G. El Fakhri

Nonrigid PET motion compensation in the lower abdomen using simultaneous tagged‐MRI and PET imaging

PURPOSE We propose a novel approach for PET respiratory motion correction using tagged-MRI and simultaneous PET-MRI acquisitions. METHODS We use a tagged-MRI acquisition followed by motion tracking in the phase domain to estimate the nonrigid deformation of biological tissues during breathing. In order to accurately estimate motion even in the presence of noise and susceptibility artifacts, we regularize the traditional HARP tracking strategy using a quadratic roughness penalty on neighboring displacement vectors (R-HARP). We then incorporate the motion fields estimated with R-HARP in the system matrix of an MLEM PET reconstruction algorithm formulated both for sinogram and list-mode data representations. This approach allows reconstruction of all detected coincidences in a single image while modeling the effect of motion both in the emission and the attenuation maps. At present, tagged-MRI does not allow estimation of motion in the lungs and our approach is therefore limited to motion correction in soft tissues. Since it is difficult to assess the accuracy of motion correction approaches in vivo, we evaluated the proposed approach in numerical simulations of simultaneous PET-MRI acquisitions using the NCAT phantom. We also assessed its practical feasibility in PET-MRI acquisitions of a small deformable phantom that mimics the complex deformation pattern of a lung that we imaged on a combined PET-MRI brain scanner. RESULTS Simulations showed that the R-HARP tracking strategy accurately estimated realistic respiratory motion fields for different levels of noise in the tagged-MRI simulation. In simulations of tumors exhibiting increased uptake, contrast estimation was 20% more accurate with motion correction than without. Signal-to-noise ratio (SNR) was more than 100% greater when performing motion-corrected reconstruction which included all counts, compared to when reconstructing only coincidences detected in the first of eight gated frames. These results were confirmed in our proof-of-principle PET-MRI acquisitions, indicating that our motion correction strategy is accurate, practically feasible, and is therefore ready to be tested in vivo. CONCLUSIONS This work shows that PET motion correction using motion fields measured with tagged-MRI in simultaneous PET-MRI acquisitions can be made practical for clinical application and that doing so has the potential to remove motion blur in whole-body PET studies of the torso.

The reliability of proton-nuclear interaction cross-section data to predict proton-induced PET images in proton therapy.

In vivo PET range verification relies on the comparison of measured and simulated activity distributions. The accuracy of the simulated distribution depends on the accuracy of the Monte Carlo code, which is in turn dependent on the accuracy of the available cross-section data for β(+) isotope production. We have explored different cross-section data available in the literature for the main reaction channels ((16)O(p,pn)(15)O, (12)C(p,pn)(11)C and (16)O(p,3p3n)(11)C) contributing to the production of β(+) isotopes by proton beams in patients. Available experimental and theoretical values were implemented in the simulation and compared with measured PET images obtained with a high-resolution PET scanner. Each reaction channel was studied independently. A phantom with three different materials was built, two of them with high carbon or oxygen concentration and a third one with average soft tissue composition. Monoenergetic and SOBP field irradiations of the phantom were accomplished and measured PET images were compared with simulation results. Different cross-section values for the tissue-equivalent material lead to range differences below 1 mm when a 5 min scan time was employed and close to 5 mm differences for a 30 min scan time with 15 min delay between irradiation and scan (a typical off-line protocol). The results presented here emphasize the need of more accurate measurement of the cross-section values of the reaction channels contributing to the production of PET isotopes by proton beams before this in vivo range verification method can achieve mm accuracy.

Scatter and cross-talk corrections in simultaneous Tc-99m/I-123 brain SPECT using constrained factor analysis and artificial neural networks

Simultaneous imaging of Tc-99m and I-123 would have a high clinical potential in the assessment of brain perfusion (Tc-99m) and neurotransmission (I-123) but is hindered by cross-talk between the two radionuclides. Monte Carlo simulations of 15 different dual-isotope studies were performed using a digital brain phantom. Several physiologic Tc-99m and I-123 uptake patterns were modeled in the brain structures. Two methods were considered to correct for cross-talk from both scattered and unscattered photons: constrained spectral factor analysis (SFA) and artificial neural networks (ANN). The accuracy and precision of reconstructed pixel values within several brain structures were compared to those obtained with an energy windowing method (WSA). In I-123 images, mean bias was close to 10% in all structures for SFA and ANN and between 14% (in the caudate nucleus) and 25% (in the cerebellum) for WSA. Tc-99m activity was overestimated by 35% in the cortex and 53% in the caudate nucleus with WSA, but by less than 9% in all structures with SFA and ANN. SFA and ANN performed well even in the presence of high-energy I-123 photons. The accuracy was greatly improved by incorporating the contamination into the SFA model or in the learning phase for ANN. SFA and ANN are promising approaches to correct for cross-talk in simultaneous Tc-99m/I-123 SPECT.

Towards coronary plaque imaging using simultaneous PET-MR: a simulation study

Coronary atherosclerotic plaque rupture is the main cause of myocardial infarction and the leading killer in the US. Inflammation is a known bio-marker of plaque vulnerability and can be assessed non-invasively using fluorodeoxyglucose-positron emission tomography imaging (FDG-PET). However, cardiac and respiratory motion of the heart makes PET detection of coronary plaque very challenging. Fat surrounding coronary arteries allows the use of MRI to track plaque motion during simultaneous PET-MR examination. In this study, we proposed and assessed the performance of a fat-MR based coronary motion correction technique for improved FDG-PET coronary plaque imaging in simultaneous PET-MR. The proposed methods were evaluated in a realistic four-dimensional PET-MR simulation study obtained by combining patient water-fat separated MRI and XCAT anthropomorphic phantom. Five small lesions were digitally inserted inside the patients coronary vessels to mimic coronary atherosclerotic plaques. The heart of the XCAT phantom was digitally replaced with the patients heart. Motion-dependent activity distributions, attenuation maps, and fat-MR volumes of the heart, were generated using the XCAT cardiac and respiratory motion fields. A full Monte Carlo simulation using Siemens mMRs geometry was performed for each motion phase. Cardiac/respiratory motion fields were estimated using non-rigid registration of the transformed fat-MR volumes and incorporated directly into the system matrix of PET reconstruction along with motion-dependent attenuation maps. The proposed motion correction method was compared to conventional PET reconstruction techniques such as no motion correction, cardiac gating, and dual cardiac-respiratory gating. Compared to uncorrected reconstructions, fat-MR based motion compensation yielded an average improvement of plaque-to-background contrast of 29.6%, 43.7%, 57.2%, and 70.6% for true plaque-to-blood ratios of 10, 15, 20 and 25:1, respectively. Channelized Hotelling observer (CHO) signal-to-noise ratio (SNR) was used to quantify plaque detectability. CHO-SNR improvement ranged from 105% to 128% for fat-MR-based motion correction as compared to no motion correction. Likewise, CHO-SNR improvement ranged from 348% to 396% as compared to both cardiac and dual cardiac-respiratory gating approaches. Based on this study, our approach, a fat-MR based motion correction for coronary plaque PET imaging using simultaneous PET-MR, offers great potential for clinical practice. The ultimate performance and limitation of our approach, however, must be fully evaluated in patient studies.

A new scatter compensation method for Ga-67 imaging using artificial neural networks

A new scatter correction method for Ga-67 based on artificial neural networks (ANN) with error backpropagation was designed and evaluated. The ANN consisted of a 37-node input layer (37 energy channels in the range 60-370 keV), an 18-node hidden layer, and a 3-node output layer to estimate the scatter-free distribution in the 93, 185 and 300 keV photopeaks. Two separate activity and attenuation distribution sets, based on a segmented realistic anthropomorphic torso phantom, were simulated. The first set was used for ANN learning and the second to evaluate the scatter correction. Our Monte Carlo simulation modeled all photon interactions in the patient, collimator and detector. Interactions simulated in the collimator included Compton and coherent scatter, and photoelectric absorption with forced production of lead K-shell X-rays. Ninety very-high-count projections were simulated and used as a basis for generating 15 Poisson noise realizations for each angle; noise levels were characteristic of 72-hour post-injection Ga-67 studies. The energy window images (WIN) used clinically were also generated for comparison. Bias and variance were computed with respect to the primary distributions over reconstructed volumes of interests in the lungs, abdomen and liver. ANN overall bias and precision in the abdomen were 5.8/spl infin/2.6% (93 keV), -0.1/spl plusmn/2.4% (185 keV) and -4.9/spl plusmn/1.8% (300 keV), and the bias in all structures was less than 19% as compared to 85% with WIN. ANN is an accurate and robust scatter correction method for Ga-67 studies.

Absolute activity quantitation from projections using an analytical approach: comparison with iterative methods in Tc-99m and I-123 brain SPECT

Estimates of SPECT activity within certain deep brain structures could be useful for clinical tasks such as early prediction of Alzheimers disease with Tc-99m or Parkinsons disease with I-123; however, such estimates are biased by poor spatial resolution and inaccurate scatter and attenuation corrections. We compared an analytical approach (AA) of more accurate quantitation to a slower iterative approach (IA). Monte Carlo simulated projections of 12 normal and 12 pathologic Tc-99m perfusion studies, as well as 12, normal and 12 pathologic I-123 neurotransmission studies, were generated using a digital brain phantom and corrected for scatter by a multispectral fitting procedure. The AA included attenuation correction by a modified Metz-Fan algorithm and activity estimation by a technique that incorporated Metz filtering to compensate for variable collimator response (VCR), IA-modeled attenuation, and VCR in the projector/backprojector of an ordered subsets-expectation maximization (OSEM) algorithm. Bias and standard deviation over the 12 normal and 12 pathologic patients were calculated with respect to the reference values in the corpus callosum, caudate nucleus, and putamen. The IA and AA yielded similar quantitation results in both Tc-99m and I-123 studies in all brain structures considered in both normal and pathologic patients. The bias with respect to the reference activity distributions was less than 7% for Tc-99m studies, but greater than 30% for I-123 studies, due to partial volume effect in the striata. Our results were validated using I-123 physical acquisitions of an anthropomorphic brain phantom. The IA yielded quantitation accuracy comparable to that obtained with IA, while requiring much less processing time. However, in most conditions, IA yielded lower noise for the same bias than did AA.

Novel Scatter Compensation of List-Mode PET Data Using Spatial and Energy Dependent Corrections

With the widespread use of positron emission tomography (PET) crystals with greatly improved energy resolution (e.g., 11.5% with LYSO as compared to 20% with BGO) and of list-mode acquisitions, the use of the energy of individual events in scatter correction schemes becomes feasible. We propose a novel scatter approach that incorporates the energy of individual photons in the scatter correction and reconstruction of list-mode PET data in addition to the spatial information presently used in clinical scanners. First, we rewrite the Poisson likelihood function of list-mode PET data including the energy distributions of primary and scatter co incidences and show that this expression yields an MLEM reconstruction algorithm containing both energy and spatial dependent corrections. To estimate the spatial distribution of scatter coincidences we use the single scatter simulation (SSS). Next, we derive two new formulae which allow estimation of the 2-D (coincidences) energy probability density functions (E-PDF) of primary and scatter coincidences from the 1-D (photons) E-PDFs associated with each photon. We also describe an accurate and robust object-specific method for estimating these 1-D E-PDFs based on a de composition of the total energy spectra detected across the scanner into primary and scattered components. Finally, we show that the energy information can be used to accurately normalize the scatter sinogram to the data. We compared the performance of this novel scatter correction incorporating both the position and energy of detected coincidences to that of the traditional approach modeling only the spatial distribution of scatter coincidences in 3-D Monte Carlo simulations of a medium cylindrical phantom and a large, nonuniform NCAT phantom. Incorporating the energy information in the scatter correction decreased bias in the activity distribution estimation by ~20% and ~40% in the cold regions of the large NCAT phantom at energy resolutions 11.5% and 20% at 511 keV, respectively, compared to when using the spatial information alone.

Simultaneous Dual Tracer PET Using Generalized Factor Analysis of Dynamic Sequences

Simultaneous dual tracer PET imaging has potential for simultaneous assessment of two physiological processes under identical conditions, but it has not been successfully achieved in the clinical setting because of the lack of potential for energy discrimination. We developed a new approach based on generalized factor analysis of dynamic sequences (GFADS) that exploits temporal differences between radiotracers and applied it to the case of simultaneous imaging of brain metabolism and dopamine transporter density (DAT) using 18FDG and 18FECNT. Monte Carlo simulations of both studies were performed in a population of anthropomorphic phantoms using 5 different specific and non-specific DAT time activity curves (TAC) previously reported in human and monkey studies. 3D acquisitions were simulated while modeling random and scatter coincidences as well as pixelated block detectors, light sharing among crystals elements and dead-time in the PET/CT scanner. Activity distributions of 18FDG as well as specific and non-specific 18FECNT-DAT were simulated separately and 25 dynamic simultaneous dual tracer studies were generated. We found very good agreement between the estimated GFADS factors and the simulated reference TACs, and between the GFADS factor images and the corresponding reference activity distributions with errors less than 7.3 plusmn 1.3%. Biases in estimation of specific DAT binding and relative metabolism activity were within 5.9 plusmn 3.6% compared to the reference values.

The effects of compensation for scatter, lead X-rays and high-energy contamination on lesion detectability and activity estimation in Ga-67 imaging

Compton scatter, lead X-rays, and high-energy contamination are major factors affecting image quality in Ga-67 imaging. Scattered photons detected in one photopeak window include photons exiting the patient at energies within the photopeak, as well as higher energy photons which have interacted in the collimator and crystal and lost energy. Furthermore, lead X-rays can be detected in the main energy photopeak (93 keV). We have previously developed two energy-based methods, based on artificial neural networks (ANN) and on a generalized spectral (GS) approach to compensate for scatter, high-energy contamination, and lead X-rays in Ga-67 imaging. For comparison, we considered also the projections that would be acquired in the clinic using the optimal energy windows (WIN) we have reported previously for tumor detection and estimation tasks for the 93, 185, and 300 keV photopeaks. The aim of the present study is to evaluate under realistic conditions the impact of these phenomena and their compensation on tumor detection and estimation tasks in Ga-67 imaging. ANN and GS were compared on the basis of performance of a three-channel Hotelling observer (CHO), in detecting the presence of a spherical tumor of unknown size embedded in an anatomic background as well as on the basis of estimation of tumor activity. Projection datasets of spherical tumors ranging from 2 to 6 cm in diameter, located at several sites in an anthropomorphic torso phantom, were simulated using a Monte Carlo program that modeled all photon interactions in the patient as well as in the collimator and the detector for all decays between 91 and 888 keV. One hundred realistic noise realizations were generated from each very-low-noise simulated projection dataset. The presence of scatter degraded both CHO signal-to-noise ratio (SNR) and estimation accuracy. On average, the presence of scatter led to a 12% reduction in CHO SNR. Correcting for scatter further diminished CHO SNR but to a lesser extent with ANN (5% reduction) than with GS (12%). Both scatter corrections improved performance in activity estimation. ANN yielded better precision (1.8% relative standard deviation) than did GS (4%) but greater average bias (5.1% with ANN, 3.6% with GS).

Quantitative simultaneous /sup 99m/Tc//sup 123/I SPECT: design study and validation with Monte Carlo simulations and physical acquisitions

Simultaneous dual isotope imaging (/sup 99m/Tc//sup 123/I) has potential clinical applications but has not been implemented in the clinic. The aim of this work was to design an artificial neural network (ANN) for crosstalk and scatter correction using a smaller number of energy windows (8) than we had previously proposed (26) to allow implementation on some clinical cameras, and to validate our approach using realistic Monte Carlo simulations and anthropomorphic brain phantom acquisitions. Monte Carlo simulations of dual isotope SPECT studies of a digital brain phantom and physical acquisitions of the striatal brain phantom were used to validate our approach. Corrected projections were reconstructed using an iterative ordered subsets expectation maximization (OSEM) algorithm that modeled nonuniform attenuation and variable collimator response in the projector/backprojector. Results: In Monte Carlo simulations, ANN26 and ANN8 yielded similarly accurate quantitation of /sup 123/I activity (bias <7%) in all brain structures. An asymmetric windowing method (AW) yielded accurate estimation in the striata (bias <7%) but not in other brain structures. The estimation bias of /sup 99m/Tc primary activity was <10% in all brain structures with ANN26 and ANN8.