The recent introduction of high-resolution molecular imaging technology is considered by many experts as a major breakthrough that will potentially lead to a revolutionary paradigm shift in health care and revolutionize clinical practice. This paper intends to balance the capabilities of the two major molecular imaging modalities used in nuclear medicine, namely positron emission tomography (PET) and single photon emission computed tomography (SPECT). The motivations are many-fold: (1) to gain a better understanding of the strengths and limitations of the two imaging modalities in the context of recent and ongoing developments in hardware and software design; (2) to emphasize that certain issues, historically and commonly thought as limitations of one technology, may now instead be viewed as challenges that can be addressed; (3) to point out that current state of the art PET and SPECT scanners can (greatly) benefit from improvements in innovative image reconstruction algorithms; and (4) to identify important areas of research in PET and SPECT imaging that will be instrumental to further improvements in the two modalities. Both technologies are poised to advance molecular imaging and have a direct impact on clinical and research practice to influence the future of molecular medicine.

PET versus SPECT: strengths, limitations and challenges

The quality of images reconstructed by statistical iterative methods depends on an accurate model of the relationship between image space and projection space through the system matrix. The elements of the system matrix for the clinical Hi-Rez scanner were derived by processing the data measured for a point source at different positions in a portion of the field of view. These measured data included axial compression and azimuthal interleaving of adjacent projections. Measured data were corrected for crystal and geometrical efficiency. Then, a whole system matrix was derived by processing the responses in projection space. Such responses included both geometrical and detection physics components of the system matrix. The response was parameterized to correct for point source location and to smooth for projection noise. The model also accounts for axial compression (span) used on the scanner. The forward projector for iterative reconstruction was constructed using the estimated response parameters. This paper extends our previous work to fully three-dimensional. Experimental data were used to compare images reconstructed by the standard iterative reconstruction software and the one modeling the response function. The results showed that the modeling of the response function improves both spatial resolution and noise properties

Fully 3-D PET reconstruction with system matrix derived from point source measurements

In this paper, the authors review the field of resolution modeling in positron emission tomography (PET) image reconstruction, also referred to as point-spread-function modeling. The review includes theoretical analysis of the resolution modeling framework as well as an overview of various approaches in the literature. It also discusses potential advantages gained via this approach, as discussed with reference to various metrics and tasks, including lesion detection observer studies. Furthermore, attention is paid to issues arising from this approach including the pervasive problem of edge artifacts, as well as explanation and potential remedies for this phenomenon. Furthermore, the authors emphasize limitations encountered in the context of quantitative PET imaging, wherein increased intervoxel correlations due to resolution modeling can lead to significant loss of precision (reproducibility) for small regions of interest, which can be a considerable pitfall depending on the task of interest.

Resolution modeling in PET imaging: Theory, practice, benefits, and pitfalls

Brain PET in small structures is challenged by low resolution inducing bias in the activity measurements. Improved spatial resolution may be obtained by using dedicated tomographs and more comprehensive modeling of the acquisition system during reconstruction. In this study, we assess the impact of resolution modeling (RM) during reconstruction on image quality and on the estimates of biologic parameters in a clinical study performed on a high-resolution research tomograph. Methods: An accelerated list-mode ordinary Poisson ordered-subset expectation maximization (OP-OSEM) algorithm, including sinogram-based corrections and an experimental stationary model of resolution, has been designed. Experimental phantom studies are used to assess contrast and noise characteristics of the reconstructed images. The binding potential of a selective tracer of the dopamine transporter is also assessed in anatomic volumes of interest in a 5-patient study. Results: In the phantom experiment, a slower convergence and a higher contrast recovery are observed for RM-OP-OSEM than for OP-OSEM for the same level of statistical noise. RM-OP-OSEM yields contrast recovery levels that could not be reached without RM as well as better visual recovery of the smallest spheres and better delineation of the structures in the reconstructed images. Statistical noise has lower variance at the voxel level with RM than without at matched resolution. In a uniform activity region, RM induces higher positive and lower negative correlations with neighboring voxels, leading to lower spatial variance. Clinical images reconstructed with RM demonstrate better delineation of cortical and subcortical structures in both time-averaged and parametric images. The binding potential in the striatum is also increased, a result similar to the one observed in the phantom study. Conclusion: In high-resolution PET, RM during reconstruction improves quantitative accuracy by reducing the partial-volume effects.

https://jnm.snmjournals.org/content/jnumed/49/6/1000.full.pdf

Impact of image-space resolution modeling for studies with the high-resolution research tomograph.

Historically, positron emission tomography (PET) systems have been based on scintillation crystals coupled to photomultipliers tubes (PMTs). However, the limited quantum efficiency, bulkiness, and relatively high cost per unit surface area of PMTs, along with the growth of new applications for PET, offers opportunities for other photodetectors. Among these, small-animal scanners, hybrid PET/MRI systems, and incorporation of time-of-flight information are of particular interest and require low-cost, compact, fast, and magnetic field compatible photodetectors. With high quantum efficiency and compact structure, avalanche photodiodes (APDs) overcome several of the drawbacks of PMTs, but this is offset by degraded signal-to-noise and timing properties. Silicon photomultipliers (SiPMs) offer an alternative solution, combining many of the advantages of PMTs and APDs. They have high gain, excellent timing properties and are insensitive to magnetic fields. At the present time, SiPM technology is rapidly developing and therefore an investigation into optimal design and operating conditions is underway together with detailed characterization of SiPM-based PET detectors. Published data are extremely promising and show good energy and timing resolution, as well as the ability to decode small scintillator arrays. SiPMs clearly have the potential to be the photodetector of choice for some, or even perhaps most, PET systems.

https://link.springer.com/content/pdf/10.1007%2Fs10439-011-0266-9.pdf

Application of Silicon Photomultipliers to Positron Emission Tomography

The LabPETtrade is a fully digital avalanche photodiode (APD) based PET scanner designed for state-of-the-art molecular and genomic imaging of small animals. Two versions of the scanner were evaluated, having 3.75 (LabPET4) and 7.5 cm axial FOV (LabPET8). The detectors are made of 2times2times12/14 mm3 LYSO and LGSO crystals assembled in phoswich pairs read out by an APD. After digital crystal identification, the average energy resolution is 24plusmn6% for LYSO and 25plusmn6% for LGSO. The scanner's overall timing resolution is 6.6 ns for LYSO/LYSO and 10.7 ns for LGSO/LGSO coincidences after coarse timing alignment. The FBP reconstructed tangential/radial resolution is 1.3/1.4 mm FWHM (2.5/2.4 mm FWTM) at the FOV center and remains below 2.1 mm FWHM (3.6 mm FWTM) within the central 4-cm diameter FOV. MLEM reconstruction of a micro resolution phantom provided clear separation of the 1.0 mm spots. With an energy window of 250-650 keV, the sensitivity is 1.1% for LabPET4 and 2.1% for LabPET8. The imaging capabilities of the scanner are demonstrated with in vivo images of rats and mice.

Performance Evaluation of the LabPET APD-Based Digital PET Scanner

One limitation in a practical implementation of statistical iterative image reconstruction is to compute a transition matrix accurately modeling the relationship between projection and image spaces. Detector response function (DRF) in positron emission tomography (PET) is broad and spatially-variant, leading to large transition matrices taking too much space to store. In this work, the authors investigate the effect of simpler DRF models on image quality in maximum likelihood expectation maximization reconstruction. The authors studied 6 cases of modeling projection/image relationship: tube/pixel geometric overlap with tubes centered on detector face; same as previous with tubes centered on DRF maximum; two different fixed-width Gaussian functions centered on DRF maximum weighing tube/pixel overlap; same as previous with a Gaussian of the same spectral resolution as DRF; analytic DRF based on linear attenuation of /spl gamma/-rays in detector arrays weighing tube/pixel overlap. The authors found that DRF oversimplification may affect visual image quality and image quantification dramatically, including artefact generation. They showed that analytic DRF yielded images of excellent quality for a small animal PET system with long, narrow detectors and generated a transition matrix for 2-D reconstruction that could be easily fitted into the memory of current stand-alone computers.

Detector response models for statistical iterative image reconstruction in high resolution PET

An iterative image reconstruction method used with an imaging system that generates projection data, the method comprises: collecting the projection data; choosing a polar or cylindrical image definition comprising a polar or cylindrical grid representation and a number of basis functions positioned according to the polar or cylindrical grid so that the number of basis functions at different radius positions of the polar or cylindrical image grid is a factor of a number of in-plane symmetries between lines of response along which the projection data are measured by the imaging system; obtaining a system probability matrix that relates each of the projection data to each basis function of the polar or cylindrical image definition; restructuring the system probability matrix into a block circulant matrix and converting the system probability matrix in the Fourier domain; storing the projection data into a measurement data vector; providing an initial polar or cylindrical image estimate; for each iteration; recalculating the polar or cylindrical image estimate according to an iterative solver based on forward and back projection operations with the system probability matrix in the Fourier domain; and converting the polar or cylindrical image estimate into a Cartesian image representation to thereby obtain a reconstructed image.

Image Reconstruction Methods Based on Block Circulant System Matrices

The LabPETTM is a fully digital avalanche photodiode (APD) based PET scanner designed for state-of-the- art molecular and genomic imaging of small animals. Two versions of the scanner were evaluated, having 3.75 (LabPET4) and 7.5 cm axial FOV (LabPET8). The detectors are made of 2x2x10/12 mm3 LYSO and LGSO crystals assembled in phoswich pairs read out by an APD. After digital crystal identification, the average energy resolution is 24 plusmn 6% for LYSO and 25 plusmn 6% for LGSO. The scanner overall timing resolution is 6.6 ns for LYSO/LYSO and 10.7 ns for LGSO/LGSO coincidences after coarse timing alignment. The FBP reconstructed tangential/radial resolution is 1.3/1.4 mm FWHM (2.5/2.4 mm FWTM) at the FOV center and remains below 2.1 mm FWHM (3.6 mm FWTM) within the central 4-cm diameter FOV. MLEM reconstruction of a micro resolution phantom provided clear separation of the 1.35 mm spots and fair identification of 1 mm spots. With an energy window of 250-650 keV, the sensitivity is 1.1% for LabPET4 and 2.1% for LabPET8. The imaging capabilities of the scanner are demonstrated with in vivo images of rats and mice.

Performance evaluation of the LabPET™ APD-based digital PET scanner

A major shortcoming of the maximum likelihood expectation maximization (ML-EM) method for reconstruction of dynamic positron emission tomography (PET) images is to decide when to stop the iterative process for image frames with largely different statistics and activity distributions. A widespread practice to overcome this problem involves overiteration of an image estimate followed by smoothing. Here, the authors investigate the qualitative and quantitative accuracy of the cross-validation procedure (CV) as a stopping rule, in comparison to overiteration and post-filtering, for the reconstruction of phantom and small animal dynamic /sup 18/F-fluorodeoxyglucose PET data acquired in two-dimensional mode. The CV stopping rule ensured visually acceptable image estimates with balanced resolution and noise characteristics. However, quantitative accuracy required some minimum number of counts per image. The effect of the number of ML-EM iterations on time-activity curves and metabolic rates of glucose extracted from image series is discussed. A dependence of the CV defined number of iterations on projection counts was found that simplifies reconstruction and reduces computation time.

V. Selivanov

Papers

Performance Evaluation of the LabPET APD-Based Digital PET Scanner

Detector response models for statistical iterative image reconstruction in high resolution PET

Image Reconstruction Methods Based on Block Circulant System Matrices

Performance evaluation of the LabPET™ APD-based digital PET scanner

Cross-validation stopping rule for ML-EM reconstruction of dynamic PET series: effect on image quality and quantitative accuracy