Brain PET imaging optimization with time of flight and point spread function modelling

PurposeTo assess the influence of reconstruction algorithms and parameters on the PET image quality of brain phantoms in order to optimize reconstruction for clinical PET brain studies in a new generation PET/CT.MethodsThe 3D Hoffman phantom that simulates ¹⁸F-fluorodeoxyglucose (FDG) distribution was imaged in a Siemens Biograph mCT TrueV PET/CT with Time of Flight (TOF) and Point Spread Function (PSF) modelling. Contrast-to-Noise Ratio (CNR), contrast and noise were studied for different reconstruction models: OSEM, OSEM + TOF, OSEM + PSF and OSEM + PSF + TOF.The 2D multi-compartment Hoffman phantom was filled to simulate 4 different tracers' spatial distribution: FDG, ¹¹C-flumazenil (FMZ), ¹¹C-Methionine (MET) and 6-¹⁸F-fluoro-l-dopa (FDOPA). The best algorithm for each tracer was selected by visual inspection. The maximization of CNR determined the optimal parameters for each reconstruction.ResultsIn the 3D Hoffman phantom, both noise and contrast increased with increasing number of iterations and decreased with increasing FWHM. OSEM + PSF + TOF reconstruction was generally superior to other reconstruction models. Visual analysis of the 2D Hoffman brain phantom suggested that OSEM + PSF + TOF is the optimum algorithm for tracers with focal uptake, such as MET or FDOPA, and OSEM + TOF for tracers with diffuse cortical uptake (i.e. FDG and FMZ). Optimization of CNR demonstrated that OSEM + TOF reconstruction must be performed with 2 iterations and a filter FWHM of 3 mm, and OSEM + PSF + TOF reconstruction with 4 iterations and 1 mm FWHM filter.ConclusionsOptimization of reconstruction algorithm and parameters has been performed to take particular advantage of the last generation PET scanner, recommending specific settings for different brain PET radiotracers.     

图形处理器在实时光学相干断层成像中的应用

光学相干断层成像(optical coherence tomography,OCT)技术在成像过程中具有极大的数据量和计算量,传统的基于中央处理器(central processing unit,CPU)的计算平台难以满足OCT实时成像的需求。图形处理器(graphics processing unit,GPU)在通用计算方面具有强大的并行处理能力和数值计算能力,可以突破OCT实时成像的瓶颈。本文对GPU做了简要介绍并阐述了GPU在OCT实时成像及功能成像中的应用及研究进展。     

Bone SPECT image reconstruction using deconvolution and wavelet transformation: Development,performance assessment and comparison in phantom and patient study with standard OSEM and resolution recovery algorithm

PurposeThe aim of this work was to introduce a new algorithm for image reconstruction in bone SPECT and to compare its performances with a commercially available standard OSEM and resolution recovery (RR) reconstruction.Materials and methodsThe algorithm was built applying the Lucy-Richardson deconvolution adn logarithmic image processing to the projections. A modification of the coefficients of wavelet decomposition was used to suppress the noise. The comparison with vendor software was performed both in a phantom study, using Signal-to-Noise ratio (SNR), Signal-to-Background ratio (SBR), spatial resolution and in clinical studies, by visual assessment of changes in contrast, spatial resolution and lesion detectability.ResultsA change in the SNR (from −4 to 40%), an increase in the SBR (from 19 to 40%), a minor improvement in spatial resolution and a similar noise level were observed in the phantom study in comparison to the standard OSEM. A decrease in the SNR, a worse spatial resolution, but only a 3 to 13 % lower SBR were achieved in comparison with the vendor supplied RR algorithm. The proposed algorithm creates patient images with better contrast and lesion detectability compared to clinically used OSEM. Compared to RR, more than half of obtained images showed better contrast and nearly half of them have better lesion detectability.ConclusionThe proposed algorithm compares favorably with the standard OSEM. Although less favorable, the comparison with RR and noise suppression algorithms, suggests that it can be used with only a slight decrease in the SBR.     

采用Evolution CDR补偿重建技术提高骨断层图像质量方法学研究

目的应用Evolution CDR补偿技术对骨断层图像质量的改善进行评价。方法随机选择32例骨断层显像,分别进行全时采集OSEM迭代处理,1／2时采集OSEM处理以及1／2时采集Evolution处理和全时采集Evolution处理。通过盲法读片,对三种不同图像采集处理技术获得图像进行图像质量评分。结果1／2时采集Evolution重建图像质量与全时采集OS-EM重建图像质量相当;而全时采集Evolution重建的图像质量明显优于全时采集OSEM重建。结论Evolution重建技术通过对CDR补偿,能降低图像噪声,明显改善图像质量,或者在得到与全时采集OSEM迭代重建相当的图像质量的情况下,可以大女降低采键时间．     

Hybrid framework for feasible modeling of an edge illumination X-ray phase-contrast imaging system at a human scale

Here, we present a hybrid approach for simulating an edge illumination X-ray phase-contrast imaging (EIXPCi) set-up using graphics processor units (GPU) with a high degree of accuracy. In this study, the applicability of pixel, mesh and non-uniform rational B-splines (NURBS) objects to carry out realistic maps of X-ray phase-contrast distribution at a human scale is accounted for by using numerical anthropomorphic phantoms and a very fast and robust simulation framework which integrates total interaction probabilities along selected X-ray paths. We exploit the mathematical and algorithmic properties of NURBS and describe how to represent human scale phantoms in an edge illumination X-ray phase-contrast model. The presented implementation allows the modeling of a variety of physical interactions of x-rays with different mathematically described objects and the recording of quantities, e.g. path integrals, interaction sites and deposited energies. Furthermore, our efficient, scalable and optimized hybrid Monte Carlo and ray-tracing projector can be used in iterative reconstruction algorithms on multi GPU heterogeneous systems. The preliminary results of our innovative approach show the fine performance of an edge illumination X-ray phase-contrast medical imaging system on various human-like soft tissues with noticeably reduced computation time. Our approach to the EIXPCi modeling confirms that building a true imaging system at a human scale should be possible and the simulations presented here aim at its future development.     

Conceptual Design and Simulation Study of an ROI-Focused Panel-PET Scanner

Positron emission tomography (PET) is an important imaging modality for clincial use. Conventionally, the PET scanner is generally built to provide a roomy enough transverse field-of-view (FOV) for imaging most adults’ torsos. However, in many cases, the region-of-interest (ROI) for imaging is usually a small area inside the human body. Therefore, to fulfill a PET system which provides an FOV comparable in size to the target ROI seems appealing and more cost effective. Meanwhile, such a PET system has the potential for portable or bedside application with the reduced system size. In this work, we have investigated the feasibility of using dual-headed panel-detectors to build an ROI-focused PET scanner. A novel windowed list-mode ordered subset expectation maximization method was developed to perform the ROI image reconstruction. With this method, the ROI of the object can be reconstructed from the coincidences whose position determined by time-of-flight (TOF) measurements was inside the ROI. Monte Carlo simulation demonstrates the feasibility of detecting lesions not less than 1 cm in diameter, with a 300 ps full width at half maximum timing resolution. As a critical system performance, the impact of TOF information on image quality has been studied and the required TOF capability was assessed. With enhanced timing resolution, the distortions and artifacts were reduced effectively. The further improved TOF capability also shows a noticeable improvement of detection performance for low uptake lesions, as well as the recovery speed of lesion contrast, which is of practical significance in the lesion detection task.     

Development of a single detector ring micro crystal element scanner: QuickPET II

This article describes a single ring version of the micro crystal element scanner (MiCES) and investigation of its spatial resolution imaging characteristics for mouse positron emission tomography (PET) imaging. This single ring version of the MiCES system, referred to as QuickPET II, consists of 18 MiCE detector modules mounted as a single ring in a vertical gantry. The system has a 5.76-cm transverse field of view and a 1.98-cm axial field of view. In addition to the scanner and data acquisition system, we have developed an iterative reconstruction that includes a model of the system's detector response function. Evaluation images of line sources and mice have been acquired. Using filtered backprojection, the resolution for a reconstructed line source has been measured at 1.2 mm full width at half maximum. F-18-2-fluoro-2-deoxyglucose mouse PET images are provided. The result shows that QuickPET II has the imaging characteristics to support high-resolution, static mouse PET studies using 18-F labeled compounds.     

基于分段线性模板先验的正电子成像重建

正电子发射成像的噪声性能较其它的一些成像方法要差得多,为了提高重建结果的分辨率和噪声特性,一般采用Bayesian重建。Bayesian方法需要选择恰当的先验,这种先验既要能够抑止重建结果的噪声,又要能够保留图像密度的突变信息。分段线性模板图像模型利用从其它模态的形态学成像得到的组织结构信息,构造适合要求的先验分布函数,由于采用的先验函数是非凸的并包含超验参数,一般的优化方法难以处理,采用动态后验模拟的方法可以很好地解决这些问题。     

Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs)

Iterative reconstruction algorithms are becoming increasingly important in electron tomography of biological samples. These algorithms, however, impose major computational demands. Parallelization must be employed to maintain acceptable running times. Graphics Processing Units (GPUs) have been demonstrated to be highly cost-effective for carrying out these computations with a high degree of parallelism. In a recent paper by Xu et al. (2010), a GPU implementation strategy was presented that obtains a speedup of an order of magnitude over a previously proposed GPU-based electron tomography implementation. In this technical note, we demonstrate that by making alternative design decisions in the GPU implementation, an additional speedup can be obtained, again of an order of magnitude. By carefully considering memory access locality when dividing the workload among blocks of threads, the GPU’s cache is used more efficiently, making more effective use of the available memory bandwidth.     

MRI/PET nonrigid breast-image registration using skin fiducial markers

We propose a finite-element method (FEM) deformable breast model that does not require elastic breast data for nonrigid PET/MRI breast image registration. The model is applicable only if the stress conditions in the imaged breast are virtually the same in PET and MRI. Under these conditions, the observed intermodality displacements are solely due the imaging/reconstruction process. Similar stress conditions are assured by use of an MRI breast-antenna replica for breast support during PET, and use of the same positioning. The tetrahedral volume and triangular surface elements are used to construct the FEM mesh from the MRI image. Our model requires a number of fiducial skin markers (FSM) visible in PET and MRI. The displacement vectors of FSMs are measured followed by the dense displacement field estimation by first distributing the displacement, vectors linearly over the breast surface and then distributing them throughout the volume. Finally, the floating MRI image is warped to a fixed PET image, by using an appropriate shape function in the interpolation from mesh nodes to voxels. We tested our model on an elastic breast phantom with simulated internal lesions and on a small number of patients imaged, with FMS using PET and MRI. Using simulated lesions (in phantom) and real lesions (in patients) visible in both PET and MRI, we established that the target registration error (TRE) is below two pet voxels.     

Patient-specific modelling of pulmonary airflow using GPU cluster for the application in medical practice

In this paper, we propose a novel patient-specific method of modelling pulmonary airflow using graphics processing unit (GPU) computation that can be applied in medical practice. To overcome the barriers imposed by computation speed, installation price and footprint to the application of computational fluid dynamics, we focused on GPU computation and the lattice Boltzmann method (LBM). The GPU computation and LBM are compatible due to the characteristics of the GPU. As the optimisation of data access is essential for the performance of the GPU computation, we developed an adaptive meshing method, in which an airway model is covered by isotropic subdomains consisting of a uniform Cartesian mesh. We found that 4(3) size subdomains gave the best performance. The code was also tested on a small GPU cluster to confirm its performance and applicability, as the price and footprint are reasonable for medical applications.     

Artificial intelligence in image reconstruction: The change is here

Innovations in CT have been impressive among imaging and medical technologies in both the hardware and software domain. The range and speed of CT scanning improved from the introduction of multidetector-row CT scanners with wide-array detectors and faster gantry rotation speeds. To tackle concerns over rising radiation doses from its increasing use and to improve image quality, CT reconstruction techniques evolved from filtered back projection to commercial release of iterative reconstruction techniques, and recently, of deep learning (DL)-based image reconstruction. These newer reconstruction techniques enable improved or retained image quality versus filtered back projection at lower radiation doses. DL can aid in image reconstruction with training data without total reliance on the physical model of the imaging process, unique artifacts of PCD-CT due to charge sharing, K-escape, fluorescence x-ray emission, and pulse pileups can be handled in the data-driven fashion. With sufficiently reconstructed images, a well-designed network can be trained to upgrade image quality over a practical/clinical threshold or define new/killer applications. Besides, the much smaller detector pixel for PCD-CT can lead to huge computational costs with traditional model-based iterative reconstruction methods whereas deep networks can be much faster with training and validation. In this review, we present techniques, applications, uses, and limitations of deep learning-based image reconstruction methods in CT.     

Sop‐GPU: Accelerating biomolecular simulations in the centisecond timescale using graphics processors

Theoretical exploration of fundamental biological processes involving the forced unraveling of multimeric proteins, the sliding motion in protein fibers and the mechanical deformation of biomolecular assemblies under physiological force loads is challenging even for distributed computing systems. Using a C_α‐based coarse‐grained self organized polymer (SOP) model, we implemented the Langevin simulations of proteins on graphics processing units (SOP‐GPU program). We assessed the computational performance of an end‐to‐end application of the program, where all the steps of the algorithm are running on a GPU, by profiling the simulation time and memory usage for a number of test systems. The ～90‐fold computational speedup on a GPU, compared with an optimized central processing unit program, enabled us to follow the dynamics in the centisecond timescale, and to obtain the force‐extension profiles using experimental pulling speeds (v_f = 1–10 μm/s) employed in atomic force microscopy and in optical tweezers‐based dynamic force spectroscopy. We found that the mechanical molecular response critically depends on the conditions of force application and that the kinetics and pathways for unfolding change drastically even upon a modest 10‐fold increase in v_f. This implies that, to resolve accurately the free energy landscape and to relate the results of single‐molecule experiments in vitro and in silico, molecular simulations should be carried out under the experimentally relevant force loads. This can be accomplished in reasonable wall‐clock time for biomolecules of size as large as 10⁵ residues using the SOP‐GPU package. Proteins 2010; © 2010 Wiley‐Liss, Inc.     

Small animal PET imaging

Positron emission tomography (PET) is well established as an important research and clinical molecular imaging modality. Although the size differences between humans and rodents create formidable challenges for the application of PET imaging in small animals, advances in technology over the past several years have enabled the translation of this imaging modality to preclinical applications. In this article we discuss the basic principles of PET instrumentation and radiopharmaceuticals, and examine the key factors responsible for the qualitative and quantitative imaging capabilities of small animal PET systems. We describe the criteria that PET imaging agents must meet, and provide examples of small animal PET imaging to give the reader a broad perspective on the capabilities and limitations of this evolving technology. A crucial driver for future advances in PET imaging is the availability of molecular imaging probes labeled with positron-emitting radionuclides. The strong translational science potential of small animal and human PET holds great promise to dramatically advance our understanding of human disease. The assessment of molecular and functional processes using imaging agents as either direct or surrogate biomarkers will ultimately enable the characterization of disease expression in individual patients and thus facilitate tailored treatment plans that can be monitored for their effectiveness in each subject.     

Advancing simulations of biological materials: applications of coarse-grained models on graphics processing unit hardware

The timescales of biological processes, primarily those inherent to the molecular mechanisms of disease, are long (>μs) and involve complex interactions of systems consisting of many atoms (>10⁶). Simulating these systems requires an advanced computational approach, and as such, coarse-grained (CG) models have been developed and highly optimised for accelerator hardware, primarily graphics processing units (GPUs). In this review, I discuss the implementation of CG models for biologically relevant systems, and show how such models can be optimised and perform well on GPU-accelerated hardware. Several examples of GPU implementations of CG models for both molecular dynamics and Monte Carlo simulations on purely GPU and hybrid CPU/GPU architectures are presented. Both the hardware and algorithmic limitations of various models, which depend greatly on the application of interest, are discussed.     

MR-Based PET Motion Correction Procedure for Simultaneous MR-PET Neuroimaging of Human Brain

Positron Emission Tomography (PET) images are prone to motion artefacts due to the long acquisition time of PET measurements. Recently, simultaneous magnetic resonance imaging (MRI) and PET have become available in the first generation of Hybrid MR-PET scanners. In this work, the elimination of artefacts due to head motion in PET neuroimages is achieved by a new approach utilising MR-based motion tracking in combination with PET list mode data motion correction for simultaneous MR-PET acquisitions. The method comprises accurate MR-based motion measurements, an intra-frame motion minimising and reconstruction time reducing temporal framing algorithm, and a list mode based PET reconstruction which utilises the Ordinary Poisson Algorithm and avoids axial and transaxial compression. Compared to images uncorrected for motion, an increased image quality is shown in phantom as well as in vivo images. In vivo motion corrected images show an evident increase of contrast at the basal ganglia and a good visibility of uptake in tiny structures such as superior colliculi.     

Patient-specific modelling of pulmonary airflow using GPU cluster for the application in medical practice

In this paper, we propose a novel patient-specific method of modelling pulmonary airflow using graphics processing unit (GPU) computation that can be applied in medical practice. To overcome the barriers imposed by computation speed, installation price and footprint to the application of computational fluid dynamics, we focused on GPU computation and the lattice Boltzmann method (LBM). The GPU computation and LBM are compatible due to the characteristics of the GPU. As the optimisation of data access is essential for the performance of the GPU computation, we developed an adaptive meshing method, in which an airway model is covered by isotropic subdomains consisting of a uniform Cartesian mesh. We found that 4³ size subdomains gave the best performance. The code was also tested on a small GPU cluster to confirm its performance and applicability, as the price and footprint are reasonable for medical applications.     

Automated Movement Correction for Dynamic PET/CT Images: Evaluation with Phantom and Patient Data

Head movement during a dynamic brain PET/CT imaging results in mismatch between CT and dynamic PET images. It can cause artifacts in CT-based attenuation corrected PET images, thus affecting both the qualitative and quantitative aspects of the dynamic PET images and the derived parametric images. In this study, we developed an automated retrospective image-based movement correction (MC) procedure. The MC method first registered the CT image to each dynamic PET frames, then re-reconstructed the PET frames with CT-based attenuation correction, and finally re-aligned all the PET frames to the same position. We evaluated the MC method''s performance on the Hoffman phantom and dynamic FDDNP and FDG PET/CT images of patients with neurodegenerative disease or with poor compliance. Dynamic FDDNP PET/CT images (65 min) were obtained from 12 patients and dynamic FDG PET/CT images (60 min) were obtained from 6 patients. Logan analysis with cerebellum as the reference region was used to generate regional distribution volume ratio (DVR) for FDDNP scan before and after MC. For FDG studies, the image derived input function was used to generate parametric image of FDG uptake constant (K_i) before and after MC. Phantom study showed high accuracy of registration between PET and CT and improved PET images after MC. In patient study, head movement was observed in all subjects, especially in late PET frames with an average displacement of 6.92 mm. The z-direction translation (average maximum = 5.32 mm) and x-axis rotation (average maximum = 5.19 degrees) occurred most frequently. Image artifacts were significantly diminished after MC. There were significant differences (P<0.05) in the FDDNP DVR and FDG Ki values in the parietal and temporal regions after MC. In conclusion, MC applied to dynamic brain FDDNP and FDG PET/CT scans could improve the qualitative and quantitative aspects of images of both tracers.     

An Effective CUDA Parallelization of Projection in Iterative Tomography Reconstruction

Projection and back-projection are the most computationally intensive parts in Computed Tomography (CT) reconstruction, and are essential to acceleration of CT reconstruction algorithms. Compared to back-projection, parallelization efficiency in projection is highly limited by racing condition and thread unsynchronization. In this paper, a strategy of Fixed Sampling Number Projection (FSNP) is proposed to ensure the operation synchronization in the ray-driven projection with Graphical Processing Unit (GPU). Texture fetching is also used utilized to further accelerate the interpolations in both projection and back-projection. We validate the performance of this FSNP approach using both simulated and real cone-beam CT data. Experimental results show that compare to the conventional approach, the proposed FSNP method together with texture fetching is 10~16 times faster than the conventional approach based on global memory, and thus leads to more efficient iterative algorithm in CT reconstruction.