Arman Rahmim

PET versus SPECT: Strengths, limitations and challenges

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.

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

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.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.

Quantification of cerebral cannabinoid receptors subtype 1 (CB1) in healthy subjects and schizophrenia by the novel PET radioligand [11C]OMAR

Several studies have examined the link between the cannabinoid CB1 receptor and several neuropsychiatric illnesses, including schizophrenia. As such, there is a need for in vivo imaging tracers so that the relationship between CB1 and schizophrenia (SZ) can be further studied. In this paper, we present our first human studies in both healthy control patients and patients with schizophrenia using the novel PET tracer, [(11)C]OMAR (JHU75528), we have shown its utility as a tracer for imaging human CB1 receptors and to investigate normal aging and the differences in the cannabinoid system of healthy controls versus patients with schizophrenia. A total of ten healthy controls and nine patients with schizophrenia were included and studied with high specific activity [(11)C]OMAR. The CB1 binding (expressed as the distribution volume; V(T)) was highest in the globus pallidus and the cortex in both controls and patients with schizophrenia. Controls showed a correlation with the known distribution of CB1 and decline of [(11)C]OMAR binding with age, most significantly in the globus pallidus. Overall, we observed elevated mean binding in patients with schizophrenia across all regions studied, and this increase was statistically significant in the pons (p<0.05), by the Students t-test. When we ran a regression of the control subjects V(T) values with age and then compared the patient data to 95% prediction limits of the linear regression, three patients fell completely outside for the globus pallidus, and in all other regions there were at least 1-3 patients outside of the prediction intervals. There was no statistically significant correlations between PET measures and the individual Brief Psychiatry Rating Score (BPRS) subscores (r=0.49), but there was a significant correlation between V(T) and the ratio of the BPRS psychosis to withdrawal score in the frontal lobe (r=0.60), and middle and posterior cingulate regions (r=0.71 and r=0.79 respectively). In conclusion, we found that [(11)C] OMAR can image human CB1 receptors in normal aging and schizophrenia. In addition, our initial data in subjects with schizophrenia seem to suggest an association of elevated binding specific brain regions and symptoms of the disease.

Partial Volume Correction Strategies in PET

In the early days of PET, the partial volume effect (PVE) was identified as a serious factor affecting image quality and limiting the accuracy of quantitative analysis. Because of the limited spatial resolution of clinical PET systems, the images are blurred by the system response so that smaller objects appear larger. Although the total number of counts is preserved, they are distributed over a larger volume. This article describes the various partial volume correction strategies used in PET and summarizes their clinical and research applications.

Accurate Event-Driven Motion Compensation in High-Resolution PET Incorporating Scattered and Random Events

With continuing improvements in spatial resolution of positron emission tomography (PET) scanners, small patient movements during PET imaging become a significant source of resolution degradation. This work develops and investigates a comprehensive formalism for accurate motion-compensated reconstruction which at the same time is very feasible in the context of high-resolution PET. In particular, this paper proposes an effective method to incorporate presence of scattered and random coincidences in the context of motion (which is similarly applicable to various other motion correction schemes). The overall reconstruction framework takes into consideration missing projection data which are not detected due to motion, and additionally, incorporates information from all detected events, including those which fall outside the field-of-view following motion correction. The proposed approach has been extensively validated using phantom experiments as well as realistic simulations of a new mathematical brain phantom developed in this work, and the results for a dynamic patient study are also presented.

Four-dimensional (4D) image reconstruction strategies in dynamic PET: Beyond conventional independent frame reconstruction

In this article, the authors review novel techniques in the emerging field of spatiotemporal four-dimensional (4D) positron emission tomography (PET) image reconstruction. The conventional approach to dynamic PET imaging, involving independent reconstruction of individual PET frames, can suffer from limited temporal resolution, high noise (especially when higher frame sampling is introduced to better capture fast dynamics), as well as complex reconstructed image noise distributions that can be very difficult and time consuming to model in kinetic parameter estimation tasks. Various approaches that seek to address some or all of these limitations are described, including techniques that utilize (a) iterative temporal smoothing, (b) advanced temporal basis functions, (c) principal components transformation of the dynamic data, (d) wavelet-based techniques, as well as (e) direct kinetic parameter estimation methods. Future opportunities and challenges with regards to the adoption of 4D and higher dimensional image reconstruction techniques are also outlined.

Statistical list-mode image reconstruction for the high resolution research tomograph

We have investigated statistical list-mode reconstruction applicable to a depth-encoding high resolution research tomograph. An image non-negativity constraint has been employed in the reconstructions and is shown to effectively remove the overestimation bias introduced by the sinogram non-negativity constraint. We have furthermore implemented a convergent subsetized (CS) list-mode reconstruction algorithm, based on previous work (Hsiao et al 2002 Conf. Rec. SPIE Med. Imaging 4684 10-19; Hsiao et al 2002 Conf. Rec. IEEE Int. Symp. Biomed. Imaging 409-12) on convergent histogram OSEM reconstruction. We have demonstrated that the first step of the convergent algorithm is exactly equivalent (unlike the histogram-mode case) to the regular subsetized list-mode EM algorithm, while the second and final step takes the form of additive updates in image space. We have shown that in terms of contrast, noise as well as FWHM width behaviour, the CS algorithm is robust and does not result in limit cycles. A hybrid algorithm based on the ordinary and the convergent algorithms is also proposed, and is shown to combine the advantages of the two algorithms (i.e. it is able to reach a higher image quality in fewer iterations while maintaining the convergent behaviour), making the hybrid approach a good alternative to the ordinary subsetized list-mode EM algorithm.

Strategies for Motion Tracking and Correction in PET

With the arrival of increasingly higher-resolution PET systems, small amounts of motion can cause significant blurring in the resulting images compared with the intrinsic resolution of the PET scanner. The authors review advanced correction methods for unwanted patient motion and for motion due to cardiac and respiratory cycles. A general theme in motion correction methods is the use of increasingly sophisticated software to make use of existing advanced hardware. In this sense, the field is open to future novel ideas (hardware and especially software) aimed at improving motion detection, characterization, and compensation.

Statistical dynamic image reconstruction in state-of-the-art high-resolution PET.

Modern high-resolution PET is now more than ever in need of scrutiny into the nature and limitations of the imaging modality itself as well as image reconstruction techniques. In this work, we have reviewed, analysed and addressed the following three considerations within the particular context of state-of-the-art dynamic PET imaging: (i) the typical average numbers of events per line-of-response (LOR) are now (much) less than unity, (ii) due to the physical and biological decay of the activity distribution, one requires robust and efficient reconstruction algorithms applicable to a wide range of statistics and (iii) the computational considerations in dynamic imaging are much enhanced (i.e., more frames to be stored and reconstructed). Within the framework of statistical image reconstruction, we have argued theoretically and shown experimentally that the sinogram non-negativity constraint (when using the delayed-coincidence and/or scatter-subtraction techniques) is especially expected to result in an overestimation bias. Subsequently, two schemes are considered: (a) subtraction techniques in which an image non-negativity constraint has been imposed and (b) implementation of random and scatter estimates inside the reconstruction algorithms, thus enabling direct processing of Poisson-distributed prompts. Both techniques are able to remove the aforementioned bias, while the latter, being better conditioned theoretically, is able to exhibit superior noise characteristics. We have also elaborated upon and verified the applicability of the accelerated list-mode image reconstruction method as a powerful solution for accurate, robust and efficient dynamic reconstructions of high-resolution data (as well as a number of additional benefits in the context of state-of-the-art PET).

The second generation HRRT - a multi-centre scanner performance investigation

The high resolution research tomograph (HRRT) is one of the most complex existing positron emission tomographs: it is the only human size scanner capable of decoding the depth of the /spl gamma/-ray interaction in the crystal, using a lutetium LSO/LYSO phoswich detector arrangement. In this study we determined basic scanner hardware characteristics, such as scanner data acquisition stability, and their variability across eleven centres. In addition a subset of the NEMA NU-2001 standards measurements was performed. We found (i) significant variability in the DOI decoding results between centres, (ii) a trend toward an increasing number of detected true coincident events as a function of elapsed time from scanner calibration likely due to a shifting energy spectrum, (iii) a count-rate dependent layer identification, (iv) scatter fraction ranging from /spl sim/ 42% to 54% where the variability was partly related to the shifting of the energy spectrum, (v) sensitivity ranging from /spl sim/5.5% to 6.5% across centres, (vi) resolution of /spl sim/(2.5 mm)/sup 3/, fairly consistent across centres, (vii) image quality which is very comparable to other scanners.