Improvement of performance of cardiac SPECT camera using curved detectors with pinholes

SPECT is primarily used in the clinic for cardiac myocardial perfusion imaging. However, for SPECT, sensitivity is impaired due to the need for collimation. System resolution FWHM is poor as well (∼ 1 cm). In this work the resolution of a curved detector was theoretically derived. The advantage of a curved detector over a flat detector with pinhole collimation was demonstrated for cardiac applications using theoretical derivations as well as a ray-tracing voxel-based forward projector. For the flat detector using parameters close to what was expected for the new multi-pinhole GE Discovery system, it is shown that using a paraboloid detector one may obtain a better system resolution (about 29% better on the average), keeping same pinhole opening. Alternately, sensitivity gains of as much as 2.25 may be obtained, for similar resolutions as a flat detector by just using a different pinhole with higher hole-diameter. © 2012 IEEE

Theoretical and numerical study of MLEM and OSEM reconstruction algorithms for motion correction in emission tomography

Patient body-motion and respiratory-motion impacts the image quality of cardiac SPECT and PET perfusion images. Several algorithms exist in the literature to correct for motion within the iterative maximum-likelihood reconstruction framework. In this work, three algorithms are derived starting with Poisson statistics to correct for patient motion. The first one is a motion compensated MLEM algorithm (MC-MLEM). The next two algorithms called MGEM-1 and MGEM-2 (short for Motion Gated OSEM, 1 and 2) use the motion states as subsets, in two different ways. Experiments were performed with NCAT phantoms (with exactly known motion) as the source and attenuation distributions. Experiments were also performed on an anthropomorphic phantom and a patient study. The SIMIND Monte Carlo simulation software was used to create SPECT projection images of the NCAT phantoms. The projection images were then modified to have Poisson noise levels equivalent to that of clinical acquisition. We investigated application of these algorithms to correction of (1) a large body-motion of 2 cm in Superior-Inferior (SI) and Anterior-Posterior (AP) directions each and (2) respiratory motion of 2 cm in SI and 0.6 cm in AP. We determined the bias with respect to the NCAT phantom activity for noiseless reconstructions as well as the bias-variance for noisy reconstructions. The MGEM-1 advanced along the bias-variance curve faster than the MC-MLEM with iterations. The MGEM-1 also lowered the noiseless bias (with respect to NCAT truth) faster with iterations, compared to the MC-MLEM algorithms, as expected with subset algorithms. For the body motion correction with two motion states, after the 9th iteration the bias was close to that of MC-MLEM at iteration 17, reducing the number of iterations by a factor of 1.89. For the respiratory motion correction with 9 motion states, based on the noiseless bias, the iteration reduction factor was approximately 7. For the MGEM-2, however, bias-plot or the bias-variance-plot saturated with iteration because of successive interpolation error. SPECT data was acquired simulating respiratory motion of 2 cm amplitude with an anthropomorphic phantom. A patient study acquired with body motion in a second rest was also acquired. The motion correction was applied to these acquisitions with the anthropomorphic phantom and the patient study, showing marked improvements of image quality with the estimated motion correction. © 2009 IEEE

Maximum-Likelihood Estimation of Glandular Fraction for Mammography and its Effect on Microcalcification Detection

Objective: Breast tissue is a mixture of adipose and fibro-glandular tissue. The risk of undetected breast cancer increases with the amount of glandular tissue in the breast. Therefore, radiologists need to know quantitative glandular fraction when diagnosing a patient. Another increasingly popular mammography protocol is eliminating the anti-scatter grid and using software algorithms to reduce scatter. This work uses a Maximum Likelihood Expectation Maximization algorithm to estimate the pixel-wise glandular fraction from images taken with an anti-scatter grid or with scatter removed algorithmically. The work also studies if presenting the pixel-wise glandular fraction image alongside the usual mammography image has the potential to improve micro-calcification detection. Approach: The algorithms are implemented and evaluated with TOPAS Geant4-generated images with known glandular fractions. These images are also taken with and without microcalcifications present to study the effects of GF-estimation on microcalcification detection. We then applied the algorithm to a few clinical DICOM images with and without microcalcifications. Results: For the TOPAS simulated images, the glandular fraction was estimated with a root mean squared error of 3.2% and 2.5% for the without and with anti-scatter grid cases. Results from DICOM clinical images (where the proper glandular fraction is unknown) show that the algorithm gives a glandular fraction within the average range expected from the literature. For microcalcification detection, the contrast-to-noise ratio improved by 17.5-548% in DICOM images and 5.1-88% in TOPAS images. Significance: This work studied the accuracy of maximum likelihood estimation for a glandular fraction on simulated and clinical images and shows an improvement in contrast to noise ratio for detecting microcalcifications, a risk factor in breast cancer.Comment: Manuscipt under peer-revie

X-ray interferometry without analyzer for breast CT application: A simulation study

Purpose: We investigate an analyzer-less x-ray interferometer with a spatially modulated phase grating (MPG) that can deliver three modalities (attenuation image, phase image, and scatter images) in breast computed tomography (BCT). The system can provide three x-ray modalities while preserving the dose to the object and can achieve attenuation image sensitivity similar to that of a standard absorption-only BCT. The MPG system works with a source, a source-grating, a single phase grating, and a detector. No analyzer is necessary. Thus, there is an approximately 2x improvement in fluence at the detector for our system compared with the same source-detector distance Talbot-Lau x-ray interferometry (TLXI) because the TLXI has an analyzer after the object, which is not required for the MPG. Approach: We investigate the MPG BCT system in simulations and find a clinically feasible system geometry. First, the mechanism of MPG interferometry is conceptually shown via Sommerfeld-Rayleigh diffraction integral simulations. Next, we investigate source coherence requirements, fringe visibility, and phase sensitivity dependence on different system parameters and find clinically feasible system geometry. Results: The phase sensitivity of MPG interferometry is proportional to object-detector distance and inversely proportional to a period of broad fringes at the detector, which is determined by the grating spatial modulation period. In our simulations, the MPG interferometry can achieve about 27% fringe visibility with clinically realistic BCT geometry of a total source-detector distance of 950 mm and source-object distance of 500 mm. Conclusions: We simulated a promising analyzer-less x-ray interferometer, with a spatially sinusoidal MPG. Our system is expected to deliver the attenuation, phase and scatter image in a single acquisition without dose or fluence detriment, compared with conventional BCT

New compartment model analysis of lean-mass and fat-mass growth with overfeeding

Objectives: Mathematical models of lean- and fat-mass growth with diet are useful to help describe and potentially predict the fat- and lean-mass change with different diets as a function of consumed protein and fat calories. Most of the existing models do not explicitly account for interdependence of fat-mass on the lean-mass and vice versa. The aim of this study was to develop a new compartmental model to describe the growth of lean and fat mass depending on the input of dietary protein and fat, and accounting for the interdependence of adipose tissue and muscle growth. Methods: The model was fitted to existing clinical data of an overfeeding trial for 23 participants (with a high-protein diet, a normal-protein diet, and a low-protein diet) and compared with the existing Forbes model. Results: Qualitatively and quantitatively, the compartment model data fit was smoother with less overall error than the Forbes model. The root means square error were 0.39, 0.93 and 0.72 kg for the new model, the Forbes model, and the modified Forbes model, respectively. Additionally, for the present model, the differences between some of the coefficients (on the cross dependence of fat and lean mass as well as on the intake diet dependence) across different diets were statistically significant (P \u3c 0.05). Conclusions: Our new Dey-model showed excellent fit to overfeeding data for 23 normal participants with some significant differences of model coefficients across diets, enabling further studies of the model coefficients for larger groups of participants with obesity or other diseases

Experimental method and statistical analysis to fit tumor growth model using SPECT/CT imaging: A preclinical study

Background: Over the last decade, several theoretical tumor-models have been developed to describe tumor growth. Oncology imaging is performed using various modalities including computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT) and fluorodeoxyglucose-positron emission tomography (FDG-PET). Our goal is to extract useful, otherwise hidden, quantitative biophysical parameters (such as growth-rate, tumor-necrotic-factor, etc.) from these serial images of tumors by fitting mathematical models to images. These biophysical features are intrinsic to the tumor types and specific to the study-subject, and expected to add valuable information on the tumor containment or spread and help treatment plans. Thus, fitting realistic but practical models and assessing parameter-errors and degree of fit is important. Methods: We implemented an existing theoretical ode-compartment model and variants and applied them for the first time, in vivo. We developed an inversion algorithm to fit the models for tumor growth for simulated as well as in vivo experimental data. Serial SPECT/CT scans of mice breast-tumors were acquired, and SPECT data was used to segment the proliferating-layers of tumors. Results: Results of noisy data simulation and inversion show that 5 out of 7 parameters were recovered to within 4.3% error. In particular, tumor growth-rate parameter was recovered to 0.07% error. For model fitting to in vivo mice-tumors, regression analysis on the P-layer volume showed R2 of 0.99 for logistic and Gompertzian while surface area model yielded R2=0.96. For the necrotic layer the R2 values were 0.95, 0.93 and 0.94 respectively for surface-area, logistic and Gompertzian. The Akaike Information Criterion (AIC) weights of the models (giving their relative probability of being the best Kullback-Leibler (K-L) model among the set of candidate models) were 0, 0.43 and 0.57 for surface-area, logistic and Gompertzian models. Conclusions: Model-fitting to mice tumor studies demonstrates feasibility of applying the models to in vivo imaging data to extract features. Akaike information criterion (AIC) evaluations show Gompertzian or logistic growth model fits in vivo breast-tumors better than surface-area based growth model

The impact of system matrix dimension on small FOV SPECT reconstruction with truncated projections

Purpose: A dedicated cardiac hybrid single photon emission computed tomography (SPECT)/CT scanner that uses cadmium zinc telluride detectors and multiple pinhole collimators for stationary acquisition offers many advantages. However, the impact of the reconstruction system matrix (SM) dimension on the reconstructed image quality from truncated projections and 19 angular samples acquired on this scanner has not been extensively investigated. In this study, the authors aimed to investigate the impact of the dimensions of SM and the use of body contour derived from adjunctive CT imaging as an object support in reconstruction on this scanner, in relation to background extracardiac activity. Methods: The authors first simulated a generic SPECT/CT system to image four NCAT phantoms with various levels of extracardiac activity and compared the reconstructions using SM in different dimensions and with/without body contour as a support for quantitative evaluations. The authors then compared the reconstructions of 18 patient studies, which were acquired on a GE Discovery NM570c scanner following injection of different radiotracers, including 99mTc-Tetrofosmin and 123I-mIBG, comparing the scanner\u27s default SM that incompletely covers the body with a large SM that incorporates a patient specific full body contour. Results: The simulation studies showed that the reconstructions using a SM that only partially covers the body yielded artifacts on the edge of the field of view (FOV), overestimation of activity and increased nonuniformity in the blood pool for the phantoms with higher relative levels of extracardiac activity. However, the impact on the quantitative accuracy in the high activity region such as the myocardium, was subtle. On the other hand, an excessively large SM that enclosed the entire body alleviated the artifacts and reduced overestimation in the blood pool, but yielded slight underestimation in myocardium and defect regions. The reconstruction using the larger SM with body contour yielded the most quantitatively accurate results in all the regions of interest for a range of uptake levels in the extracardiac regions. In patient studies, the SM incorporating patient specific body contour minimized extracardiac artifacts, yielded similar myocardial activity, lower blood pool activity, and subsequently improved myocardium-to-blood pool contrast (p\u3c0.0001) by an average of 7% (range 0%-18%) across all the patients, compared to the reconstructions using the scanner\u27s default SM. Conclusions: Their results demonstrate that using a large SM that incorporates a CT derived body contour in the reconstruction could improve quantitative accuracy within the FOV for clinical studies with high extracardiac activity

LROC Investigation of Three Strategies for Reducing the Impact of Respiratory Motion on the Detection of Solitary Pulmonary Nodules in SPECT

The objective of this investigation was to determine the effectiveness of three motion reducing strategies in diminishing the degrading impact of respiratory motion on the detection of small solitary pulmonary nodules (SPNs) in single-photon emission computed tomographic (SPECT) imaging in comparison to a standard clinical acquisition and the ideal case of imaging in the absence of respiratory motion. To do this nonuniform rational B-spline cardiac-torso (NCAT) phantoms based on human-volunteer CT studies were generated spanning the respiratory cycle for a normal background distribution of Tc-99 m NeoTect. Similarly, spherical phantoms of 1.0-cm diameter were generated to model small SPN for each of the 150 uniquely located sites within the lungs whose respiratory motion was based on the motion of normal structures in the volunteer CT studies. The SIMIND Monte Carlo program was used to produce SPECT projection data from these. Normal and single-lesion containing SPECT projection sets with a clinically realistic Poisson noise level were created for the cases of 1) the end-expiration (EE) frame with all counts, 2) respiration-averaged motion with all counts, 3) one fourth of the 32 frames centered around EE (Quarter Binning), 4) one half of the 32 frames centered around EE (Half Binning), and 5) eight temporally binned frames spanning the respiratory cycle. Each of the sets of combined projection data were reconstructed with RBI-EM with system spatial-resolution compensation (RC). Based on the known motion for each of the 150 different lesions, the reconstructed volumes of respiratory bins were shifted so as to superimpose the locations of the SPN onto that in the first bin (Reconstruct and Shift). Five human observers performed localization receiver operating characteristics (LROC) studies of SPN detection. The observer results were analyzed for statistical significance differences in SPN detection accuracy among the three correction strategies, the standard acquisition, and the ideal case of the absence of respiratory motion. Our human-observer LROC determined that Quarter Binning and Half Binning strategies resulted in SPN detection accuracy statistically significantly below (P \u3c 0.05) that of standard clinical acquisition, whereas the Reconstruct and Shift strategy resulted in a detection accuracy not statistically significantly different from that of the ideal case. This investigation demonstrates that tumor detection based on acquisitions associated with less than all the counts which could potentially be employed may result in poorer detection despite limiting the motion of the lesion. The Reconstruct and Shift method results in tumor detection that is equivalent to ideal motion correction

Creation of 3D Digital Anthropomorphic Phantoms which Model Actual Patient Non-rigid Body Motion as Determined from MRI and Position Tracking Studies of Volunteers

Background: Patient motion during emission imaging can create artifacts in the reconstructed emission distributions, which may mislead the diagnosis. For example, in myocardial-perfusion imaging, these artifacts can be mistaken for defects. Various software and hardware approaches have been developed to detect and compensate for motion. There are various ways of testing the effectiveness of motion correction methods applied in emission tomography, including the use of realistic digital anthropomorphic phantoms. Purpose: The purpose of this study was to create 3D digital anthropomorphic phantoms based on MRI data of volunteers undergoing a series of clinically relevant motions. These phantoms with combined position tracking were used to investigate both imaging-data-driven and motion tracking strategies to estimate and correct for patient motion. Methods: MRI scans were obtained of volunteers undergoing a series of clinically relevant movements. During the MRI, the motions were recorded by near-infra-red cameras tracking using external markers on the chest and abdomen. Individual-specific extended cardiac-torso (XCAT) phantoms were created fit to our volunteer MRI imaging data representing pre- and post-motion states. These XCAT phantoms were then used to generate activity and attenuation distributions. Monte Carlo methods will then be performed to simulate SPECT acquisitions, which will be used to evaluate various motion estimation and correction strategies. Results: Three volunteers were scanned in the MRI with concurrent external motion tracking. Each volunteer performed five separate motions including an axial slide, roll, shoulder twist, spine bend, and arm motion. These MRI scans were then manually digitalized into 3D anthropomorphic XCAT phantoms. Activity and attenuation distributions were created for each XCAT phantom, representing fifteen individual-specific motions. Conclusions: Our results will be combined with the external motion tracking data to determine if external motion tracking accurately reflects heart position in patients undergoing cardiac SPECT imaging. This data will also be used to evaluate other motion correction methods in the future