Abstract
Real-time positron emission tomography (PET) may provide information from first-shot images, enable PET-guided biopsies, and allow awake animal studies. Fully-3D iterative reconstructions yield the best images in PET, but they are too slow for real-time imaging. Analytical methods such as Fourier back projection (FBP) are very fast, but yield images of poor quality with artifacts due to noise or data incompleteness. In this work, an image reconstruction based on the pseudoinverse of the system response matrix (SRM) is presented. w. To implement the pseudoinverse method, the reconstruction problem is separated into two stages. First, the axial part of the SRM is pseudo-inverted (PINV) to rebin the 3D data into 2D datasets. Then, the resulting 2D slices can be reconstructed with analytical methods or by applying the pseudoinverse algorithm again. The proposed two-step PINV reconstruction yielded good-quality images at a rate of several frames per second, compatible with real time applications. Furthermore, extremely fast direct PINV reconstruction of projections of the 3D image collapsed along specific directions can be implemented.
Highlights
Fourier rebinning (FORE) provided the best axial resolution and less noise, while PINV, on the other hand, provided a more uniform resolution and noise level across the field of view (FOV), clearly improving single-slice rebinning (SSRB) for off-axis values, where it approached the best results of FORE
We studied the isolated 2D reconstruction with Fourier back projection (FBP) and PINV from the same FORE
We showed that pseudoinverse reconstruction of 3D positron emission tomography (PET) data using PINVz +
Summary
Positron emission tomography (PET) is widely used in medical imaging. In many applications of PET such as first-shot tests for chemicals [1] or PET image-guided surgery or biopsy [2], it is crucial to obtain images as quickly as possible. The frame rate achievable will depend on the sensitivity of the acquisition system, the hardware used for data acquisition, and the software employed for data processing and image reconstruction [3]. State-of-the-art PET systems [4,5,6] can process up to 10 million coincidences per second, providing useful images in very short time frames. The challenge to be considered here is to provide useful quasi real-time images, that is, at a rate of a few frames per second with appropriate image quality
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