Larry Pierce

The impact of respiratory motion on tumor quantification and delineation in static PET/CT imaging

Our aim is to investigate the impact of respiratory motion on tumor quantification and delineation in static PET/CT imaging using a population of patient respiratory traces. A total of 1295 respiratory traces acquired during whole body PET/CT imaging were classified into three types according to the qualitative shape of their signal histograms. Each trace was scaled to three diaphragm motion amplitudes (6 mm, 11 mm and 16 mm) to drive a whole body PET/CT computer simulation that was validated with a physical phantom experiment. Three lung lesions and one liver lesion were simulated with diameters of 1 cm and 2 cm. PET data were reconstructed using the OS-EM algorithm with attenuation correction using CT images at the end-expiration phase and respiratory-averaged CT. The errors of the lesion maximum standardized uptake values (SUV(max)) and lesion volumes between motion-free and motion-blurred PET/CT images were measured and analyzed. For respiration with 11 mm diaphragm motion and larger quiescent period fraction, respiratory motion can cause a mean lesion SUV(max) underestimation of 28% and a mean lesion volume overestimation of 130% in PET/CT images with 1 cm lesions. The errors of lesion SUV(max) and volume are larger for patient traces with larger motion amplitudes. Smaller lesions are more sensitive to respiratory motion than larger lesions for the same motion amplitude. Patient respiratory traces with relatively larger quiescent period fraction yield results less subject to respiratory motion than traces with long-term amplitude variability. Mismatched attenuation correction due to respiratory motion can cause SUV(max) overestimation for lesions in the lower lung region close to the liver dome. Using respiratory-averaged CT for attenuation correction yields smaller mismatch errors than those using end-expiration CT. Respiratory motion can have a significant impact on static oncological PET/CT imaging where SUV and/or volume measurements are important. The impact is highly dependent upon motion amplitude, lesion location and size, attenuation map and respiratory pattern. To overcome the motion effect, motion compensation techniques may be necessary in clinical practice to improve the tumor quantification for determining the response to therapy or for radiation treatment planning.

Quiescent period respiratory gating for PET∕CT

PURPOSE To minimize respiratory motion artifacts, this work proposes quiescent period gating (QPG) methods that extract PET data from the end-expiration quiescent period and form a single PET frame with reduced motion and improved signal-to-noise properties. METHODS Two QPG methods are proposed andevaluated. Histogram-based quiescent period gating (H-QPG) extracts a fraction of PET data determined by a window of the respiratory displacement signal histogram. Cycle-based quiescent period gating (C-QPG) extracts data with a respiratory displacement signal below a specified threshold of the maximum amplitude of each individual respiratory cycle. Performances of both QPG methods were compared to ungated and five-bin phase-gated images across 21 FDG-PET/CT patient data sets containing 31 thorax and abdomen lesions as well as with computer simulations driven by 1295 different patient respiratory traces. Image quality was evaluated in terms of the lesion SUV(max) and the fraction of counts included in each gate as a surrogate for image noise. RESULTS For all the gating methods, image noise artifactually increases SUV(max) when the fraction of counts included in each gate is less than 50%. While simulation data show that H-QPG is superior to C-QPG, the H-QPG and C-QPG methods lead to similar quantification-noise tradeoffs in patient data. Compared to ungated images, both QPG methods yield significantly higher lesion SUV(max). Compared to five-bin phase gating, the QPG methods yield significantly larger fraction of counts with similar SUV(max) improvement. Both QPG methods result in increased lesion SUV(max) for patients whose lesions have longer quiescent periods. CONCLUSIONS Compared to ungated and phase-gated images, the QPG methods lead to images with less motion blurring and an improved compromise between SUV(max) and fraction of counts. The QPG methods for respiratory motion compensation could effectively improve tumor quantification with minimal noise increase.

Quantitative assessment of dynamic PET imaging data in cancer imaging.

Clinical imaging in positron emission tomography (PET) is often performed using single-time-point estimates of tracer uptake or static imaging that provides a spatial map of regional tracer concentration. However, dynamic tracer imaging can provide considerably more information about in vivo biology by delineating both the temporal and spatial pattern of tracer uptake. In addition, several potential sources of error that occur in static imaging can be mitigated. This review focuses on the application of dynamic PET imaging to measuring regional cancer biologic features and especially in using dynamic PET imaging for quantitative therapeutic response monitoring for cancer clinical trials. Dynamic PET imaging output parameters, particularly transport (flow) and overall metabolic rate, have provided imaging end points for clinical trials at single-center institutions for years. However, dynamic imaging poses many challenges for multicenter clinical trial implementations from cross-center calibration to the inadequacy of a common informatics infrastructure. Underlying principles and methodology of PET dynamic imaging are first reviewed, followed by an examination of current approaches to dynamic PET image analysis with a specific case example of dynamic fluorothymidine imaging to illustrate the approach.

Resolution Properties of a Prototype Continuous Miniature Crystal Element (cMiCE) Scanner

Continuous miniature crystal element (cMiCE) detectors are a potentially lower cost alternative for high resolution discrete crystal PET detector designs. We report on performance characteristics of a prototype PET scanner consisting of two cMiCE detector modules. Each cMiCE detector is comprised of a 50 × 50 × 8 mm3 LYSO crystal coupled to a 64 channel multi-anode PMT. The cMiCE detectors use a statistics-based positioning method based upon maximum likelihood estimation for event positioning. By this method, cMiCE detectors can also provide some depth of interaction event positioning information. For the prototype scanner, the cMiCE detectors were positioned across from one another on a horizontal gantry with a detector spacing of 10.7 cm. Full tomographic data were collected and reconstructed using single slice rebinning and filtered back projection with no smoothing. The average image resolutions in X (radial), Y (transverse) and Z (axial) were 1.05 ± 0.08 mm, 0.99 ± 0.07 mm, 1.24 ± 0.31 mm FWHM. These initial imaging results from a prototype imaging system demonstrate the outstanding image resolution performance that can be achieved using the potentially lower cost cMiCE detectors.

A Digital Reference Object to Analyze Calculation Accuracy of PET Standardized Uptake Value.

PURPOSE To determine the extent of variations in computing standardized uptake value (SUV) by body weight (SUV(BW)) among different software packages and to propose a Digital Imaging and Communications in Medicine (DICOM) reference test object to ensure the standardization of SUV computation between medical image viewing workstations. MATERIALS AND METHODS Research ethics board approval was not necessary because this study only evaluated images of a phantom. A synthetic set of positron emission tomographic (PET)/computed tomographic (CT) image data, called a digital reference object (DRO), with known SUV was created. The DRO was sent to 16 sites and evaluated on 21 different PET/CT display software packages. Users were asked to draw various regions of interest (ROIs) on specific features and report the maximum, minimum, mean, and standard deviation of the SUVs for each ROI. Numerical tolerances were defined for each metric, and the fraction of reported values within the tolerance was recorded, as was the mean, standard deviation, and range of the metrics. RESULTS The errors in reported maximum SUV ranged from -37.8% to 0% for an isolated voxel with 4.11:1 target-to-background activity level, and errors in the reported mean SUV ranged from -1.6% to 100% for a region with controlled noise. There was also a range of errors in the less commonly used metrics of minimum SUV and standard deviation SUV. CONCLUSION The variability of computed SUV(BW) between different software packages is substantial enough to warrant the introduction of a reference standard for medical image viewing workstations.

Dual-radioisotope PET data acquisition and analysis

This study experimentally evaluates a novel approach for dual radioisotope PET imaging. The method relies on one radioisotope being a pure positron emitter and the other radioisotope emitting a prompt high energy gamma along with the positron. Using the differential count rates of dual and triple coincidences allows for quantitative reconstruction of the individual radioisotope activities. The objective of the present study was to perform an experimental proof-of-principle test of the method. We used two cMiCE detector modules (with 400 keV lower energy threshold) mounted directly across from one another and a third module (850 keV threshold) at 90 degrees to the first two modules. Coincidence logic was set to either monitor a coincidence between modules 1 and 2 and NOT 3, or a coincidence between all three modules. These were used to measure dual and triple coincidence count rates from 22Na and 68Ge point sources, scanned either separately or together. While 68Ge was considered to be a pure positron emitter, for 22Na, 90.4% of decays produce a positron and a 1.275 MeV prompt gamma. In addition to the count rate test, full tomographic data were collected and images reconstructed for both dual and triple coincidence data sets. Reconstructed images demonstrate the ability of the method to clearly distinguish between 22Na and 68Ge sources based on triple-coincidence criterium. For both dual and triple coincidence event modes, the coincidence rates for simultaneous acquisition of the 22Na and 68Ge sources were 10–19% higher than the sum of the coincident rates for the radioisotopes acquired individually. We speculate this is due to energy pulse-pile up and are continuing evaluations of this effect. We were able to demonstrate the basic validity of differentiating the individual activity levels of 22Na and 68Ge sources even when acquired at the same time. Quantitative accuracy can likely be improved using normalization methods, and evaluations of this approach are ongoing.

Multiplexing strategies for monolithic crystal PET detector modules

To reduce the number of output channels and associated cost in PET detectors, strategies to multiplex the signal channels have been investigated by several researchers. This work aims to find an optimal multiplexing strategy for detector modules consisting of a monolithic LYSO scintillator coupled to a 64-channel PMT. We apply simulated multiplexing strategies to measured data from two continuous miniature crystal element (cMiCE) detector modules. The strategies tested include standard methods such as row column summation and its variants, as well as new data-driven methods involving the principal components of measured data and variants of those components. The detector positioning resolution and bias are measured for each multiplexing strategy and the results are compared. The mean FWHM over the entire detector was 1.23 mm for no multiplexing (64 channels). Using 16 principal component channels yielded a mean FWHM resolution of 1.21 mm, while traditional row/column summation (16 channels) yielded 1.28 mm. Using 8 principal component output channels resulted in a resolution of 1.30 mm. Using the principal components of the calibration data to guide the multiplexing scheme appears to be a viable method for reducing the number of output data channels. Further study is needed to determine if the depth-of-interaction resolution can be preserved with this multiplexing scheme.

Resolution properties of a prototype continuous miniature crystal element (cMiCE) scanner

Continuous miniature crystal element (cMiCE) detectors are a potentially lower cost alternative to high resolution discrete crystal PET detector designs. We report on performance characteristics of a prototype PET scanner consisting of two cMiCE detector modules. Each cMiCE detector is comprised of a 50 mm by 50 mm by 8 mm LYSO crystal coupled to a 64 channel multi-anode PMT. The cMiCE detectors use a statistics-based positioning method based upon the maximum likelihood method for event positioning. In addition, cMiCE detectors can provide some depth of interaction event positioning information. For the prototype scanner, the cMiCE detectors were positioned across from one another on a horizontal gantry with a detector spacing of 10.1 cm. Full tomographic data were collected by placing the object to be imaged on a rotating stage. Data were collected in a step and shoot fashion with 6 degree angular steps. Data were collected for point sources placed at 1, 5, 10 and 15 mm radial offset from the center of the imaging field of view. Data were binned using single slice rebinning and reconstructed using filtered back projection with a ramp filter. The average image resolutions for X (radial), Y (transverse) and Z (axial) were 1.09 mm, 0.99 mm, 1.25 mm FWHM, respectively. The initial imaging results from a prototype cMiCE imaging system demonstrate the outstanding image resolution performance than can be achieved using cMiCE detectors.

Determining block detector positions for PET scanners

We present an algorithm for accurate localization of block detectors in a positron emission tomography (PET) scanner. Accurate reconstruction of PET images requires precise knowledge of the physical position and orientation of the detectors. However, in some systems, block detector positioning and orientation can have relatively large tolerances, leading to implicit errors in the coincidence line-of-response (LOR) positioning. To compensate we utilize a rotating point source phantom where the rotational angle of the phantom is used to precisely determine the location of each scintillator crystal within a detector block. The aggregate block positions are then applied to the system model to determine the true location of each LOR. Images reconstructed with the more accurate LOR positioning demonstrate improved image fidelity.

A machine learning method for fast and accurate characterization of depth-of-interaction gamma cameras

Measuring the depth-of-interaction (DOI) of gamma photons enables increasing the resolution of emission imaging systems. Several design variants of DOI-sensitive detectors have been recently introduced to improve the performance of scanners for positron emission tomography (PET). However, the accurate characterization of the response of DOI detectors, necessary to accurately measure the DOI, remains an unsolved problem. Numerical simulations are, at the state of the art, imprecise, while measuring directly the characteristics of DOI detectors experimentally is hindered by the impossibility to impose the depth-of-interaction in an experimental set-up. In this article we introduce a machine learning approach for extracting accurate forward models of gamma imaging devices from simple pencil-beam measurements, using a nonlinear dimensionality reduction technique in combination with a finite mixture model. The method is purely data-driven, not requiring simulations, and is applicable to a wide range of detector types. The proposed method was evaluated both in a simulation study and with data acquired using a monolithic gamma camera designed for PET (the cMiCE detector), demonstrating the accurate recovery of the DOI characteristics. The combination of the proposed calibration technique with maximum- a posteriori estimation of the coordinates of interaction provided a depth resolution of  ≈1.14 mm for the simulated PET detector and  ≈1.74 mm for the cMiCE detector. The software and experimental data are made available at http://occiput.mgh.harvard.edu/depthembedding/.