KEYWORDS: Positron emission tomography, Monte Carlo methods, Image restoration, Scanners, Matrices, 3D image processing, Image quality, Testing and analysis, Reconstruction algorithms, Medical imaging
Real-time Positron Emission Tomography (PET) has the potential to become a new imaging tool providing useful information, such as first-shot images, medical intervention guidance, information about patient position and motion, and to perform PET image guided biopsy. Fully-3D iterative reconstruction methods in PET provide highest quality images, but they are still not suitable for real-time imaging due to their large computational time requirements. On the other hand, analytical methods are much faster, but they exhibit low-quality images and artifacts when using noisy or incomplete data. We propose an alternative reconstruction method based on the pseudoinverse of the System Response Matrices (SRM), which can be very fast while yielding good quality images. The reconstruction problem is separated into two independent ones. First, the axial part of the SRM is pseudoinverted and used to rebin in the axial direction 3D data into 2D datasets with resolution recovery. The resulting 2D datasets can be reconstructed with standard analytical methods such as Filtered Back-Projection (FBP), or with another in-plane pseudoinverse algorithm. Pseudoinverse rebinning is as fast as standard Single Slice ReBinning (SSRB), but with image quality comparable to FOurierREbinning (FORE). With regards to the transaxial image reconstruction, pseudoinverse rebinning is as fast as FBP, but obtains improved resolution recovery and uniformity. Overall, the two-step psudoinverse reconstruction yields much more acceptable images than SSRB+FBP, at a rate of several frames per second, compatible with real time applications.
Most current Positron Emission Tomography (PET) scanners use pixelated detector crystals, and the crystal pitch limits the sampling and the image resolution. In this paper we present a maximum-likelihood based method to go beyond the existing discrete sampling in PET scanners. After an initial standard image reconstruction, the projection of the reconstructed image is used to redistribute the counts of each original LOR among several subLORs. The new dataset with increased sampling is reconstructed again, obtaining improved image resolution without increasing the noise. The procedure can be repeated several times for further improvements, being each reconstruction a super-iteration. We validated the method with data acquired with the preclinical Super Argus PET/CT scanner. We used the NEMA NU4- 2008 for the Super Argus PET/CT scanner to quantitatively measure the image quality improvement, which resulted in a Recovery Coefficient (RC) increase of 14% for the smallest rod. Results with in-vivo acquisitions of a rat cardiac study injected with FDG also confirm the improvement in image quality. The proposed method can be considered a generalization of standard reconstruction algorithms, which is able to achieve better images at the expense of increasing the reconstruction time.
The presence of motion during the relatively long PET acquisitions is a very common problem, especially with awake animals, infants and patients with neurological disorders. External motion can be detected based on the optical tracking of markers placed on the skin of the patient, but it needs additional hardware and a somehow complex integration with the PET data. The possibility of motion detection directly from the acquired PET data would overcome these limitations. In this work, we propose the use of the centroid of lines of response to identify long motion free frames (more than 2.5 seconds). In these frames we identify in real-time the location of 18F markers placed on the head of the rat with the radiotracer labeled with 18F. We evaluated the performance of the proposed method in a preclinical PET/CT scanner with an awake rat injected with 600 μCi and four 18F sources attached in its head. After solid rigid motion compensation, we reconstruct an image that use 70% events of the acquisition, and the resolution is comparable with the motion-free frames.
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