IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium最新文献

An inter-operative image-guided surgery system is described in which the CT volume is pre-registered to the physical scanner, allowing easier workflow and small field-of-view update scans.

Previous studies showed that iterative image reconstruction algorithms may produce overestimations of activity in low-activity regions in low-count frames. The purpose of this study was (1) to evaluate the quantitative accuracy of the MOLAR list-mode iterative reconstruction method in the context of ligand-receptor PET studies in low counts, and (2) to determine the minimum noise equivalent counts (NEC) per frame to avoid bias. Evaluation of clinical data was performed for 4 tracers using dynamic brain PET studies. True activity was estimated from high-statistics frames (300s) and ROI analysis was performed to evaluate bias in low-activity regions in short acquisition frames (10-30s) from matching times. Bias in the ROI mean values was analyzed as function of NEC. In addition, accuracy was assessed using Hoffman phantom data and simulated list mode data based on human data, but without scatter and randoms.Unlike previous results, small biases of -3±3% for low statistics region across the 4 tracers were found for NEC >100K in each frame. Very similar results were found in the phantom and simulation data. We conclude that the MOLAR iterative reconstruction method provides accurate results even in very low-count frames. This improved performance may be attributed to some of the unique characteristics of MOLAR including randoms estimation from singles, iterative estimation of scatter within the algorithm, component-based normalization, and incorporation of a line-spread function model in the reconstruction.

In this paper, we consider a prototype of an adaptive SPECT system, and we use simulation to objectively assess the system's performance with respect to a conventional, non-adaptive SPECT system. Objective performance assessment is investigated for a clinically relevant task: the detection of tumor necrosis at a known location and in a random lumpy background. The iterative maximum-likelihood expectation-maximization (MLEM) algorithm is used to perform image reconstruction. We carried out human observer studies on the reconstructed images and compared the probability of correct detection when the data are generated with the adaptive system as opposed to the non-adaptive system. Task performance is also assessed by using a channelized Hotelling observer, and the area under the receiver operating characteristic curve is the figure of merit for the detection task. Our results show a large performance improvement of adaptive systems versus non-adaptive systems and motivate further research in adaptive medical imaging.

The influence of a finite positron annihilation distance represents a fundamental limit to the spatial resolution of PET scanners. It is appreciated that this effect is a minor concern in whole-body F18 imaging, but it does represent an issue when imaging with higher energy isotopes such as N13 or Rb82. This effect is especially relevant for imaging tasks along tissue gradients such as the cardiac/lung boundary and diaphragm/lung boundary. This work presents a method to determine the positron range effect from a CT scan and to model this effect as shift-variant, anisotropic kernels. The positron annihilation distance across boundaries of tissues is represented with a simple model, which can be quickly derived from CT scans and applied in the reconstruction of PET images. The positron range compensation map is applied in a modified OSEM algorithm to simulated and measured data.

We present a direct method for producing images of kinetic parameters from list mode PET data. The time-activity curve for each voxel is described by a one-tissue compartment, 2-parameter model. Extending previous EM algorithms, a new spatiotemporal complete data space was introduced to optimize the maximum likelihood function. This leads to a straightforward parametric image update equation with moderate additional computation requirements compared to the conventional algorithm. Qualitative and quantitative evaluations were performed using 2D (x,t) and 4D (x,y,z,t) simulated list mode data for a brain receptor study. Comparisons with the two-step approach (frame-based reconstruction followed by voxel-by-voxel parameter estimation) show that the proposed method can lead to accurate estimation of the parametric image values with reduced variance, especially for the volume of distribution (V(T)).

We report on methods to speed up the calibration process for a continuous miniature crystal element (cMiCE) detector. Our cMiCE detector is composed of a 50 mm by 50 mm by 8 mm thick LYSO crystal coupled to a 64-channel, flat panel photomultiplier tube (PMT). This detector is a lower cost alternative to designs that use finely pixilated individual crystal detectors. It achieves an average intrinsic spatial resolution of ~1.4 mm full width at half maximum (FWHM) over the useful face of the detector through the use of a statistics based positioning algorithm. A drawback to the design is the length of time it takes to calibrate the detector. We report on three methods to speed up this process. The first method is to use multiple point fluxes on the surface of the detector to calibrate different points of the detector from a single data acquisition. This will work as long as the point fluxes are appropriately spaced on the detector so that there is no overlap of signal. A special multi-source device that can create up to 16 point fluxes has been custom designed for this purpose. The second scheme is to characterize the detector with coarser sampling and use interpolation to create look up tables with the desired detector sampling (e.g., 0.25 mm). The intrinsic spatial resolution performance will be investigated for sampling intervals of 0.76 mm, 1.013 mm, 1.52 mm and 2.027 mm. The third method is to adjust the point flux diameter by varying the geometry of the setup. By bringing the coincidence detector array closer to the point source array both the spot size and the coincidence counting rate will increase. We will report on the calibration setup factor we are able to achieve while maintaining an average intrinsic spatial resolution of ~1.4 mm FWHM for the effective imaging area of our cMiCE detector.

We have previously reported performance characteristics of a cMiCE detector composed of a 50 mm by 50 mm by 8 mm thick slab of LYSO, coupled to a 64 channel flat-panel PMT. In that work, all 64 PMT channels were digitized and a statistics-based positioning method was used for event positioning. In characterizing the detector, the intrinsic spatial resolution performance for the corner sections of the crystal was degraded compared to the central section of the crystal, even when using our SBP method. It is our belief that the poorer positioning performance at the corners is because much of the light is lost (i.e., not collected by our PMT). To offset this problem, we propose to place light sensors (i.e., micro-pixel avalanche photo diodes, MAPD) at the corners along the short edge of the crystal. The new design would require an additional 8 MAPD devices. Monte Carlo simulation was used to compare the performance of the original cMiCE design and this new enhanced design. Simulation results using DETECT2000 are presented. In addition to doing light ray tracing, GEANT was used to track gamma interactions (i.e., Compton scatter and photoelectric absorption) in the crystal. Thus the simulations include the effects of Compton scatter in the detector. Results indicate that adding the sensors improves the intrinsic spatial resolution performance from 0.99 mm FWHM to 0.79 mm FWHM for the corner section of the crystal, thereby nearly matching the intrinsic spatial resolution of the center section of the crystal (i.e., 0.73 mm FWHM). These results are based upon using dual-DOI look up tables. Additional results were that energy histograms were better using just the 64 channels from the flat panel PMT than using all 72 signal channels.

With the widespread availability of SPECT/CT systems it has become feasible to incorporate prior knowledge about anatomical boundaries into the SPECT reconstruction process, thus improving observer performance on tasks of clinical interest. We determine the optimal anatomical-prior strength for lesion search by measuring area under the LROC curve using human observers. We conclude that prior strength should be chosen assuming that only organ boundaries are available, even if lesion boundaries will also be known some of the time. We also test whether or not the presence of anatomical priors affects the observer's strategy, and conclude that mixing images with and without priors does not hurt reader performance when priors are not available. Finally, we examine whether using an anatomical prior in SPECT reconstruction helps observer performance when the observer already knows the possible lesion location, and conclude for this task anatomical priors do not provide the same improvement seen in search tasks.

Our current approach to fusion of CTCA and PET perfusion data uses the epicardial surface from the perfusion data onto which the CT coronary arteries are aligned and warped. This work was undertaken to improve the alignment and the display realism by using CT epicardial boundary information. PET and CTCA images from a combined scanner were used. Based on the location of the LV detected from PET during standard perfusion processing, the LV chamber of the CT was located. Hounsfield units were used to define an estimated endocardial surface in the CT. Based on the endocardial surface, the epicardial boundary was detected, again using Hounsfield units, or when that failed, by estimating its position based on the detected endocardium. Coronary arteries were detected using a commercial program; the epicardial surface was forced to be congruent with all detected artery points. A confidence factor in each epicardial boundary point was maintained based on how each was detected, whether through threshold, through estimation, or by using he coronary artery points. The epicardial boundary surface points were nonlinearly filtered; erroneous surface points, as defined by local properties and confidence factors, were replaced with values interpolated from the nearest points deemed more accurate. The resulting epicardial surface was linearly aligned to the epicardial boundary detected from the PET, and the CT boundaries were then color-coded based on the PET perfusion. Resulting surfaces were much more realistic than those created using PET epicardial boundaries (Fig 1.) Forcing the CT epicardial surface to lie on the detected coronary arteries eliminated problems with alignment and warping of the coronary arteries onto the PET surface.