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 <sup>18</sup>F markers placed on the head of the rat with the radiotracer labeled with <sup>18</sup>F. We evaluated the performance of the proposed method in a preclinical PET/CT scanner with an awake rat injected with 600 μCi and four <sup>18</sup>F 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.
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.
Dynamic PET imaging is usually performed dividing the acquired data into time frames which are reconstructed independently and then fitted using a kinetic model. This approach requires many image reconstructions, and data corrections, and the use of short frames usually produces noisy images with significant positive bias. In this work we propose to use a generalized version of the method of moments (MoM), already in use in other fields such as fluorescence decay studies, to address these problems. In the MoM, the events of the list-mode data are weighted based on the time they were detected and stored in sinograms. These sinograms are reconstructed with standard algorithms, and the dynamic parameters of interest are derived from the resulting images using algebraic relations, which depend on the specific dynamic model and selected set of weights. The method was evaluated with data from preclinical and clinical scanners with several dynamical studies such as a decaying 13N phantom acquired with the Biograph TP scanner and a PatLak analysis in the myocardium region of a mouse injected with 18F-FDG, reaching in all cases similar results to the ones obtained using frames. We also successfully tested the MoM with more complex dynamic models with simulated data obtained with dPETSTEP. In summary, the MoM applied to dynamic PET has the potential to be a very effective way to reduce the computational cost and bias in many different studies.
Ultrasound Computer Tomography is an exciting new technology mostly aimed at breast cancer imaging. Due to the complex interaction of ultrasound with human tissue, the large amount of raw data, and the large volumes of interest, both image acquisition and image reconstruction are challenging. Following the idea of open science, the long term goal of the USCT reference database is establishing open and easy to use data and code interfaces and stimulating the exchange of available reconstruction algorithms and raw data sets of different USCT devices. The database was established with freely available and open licensed USCT data for comparison of reconstruction algorithms, and will be maintained and updated. Additionally, the feedback about data and system architecture of the scientists working on reconstruction methods will be published to help to drive further development of the various measurement setups.
The reconstruction of acoustic attenuation maps for transmission Ultrasound Computed Tomography (USCT) based on
the standard least-squares full wave inversion method requires the accurate knowledge of the sound speed map in the
region under study. Any deviation in the reconstructed speed maps creates a very significant bias in the attenuation map,
as the standard least-squares misfit function is more sensitive to time misalignments than to amplitude differences of the
signals. In this work, we propose a generalized misfit function which includes an additional term that accounts for the
amplitude differences between the measured and the estimated signals. The functional gradients used to minimize the
proposed misfit function were obtained using an adjoint field formulation and the fractional Laplacian wave equation.
The forward and backward wave propagation was obtained with the parallelized GPU version of the software k-Wave
and the optimization was performed with a line search method. A numerical phantom simulating breast tissue and
synthetic noisy data were used to test the performance of the proposed misfit function. The attenuation was reconstructed
based on a converged speed map. An edge-preserving regularization method based on total variation was also
implemented. To quantify the quality of the results, the mean values and their standard deviations in several regions of
interest were analyzed and compared to the reference values. The proposed generalized misfit function decreases
considerably the bias in the attenuation map caused by the deviations in the speed map in all the regions of interest