point spread function (PSF), and this PSF was used to obtain each voxel’s intravoxel velocity distribution. The phasecontrast MRI signal of each voxel was simulated by taking the Fourier transform of this distribution. To account for the fact that voxels cannot be positioned exactly at the wall in an MR-experiment, all simulations were carried out for ten different voxel positions uniformly distributed over one voxel length. In the LE method the spatial velocity derivative was estimated as the velocity difference between the two adjacent nearwall voxels divided by the distance between them. In the wall-based method, WSS was estimated by dividing the linear interpolated velocity at one voxel distance from the wall by the distance to the wall. Errors in segmentations of wall position were accounted for by modeling them as normally distributed with a standard deviation of 1/4 voxel size. In the FVE-based method, the WSS was obtained by first estimating the intravoxel velocity profile via a simulated FVE measurement and then computing the spatial velocity derivative near the wall. Note that the FVE-method uses larger voxels.

Effect of X-Ray Computed Tomography Scanning Parameters on the Estimated Porosity of Foam Specimens

X-Ray Computed Tomography (CT) scanning is an effective method for estimating the porosity of various engineering materials and biomedical specimens such as tissue scaffolds and bones. However, the scanning and analysis parameters play a significant role in the accuracy of the porosity value determined from CT scan. This paper presents details of an investigation carried out to understand the effect of system parameters, namely the voxel size, X-ray focal spot size and segmentation threshold, on the estimated porosity by taking an example of safety-critical foam used for impact protection applications. Different voxel resolutions and focal spot sizes are selected in a total of 12 scanning tests and the effect of segmentation threshold is analyzed on each of these tests. The study indicates that the obtained porosity value is greatly influenced by the choice of voxel size at larger spot sizes and less influenced at smaller spot sizes. The threshold also has significant effect on the porosity value, especially at larger voxel sizes.

Magnetic resonance spectroscopy of the canine brain at 3.0 T and 7.0 T

The purpose of this study was to evaluate the feasibility of proton magnetic resonance spectroscopy (1H MRS) to study the concentration of metabolites in the brain of dogs at 3.0 and 7.0T. Four healthy male beagles were scanned using 3.0T and 7.0T human magnetic resonance imaging (MRI) units. The results obtained showed that all dogs had excellent quality spectra for a small (1cm3) and large (8cm3) voxel at 3.0T, whereas only 2 dogs had high quality spectra at 7.0T due to insufficient water suppression. 1H MRS at 3.0T appears to be a reliable method to study metabolite concentrations in the canine brain. The development of more advanced water suppression techniques is necessary to improve the results at 7.0T.

Monte Carlo Investigation of Rebinning Material Density Distributions of Lung Parenchyma Phantoms in Proton Therapy

In this work we present a Monte Carlo study of proton irradiation of lung parenchyma phantoms for particle energies that are typically used for proton therapy, ranging from 150 to 200 MeV. The Bragg peaks of the proton beams were scored in a water phantom distal to voxelized slabs of lung material. A detailed lung parenchyma phantom was modeled and converted into a voxelized structure, with a resolution similar to that obtained by computed tomography, to study differences in the dose deposited by the proton beams distal to the phantom caused by merging small structures into larger voxels. The results show that the Bragg peak dose in water can vary by up to 11%, the distal edge degradation can be as large as 1.1 mm, and the maximum observed changes in the range at 90% of the dose are 0.4 mm in water. From our results, we conclude that computational proton dose predictions in a lung are associated with large uncertainties. To improve the accuracy of dose calculations, a more detailed model of lung parenchyma is needed.