4D numerical observer for lesion detection in respiratory-gated PET.

PURPOSE: Respiratory-gated positron emission tomography (PET)/computed tomography protocols reduce lesion smearing and improve lesion detection through a synchronized acquisition of emission data. However, an objective assessment of image quality of the improvement gained from respiratory-gated PET is mainly limited to a three-dimensional (3D) approach. This work proposes a 4D numerical observer that incorporates both spatial and temporal informations for detection tasks in pulmonary oncology. METHODS: The authors propose a 4D numerical observer constructed with a 3D channelized Hotelling observer for the spatial domain followed by a Hotelling observer for the temporal domain. Realistic (18)F-fluorodeoxyglucose activity distributions were simulated using a 4D extended cardiac torso anthropomorphic phantom including 12 spherical lesions at different anatomical locations (lower, upper, anterior, and posterior) within the lungs. Simulated data based on Monte Carlo simulation were obtained using geant4 application for tomographic emission (GATE). Fifty noise realizations of six respiratory-gated PET frames were simulated by GATE using a model of the Siemens Biograph mMR scanner geometry. PET sinograms of the thorax background and pulmonary lesions that were simulated separately were merged to generate different conditions of the lesions to the background (e.g., lesion contrast and motion). A conventional ordered subset expectation maximization (OSEM) reconstruction (5 iterations and 6 subsets) was used to obtain: (1) gated, (2) nongated, and (3) motion-corrected image volumes (a total of 3200 subimage volumes: 2400 gated, 400 nongated, and 400 motion-corrected). Lesion-detection signal-to-noise ratios (SNRs) were measured in different lesion-to-background contrast levels (3.5, 8.0, 9.0, and 20.0), lesion diameters (10.0, 13.0, and 16.0 mm), and respiratory motion displacements (17.6-31.3 mm). The proposed 4D numerical observer applied on multiple-gated images was compared to the conventional 3D approach applied on the nongated and motion-corrected images. RESULTS: On average, the proposed 4D numerical observer improved the detection SNR by 48.6% (p < 0.005), whereas the 3D methods on motion-corrected images improved by 31.0% (p < 0.005) as compared to the nongated method. For all different conditions of the lesions, the relative SNR measurement (Gain = SNRObserved/SNRNongated) of the 4D method was significantly higher than one from the motion-corrected 3D method by 13.8% (p < 0.02), where Gain4D was 1.49 ± 0.21 and Gain3D was 1.31 ± 0.15. For the lesion with the highest amplitude of motion, the 4D numerical observer yielded the highest observer-performance improvement (176%). For the lesion undergoing the smallest motion amplitude, the 4D method provided superior lesion detectability compared with the 3D method, which provided a detection SNR close to the nongated method. The investigation on a structure of the 4D numerical observer showed that a Laguerre-Gaussian channel matrix with a volumetric 3D function yielded higher lesion-detection performance than one with a 2D-stack-channelized function, whereas a different kind of channels that have the ability to mimic the human visual system, i.e., difference-of-Gaussian, showed similar performance in detecting uniform and spherical lesions. The investigation of the detection performance when increasing noise levels yielded decreasing detection SNR by 27.6% and 41.5% for the nongated and gated methods, respectively. The investigation of lesion contrast and diameter showed that the proposed 4D observer preserved the linearity property of an optimal-linear observer while the motion was present. Furthermore, the investigation of the iteration and subset numbers of the OSEM algorithm demonstrated that these parameters had impact on the lesion detectability and the selection of the optimal parameters could provide the maximum lesion-detection performance. The proposed 4D numerical observer outperformed the other observers for the lesion-detection task in various lesion conditions and motions. CONCLUSIONS: The 4D numerical observer shows substantial improvement in lesion detectability over the 3D observer method. The proposed 4D approach could potentially provide a more reliable objective assessment of the impact of respiratory-gated PET improvement for lesion-detection tasks. On the other hand, the 4D approach may be used as an upper bound to investigate the performance of the motion correction method. In future work, the authors will validate the proposed 4D approach on clinical data for detection tasks in pulmonary oncology.

Dual-tracer PET using generalized factor analysis of dynamic sequences.

PURPOSE: With single-photon emission computed tomography, simultaneous imaging of two physiological processes relies on discrimination of the energy of the emitted gamma rays, whereas the application of dual-tracer imaging to positron emission tomography (PET) imaging has been limited by the characteristic 511-keV emissions. PROCEDURES: To address this limitation, we developed a novel approach based on generalized factor analysis of dynamic sequences (GFADS) that exploits spatio-temporal differences between radiotracers and applied it to near-simultaneous imaging of 2-deoxy-2-[(18)F]fluoro-D-glucose (FDG) (brain metabolism) and (11)C-raclopride (D2) with simulated human data and experimental rhesus monkey data. We show theoretically and verify by simulation and measurement that GFADS can separate FDG and raclopride measurements that are made nearly simultaneously. RESULTS: The theoretical development shows that GFADS can decompose the studies at several levels: (1) It decomposes the FDG and raclopride study so that they can be analyzed as though they were obtained separately. (2) If additional physiologic/anatomic constraints can be imposed, further decomposition is possible. (3) For the example of raclopride, specific and nonspecific binding can be determined on a pixel-by-pixel basis. We found good agreement between the estimated GFADS factors and the simulated ground truth time activity curves (TACs), and between the GFADS factor images and the corresponding ground truth activity distributions with errors less than 7.3 ± 1.3 %. Biases in estimation of specific D2 binding and relative metabolism activity were within 5.9 ± 3.6 % compared to the ground truth values. We also evaluated our approach in simultaneous dual-isotope brain PET studies in a rhesus monkey and obtained accuracy of better than 6 % in a mid-striatal volume, for striatal activity estimation. CONCLUSIONS: Dynamic image sequences acquired following near-simultaneous injection of two PET radiopharmaceuticals can be separated into components based on the differences in the kinetics, provided their kinetic behaviors are distinct.

Improvement in lesion detection with whole-body oncologic time-of-flight PET.

UNLABELLED: Time-of-flight (TOF) PET has great potential in whole-body oncologic applications, and recent work has demonstrated qualitatively in patient studies the improvement that can be achieved in lesion visibility. The aim of this work was to objectively quantify the improvement in lesion detectability that can be achieved in lung and liver lesions with whole-body (18)F-FDG TOF PET in a cohort of 100 patients as a function of body mass index, lesion location and contrast, and scanning time. METHODS: One hundred patients with BMIs ranging from 16 to 45 were included in this study. Artificial 1-cm spheric lesions were imaged separately in air at variable locations of each patient's lung and liver, appropriately attenuated, and incorporated in the patient list-mode data with 4 different lesion-to-background contrast ranges. The fused studies with artificial lesion present or absent were reconstructed using a list-mode unrelaxed ordered-subsets expectation maximization with chronologically ordered subsets and a gaussian TOF kernel for TOF reconstruction. Conditions were compared on the basis of performance of a 3-channel Hotelling observer signal-to-noise ratio in detecting the presence of a sphere of unknown size on an anatomic background while modeling observer noise. RESULTS: TOF PET yielded an improvement in lesion detection performance (3-channel Hotelling observer signal-to-noise ratio) over non-TOF PET of 8.3% in the liver and 15.1% in the lungs. The improvement in all lesions was 20.3%, 12.0%, 9.2%, and 7.5% for mean contrast values of 2.0:1, 3.2:1, 4.4:1, and 5.7:1, respectively. Furthermore, this improvement was 9.8% in patients with a BMI of less than 30 and 11.1% in patients with a BMI of 30 or more. Performance plateaued faster as a function of number of iterations with TOF than non-TOF. CONCLUSION: Over all contrasts and body mass indexes, oncologic TOF PET yielded a significant improvement in lesion detection that was greater for lower lesion contrasts. This improvement was achieved without compromising other aspects of PET imaging.

Quantitative simultaneous 99mTc/123I cardiac SPECT using MC-JOSEM.

Simultaneous rest 99mTc-Sestamibi/ 123I-BMIPP cardiac SPECT imaging has the potential to replace current clinical 99mTc-Sestamibi rest/stress imaging and therefore has great potential in the case of patients with chest pain presenting to the emergency department. Separation of images of these two radionuclides is difficult, however, because their emission energies are close. The authors previously developed a fast Monte Carlo (MC)-based joint ordered-subset expectation maximization (JOSEM) iterative reconstruction algorithm (MC-JOSEM), which simultaneously compensates for scatter and cross talk as well as detector response within the reconstruction algorithm. In this work, the authors evaluated the performance of MC-JOSEM in a realistic population of 99mTc/123I studies using cardiac phantom data on a Siemens e.cam system using a standard cardiac protocol. The authors also compared the performance of MC-JOSEM for estimation tasks to that of two other methods: standard OSEM using photopeak energy windows without scatter correction (NSC-OSEM) and standard OSEM using a Compton-scatter energy window for scatter correction (SC-OSEM). For each radionuclide the authors separately acquired high-count projections of radioactivity in the myocardium wall, liver, and soft tissue background compartments of a water-filled torso phantom, and they generated synthetic projections of various dual-radionuclide activity distributions. Images of different combinations of myocardium wall/background activity concentration ratios for each radionuclide were reconstructed by NSC-OSEM, SC-OSEM, and MC-JOSEM. For activity estimation in the myocardium wall, MC-JOSEM always produced the best relative bias and relative standard deviation compared with NSC-OSEM and SC-OSEM for all the activity combinations. On average, the relative biases after 100 iterations were 8.1% for 99mTc and 3.7% for 123I with MC-JOSEM, 39.4% for 99mTc and 23.7% for 123I with NSC-OSEM, and 20.9% for 99mTc with SC-OSEM. The relative standard deviations after 30 iterations were 0.7% for 99mTc and 1.0% for 123I with MC-JOSEM, as compared to 1.1% for 99mTc and 1.2% for 123I with NSC-OSEM and 1.3% for 99mTc with SC-OSEM. Finally, the authors compared the relative standard deviation after 30 iterations with the minimum theoretical variance on activity estimation, the Cramer-Rao lower bound (CRB), and with the biased CRB. The measured precision was larger than the biased bound values by factors of 2-4, suggesting that further improvement could be made to the method.

Sequential and simultaneous dual-isotope brain SPECT: comparison with PET for estimation and discrimination tasks in early Parkinson disease.

Parkinson disease (PD) is the second most frequently occurring cerebral degenerative disease, after Alzheimer disease. Treatments are available, but their efficacy is diminished unless they are administered in the early stages. Therefore, early identification of PD is crucial. In addition to providing perfectly registered studies, simultaneous 99mTc/123I imaging makes possible the assessment of pre- and postsynaptic neurotransmission functions under identical physiological conditions, while doubling the number of counts for the same total imaging time. These advantages are limited, however, by cross talk between the two radionuclides due to the close emission energies of 99mTc (140 keV) and 123I (159 keV). PET, on the other hand, provides good temporal and spatial resolution and sensitivity but usually requires the use of a single radionuclide. In the present work, the authors compared brain PET with sequential and simultaneous dual-isotope SPECT for the task of estimating striatal activity concentration and striatal size for a normal brain and two stages of early PD. Realistic Monte Carlo simulations of a time-of-flight PET scanner and gamma cameras were performed while modeling all interactions in the brain, collimator (gamma camera) and crystal (detector block in PET), as well as population biological variability of pre- and postsynaptic uptake. For SPECT imaging, we considered two values of system energy resolution and scanners with two and three camera heads. The authors used the Cramer-Rao bound, as a surrogate for the best theoretical performance, to optimize the SPECT acquisition energy windows and objectively compare PET and SPECT. The authors determined the discrimination performance between 500 simulated subjects in every disease stage as measured by the area under the ROC curve (AUC). The discrimination accuracy between a normal subject and a subject in the prodromal disease stage was AUC = 0.924 with PET, compared to 0.863 and 0.831 with simultaneous and sequential SPECT, respectively. The significant improvement in performance obtained with simultaneous dual-isotope SPECT compared to sequential imaging (p = 0.019) was due primarily to the increased number of counts detected and resulted in comparable performance when performing simultaneous SPECT on a two-head camera with 9.2% energy resolution to that obtained with sequential SPECT on a three-head camera with 6.2% energy resolution.