Ziad Burbar

Simultaneous MR/PET Imaging of the Human Brain: Feasibility Study

The purpose of this study was to apply a magnetic resonance (MR) imaging-compatible positron emission tomographic (PET) detector technology for simultaneous MR/PET imaging of the human brain and skull base. The PET detector ring consists of lutetium oxyorthosilicate (LSO) scintillation crystals in combination with avalanche photodiodes (APDs) mounted in a clinical 3-T MR imager with use of the birdcage transmit/receive head coil. Following phantom studies, two patients were simultaneously examined by using fluorine 18 fluorodeoxyglucose (FDG) PET and MR imaging and spectroscopy. MR/PET data enabled accurate coregistration of morphologic and multifunctional information. Simultaneous MR/PET imaging is feasible in humans, opening up new possibilities for the emerging field of molecular imaging.

Statistical list-mode image reconstruction for the high resolution research tomograph

We have investigated statistical list-mode reconstruction applicable to a depth-encoding high resolution research tomograph. An image non-negativity constraint has been employed in the reconstructions and is shown to effectively remove the overestimation bias introduced by the sinogram non-negativity constraint. We have furthermore implemented a convergent subsetized (CS) list-mode reconstruction algorithm, based on previous work (Hsiao et al 2002 Conf. Rec. SPIE Med. Imaging 4684 10-19; Hsiao et al 2002 Conf. Rec. IEEE Int. Symp. Biomed. Imaging 409-12) on convergent histogram OSEM reconstruction. We have demonstrated that the first step of the convergent algorithm is exactly equivalent (unlike the histogram-mode case) to the regular subsetized list-mode EM algorithm, while the second and final step takes the form of additive updates in image space. We have shown that in terms of contrast, noise as well as FWHM width behaviour, the CS algorithm is robust and does not result in limit cycles. A hybrid algorithm based on the ordinary and the convergent algorithms is also proposed, and is shown to combine the advantages of the two algorithms (i.e. it is able to reach a higher image quality in fewer iterations while maintaining the convergent behaviour), making the hybrid approach a good alternative to the ordinary subsetized list-mode EM algorithm.

The second generation HRRT - a multi-centre scanner performance investigation

The high resolution research tomograph (HRRT) is one of the most complex existing positron emission tomographs: it is the only human size scanner capable of decoding the depth of the /spl gamma/-ray interaction in the crystal, using a lutetium LSO/LYSO phoswich detector arrangement. In this study we determined basic scanner hardware characteristics, such as scanner data acquisition stability, and their variability across eleven centres. In addition a subset of the NEMA NU-2001 standards measurements was performed. We found (i) significant variability in the DOI decoding results between centres, (ii) a trend toward an increasing number of detected true coincident events as a function of elapsed time from scanner calibration likely due to a shifting energy spectrum, (iii) a count-rate dependent layer identification, (iv) scatter fraction ranging from /spl sim/ 42% to 54% where the variability was partly related to the shifting of the energy spectrum, (v) sensitivity ranging from /spl sim/5.5% to 6.5% across centres, (vi) resolution of /spl sim/(2.5 mm)/sup 3/, fairly consistent across centres, (vii) image quality which is very comparable to other scanners.

Variance reduction on randoms from coincidence histograms for the HRRT

A new algorithm for variance reduction on random coincidences (VRR) has been validated for the HRRT. VRR is crucial to achieve quantitation for low statistics dynamic studies reconstructed with iterative methods based on ordinary Poisson model. On HRRT, VRR cannot be performed in projection space since individual LORs are mixed after histogramming in parallel projection space using nearest neighbor approximation and axial compression. The proposed algorithm uses the classical random rate equation on the 4.5 109LORs. However, crystal singles are registered at block level and have lower deadtime than coincidences. Variations in layer identification with countrate were reported biasing random estimation from block singles. Our method overcomes these problems by estimating the singles per crystal from delayed coincidences. A singles map is created histogramming every delayed event into 2 singles. Each element represents the number of coincidences between that crystal and the ones in the 5 opposite coincident heads. The algorithm finds iteratively the crystal singles rates compatible with the delayed coincidence events. The method has been validated on decaying phantoms. We compared estimated and measured block singles to identify deadtime difference between singles and coincidences

Evaluation of single photon transmission for the HRRT

A dedicated whole human brain positron emission tomograph (PET), the High Resolution Research Tomograph (ECAT HRRT) is utilized to evaluate attenuation correction using single photon based transmission scanning. The patented transmission procedure uses a 740 MBq Cs-137 point source, which is extended into the FOV and collimated to flood the opposing detectors only. An attenuation map is then calculated iteratively using a blank and transmission scan, scaled to 511 keV, and re-projected using inverse Fourier rebinning to estimate the 3D attenuation correction. We have evaluated the accuracy of the single-based transmission procedure and attenuation correction process. In particular, we compare variance weighted OSEM and a dedicated TR algorithm with regularization (MAP-TR) for the reconstruction of the /spl mu/-image. Contamination from emission is estimated from a mock scan without moving the source. Results of a measurement of the patient dose during HRRT transmission scans show a 4 times lower dose compared to patient transmission scans on the ECAT HR.

Coincidence time alignment of high resolution planar detectors

Planar Quadrant Shared Array PET detectors are highly dependent upon accurate timing to improve their coincidence efficiency. Techniques are described to determine the timing characteristics of the planar detectors used in the CPS HRRT PET tomograph, and to compensate for manufacturing tolerances.

Data Processing Methods for a High Throughput Brain Imaging PET Research Center

We describe a computer system designed to meet the data processing needs of a high-volume brain PET research center based on the High Resolution Research Tomograph (HRRT). Listmode data are collected by an acquisition computer and stored on a high-speed disk. A workflow management program transfers the data through a gigabit network, rebins events into sinograms, and calculates correction factors. Reconstruction jobs are performed on a 64 processor cluster. We developed methods for dynamically allocating subclusters from the pool of available nodes, and reconstructing multiple images on multiple subclusters simultaneously. We also studied overall workflow. In our initial plan, scatter and randoms calculation unexpectedly became a bottleneck. We therefore adjusted our plan so that scatter estimation was performed initially in low resolution, and later expanded to high resolution.

True 3D iterative scatter correction for small bore long axial FOV scanner

Two region of interest were selected. A hot region is around the Basal Ganglia, and the other region is around the Cerebellum as shown in both figure 2. A time activity curve was then generated for both the hot and cold region for the same study being generated with 2D and 3D scatter correction. Figure 3-a shows the hot region for both scatter 2D and 3D. Both regions have the same trend and value. However, looking at figure 3-b that shows the cold region, it shows that scatter were over estimated in the 2D scatter correction case.

Stereo computer vision system for measuring movement of patient's head in PET scanning

This paper introduces a stereo computer vision system developed at CPS Innovations for measuring the movement of a patients head in brain PET scan. A pair of monochrome CCD cameras is installed to acquire the images of the patients head from two different directions. Simple features (three round dots) on a small target attached to the patients head are recognized by applying a feature-matching algorithm. The position and orientation of the target is obtained through triangulation calculations. The methods and processes for camera calibration, feature-matching, and measurement accuracy assessment are discussed. The preliminary experiment shows that the root-mean-square measurement error of the system is less than 0.36mm. The system is low cost, compact in size, less intrusive to the patient, and offers robust measurement with sufficient accuracy. It provides an industrial solution for measuring the movement of a patients head in PET scanning, and can be easily integrated with brain PET scanners such as the HRRT

Clinical time OSEM3D: infrastructure issues

In previous work we compared two parallel algorithms for calculating 3D forward and backprojection on a distributed-memory cluster computer. These two methods were used to develop an implementation of fully three-dimensional ordered subset expectation maximization iterative reconstruction for emission tomography (OSEM3D). It is, however, necessary to embed these computational kernels in an environment that supports efficient data movement and other infrastructural operations, such as process management. Here we briefly describe two particular components of the infrastructure: the I/O subsystem and the service demon. For the I/O subsystem, a fortuitous relationship between the traditional representation of the fully three-dimensional sinogram S/sub 4DO/(/spl theta/,z,/spl phi/,r) and the distributed representation for image space decomposition permits an efficient solution involving minimal data movement. The service demon is similar to others, but contains additional recovery-oriented mechanisms for minimizing mean time to repair. We conclude with performance benchmarks for fully 3D reconstruction for a scanner that produces a significant volume of data.