Uygar Tuna

Gap-Filling for the High-Resolution PET Sinograms With a Dedicated DCT-Domain Filter

High-resolution positron emission tomography (PET) scanners have brought many improvements to the nuclear medicine imaging field. However, the mechanical limitations in the construction of the scanners introduced gaps between the detectors, and accordingly, to the acquired projection data. When the methods requiring full-sinogram dataset, e.g., filtered backprojection (FBP) are applied, the missing parts degrade the reconstructed images. In this study, we aim to compensate the sinograms for the missing parts, i.e., gaps. For the gap filling, we propose an iterative discrete-cosine transform (DCT) domain method with two versions: (1) with basic DCT domain filter and (2) with dedicated and gap-dependent DCT domain filter. For the testing of the methods, 2-D FBP reconstructions were applied to the gap-filled sinograms. The proposed DCT domain gap-filling method with two different filters was compared to the constrained Fourier space (CFS) method. For the quantitative analysis, we used numerical phantoms at eight different Poisson noise levels with 100 realizations. Mean-square error, bias, and variance evaluations were performed over the selected regions of interest. Only the dedicated gap-dependent DCT domain filter showed quantitative improvement in all regions, at each noise level. We also assessed the methods visually with a [11C] raclopride human brain study reconstructed by 2-D FBP after gap filling. The visual comparisons of the methods showed that the gap filling with both DCT domain filters performed better than the CFS method. The proposed technique can be used for the sinograms, not only with limited range of projections as in the high-resolution research tomograph (ECAT HRRT) PET scanner, but also with detector failure artifacts.

Interpolation for the gap-filling of the HRRT PET sinograms by using the slices in the direction of the radial samples

The ECAT High Resolution Research Tomograph (HRRT) (CTI PET Systems, Knoxville, TN, USA) is the state-of-the-art positron emission tomography (PET) scanner in the nuclear medicine imaging field. The gantry of the HRRT PET scanner has detector-free regions. These regions between the detector blocks introduce missing parts to the acquired PET data. Without the estimation of the missing parts of the sinogram data, the methods which require full sinogram dataset give undesired results. Previously, we proposed the iterative discrete cosine transform (DCT) domain gap-filling method which gave better quantitative and visual results than the previously published gap-filling methods. The gap-filling methods published so far estimate the missing parts only on the transaxial slices while they are ignoring the information from the neighboring transaxial slices. In this study, we propose a non-iterative gap-filling method for the compensation of the HRRT PET sinogram data by using the slices in the direction of radial samples. We compared the results of this new method with the results of the improved version of the DCT domain gap-filling approach. We used 3D numerical phantom sinogram at eight different Poisson noise levels for checking the regional quantitative results. For visual assessment, we employed a [11C]-raclopride human brain study. After the missing parts were estimated, we applied 2D filtered backprojection which obligates full sinogram dataset. The results showed that the non-iterative interpolation by using the slices in the direction of radial samples gave similar results as the DCT domain data estimation method. Its simplicity and the short processing time over the DCT domain gap-filling method are the notable advantageous properties of this non-iterative gap-filling approach.

Gap-filling methods for 3D PlanTIS data

The range of positron emitters and their labeled compounds have led to high-resolution PET scanners becoming widely used, not only in clinical and pre-clinical studies but also in plant studies. A high-resolution PET scanner, plant tomographic imaging system (PlanTIS), was designed to study metabolic and physiological functions of plants noninvasively. The gantry of the PlanTIS scanner has detector-free regions. Even when the gantry of the PlanTIS is rotated during the scan, these regions result in missing sinogram bins in the acquired data. Missing data need to be estimated prior to the analytical image reconstructions in order to avoid artifacts in the final reconstructed images. In this study, we propose three gap-filling methods for estimation of the unique gaps existing in the 3D PlanTIS sinogram data. The 3D sinogram data were gap-filled either by linear interpolation in the transaxial planes or by the bicubic interpolation method (proposed for the ECAT high-resolution research tomograph) in the transradial planes or by the inpainting method in the transangular planes. Each gap-filling method independently compensates for slices in one of three orthogonal sinogram planes (transaxial, transradial and transangular planes). A 3D numerical Shepp-Logan phantom and the NEMA image quality phantom were used to evaluate the methods. The gap-filled sinograms were reconstructed using the analytical 3D reprojection (3DRP) method. The NEMA phantom sinograms were also reconstructed by the iterative reconstruction method, ordered subsets maximum a posteriori one step late (OSMAPOSL), to compare the results of gap filling followed by 3DRP with the results of OSMAPOSL reconstruction without gap filling. The three methods were evaluated quantitatively (by mean square error and coefficients of variation) over the selected regions of the 3D numerical Shepp-Logan phantom at eight different Poisson noise levels. Moreover, the NEMA phantom scan data were used in visual assessments of the methods. We observed that all methods improved the reconstructed images both quantitatively and visually. Therefore, the proposed gap-filling methods followed by the analytical 3DRP are alternative for the reconstructions of not only the 3D PlanTIS data, but also other PET scanner data of the ClearPET family.

Compensation of missing wedge effects with sequential statistical reconstruction in electron tomography.

Electron tomography (ET) of biological samples is used to study the organization and the structure of the whole cell and subcellular complexes in great detail. However, projections cannot be acquired over full tilt angle range with biological samples in electron microscopy. ET image reconstruction can be considered an ill-posed problem because of this missing information. This results in artifacts, seen as the loss of three-dimensional (3D) resolution in the reconstructed images. The goal of this study was to achieve isotropic resolution with a statistical reconstruction method, sequential maximum a posteriori expectation maximization (sMAP-EM), using no prior morphological knowledge about the specimen. The missing wedge effects on sMAP-EM were examined with a synthetic cell phantom to assess the effects of noise. An experimental dataset of a multivesicular body was evaluated with a number of gold particles. An ellipsoid fitting based method was developed to realize the quantitative measures elongation and contrast in an automated, objective, and reliable way. The method statistically evaluates the sub-volumes containing gold particles randomly located in various parts of the whole volume, thus giving information about the robustness of the volume reconstruction. The quantitative results were also compared with reconstructions made with widely-used weighted backprojection and simultaneous iterative reconstruction technique methods. The results showed that the proposed sMAP-EM method significantly suppresses the effects of the missing information producing isotropic resolution. Furthermore, this method improves the contrast ratio, enhancing the applicability of further automatic and semi-automatic analysis. These improvements in ET reconstruction by sMAP-EM enable analysis of subcellular structures with higher three-dimensional resolution and contrast than conventional methods.

Data estimation for the ECAT HRRT sinograms by utilizing the DCT domain

The ECAT High Resolution Research Tomograph (HRRT) (CTI PET Systems, Knoxville, TN, USA) is a milestone positron emission tomography scanner in nuclear medicine imaging field. The gantry port of the HRRT is constructed by eight detector panels each of which is separated by 17 mm gaps from its neighboring detector panels. The existence of the gaps results in the missing parts in the acquired projection data. Consequently, the quantification of the reconstructed images is degraded if a method which requires complete dataset, such as Fourier rebinning or filtered backprojection (FBP), is applied without estimation of the missing data.

PET sinogram denoising by block-matching and 3D filtering

We consider the denoising of noisy positron emission tomography (PET) sinograms by sparse 3D transform domain collaborative filtering. The filtering operation is realized by block-matching and 3D (BM3D) filtering algorithm that groups similar 2D sinogram fragments into 3D groups and filters each group by collaborative filtering. This filtering jointly filters the similar blocks in the transform domain and offers a way for efficient denoising while preserving essential information of each sinogram fragment. The obtained results are compared with conventional radial filtering and stackgram filtering that utilizes an intermediate stage between sinogram and image domains, called stackgram domain. Both quantitative and qualitative comparisons show that the proposed approach to filter the sinogram with BM3D filter outperforms the other filtering methods.

The AX-PET concept: New developments and tomographic imaging

The Axial PET (AX-PET) concept proposes a novel detection geometry for PET, based on layers of long scintillating crystals axially aligned with the bore axis. Arrays of wavelength shifting (WLS) strips are placed orthogonally and underneath the crystal layers; both crystals and strips are individually readout by G-APDs. The axial coordinate is obtained from the WLS signals by means of a Center-of-Gravity method combined with a cluster algorithm. This design allows spatial resolution and sensitivity to be decoupled and thus simultaneously optimized. In this work we present the latest results obtained with the 2-module AX-PET scanner prototype, which consists of 6 radial layers of 8 LYSO crystals each (crystal size: 3 × 3 × 100 mm3). The WLS arrays comprise 26 strips (3-mm wide) per layer. The estimated energy resolution from point-like measurements is 11.8% (FWHM at 511 keV). The intrinsic spatial resolution was measured for the two modules in coincidence at two different configurations using point-like sources, showing very little degradation when the modules were placed oblique to each other. The axial spatial resolution was 1.5 mm (FWHM) in all the studied cases. Tomographic data of extended phantoms filled with fluorine-18 have been acquired. Imaging a larger transaxial Field-of-View (when compared to the previous measurement campaign) was possible thanks to implementing secondary motion of one of the modules. We have also developed various reconstruction approaches which take into account the particular nature of AX-PET data, as well as a count rate model which allowed us to develop an acquisition protocol able to compensate for count losses. The reconstructed phantom images confirm the imaging capabilities of AX-PET, and the recent advancements in the DAQ let us expect significant improvements for future campaigns.

Image quantification in high-resolution PET assessed with a new anthropomorphic brain phantom

Choice of the PET scanner and image reconstruction parameters have significant impact in quantitative positron-emission tomography (PET). Hoffman phantom is probably the most widely used test object for assessing this impact in brain PET studies. In high-resolution PET, however, its usability is questionable due to lesser partial-volume effect. Futhermore, Hoffman phantom is cylindrical and does not offer realistic attenuation effect for the skull. In the current work we used a novel brain phantom that was produced using a 3D-printer, and provides realistic head contour and skull attenuation effect. We scanned the phantom with latest generation whole-body PET/MR (Philips Ingenuity TF) and PET/CT (GE Discovery 690) scanners and in a brain dedicated high-resolution scanner (Siemens HRRT) to evaluate its usability for intra- and inter-scanner comparisons with regard to PET brain imaging. In all scanners reconstruction algorithm choice and number of iterations had significant impact on anatomical gray matter ROI values. As compared to the HRRT, whole-body scanners showed 3% to 15% (Philips Ingenuity TF) and 0% to 5% (GE D690) negative biases in gray matter ROIs, when iterative reconstruction with high number of iterations but without resolution modeling was used. Whereas, low number of iterations in Philips Ingenuity yielded negative biases of 7% to 19%, but inclusion of resolution modeling in GE D690 yielded 19% to 7% higher values. In the HRRT count statistics related negative bias of up to 6% was seen, when iterative reconstruction without resolution modeling was used. We conclude that the new three-dimensional brain phantom is suitable for assessing the impact of reconstruction parameters both within and between scanners. However, the lack of ground truth values hampers the interpretation of the results, and furthermore, the small differences we saw between whole-body and brain-dedicated scanners might be due to limited resolution of the 3D-printing.

Low count PET sinogram denoising

Poisson noise is characteristic of count accumulation in positron emission tomography (PET) sinograms. We consider the denoising of low count PET sinograms by the following filters: block-matching and 3D (BM3D) filter, radial filter and stackgram filter. We also study the effect of first stabilizing the sinogram noise variance with Anscombe transformation, denoising the sinogram by the three methods and finally using exact unbiased inverse transformation to get back to the original sinogram domain. Quantitative results show that for the entire image the BM3D filter outperforms the other methods, but more careful region of interest (ROI) based analysis reveals that for the ROls in the central area of the image radial filtering with arithmetic mean filter gives the best quantitative results. Anscombe transformation has beneficial effect on BM3D filtering results. For the other filters its effect is negligible.

A Monte-Carlo based model of the AX-PET demonstrator and its experimental validation

AX-PET is a novel PET detector based on axially oriented crystals and orthogonal wavelength shifter (WLS) strips, both individually read out by silicon photo-multipliers. Its design decouples sensitivity and spatial resolution, by reducing the parallax error due to the layered arrangement of the crystals. Additionally the granularity of AX-PET enhances the capability to track photons within the detector yielding a large fraction of inter-crystal scatter events. These events, if properly processed, can be included in the reconstruction stage further increasing the sensitivity. Its unique features require dedicated Monte-Carlo simulations, enabling the development of the device, interpreting data and allowing the development of reconstruction codes. At the same time the non-conventional design of AX-PET poses several challenges to the simulation and modeling tasks, mostly related to the light transport and distribution within the crystals and WLS strips, as well as the electronics readout. In this work we present a hybrid simulation tool based on an analytical model and a Monte-Carlo based description of the AX-PET demonstrator. It was extensively validated against experimental data, providing excellent agreement.