Maria Carla Gilardi

Physical performance of the new hybrid PET∕CT Discovery-690.

PURPOSE The aim of this work was the assessment of the physical performance of the new hybrid PET∕CT system: Discovery-690. METHODS The Discovery-690 combines a lutetium-yttrium-orthosilicate (LYSO) block detector designed PET tomograph with a 64-slice CT scanner. The system is further characterized by a dedicated powerful computing platform implementing fully 3D-PET iterative reconstruction algorithms. These algorithms can account for time of flight (TOF) information and∕or a 3D model of the PET point spread function (PSF). PET physical performance was measured following NEMA NU-2-2007 procedures. Furthermore, specific tests were used: (i) to measure the energy and timing resolution of the PET system and (ii) to evaluate image quality, by using phantoms representing different clinical conditions (e.g., brain and whole body). Data processing and reconstructions were performed as required by standard procedures. Further reconstructions were carried out to evaluate the performance of the new reconstruction algorithms. In particular, four algorithms were considered for the reconstruction of the PET data: (i) HD = standard configuration, without TOF and PSF, (ii) TOF = HD + TOF, (iii) PSF = HD + PSF, and (iv) TOFPSF = HD + TOF + PSF. RESULTS The transverse (axial) spatial resolution values were 4.70 (4.74) mm and 5.06 (5.55) mm at 1 cm and 10 cm off axis, respectively. Sensitivity (average between 0 and 10 cm) was 7.5 cps∕kBq. The noise equivalent count rate (NECR) peak was 139.1 kcps at 29.0 kBq∕ml. The scatter fraction at the NECR peak was 37%. The correction accuracy for the dead time losses and random event counts had a maximum absolute error below the NECR peak of 2.09%. The average energy and timing resolution were 12.4% and 544.3 ps, respectively. PET image quality was evaluated with the NEMA IEC Body phantom by using four reconstruction algorithms (HD, TOF, PSF, and TOFPSF), as previously described. The hot contrast (after 3 iterations and for a lesion∕background activity ratio of 4:1) for the spheres of 10, 13, 17, and 22 mm was (HD) 29.8, 45.4, 55.4, and 68.1%; (TOF) 39.9, 53.5, 62.7, and 72.2%; (PSF) 28.3, 47.3, 60.4, and 71.8%; (TOFPSF) 43.8, 62.9, 70.6, and 76.4%. The cold contrast for the spheres of 28 and 37 mm was (HD) 62.4 and 65.2%; (TOF) 77.1 and 81.4%; (PSF) 62.0 and 65.2%; (TOFPSF) 77.3 and 81.6%. Similar hot and cold contrast trends were found during the analyses of other phantoms representing different clinical conditions (brain and whole body). Nevertheless, the authors observed a predominant role of either TOF or PSF, depending on the specific characteristics and dimensions of the phantoms. CONCLUSIONS Discovery-690 shows very good PET physical performance for all the standard NEMA NU-2-2007 measurements. Furthermore, the new reconstruction algorithms available for PET data (TOF and PSF) allow further improvements of the D-690 image quality performance both qualitatively and quantitatively.

Image-based point spread function implementation in a fully 3D OSEM reconstruction algorithm for PET

The interest in positron emission tomography (PET) and particularly in hybrid integrated PET/CT systems has significantly increased in the last few years due to the improved quality of the obtained images. Nevertheless, one of the most important limits of the PET imaging technique is still its poor spatial resolution due to several physical factors originating both at the emission (e.g. positron range, photon non-collinearity) and at detection levels (e.g. scatter inside the scintillating crystals, finite dimensions of the crystals and depth of interaction). To improve the spatial resolution of the images, a possible way consists of measuring the point spread function (PSF) of the system and then accounting for it inside the reconstruction algorithm. In this work, the system response of the GE Discovery STE operating in 3D mode has been characterized by acquiring (22)Na point sources in different positions of the scanner field of view. An image-based model of the PSF was then obtained by fitting asymmetric two-dimensional Gaussians on the (22)Na images reconstructed with small pixel sizes. The PSF was then incorporated, at the image level, in a three-dimensional ordered subset maximum likelihood expectation maximization (OS-MLEM) reconstruction algorithm. A qualitative and quantitative validation of the algorithm accounting for the PSF has been performed on phantom and clinical data, showing improved spatial resolution, higher contrast and lower noise compared with the corresponding images obtained using the standard OS-MLEM algorithm.

Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy

BACKGROUND Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinsons disease (PD) and Progressive Supranuclear Palsy (PSP). METHOD Morphological T1-weighted Magnetic Resonance Images (MRIs) of PD patients (28), PSP patients (28) and healthy control subjects (28) were used by a supervised machine learning algorithm based on the combination of Principal Components Analysis as feature extraction technique and on Support Vector Machines as classification algorithm. The algorithm was able to obtain voxel-based morphological biomarkers of PD and PSP. RESULTS The algorithm allowed individual diagnosis of PD versus controls, PSP versus controls and PSP versus PD with an Accuracy, Specificity and Sensitivity>90%. Voxels influencing classification between PD and PSP patients involved midbrain, pons, corpus callosum and thalamus, four critical regions known to be strongly involved in the pathophysiological mechanisms of PSP. COMPARISON WITH EXISTING METHODS Classification accuracy of individual PSP patients was consistent with previous manual morphological metrics and with other supervised machine learning application to MRI data, whereas accuracy in the detection of individual PD patients was significantly higher with our classification method. CONCLUSIONS The algorithm provides excellent discrimination of PD patients from PSP patients at an individual level, thus encouraging the application of computer-based diagnosis in clinical practice.

Enhanced 3D PET OSEM reconstruction using inter-update Metz filtering

We present an enhancement of the OSEM (ordered set expectation maximization) algorithm for 3D PET reconstruction, which we call the inter-update Metz filtered OSEM (IMF-OSEM). The IMF-OSEM algorithm incorporates filtering action into the image updating process in order to improve the quality of the reconstruction. With this technique, the multiplicative correction image--ordinarily used to update image estimates in plain OSEM--is applied to a Metz-filtered version of the image estimate at certain intervals. In addition, we present a software implementation that employs several high-speed features to accelerate reconstruction. These features include, firstly, forward and back projection functions which make full use of symmetry as well as a fast incremental computation technique. Secondly, the software has the capability of running in parallel mode on several processors. The parallelization approach employed yields a significant speed-up, which is nearly independent of the amount of data. Together, these features lead to reasonable reconstruction times even when using large image arrays and non-axially compressed projection data. The performance of IMF-OSEM was tested on phantom data acquired on the GE Advance scanner. Our results demonstrate that an appropriate choice of Metz filter parameters can improve the contrast-noise balance of certain regions of interest relative to both plain and post-filtered OSEM, and to the GE commercial reprojection algorithm software.

Detection and compensation of organ/lesion motion using 4D-PET/CT respiratory gated acquisition techniques

PURPOSE To describe the degradation effects produced by respiratory organ and lesion motion on PET/CT images and to define the role of respiratory gated (RG) 4D-PET/CT techniques to compensate for such effects. METHODS Based on the literature and on our own experience, technical recommendations and clinical indications for the use of RG 4D PET/CT have been outlined. RESULTS RG 4D-PET/CT techniques require a state of the art PET/CT scanner, a respiratory monitoring system and dedicated acquisition and processing protocols. Patient training is particularly important to obtain a regular breathing pattern. An adequate number of phases has to be selected to balance motion compensation and statistical noise. RG 4D PET/CT motion free images may be clinically useful for tumour tissue characterization, monitoring patient treatment and target definition in radiation therapy planning. CONCLUSIONS RG 4D PET/CT is a valuable tool to improve image quality and quantitative accuracy and to assess and measure organ and lesion motion for radiotherapy planning.

Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach.

Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR)-related biomarkers have been recently identified by the use of machine learning methods for the in vivo differential diagnosis of AD. However, the vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will (MCIc) or not convert (MCInc) to AD. Morphological T1-weighted MRIs of 137 AD, 76 MCIc, 134 MCInc, and 162 healthy controls (CN) selected from the Alzheimers disease neuroimaging initiative (ADNI) cohort, were used by an optimized machine learning algorithm. Voxels influencing the classification between these AD-related pre-clinical phases involved hippocampus, entorhinal cortex, basal ganglia, gyrus rectus, precuneus, and cerebellum, all critical regions known to be strongly involved in the pathophysiological mechanisms of AD. Classification accuracy was 76% AD vs. CN, 72% MCIc vs. CN, 66% MCIc vs. MCInc (nested 20-fold cross validation). Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients.

Implementation and evaluation of a 3D one-step late reconstruction algorithm for 3D positron emission tomography brain studies using median root prior

Abstract. A fully three-dimensional (3D) one-step late (OSL), maximum a posteriori (MAP) reconstruction algorithm based on the median root prior (MRP) was implemented and evaluated for the reconstruction of 3D positron emission tomography (PET) studies. The algorithm uses the ordered subsets (OS) scheme for convergence acceleration and data update during iterations. The algorithm was implemented using the software package developed within the EU project PARAPET (www.brunel.ac.uk/~masrppet). The MRP algorithm was evaluated using experimental phantom and real 3D PET brain studies. Various experimental set-ups in terms of activity distribution and counting statistics were considered. The performance of the algorithm was assessed by calculating figures of merit such as: contrast, coefficient of variation, activity ratio between two regions and full width at half of maximum for resolution measurements. The performance of MRP was compared with that of 3D ordered subsets-expectation maximisation (OSEM) and 3D re-projection (3DRP) algorithms. In all the experimental situations considered, MRP showed: (1) convergence to a stable solution, (2) effectiveness in noise reduction, particularly for low statistics data, (3) good preservation of spatial details. Compared with the OSEM and 3DRP algorithms, MRP provides comparable or better results depending on the parameters used for the reconstruction of the images.

Integration of mRNA Expression Profile, Copy Number Alterations, and microRNA Expression Levels in Breast Cancer to Improve Grade Definition

Defining the aggressiveness and growth rate of a malignant cell population is a key step in the clinical approach to treating tumor disease. The correct grading of breast cancer (BC) is a fundamental part in determining the appropriate treatment. Biological variables can make it difficult to elucidate the mechanisms underlying BC development. To identify potential markers that can be used for BC classification, we analyzed mRNAs expression profiles, gene copy numbers, microRNAs expression and their association with tumor grade in BC microarray-derived datasets. From mRNA expression results, we found that grade 2 BC is most likely a mixture of grade 1 and grade 3 that have been misclassified, being described by the gene signature of either grade 1 or grade 3. We assessed the potential of the new approach of integrating mRNA expression profile, copy number alterations, and microRNA expression levels to select a limited number of genomic BC biomarkers. The combination of mRNA profile analysis and copy number data with microRNA expression levels led to the identification of two gene signatures of 42 and 4 altered genes (FOXM1, KPNA4, H2AFV and DDX19A) respectively, the latter obtained through a meta-analytical procedure. The 42-based gene signature identifies 4 classes of up- or down-regulated microRNAs (17 microRNAs) and of their 17 target mRNA, and the 4-based genes signature identified 4 microRNAs (Hsa-miR-320d, Hsa-miR-139-5p, Hsa-miR-567 and Hsa-let-7c). These results are discussed from a biological point of view with respect to pathological features of BC. Our identified mRNAs and microRNAs were validated as prognostic factors of BC disease progression, and could potentially facilitate the implementation of assays for laboratory validation, due to their reduced number.

An Object-Oriented Library for 3D PET Reconstruction Using Parallel Computing

We present a object-oriented library of C++ features for 3D PET reconstruction. This library has been designed so that it can be used for many algorithms and scanncr geometries. Its flexibility, portability and modular design have helped greatly to (a) develop new iterative algorithms, (b) compare iterative and analytic methods using simulated, phantom and patient data, (c) adapt and apply the developed reconstruction algorithms to different designs of tomographs. As 3D iterative reconstruction algorithms are time consuming, the library contains classes and functions to run parts of the reconstruction in parallel, using parallel platforms with a distributed memory architecture.

Performance measurements for the PET/CT Discovery-600 using NEMA NU 2-2007 standards.

PURPOSE The aim of this study was to assess the performance measurements of the new PET/CT system Discovery-600 (D-600, GEMS, Milwaukee, WI). METHODS Performance measures were obtained with the National Electrical Manufacturers Association (NEMA) NU 2-2007 procedures. RESULTS The transverse (axial) spatial resolution FWHMs were 4.9 (5.6) mm and 5.6 (6.4) mm at 1 and 10 cm off axis, respectively. The sensitivity (average at 0 and 10 cm) was 9.6 cps/kBq. The scatter fraction was 36.6% (low energy threshold: 425 keV). The NEC peak rate (k=1) was 75.2 kcps at 12.9 kBq/cc. The hot contrasts for 10, 13, 17, and 22 mm spheres were 41%, 51%, 62%, and 73% and the cold contrasts for 28 and 37 mm spheres were 68% and 72%. CONCLUSIONS The Discovery-600 has good performance for the NEMA NU 2-2007 parameters, particularly in improved sensitivity compared to the scanners of the same Discovery family, D-ST and D-STE.