The Parkinson's progression markers initiative (PPMI) - establishing a PD biomarker cohort.

ObjectiveThe Parkinson's Progression Markers Initiative (PPMI) is an observational, international study designed to establish biomarker-defined cohorts and identify clinical, imaging, genetic, and biospecimen Parkinson's disease (PD) progression markers to accelerate disease-modifying therapeutic trials.MethodsA total of 423 untreated PD, 196 Healthy Control (HC) and 64 SWEDD (scans without evidence of dopaminergic deficit) subjects were enrolled at 24 sites. To enroll PD subjects as early as possible following diagnosis, subjects were eligible with only asymmetric bradykinesia or tremor plus a dopamine transporter (DAT) binding deficit on SPECT imaging. Acquisition of data was standardized as detailed at www.ppmi-info.org.ResultsApproximately 9% of enrolled subjects had a single PD sign at baseline. DAT imaging excluded 16% of potential PD subjects with SWEDD. The total MDS-UPDRS for PD was 32.4 compared to 4.6 for HC and 28.2 for SWEDD. On average, PD subjects demonstrated 45% and 68% reduction in mean striatal and contralateral putamen Specific Binding Ratios (SBR), respectively. Cerebrospinal fluid (CSF) was acquired from >97% of all subjects. CSF (PD/HC/SWEDD pg/mL) α-synuclein (1845/2204/2141) was reduced in PD vs HC or SWEDD (P < 0.03). Similarly, t-tau (45/53) and p-tau (16/18) were reduced in PD versus HC (P < 0.01).InterpretationPPMI has detailed the biomarker signature for an early PD cohort defined by clinical features and imaging biomarkers. This strategy provides the framework to establish biomarker cohorts and to define longitudinal progression biomarkers to support future PD treatment trials

Quantification of dopaminergic neurotransmission SPECT studies with 123 l-labelled radioligands

Dopaminergic neurotransmission SPECT studies with 123I-labelled radioligands can help in the diagnosis of neurological and psychiatric disorders such as Parkinson¿s disease and schizophrenia. Nowadays, interpretation of SPECT images is based mainly on visual assessment by experienced observers. However, a quantitative evaluation of the images is recommended in current clinical guidelines. Quantitative information can help diagnose the disease at the early pre-clinical stages, follow its progression and assess the effects of treatment strategies. SPECT images are affected by a number of effects that are inherent in the image formation: attenuation and scattering of photons, system response and partial volume effect. These effects degrade the contrast and resolution of the images and, as a consequence, the real activity distribution of the radiotracer is distorted. Whilst the photon emission of 123I is dominated by a low-energy line of 159 keV, it also emits several high-energy lines. When 123I-labelled radioligands are used, a non-negligible fraction of high-energy photons undergoes backscattering in the detector and the gantry and reaches the detector within the energy window. In this work, a complete methodology for the compensation of all the degrading effects involved in dopaminergic neurotransmission SPECT imaging with 123I is presented. The proposed method uses Monte Carlo simulation to estimate the scattered photons detected in the projections. For this purpose, the SimSET Monte Carlo code was modified so as to adapt it to the more complex simulation of high-energy photons emitted by 123I. Once validated, the modified SimSET code was used to simulate 123I SPECT studies of an anthropomorphic striatal phantom using different imaging systems. The projections obtained showed that scatter is strongly dependent on the imaging system and comprises at least 40% of the detected photons. Applying the new methodology demonstrated that absolute quantification can be achieved when the method includes accurate compensations for all the degrading effects. When the method did not include correction for all degradations, calculated values depended on the imaging system, although a linear relationship was found between calculated and true values. It was also found that partial volume effect and scatter corrections play a major role in the recovery of nominal values. Despite the advantages of absolute quantification, the computational and methodological requirements needed severely limit the possibility of application in clinical routine. Thus, for the time being, absolute quantification is limited to academic studies and research trials. In a clinical context, reliable, simple and rapid methods are needed, thus, semi-quantitative methods are used. Diagnosis also requires the establishment of robust reference values for healthy controls. These values are usually derived from a large data pool obtained in multicentre clinical trials. The comparison between the semi-quantitative values obtained from a patient and the reference is only feasible if the quantitative values have been previously standardised, i.e. they are independent of the gamma camera, acquisition protocol, reconstruction parameters and quantification procedure applied. Thus, standardisation requires that the calculated values are compensated somehow for all the image-degrading phenomena. In this thesis dissertation, a methodology for the standardisation of the quantitative values extracted from dopaminergic neurotransmission SPECT studies with 123I is evaluated using Monte Carlo simulation. This methodology is based on the linear relationship found between calculated and true values for a group of studies corresponding to different subjects with non-negligible anatomical and tracer uptake differences. Reconstruction and quantification methods were found to have a high impact on the linearity of the relationship and on the accuracy of the standardised results

MR-based attenuation correction and scatter correction in neurological PET/MR imaging with 18F-FDG

The aim was to investigate the effects of MR-based attenuation correction (MRAC) and scatter correction to positron emission tomography (PET) image quantification in neurological PET/MR with 18F-FDG. A multi-center phantom study was conducted to investigate the effect of MRAC between PET/MR and PET/CT systems (I). An MRAC method to derive bone from T1-weighted MR images was developed (II, III). Finally, scatter correction accuracy with MRAC was investigated (IV). The results show that the quantitative accuracy in PET is well-comparable be-tween PET/MR and PET/CT systems when an attenuation correction method resembling CT-based attenuation correction (CTAC) is implemented. This al-lows achieving of a PET bias within standard uptake value (SUV) quantification repeatability (< 10 % error) and is within the repeatability of PET in most sys-tems and brain regions (< 5 % error). In addition, MRAC considering soft tissue, air and bone can be derived using T1-weighted images alone. The improved version of the MRAC method allows achieving a quantitative accuracy feasible for advanced applications (< 5 % error). MRAC has a minor effect on the scatter correction accuracy (< 3 % error), even when using MRAC without bone. In conclusion, MRAC can be considered the largest contributing factor to PET quantification bias in 18F-FDG neurological PET/MR. This finding is not explicitly limited only to 18F-FDG imaging. Once an MRAC method that performs close to CTAC is implemented, there is no reason why a PET/MR system would perform differently from a PET/CT system. Such an MRAC method has been developed and is freely available (http://bit.ly/2fx6Jjz). Scatter correction can be considered a non-issue in neurological PET/MR imaging when using 18F-FD

Impact of New Scatter Correction Strategies on High-Resolution Research Tomograph Brain PET Studies

The aim of this study is to evaluate the impact of different scatter correction strategies on quantification of high-resolution research tomograph (HRRT) data for three tracers covering a wide range in kinetic profiles. Healthy subjects received dynamic HRRT scans using either (R)-[C-11]verapamil (n = 5), [C-11]raclopride (n = 5) or [C-11]flumazenil (n = 5). To reduce the effects of patient motion on scatter scaling factors, a margin in the attenuation correction factor (ACF) sinogram was applied prior to 2D or 3D single scatter simulation (SSS). Some (R)-[C-11]verapamil studies showed prominent artefacts that disappeared with an ACF-margin of 10 mm or more. Use of 3D SSS for (R)-[C-11]verapamil showed a statistically significant increase in volume of distribution compared with 2D SSS (p 0.05). When there is a patient motion-induced mismatch between transmission and emission scans, applying an ACF-margin resulted in more reliable scatter scaling factors but did not change (and/or deteriorate) quantification

An integrated MR/PET system: prospective applications

Radiology is strongly depending on medical imaging technology and consequently directing technological progress. A novel technology can only be established, however, if improved diagnostic accuracy influence on therapeutic management and/or overall reduced cost can be evidenced. It has been demonstrated recently that Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) can technologically be integrated into one single hybrid system. Some scientific arguments on the benefits are obvious, e.g., that simultaneous imaging of morphological and functional information will improve tissue characterization. However, crossfire of questions still remains: What unmet radiological needs are addressed by the novel system? What level of hardware integration is reasonable, or would software-based image co-registration be sufficient? Will MR/PET achieve higher diagnostic accuracy compared to separate imaging? What is the added value compared to other hybrid imaging modalities like PET/CT? And finally, is the system economically reasonable and has the potential to reduce overall costs for therapy planning and monitoring? This article tries to highlight some perspectives of applying an integrated MR/PET system for simultaneous morphologic and functional imaging

Quantitative PET image reconstruction employing nested expectation-maximization deconvolution for motion compensation

Bulk body motion may randomly occur during PET acquisitions introducing blurring, attenuation-emission mismatches and, in dynamic PET, discontinuities in the measured time activity curves between consecutive frames. Meanwhile, dynamic PET scans are longer, thus increasing the probability of bulk motion. In this study, we propose a streamlined 3D PET motion-compensated image reconstruction (3D-MCIR) framework, capable of robustly deconvolving intra-frame motion from a static or dynamic 3D sinogram. The presented 3D-MCIR methods need not partition the data into multiple gates, such as 4D MCIR algorithms, or access list-mode (LM) data, such as LM MCIR methods, both associated with increased computation or memory resources. The proposed algorithms can support compensation for any periodic and non-periodic motion, such as cardio-respiratory or bulk motion, the latter including rolling, twisting or drifting. Inspired from the widely adopted point-spread function (PSF) deconvolution 3D PET reconstruction techniques, here we introduce an image-based 3D generalized motion deconvolution method within the standard 3D maximum-likelihood expectation-maximization (ML-EM) reconstruction framework. In particular, we initially integrate a motion blurring kernel, accounting for every tracked motion within a frame, as an additional MLEM modeling component in the image space (integrated 3D-MCIR). Subsequently, we replaced the integrated model component with a nested iterative Richardson-Lucy (RL) image-based deconvolution method to accelerate the MLEM algorithm convergence rate (RL-3D-MCIR). The final method was evaluated with realistic simulations of whole-body dynamic PET data employing the XCAT phantom and real human bulk motion profiles, the latter estimated from volunteer dynamic MRI scans. In addition, metabolic uptake rate Ki parametric images were generated with the standard Patlak method. Our results demonstrate significant improvement in contrast-to-noise ratio (CNR) and noise-bias performance in both dynamic and parametric images. The proposed nested RL-3D-MCIR method is implemented on the Software for Tomographic Image Reconstruction (STIR) open-source platform and is scheduled for public release

Radiotracers for SPECT imaging: Current scenario and future prospects

Single photon emission computed tomography (SPECT) has been the cornerstone of nuclear medicine and today it is widely used to detect molecular changes in cardiovascular, neurological and oncological diseases. While SPECT has been available since the 1980s, advances in instrumentation hardware, software and the availability of new radiotracers that are creating a revival in SPECT imaging are reviewed in this paper. The biggest change in the last decade has been the fusion of CT with SPECT, which has improved attenuation correction and image quality. Advances in collimator design, replacement of sodium iodide crystals in the detectors with cadmium zinc telluride (CZT) detectors as well as advances in software and reconstruction algorithms have all helped to retain SPECT as a much needed and used technology. Today, a wide spectrum of radiotracers is available for use in cardiovascular, neurology and oncology applications. The development of several radiotracers for neurological disorders is briefly described in this review, including [ 123I]FP-CIT (DaTSCAN ™) available for Parkinson's disease. In cardiology, while technetium-99m labeled tetrofosmin and technetium-99m labeled sestamibi have been well known for myocardial perfusion imaging, we describe a recently completed multicenter clinical study on the use of [ 123I]mIBG (AdreView ™) for imaging in chronic heart failure patients. For oncology, while bone scanning has been prevalent, newer radiotracers that target cancer mechanisms are being developed. Technetium-99m labeled RGD peptides have been reported in the literature that can be used for imaging angiogenesis, while technetium-99m labeled duramycin has been used to image apoptosis. While PET/CT is considered to be the more advanced technology particularly for oncology applications, SPECT continues to be the modality of choice and the workhorse in many hospitals and nuclear medicine centers. The cost of SPECT instruments also makes them more attractive in developing countries where the cost of a scan is still prohibitive for many patients

Effect of scatter correction when comparing attenuation maps: Application to brain PET/MR

Email Print Request Permissions In PET imaging, attenuation and scatter corrections are an essential requirement to accurately quantify the radionuclide uptake. In the context of PET/MR scanners, obtaining the attenuation information can be challenging. Various authors have quantified the effect of an imprecise attenuation map on the reconstructed PET image but its influence on scatter correction has usually been ignored. In this paper, we investigate the effects of imperfect attenuation maps (μmaps) on the scatter correction in a simulation setting. We focused our study on three μmaps: the reference μmap derived from a CT image, and two MR-based methods. Two scatter estimation strategies were implemented: a μmap-specific scatter estimation and an ideal scatter estimation relying only on the reference CT μmap. The scatter estimation used the Single Scatter Simulation algorithm with tail-fitting. The results show that, for FDG brain PET, regardless of the μmap used in the reconstruction, the difference on PET images between μmap-specific and ideal scatter estimations is small (less than 1%). More importantly, the relative error between attenuation correction methods does not change depending on the scatter estimation method included in the simulation and reconstruction process. This means that the effect of errors in the μmap on the PET image is dominated by the attenuation correction, while the scatter estimate is relatively unaffected. Therefore, while scatter correction improves reconstruction accuracy, it is unnecessary to include scatter in the simulation when comparing different attenuation correction methods for brain PET/MR