Scatter Correction Based on GPU-Accelerated Full Monte Carlo Simulation for Brain PET/MRI.

Accurate scatter correction is essential for qualitative and quantitative PET imaging. Until now, scatter correction based on Monte Carlo simulation (MCS) has been recognized as the most accurate method of scatter correction for PET. However, the major disadvantage of MCS is its long computational time, which makes it unfeasible for clinical usage. Meanwhile, single scatter simulation (SSS) is the most widely used method for scatter correction. Nevertheless, SSS has the disadvantage of limited robustness for dynamic measurements and for the measurement of large objects. In this work, a newly developed implementation of MCS using graphics processing unit (GPU) acceleration is employed, allowing full MCS-based scatter correction in clinical 3D brain PET imaging. Starting from the generation of annihilation photons to their detection in the simulated PET scanner, all relevant physical interactions and transport phenomena of the photons were simulated on GPUs. This resulted in an expected distribution of scattered events, which was subsequently used to correct the measured emission data. The accuracy of the approach was validated with simulations using GATE (Geant4 Application for Tomography Emission), and its performance was compared to SSS. The comparison of the computation time between a GPU and a single-threaded CPU showed an acceleration factor of 776 for a voxelized brain phantom study. The speedup of the MCS implemented on the GPU represents a major step toward the application of the more accurate MCS-based scatter correction for PET imaging in clinical routine.

Prediction of Alzheimer's Dementia in Patients with Amnestic Mild Cognitive Impairment in Clinical Routine: Incremental Value of Biomarkers of Neurodegeneration and Brain Amyloidosis Added Stepwise to Cognitive Status.

The aim of this study was to evaluate the incremental benefit of biomarkers for prediction of Alzheimer's disease dementia (ADD) in patients with mild cognitive impairment (MCI) when added stepwise in the order of their collection in clinical routine. The model started with cognitive status characterized by the ADAS-13 score. Hippocampus volume (HV), cerebrospinal fluid (CSF) phospho-tau (pTau), and the FDG t-sum score in an AD meta-region-of-interest were compared as neurodegeneration markers. CSF-Aß1-42 was used as amyloidosis marker. The incremental prognostic benefit from these markers was assessed by stepwise Kaplan-Meier survival analysis in 402 ADNI MCI subjects. Predefined cutoffs were used to dichotomize patients as 'negative' or 'positive' for AD characteristic alteration with respect to each marker. Among the neurodegeneration markers, CSF-pTau provided the best incremental risk stratification when added to ADAS-13. FDG PET outperformed HV only in MCI subjects with relatively preserved cognition. Adding CSF-Aß provided further risk stratification in pTau-positive subjects, independent of their cognitive status. Stepwise integration of biomarkers allows stepwise refinement of risk estimates for MCI-to-ADD progression. Incremental benefit strongly depends on the patient's status according to the preceding diagnostic steps. The stepwise Kaplan-Meier curves might be useful to optimize diagnostic workflow in individual patients.

Mental speed is associated with the shape irregularity of white matter MRI hyperintensity load.

Brain MRI white matter hyperintensities (WMHs) are common in elderly subjects. Their impact on cognition, however, appears highly variable. Complementing conventional scoring of WMH load (volume and location) by quantitative characterization of the shape irregularity of WMHs might improve the understanding of the relationship between WMH load and cognitive performance. Here we propose the "confluency sum score" (COSU) as a marker of the total shape irregularity of WMHs in the brain. The study included two independent patient samples: 87 cognitively impaired geriatric inpatients from a prospective neuroimaging study (iDSS) and 198 subjects from the National Alzheimer's Coordinating Center (NACC) database (132 with, 66 w/o cognitive impairment). After automatic segmentation and clustering of the WMHs on FLAIR (LST toolbox, SPM8), the confluency of the i-th contiguous WMH cluster was computed as confluencyi = [1/(36π)∙surfacei3/volumei2]1/3-1. The COSU was obtained by summing the confluency over all WMH clusters. COSU was tested for correlation with CERAD-plus subscores. Correlation analysis was restricted to subjects with at least moderate WMH load (≥ 13.5 ml; iDSS / NACC: n = 52 / 80). In the iDSS sample, among the 12 CERAD-plus subtests the trail making test A (TMT-A) was most strongly correlated with the COSU (Spearman rho = -0.345, p = 0.027). TMT-A performance was not associated with total WMH volume (rho = 0.147, p = 0.358). This finding was confirmed in the NACC sample (rho = -0.261, p = 0.023 versus rho = -0.040, p = 0.732). Cognitive performance in specific domains including mental speed and fluid abilities seems to be more strongly associated with the shape irregularity of white matter MRI hyperintensities than with their volume.

A review of the use and potential of the GATE Monte Carlo simulation code for radiation therapy and dosimetry applications.

In this paper, the authors' review the applicability of the open-source GATE Monte Carlo simulation platform based on the GEANT4 toolkit for radiation therapy and dosimetry applications. The many applications of GATE for state-of-the-art radiotherapy simulations are described including external beam radiotherapy, brachytherapy, intraoperative radiotherapy, hadrontherapy, molecular radiotherapy, and in vivo dose monitoring. Investigations that have been performed using GEANT4 only are also mentioned to illustrate the potential of GATE. The very practical feature of GATE making it easy to model both a treatment and an imaging acquisition within the same framework is emphasized. The computational times associated with several applications are provided to illustrate the practical feasibility of the simulations using current computing facilities.

EduGATE - basic examples for educative purpose using the GATE simulation platform.

EduGATE is a collection of basic examples to introduce students to the fundamental physical aspects of medical imaging devices. It is based on the GATE platform, which has received a wide acceptance in the field of simulating medical imaging devices including SPECT, PET, CT and also applications in radiation therapy. GATE can be configured by commands, which are, for the sake of simplicity, listed in a collection of one or more macro files to set up phantoms, multiple types of sources, detection device, and acquisition parameters. The aim of the EduGATE is to use all these helpful features of GATE to provide insights into the physics of medical imaging by means of a collection of very basic and simple GATE macros in connection with analysis programs based on ROOT, a framework for data processing. A graphical user interface to define a configuration is also included.

GATE based Monte Carlo simulation of planar scintigraphy to estimate the nodular dose in radioiodine therapy for autonomous thyroid adenoma.

The recommended target dose in radioiodine therapy of solitary hyperfunctioning thyroid nodules is 300-400Gy and therefore higher than in other radiotherapies. This is due to the fact that an unknown, yet significant portion of the activity is stored in extranodular areas but is neglected in the calculatory dosimetry. We investigate the feasibility of determining the ratio of nodular and extranodular activity concentrations (uptakes) from post-therapeutically acquired planar scintigrams with Monte Carlo simulations in GATE. The geometry of a gamma camera with a high energy collimator was emulated in GATE (Version 5). A geometrical thyroid-neck phantom (GP) and the ICRP reference voxel phantoms "Adult Female" (AF, 16ml thyroid) and "Adult Male" (AM, 19ml thyroid) were used as source regions. Nodules of 1ml and 3ml volume were placed in the phantoms. For each phantom and each nodule 200 scintigraphic acquisitions were simulated. Uptake ratios of nodule and rest of thyroid ranging from 1 to 20 could be created by summation. Quantitative image analysis was performed by investigating the number of simulated counts in regions of interest (ROIs). ROIs were created by perpendicular projection of the phantom onto the camera plane to avoid a user dependant bias. The ratio of count densities in ROIs over the nodule and over the contralateral lobe, which should be least affected by nodular activity, was taken to be the best available measure for the uptake ratios. However, the predefined uptake ratios are underestimated by these count density ratios: For an uptake ratio of 20 the count ratios range from 4.5 (AF, 1ml nodule) to 15.3 (AM, 3ml nodule). Furthermore, the contralateral ROI is more strongly affected by nodular activity than expected: For an uptake ratio of 20 between nodule and rest of thyroid up to 29% of total counts in the ROI over the contralateral lobe are caused by decays in the nodule (AF 3 ml). In the case of the 1ml nodules this effect is smaller: 9-11% (AF) respectively 7-8% (AM). For each phantom, the dependency of count density ratios upon uptake ratios can be modeled well by both linear and quadratic regression (quadratic: r(2)>0.99), yielding sets of parameters which in reverse allow the computation of uptake ratios (and thus dose) from count density ratios. A single regression model obtained by fitting the data of all simulations simultaneously did not provide satisfactory results except for GP, while underestimating the true uptake ratios in AF and overestimating them in AM. The scintigraphic count density ratios depend upon the uptake ratios between nodule and rest of thyroid, upon their volumes, and their respective position in a non-trivial way. Further investigations are required to derive a comprehensive rule to calculate the uptake or dose ratios based on post-therapeutic scintigraphy.

Integration of amplitude and phase statistics for complete artifact removal in independent components of neuromagnetic recordings.

In magnetoencephalography (MEG) and electroencephalography (EEG), independent component analysis is widely applied to separate brain signals from artifact components. A number of different methods have been proposed for the automatic or semiautomatic identification of artifact components. Most of the proposed methods are based on amplitude statistics of the decomposed MEG/EEG signal. We present a fully automated approach based on amplitude and phase statistics of decomposed MEG signals for the isolation of biological artifacts such as ocular, muscle, and cardiac artifacts (CAs). The performance of different artifact identification measures was investigated. In particular, we show that phase statistics is a robust and highly sensitive measure to identify strong and weak components that can be attributed to cardiac activity, whereas a combination of different measures is needed for the identification of artifacts caused by ocular and muscle activity. With the introduction of a rejection performance parameter, we are able to quantify the rejection quality for eye blinks and CAs. We demonstrate in a set of MEG data the good performance of the fully automated procedure for the removal of cardiac, ocular, and muscle artifacts. The new approach allows routine application to clinical measurements with small effect on the brain signal.

MR-based attenuation correction for torso-PET/MR imaging: pitfalls in mapping MR to CT data.

PURPOSE: MR-based attenuation correction (AC) will become an integral part of combined PET/MR systems. Here, we propose a toolbox to validate MR-AC of clinical PET/MRI data sets. METHODS: Torso scans of ten patients were acquired on a combined PET/CT and on a 1.5-T MRI system. MR-based attenuation data were derived from the CT following MR-CT image co-registration and subsequent histogram matching. PET images were reconstructed after CT- (PET(CT)) and MR-based AC (PET(MRI)). Lesion-to-background (L/B) ratios were estimated on PET(CT) and PET(MRI). RESULTS: MR-CT histogram matching leads to a mean voxel intensity difference in the CT- and MR-based attenuation images of 12% (max). Mean differences between PET(MRI) and PET(CT) were 19% (max). L/B ratios were similar except for the lung where local misregistration and intensity transformation leads to a biased PET(MRI). CONCLUSION: Our toolbox can be used to study pitfalls in MR-AC. We found that co-registration accuracy and pixel value transformation determine the accuracy of PET(MRI).

Quantifying the A1AR distribution in peritumoural zones around experimental F98 and C6 rat brain tumours.

Quantification of growth in experimental F98 and C6 rat brain tumours was performed on 51 rat brains, 17 of which have been further assessed by 3D tumour reconstruction. Brains were cryosliced and radio-labelled with a ligand of the peripheral type benzodiazepine-receptor (pBR), (3)H-Pk11195 [(1-(2-chlorophenyl)-N-methyl-N-(1-methyl-propylene)-3-isoquinoline-carboxamide)] by receptor autoradiography. Manually segmented and automatically registered tumours have been 3D-reconstructed for volumetric comparison on the basis of (3)H-Pk11195-based tumour recognition. Furthermore automatically computed areas of -300 microm inner (marginal) zone as well as 300 microm and 600 microm outer tumour space were quantified. These three different regions were transferred onto other adjacent slices that had been labelled by receptor autoradiography with the A(1) Adenosine receptor (A(1)AR)-ligand (3)H-CPFPX ((3)H-8-cyclopentyl-3-(3-fluorpropyl)-1-propylxanthine) for quantitative assessment of A(1)AR in the three different tumour zones. Hence, a method is described for quantifying various receptor protein systems in the tumour as well as in the marginal invasive zones around experimentally implanted rat brain tumours and their representation in the tumour microenvironment as well as in 3D space. Furthermore, a tool for automatically reading out radio-labelled rat brain slices from auto radiographic films was developed, reconstructed into a consistent 3D-tumour model and the zones around the tumour were visualized. A(1)AR expression was found to depend upon the tumour volume in C6 animals, but is independent on the time of tumour development. In F98 animals, a significant increase in A(1)AR receptor protein was found in the Peritumoural zone as a function of time of tumour development and tumour volume.