Elena Herranz

The neuroinflammatory component of gray matter pathology in multiple sclerosis

In multiple sclerosis (MS), using simultaneous magnetic resonance–positron emission tomography (MR‐PET) imaging with 11C‐PBR28, we quantified expression of the 18kDa translocator protein (TSPO), a marker of activated microglia/macrophages, in cortex, cortical lesions, deep gray matter (GM), white matter (WM) lesions, and normal‐appearing WM (NAWM) to investigate the in vivo pathological and clinical relevance of neuroinflammation.

In vivo characterization of cortical and white matter neuroaxonal pathology in early multiple sclerosis.

Neuroaxonal pathology is a main determinant of disease progression in multiple sclerosis; however, its underlying pathophysiological mechanisms, including its link to inflammatory demyelination and temporal occurrence in the disease course are still unknown. We used ultra-high field (7 T), ultra-high gradient strength diffusion and T1/T2-weighted myelin-sensitive magnetic resonance imaging to characterize microstructural changes in myelin and neuroaxonal integrity in the cortex and white matter in early stage multiple sclerosis, their distribution in lesional and normal-appearing tissue, and their correlations with neurological disability. Twenty-six early stage multiple sclerosis subjects (disease duration ≤5 years) and 24 age-matched healthy controls underwent 7 T T2*-weighted imaging for cortical lesion segmentation and 3 T T1/T2-weighted myelin-sensitive imaging and neurite orientation dispersion and density imaging for assessing microstructural myelin, axonal and dendrite integrity in lesional and normal-appearing tissue of the cortex and the white matter. Conventional mean diffusivity and fractional anisotropy metrics were also assessed for comparison. Cortical lesions were identified in 92% of early multiple sclerosis subjects and they were characterized by lower intracellular volume fraction (P = 0.015 by paired t-test), lower myelin-sensitive contrast (P = 0.030 by related-samples Wilcoxon signed-rank test) and higher mean diffusivity (P = 0.022 by related-samples Wilcoxon signed-rank test) relative to the contralateral normal-appearing cortex. Similar findings were observed in white matter lesions relative to normal-appearing white matter (all P < 0.001), accompanied by an increased orientation dispersion (P < 0.001 by paired t-test) and lower fractional anisotropy (P < 0.001 by related-samples Wilcoxon signed-rank test) suggestive of less coherent underlying fibre orientation. Additionally, the normal-appearing white matter in multiple sclerosis subjects had diffusely lower intracellular volume fractions than the white matter in controls (P = 0.029 by unpaired t-test). Cortical thickness did not differ significantly between multiple sclerosis subjects and controls. Higher orientation dispersion in the left primary motor-somatosensory cortex was associated with increased Expanded Disability Status Scale scores in surface-based general linear modelling (P < 0.05). Microstructural pathology was frequent in early multiple sclerosis, and present mainly focally in cortical lesions, whereas more diffusely in white matter. These results suggest early demyelination with loss of cells and/or cell volumes in cortical and white matter lesions, with additional axonal dispersion in white matter lesions. In the cortex, focal lesion changes might precede diffuse atrophy with cortical thinning. Findings in the normal-appearing white matter reveal early axonal pathology outside inflammatory demyelinating lesions.

Feasibility assessment of the interactive use of a Monte Carlo algorithm in treatment planning for intraoperative electron radiation therapy.

This work analysed the feasibility of using a fast, customized Monte Carlo (MC) method to perform accurate computation of dose distributions during pre- and intraplanning of intraoperative electron radiation therapy (IOERT) procedures. The MC method that was implemented, which has been integrated into a specific innovative simulation and planning tool, is able to simulate the fate of thousands of particles per second, and it was the aim of this work to determine the level of interactivity that could be achieved. The planning workflow enabled calibration of the imaging and treatment equipment, as well as manipulation of the surgical frame and insertion of the protection shields around the organs at risk and other beam modifiers. In this way, the multidisciplinary team involved in IOERT has all the tools necessary to perform complex MC dosage simulations adapted to their equipment in an efficient and transparent way. To assess the accuracy and reliability of this MC technique, dose distributions for a monoenergetic source were compared with those obtained using a general-purpose software package used widely in medical physics applications. Once accuracy of the underlying simulator was confirmed, a clinical accelerator was modelled and experimental measurements in water were conducted. A comparison was made with the output from the simulator to identify the conditions under which accurate dose estimations could be obtained in less than 3 min, which is the threshold imposed to allow for interactive use of the tool in treatment planning. Finally, a clinically relevant scenario, namely early-stage breast cancer treatment, was simulated with pre- and intraoperative volumes to verify that it was feasible to use the MC tool intraoperatively and to adjust dose delivery based on the simulation output, without compromising accuracy. The workflow provided a satisfactory model of the treatment head and the imaging system, enabling proper configuration of the treatment planning system and providing good accuracy in the dosage simulation.

Improved image reconstruction in small animal PET using a priori estimates of single-pixel events

Most small animal PET scanners are based on arrays of pixelated scintillators crystals. As the read-out of individual pixels would be too expensive, identification of the crystal of interaction is usually made by center of energy methods based for instance on Anger logic. This allows for a reduction in the number of signals to be acquired, but prevents the identification of multi-hit events, that is, events in that one (or several) photon produces several hits in the detector, thus blurring the correct positioning of the interaction. Improving the identification of the pixel of interaction is pursued in this work by combining all the information acquired by the scanner without increasing the number of signals. The probability for every individual event for being single or multi-pixel is estimated from the XY positioning and energy information. This probability is fed into a 3D-OSEM iterative statistical reconstruction method. Every coincidence event detected may be analyzed combining information such as deposited energy, PMT XY location, time difference between both singles of the coincidence and coincidence and single rates, if available. With the proposed method, improved peak/noise ratio and better resolution are obtained without the introduction of additional hardware.

Heterogeneous pathological processes account for thalamic degeneration in multiple sclerosis: Insights from 7 T imaging

Background: Thalamic degeneration impacts multiple sclerosis (MS) prognosis. Objective: To investigate heterogeneous thalamic pathology, its correlation with white matter (WM), cortical lesions and thickness, and as function of distance from cerebrospinal fluid (CSF). Methods: In 41 MS subjects and 17 controls, using 3 and 7 T imaging, we tested for (1) differences in thalamic volume and quantitative T2* (q-T2*) (2) globally and (3) within concentric bands originating from the CSF/thalamus interface; (4) the relation between thalamic, cortical, and WM metrics; and (5) the contribution of magnetic resonance imaging (MRI) metrics to clinical scores. We also assessed MS thalamic lesion distribution as a function of distance from CSF. Results: Thalamic lesions were mainly located next to the ventricles. Thalamic volume was decreased in MS versus controls (p < 10−2); global q-T2* was longer in secondary progressive multiple sclerosis (SPMS) only (p < 10−2), indicating myelin and/or iron loss. Thalamic atrophy and longer q-T2* correlated with WM lesion volume (p < 0.01). In relapsing-remitting MS, q-T2* thalamic abnormalities were located next to the WM (p < 0.01 (uncorrected), p = 0.09 (corrected)), while they were homogeneously distributed in SPMS. Cortical MRI metrics were the strongest predictors of clinical outcome. Conclusion: Heterogeneous pathological processes affect the thalamus in MS. While focal lesions are likely mainly driven by CSF-mediated factors, overall thalamic degeneration develops in association with WM lesions.

Validation of PeneloPET against two small animal PET scanners

PeneloPET is a Monte Carlo application based on PENELOPE. We present here the new features and results of validation tests for the new version of PeneloPET that has been compared against data from real scanners. PeneloPET was built as a powerful tool for PET simulation, it is easy to use, fast and very accurate. Recently, many improvements have been made in the code with the incorporation of a very realistic signal processing chain and by adding the possibility of running simulations in parallel mode on cluster computers. A comparison between data obtained with two small-animal scanners and the results of PeneloPET simulations has been performed. The small-animal PET scanners were an eXplore Vista DR (GEHC) and a partial-ring, rotating rPET (SUINSA medical systems). Intrinsic resolution, scatter fractions, noise equivalent count rates and sensitivity measurements for the real acquisitions and simulations were compared. NEMA protocol was applied using mouse-size and rat-size cylinders, spheres and line sources as phantoms. Results show small differences (less than 10%) between real acquisitions and simulated data, proving that PeneloPET is an accurate tool for PET simulations.

Deadtime and pile-up correction method based on the singles to coincidences ratio for PET

The count rate in a PET scanner as a function of the activity in the Field of View (FOV) has a non-linear contribution coming from deadtime, pile-up and random coincidences. These effects must be estimated accurately and corrected in order to perform quantitative PET studies. For a given scanner and acquisition system, the relative importance of deadtime and pile-up effects still depends on the size and materials of the objects being imaged. These facts difficult to devise a universal correction method that yields accurate results for any kind of acquisition. In this work we show that, in a PET scanner, there is a linear relationship between the effective deadtime for coincidences, τ, (which takes into account deadtime and pile-up losses and gains within the energy window) and the Singles to Coincidences Ratio (SCRm) measured by the scanner. This relation has been recovered both in simulations and real data. This allows us to devise a simple method which, requiring only two calibration acquisitions for each energy window, one with high SCRm and one with low SCRm, is able to estimate accurately deadtime and pile up corrections for any other acquisition performed in the same scanner. Simulations show that corrected count rates are accurate within 5%, even when high activities are present in the FOV.

Frequency selective signal extrapolation for compensation of missing data in sinograms

We present a method to compensate for missing projection data in positron emission tomography (PET), which may result from gaps between adjacent detectors or from malfunctioning detectors. To prevent artifacts in the reconstruction when using Fourier rebinning (FORE) or analytical reconstruction algorithms, filling the data gaps is required. This new approach for sinogram data interpolation prior to reconstruction is based on a simple iterative frequency selective signal extrapolation method initially proposed and successfully applied for error concealment in image and video communication. In this work the method has been improved taking advantage of well known restrictions in the allowed region of frequencies of the sinograms. We compare quantitatively the results of this technique with previously proposed gap filling procedures in both the sinograms and in their reconstructed images. The proposed technique outperforms those methods without requiring too much computational time.

Quantification limits of iterative PET reconstruction algorithms and improved estimation of kinetic constants

Quantification of tracer kinetics is often accomplished from time-activity curves of a region of interest of dynamic PET images. The choice of reconstruction method may affect the time-activity curves and hence the estimated kinetic parameters. Several studies have shown that statistical-iterative methods, due to non-negativity constrains, may exhibit a quantification bias in low activity regions and thus these methods, in spite of the better image quality they provide, are seldom used to estimate kinetic constants. By means of realistic dynamic simulations, we have investigated the quantitative properties of statistical-iterative (OSEM, both 2D and 3D) and FBP reconstruction methods and the accuracy of the kinetic parameters derived from images reconstructed with each algorithm. We focus on the procedure to fit kinetic constants to data. Our results show that, with appropriate measures to account for quantification bias, iterative reconstructions may be suited to derive kinetic constants from dynamic PET acquisitions.

Revised consistency conditions for PET data

Tomographic Data Consistency Conditions (TDCC) are frequently employed to improve the quality of PET data. However, most of these consistency conditions were derived from X-ray computerized tomography (CT) and their validity for other imaging modalities has not been well established. For instance, it is well known from (X-ray) CT data that the sum of the projection data from one view of the parallel-beam projections is a constant independent of the view-angle. This consistency condition is based on well-known mathematical properties of the Radon transform and yields good results when employed in noise removal or sinogram restoration. But this consistency condition assumes that emission and detection of radiation occur within a thin (ideally with zero width) line-of- response (LOR), with a flat probability distribution of the detection (in PET) or absorption (X-ray) along such LOR This assumption, being valid for CT, is not realistic for PET acquisitions. Thus, TDCC for PET should be revised in order to check their validity with more realistic detection models. In this work we review the main differences between PET and CT data and study whether these consistency conditions should be modified in order to take into account the dependence of the probabilities on the distance to the center of the line-of-response. Results from simulations are also presented to illustrate the importance of these effects. They indicate that some consistency conditions can be violated at the 10% level.