Adam M. Alessio

Application and Evaluation of a Measured Spatially Variant System Model for PET Image Reconstruction

Accurate system modeling in tomographic image reconstruction has been shown to reduce the spatial variance of resolution and improve quantitative accuracy. System modeling can be improved through analytic calculations, Monte Carlo simulations, and physical measurements. The purpose of this work is to improve clinical fully-3-D reconstruction without substantially increasing computation time. We present a practical method for measuring the detector blurring component of a whole-body positron emission tomography (PET) system to form an approximate system model for use with fully-3-D reconstruction. We employ Monte Carlo simulations to show that a non-collimated point source is acceptable for modeling the radial blurring present in a PET tomograph and we justify the use of a Na22 point source for collecting these measurements. We measure the system response on a whole-body scanner, simplify it to a 2-D function, and incorporate a parameterized version of this response into a modified fully-3-D OSEM algorithm. Empirical testing of the signal versus noise benefits reveal roughly a 15% improvement in spatial resolution and 10% improvement in contrast at matched image noise levels. Convergence analysis demonstrates improved resolution and contrast versus noise properties can be achieved with the proposed method with similar computation time as the conventional approach. Comparison of the measured spatially variant and invariant reconstruction revealed similar performance with conventional image metrics. Edge artifacts, which are a common artifact of resolution-modeled reconstruction methods, were less apparent in the spatially variant method than in the invariant method. With the proposed and other resolution-modeled reconstruction methods, edge artifacts need to be studied in more detail to determine the optimal tradeoff of resolution/contrast enhancement and edge fidelity.

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.

Modeling and incorporation of system response functions in 3-D whole body PET

Appropriate application of spatially variant system models can correct for degraded resolution response and mispositioning errors. This paper explores the detector blurring component of the system model for a whole body positron emission tomography (PET) system and extends this factor into a more general system response function to account for other system effects including the influence of Fourier rebinning (FORE). We model the system response function as a three-dimensional (3-D) function that blurs in the radial and axial dimension and is spatially variant in radial location. This function is derived from Monte Carlo simulations and incorporates inter-crystal scatter, crystal penetration, and the blurring due to the FORE algorithm. The improved system model is applied in a modified ordered subsets expectation maximization (OSEM) algorithm to reconstruct images from rebinned, fully 3-D PET data. The proposed method effectively removes the spatial variance in the resolution response, as shown in simulations of point sources. Furthermore, simulation and measured studies show the proposed method improves quantitative accuracy with a reduction in tumor bias compared to conventional OSEM on the order of 10%-30% depending on tumor size and smoothing parameter

Noise and signal properties in PSF-based fully 3D PET image reconstruction: an experimental evaluation.

The addition of accurate system modeling in PET image reconstruction results in images with distinct noise texture and characteristics. In particular, the incorporation of point spread functions (PSF) into the system model has been shown to visually reduce image noise, but the noise properties have not been thoroughly studied. This work offers a systematic evaluation of noise and signal properties in different combinations of reconstruction methods and parameters. We evaluate two fully 3D PET reconstruction algorithms: (1) OSEM with exact scanner line of response modeled (OSEM+LOR), (2) OSEM with line of response and a measured point spread function incorporated (OSEM+LOR+PSF), in combination with the effects of four post-reconstruction filtering parameters and 1-10 iterations, representing a range of clinically acceptable settings. We used a modified NEMA image quality (IQ) phantom, which was filled with 68Ge and consisted of six hot spheres of different sizes with a target/background ratio of 4:1. The phantom was scanned 50 times in 3D mode on a clinical system to provide independent noise realizations. Data were reconstructed with OSEM+LOR and OSEM+LOR+PSF using different reconstruction parameters, and our implementations of the algorithms match the vendors product algorithms. With access to multiple realizations, background noise characteristics were quantified with four metrics. Image roughness and the standard deviation image measured the pixel-to-pixel variation; background variability and ensemble noise quantified the region-to-region variation. Image roughness is the image noise perceived when viewing an individual image. At matched iterations, the addition of PSF leads to images with less noise defined as image roughness (reduced by 35% for unfiltered data) and as the standard deviation image, while it has no effect on background variability or ensemble noise. In terms of signal to noise performance, PSF-based reconstruction has a 7% improvement in contrast recovery at matched ensemble noise levels and 20% improvement of quantitation SNR in unfiltered data. In addition, the relations between different metrics are studied. A linear correlation is observed between background variability and ensemble noise for all different combinations of reconstruction methods and parameters, suggesting that background variability is a reasonable surrogate for ensemble noise when multiple realizations of scans are not available.

Cine CT for Attenuation Correction in Cardiac PET/CT

In dual-modality PET/CT systems, the CT scan provides the attenuation map for PET attenuation correction. The current clinical practice of obtaining a single helical CT scan provides only a snapshot of the respiratory cycle, whereas PET occurs over multiple respiratory cycles. Misalignment of the attenuation map and emission image because of respiratory motion causes errors in the attenuation correction factors and artifacts in the attenuation-corrected PET image. To rectify this problem, we evaluated the use of cine CT, which acquires multiple low-dose CT images during a respiratory cycle. We evaluated the average and the intensity-maximum image of cine CT for cardiac PET attenuation correction. Methods: Cine CT data and cardiac PET data were acquired from a cardiac phantom and from multiple patient studies. The conventional helical CT, cine CT, and PET data of an axially translating phantom were evaluated with and without respiratory motion. For the patient studies, we acquired 2 cine CT studies for each PET acquisition in a rest–stress 13N-ammonia protocol. Three readers visually evaluated the alignment of 74 attenuation image sets versus the corresponding emission image and determined whether the alignment provided acceptable or unacceptable attenuation-corrected PET images. Results: In the phantom study, the attenuation correction from helical CT caused a major artifactual defect in the lateral wall on the PET image. The attenuation correction from the average and from the intensity-maximum cine CT images reduced the defect by 20% and 60%, respectively. In the patient studies, 77% of the cases using the average of the cine CT images had acceptable alignment and 88% of the cases using the intensity maximum of the cine CT images had acceptable alignment. Conclusion: Cine CT offers an alternative to helical CT for compensating for respiratory motion in the attenuation correction of cardiac PET studies. Phantom studies suggest that the average and the intensity maximum of the cine CT images can reduce potential respiration-induced misalignment errors in attenuation correction. Patient studies reveal that cine CT provides acceptable alignment in most cases and suggest that the intensity-maximum cine image offers a more robust alternative to the average cine image.

Tumor delineation using PET in head and neck cancers: Threshold contouring and lesion volumes

Tumor boundary delineation using positron emission tomography (PET) is a promising tool for radiation therapy applications. In this study we quantify the uncertainties in tumor boundary delineation as a function of the reconstruction method, smoothing, and lesion size in head and neck cancer patients using FDG-PET images and evaluate the dosimetric impact on radiotherapy plans. FDG-PET images were acquired for eight patients with a GE Advance PET scanner. In addition, a 20 cm diameter cylindrical phantom with six FDG-filled spheres with volumes of 1.2 to 26.5 cm3 was imaged. PET emission scans were reconstructed with the OSEM and FBP algorithms with different smoothing parameters. PET-based tumor regions were delineated using an automatic contouring function set at progressively higher threshold contour levels and the resulting volumes were calculated. CT-based tumor volumes were also contoured by a physician on coregistered PET/CT patient images. The intensity value of the threshold contour level that returns 100% of the actual volume, I(V100), was measured. We generated intensity-modulated radiotherapy (IMRT) plans for an example head and neck patient, treating 66 Gy to CT-based gross disease and 54 Gy to nodal regions at risk, followed by a boost to the FDG-PET-based tumor. The volumes of PET-based tumors are a sensitive function of threshold contour level for all patients and phantom datasets. A 5% change in threshold contour level can translate into a 200% increase in volume. Phantom data indicate that I(V100) can be set as a fraction, f, of the maximum measured uptake. Fractional threshold values in the cylindrical water phantom range from 0.23 to 0.51. Both the fractional threshold and the threshold-volume curve are dependent on lesion size, with lesions smaller than approximately 5 cm3 displaying a more pronounced sensitivity and larger fractional threshold values. The threshold-volume curves and fractional threshold values also depend on the reconstruction algorithm and smoothing filter with more smoothing requiring a higher fractional threshold contour level. The threshold contour level affects the tumor size, and therefore the ultimate boost dose that is achievable with IMRT. In an example head and neck IMRT plan, the D95 of the planning target volume decreased from 7770 to 7230 cGy for 42% vs. 55% contour threshold levels. PET-based tumor volumes are strongly affected by the choice of threshold level. This can have a significant dosimetric impact. The appropriate threshold level depends on lesion size and image reconstruction parameters. These effects should be carefully considered when using PET contour and/or volume information for radiotherapy applications.

Ultra-low dose CT attenuation correction for PET/CT

A challenge for positron emission tomography/computed tomography (PET/CT) quantitation is patient respiratory motion, which can cause an underestimation of lesion activity uptake and an overestimation of lesion volume. Several respiratory motion correction methods benefit from longer duration CT scans that are phase matched with PET scans. However, even with the currently available, lowest dose CT techniques, extended duration cine CT scans impart a substantially high radiation dose. This study evaluates methods designed to reduce CT radiation dose in PET/CT scanning. We investigated selected combinations of dose reduced acquisition and noise suppression methods that take advantage of the reduced requirement of CT for PET attenuation correction (AC). These include reducing CT tube current, optimizing CT tube voltage, adding filtration, CT sinogram smoothing and clipping. We explored the impact of these methods on PET quantitation via simulations on different digital phantoms. CT tube current can be reduced much lower for AC than that in low dose CT protocols. Spectra that are higher energy and narrower are generally more dose efficient with respect to PET image quality. Sinogram smoothing could be used to compensate for the increased noise and artifacts at radiation dose reduced CT images, which allows for a further reduction of CT dose with no penalty for PET image quantitation. When CT is not used for diagnostic and anatomical localization purposes, we showed that ultra-low dose CT for PET/CT is feasible. The significant dose reduction strategies proposed here could enable respiratory motion compensation methods that require extended duration CT scans and reduce radiation exposure in general for all PET/CT imaging.

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.

Dual Energy CT Attenuation Correction Methods for Quantitative Assessment of Response to Cancer Therapy with PET/CT Imaging

We hypothesize that improved quantification for PET imaging of high atomic number materials can be achieved by combining low-dose x-ray imaging with dual energy CT for PET attenuation correction. Improved quantification of tracer uptake will lead to improved patient outcomes by providing more accurate information for therapeutic choices. Accurate PET/CT measurements of early response will be critical in determining the best cancer therapy option for each patient in a timely manner and in sparing patients the morbidity and cost of ineffective treatments. We first evaluate the potential errors in PET images arising from CT-based attenuation correction when iodine-based contrast is incorrectly classified as bone when forming the linear attenuation coefficient image. We then investigate two methods of reducing errors in the linear attenuation image: an approximate, but fast, hybrid classification/scaling algorithm and a model-based dual-energy CT method that incorporates the polyenergetic spectrum and a noise model in an iterative reconstruction method. Both methods are shown to reduce errors in the estimated linear attenuation coefficient image, but require further study to determine the effects of noise propagation if low-dose CT scans are used for the estimation of the linear attenuation image.

Model-Based Iterative Reconstruction Versus Adaptive Statistical Iterative Reconstruction and Filtered Back Projection in Liver 64-MDCT: Focal Lesion Detection, Lesion Conspicuity, and Image Noise

OBJECTIVE The purpose of this study is to compare three CT image reconstruction algorithms for liver lesion detection and appearance, subjective lesion conspicuity, and measured noise. MATERIALS AND METHODS Thirty-six patients with known liver lesions were scanned with a routine clinical three-phase CT protocol using a weight-based noise index of 30 or 36. Image data from each phase were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR). Randomized images were presented to two independent blinded reviewers to detect and categorize the appearance of lesions and to score lesion conspicuity. Lesion size, lesion density (in Hounsfield units), adjacent liver density (in Hounsfield units), and image noise were measured. Two different unblinded truth readers established the number, appearance, and location of lesions. RESULTS Fifty-one focal lesions were detected by truth readers. For blinded reviewers compared with truth readers, there was no difference for lesion detection among the reconstruction algorithms. Lesion appearance was statistically the same among the three reconstructions. Although one reviewer scored lesions as being more conspicuous with MBIR, the other scored them the same. There was significantly less background noise in air with MBIR (mean [± SD], 2.1 ± 1.4 HU) than with ASIR (8.9 ± 1.9 HU; p < 0.001) or FBP (10.6 ± 2.6 HU; p < 0.001). Mean lesion contrast-to-noise ratio was statistically significantly higher for MBIR (34.4 ± 29.1) than for ASIR (6.5 ± 4.9; p < 0.001) or FBP (6.3 ± 6.0; p < 0.001). CONCLUSION In routine-dose clinical CT of the liver, MBIR resulted in comparable lesion detection, lesion characterization, and subjective lesion conspicuity, but significantly lower background noise and higher contrast-to-noise ratio compared with ASIR or FBP. This finding suggests that further investigation of the use of MBIR to enable dose reduction in liver CT is warranted.