Browsing by Subject "Positron emission tomography"

The use of positron emission tomography (PET) in radiation therapy has continued to grow, especially since the development of combined computed tomography (CT) and PET imaging system in the early 1990s. Today, the biggest use of PET-CT is in oncology, where a glucose analog radiotracer is rapidly incorporated into the metabolic pathways of a variety of cancers. Images representing the in-vivo distribution of this radiotracer are used for the staging, delineation and assessment of treatment response of patients undergoing chemotherapy or radiation therapy. While PET offers the ability to provide functional information, the imaging quality of PET is adversely affected by its lower spatial resolution. It also has unfavorable image noise characteristics due to radiation dose concerns and patient compliance. These factors result in PET images having less detail and lower signal-to-noise (SNR) properties compared to images produced by CT. This complicates the use of PET within many areas of radiation oncology, but particularly the delineation of targets for radiation therapy and the assessment of patient response to therapy. The development of segmentation methods that can provide accurate object identification in PET images under a variety of imaging conditions has been a goal of the imaging community for years. The goal of this thesis are to: (1) investigate the effect of filtering on segmentation methods; (2) investigate whether combining individual segmentation methods can improve segmentation accuracy; (3) investigate whether the consensus volumes can be useful in aiding physicians of different experience in defining gross tumor volumes (GTV) for head-and-neck cancer patients; and (4) to investigate whether consensus volumes can be useful in assessing early treatment response in head-and-neck cancer patients.

For this dissertation work, standard spherical objects of volumes ranging from 1.15 cc to 37 cc and two irregularly shaped objects of volume 16 cc and 32 cc formed by deforming high density plastic bottles were placed in a standardized image quality phantom and imaged at two contrasts (4:1 or 8:1 for spheres, and 4.5:1 and 9:1 for irregular) and three scan durations (1, 2 and 5 minutes). For the work carried out into the comparison of images filters, Gaussian and bilateral filters matched to produce similar image signal to noise (SNR) in background regions were applied to raw unfiltered images. Objects were segmented using thresholding at 40% of the maximum intensity within a region-of-interest (ROI), an adaptive thresholding method which accounts for the signal of the object as well as background, k-means clustering, and a seeded region-growing method adapted from the literature. Quality of the segmentations was assessed using the Dice Similarity Coefficient (DSC) and symmetric mean absolute surface distance (SMASD). Further, models describing how DSC varies with object size, contrast, scan duration, filter choice and segmentation method were fitted using generalized estimating equations (GEEs) and standard regression for comparison. GEEs accounted for the bounded, correlated and heteroscedastic nature of the DSC metric. Our analysis revealed that object size had the largest effect on DSC for spheres, followed by contrast and scan duration. In addition, compared to filtering images with a 5 mm full-width at half maximum (FWHM) Gaussian filter, a 7 mm bilateral filter with moderate pre-smoothing (3 mm Gaussian (G3B7)) produced significant improvements in 3 out of the 4 segmentation methods for spheres. For the irregular objects, time had the biggest effect on DSC values, followed by contrast.

For the study of applying consensus methods to PET segmentation, an additional gradient based method was included into the collection individual segmentation methods used for the filtering study. Objects in images acquired for 5 minute scan durations were filtered with a 5 mm FWHM Gaussian before being segmented by all individual methods. Two approaches of creating a volume reflecting the agreement between the individual methods were investigated. First, a simple majority voting scheme (MJV), where individual voxels segmented by three or more of the individual methods are included in the consensus volume, and second, the Simultaneous Truth and Performance Level Estimation (STAPLE) method which is a maximum likelihood methodology previously presented in the literature but never applied to PET segmentation. Improvements in accuracy to match or exceed the best performing individual method were observed, and importantly, both consensus methods provided robustness against poorly performing individual methods. In fact, the distributions of DSC and SMASD values for the MJV and STAPLE closely match the distribution that would result if the best individual method result were selected for all objects (the best individual method varies by objects). Given that the best individual method is dependent on object type, size, contrast, and image noise and the best individual method is not able to be known before segmentation, consensus methods offer a marked improvement over the current standard of using just one of the individual segmentation methods used in this dissertation.

To explore the potential application of consensus volumes to radiation therapy, the MJV consensus method was used to produce GTVs in a population of head and neck cancer patients. This GTV and one created using simple 40% thresholding were then available to be used as a guidance volume for an attending head and neck radiation oncologist and a resident who had completed their head and neck rotation. The task for each physician was to manually delineate GTVs using the CT and PET images. Each patient was contoured three times by each physician- without guidance and with guidance using either the MJV consensus volume or 40% thresholding. Differences in GTV volumes between physicians were not significant, nor were differences between the GTV volumes regardless of the guidance volume available to the physicians. However, on average, 15-20% of the provided guidance volume lay outside the final physician-defined contour.

In the final study, the MJV and STAPLE consensus volumes were used to extract maximum, peak and mean SUV measurements in two baseline PET scans and one PET scan taken during patients' prescribed radiation therapy treatments. Mean SUV values derived from consensus volumes showed smaller variability compared to maximum SUV values. Baseline and intratreatment variability was assessed using a Bland-Altman analysis which showed that baseline variability in SUV was lower than intratreatment changes in SUV.

The techniques developed and reported in this thesis demonstrate how filter choice affects segmentation accuracy, how the use of GEEs more appropriately account for the properties of a common segmentation quality metric, and how consensus volumes not only provide an accuracy on par with the single best performing individual method in a given activity distribution, but also exhibit a robustness against variable performance of individual segmentation methods that make up the consensus volume. These properties make the use of consensus volumes appealing for a variety of tasks in radiation oncology.

Positron emission tomography (PET) is a nuclear medicine diagnostic imaging exam of metabolic processes in the body. Radiotracers, which consist of positron emitting radioisotopes and a molecular probe, are introduced into the body, emitted radiation is detected, and tomographic images are reconstructed. The primary clinical PET application is in oncology using a glucose analogue radiotracer, which is avidly taken up by some cancers.

It is well known that PET performance and image quality degrade as body size increases, and epidemiological studies over the past two decades show that the adult US population's body size has increased dramatically and continues to increase. Larger patients have more attenuating material that increases the number of emitted photons that are scattered or absorbed within the body. Thus, for a fixed amount of injected radioactivity and acquisition duration, the number of measured true coincidence events will decrease, and the background fractions will increase. Another size-related factor, independent of attenuation, is the volume throughout which the measured coincidence counts are distributed: for a fixed acquisition duration, as the body size increases, the counts are distributed over a larger area. This is true for both a fixed amount of radioactivity, where the concentration decreases as size increases, and a fixed concentration, where the amount radioactivity increases with size.

Time-of-flight (TOF) PET is a recently commercialized technology that allows the localization, with a certain degree of error, of a positron annihilation using timing differences in the detection of coincidence photons. Both heuristic and analytical evaluations predict that TOF PET will have improved performance and image quality compared to non-TOF PET, and this improvement increases as body size increases. The goal of this dissertation is to parameterize the image quality improvement of TOF PET compared to non-TOF PET as a function of body size. Currently, no standard for comparison exists.

Previous evaluations of TOF PET's improvement have been made with either computer-simulated data or acquired data using a few discrete phantom sizes. A phantom that represents a range of attenuating dimensions, that can have a varying radioactivity distribution, and that can have radioactive inserts positioned throughout its volume would facilitate characterizing PET system performance and image quality as a function of body size. A fillable, tapered phantom, was designed, simulated, and constructed. The phantom has an oval cross-section ranging from 38.5 × 49.5 cm to 6.8 × 17.8 cm, a length of 51.1 cm, a mass of 6 kg (empty), a mass of 42 kg (water filled), and 1.25-cm acrylic walls.

For this dissertation research, PET image quality was measured using multiple, small spheres with diameters near the spatial resolution of clinical whole-body PET systems. Measurements made on a small sphere, which typically include a small number of image voxels, are susceptible to fluctuations over the few voxels, so using multiple spheres improves the statistical power of the measurements that, in turn, reduces the influence of these fluctuations. These spheres were arranged in an array and mounted throughout the tapered phantom's volume to objectively measure image quality as a function of body size. Image quality is measured by placing regions of interest on images and calculating contrast recovery, background variability, and signal to noise ratio.

Image quality as a function of body size was parameterized for TOF compared to non-TOF PET using 46 1.0-cm spheres positioned in six different body sizes in a fillable, tapered phantom. When the TOF and non-TOF PET images were reconstructed for matched contrast, the square of the ratio of the images' signal-to-noise ratios for TOF to non-TOF PET was plotted as a function, f(D), of the radioactivity distribution size, D, in cm. A linear regression was fit to the data: f(D) = 0.108D - 1.36. This was compared to the ratio of D and the localization error, σ_d, based on the system timing resolution, which is approximately 650 ps for the TOF PET system used for this research. With the image quality metrics used in this work, the ratio of TOF to non-TOF PET fits well to a linear relationship and is parallel to D/σ_d. For D < 20 cm, there is no image quality improvement, but for radioactivity distributions D > 20 cm, TOF PET improves image quality over non-TOF PET. PET imaging's clinical use has increased over the past decade, and TOF PET's image quality improvement for large patients makes TOF an important new technology because the occurrence of obesity in the US adult population continues to increase.

The Standardized Uptake Value (SUV) is a method for semiquantitative evaluation of radiotracer accumulation on PET scans. Changes in SUV can be used to determine treatment response. However, SUV measurements are influenced by a variety of biological and technological factors, including image reconstruction parameters.

There are other semiquantitative metrics used in PET that relate to the total metabolic activity of a tumor. Current metrics of this type (e.g., Total Lesion Glycolysis) use a combination of SUV and an object volume. Such concentration-based metrics may not capture all radioactivity of an object. We propose a more direct method to assess total radiotracer uptake (TRU): the total radioactivity in a large VOI is measured and background is subtracted.

Phantom studies were performed to assess the effect of image reconstruction parameters on SUV, and to compare the TRU with concentration-based metrics. Patient images were evaluated to estimate the percent error of the TRU metric in imaging of humans.

Methods:

A whole body phantom with 1 cm hot spheres was scanned with a GE Discovery 690 PET/CT scanner, with time of flight (TOF) capability. Data were reconstructed several different ways to examine the effect of image matrix size, amount of smoothing, field of view (FOV) size, TOF vs. non-TOF reconstruction, iterations of reconstruction algorithm, and image matrix shift on SUV.

An additional whole body phantom was scanned on the same system to compare the accuracy and variability of the new TRU metric with existing measures.

Results:

Reconstruction parameters had substantial effects on SUV for 1 cm spheres. Varying the FOV from 35 to 70 cm produced an 11% change in average normalized SUV. Changing the image matrix size from 128x128 pixels to 256x256 pixels produced an 5.3% difference. Shifting the image matrix produced up to a 12% change in SUV. TOF vs. non-TOF reconstruction resulted in up to a 29% difference in SUV for two iterations.

The TRU method was more accurate than TLG and SUV for all sphere types in images with 0 mm to 10 mm of smoothing. Mean errors of TRU were between 1-12%. The TRU method was less variable than TLG in unsmoothed images with acquisition lengths of 1, 2, and 4 minutes. Coefficients of variation were between from 2-17% for TRU measurements, compared to 5-19% for TLG measurements. Simulation of TRU applied to human images shows potential error from 10-18% for 10:1 lesions 1-4 cm in diameter.

Conclusions:

Changes in image reconstruction parameters could significantly influence the SUV for small, 1 cm lesions. These effects are reduced for larger, 2.5 cm lesions.

TRU can accurately quantify small lesions in a phantom study. In some cases, TRU is less variable than TLG and SUV. Computer simulations of error in TRU when applied to human studies show low percentage errors for realistic tumor contrasts and volumes.

Quantitative measurements in PET imaging have recently become more widespread as a way to diagnose and stage many types of malignant cancer. Currently patients need to have follow-up scans performed on the same PET system due to technical factors. Multi-clinic studies using quantitative PET measurements are also confounded by these technological factors. This work aims to evaluate the use of commonly available phantoms to cross-calibrate processing parameters to equalize small lesion quantitation. The method was verified using an abdomen phantom with small hot sphere inserts, as well as a smaller phantom with small hot sphere inserts.

Methods: A GE Discovery 690 and STE were used. Both time-of-flight (TOF) and non-TOF images were used from the D690. Jaszczak phantoms with hot rod and cold rod inserts were scanned on both systems consecutively for 20 minutes. Images were reconstructed with a range of iterations and post-smoothed (PS) with 2-10 mm of smoothing. Automated analysis of the images used the CT images to find rods and then calculate a rod to background ratio for each rod sector, PET image variant, and scanner. A target rod contrast could then be chosen and parameters determined for both systems separately to equalize rod contrast. Iteration-based resolution control and PS were both evaluated. To verify, an abdomen phantom was filled with a low background activity and ten 10-mm diameter spheres filled with FDG and CT contrast. In order to evaluate any size dependence, six 10-mm diameter spheres filled with FDG and CT contrast were placed inside a Jaszczak container filled with low background activity. An automatic CT-based analysis of the spheres was performed, obtaining mean and maximum values across the spheres.

Results: Small sphere quantitation differed substantially for similar processing between systems. However, sphere quantitation matched well when cross-calibrating the DSTE and non-TOF D690 Jaszczak phantom images by independently limiting iterations. Doing the same process with post-smoothing yielded similar results, with high iteration PS performing slightly better than PS at iterations used clinically at Duke for twenty-minute scans. Equalizing TOF images from the D690 with DSTE images with spheres placed in an abdomen phantom resulted in relatively poor correlation, but correlated well with spheres placed inside the Jaszczak phantom. Shorter scan durations behaved similarly to the twenty-minute scans.

Conclusions: Both Jaszczak phantoms worked well for cross-calibrating processing parameters to equalize quantitation in small lesions for non-TOF imaging. Iterations and PS could both be used to control resolution. It appears the best method is to use PS to fine-tune the resolution. The size dependence of TOF, and PET in general, seems to be an issue.

Positron Emission Tomography is a coincidence-detection-based nuclear imaging modality that has increased in clinical prevalence over the last two decades. Measures have recently been taken to improve the practice, specifically the synergistic combination with CT, and implementation of iterative reconstruction. The time-of- flight (TOF) technique is another improvement theorized early in PET development, which reduces image noise by measuring the difference in coincident photon detection times. It was difficult to implement at the time of inception because of limited technologies, but better detectors and electronics have recently made TOF feasible for clinical use. Its gain in image quality has been measured by various methods, but is difficult to quantify because of tradeoffs inherent in count-based imaging. This work set out to investigate the image quality gained with TOF imaging by determining the effective non-TOF scan time required to achieve equivalent image quality as TOF.

Methods: We used the TOF-capable GE Discovery 690 PET/CT scanner with ~600 ps timing resolution to acquire high-count list-mode data of hot spheres, cold bottles, and a novel low-contrast bead insert housed in three phantoms of increasing diameters. These data were reconstructed with and without TOF information into shorter images of 30 sec, 1, 2, 4 and 8 min, using the OS-EM reconstruction algorithm with 16 subsets and 1, 2, 3, 5 and 10 iterations each. Up to 16 replicates of each image were produced. Regions of interest were drawn on the high-count images and subsequently applied to all images in each set. These data were averaged across the replicate image sets for statistical power and were used to calculate contrast, background variability and replicate variability for regions within each phantom scan. Background variability was measured as the standard deviation of 1 cm ROI means spread throughout the background, while replicate noise was measured as the pixel deviation across replicated images. The contrast for each unique phantom region and scan time were plotted versus the two noise measures, and a unique quantification method was devised to calculate the scan time equivalent for images reconstructed with TOF versus those without.

Results: Visual evaluation showed universal improvement in image quality. Hot spheres were more easily resolved, cold regions were colder, and the low-contrast phantom became clearer overall. Gains were also higher as a function of phantom size. Plotting contrast versus the two variability measures demonstrated greater gains for larger phantoms than small.

The quantification method delivered easily interpretable results that correlated with visual and graphical evaluation. Hot spheres showed between 1.6× and 2.5× scan time gain factor, while cold bottles showed between 3.8× and 4.3× gain, when measuring background variability as the noise component. Three areas of the low- contrast insert were considered, and showed results generally lying between those of the cold and hot inserts, with one exception demonstrating 9.15× and 10.35× gains for the background and replicate variability measures, respectively.

Measuring gains using the replicate noise demonstrated similar quality gain as the background variability.

Conclusions: The results of this work agree with previous studies stating that TOF information contributes significantly to PET image quality when utilized during reconstruction, specifically for hot lesions and cold regions. This was shown visually, graphically, and quantitatively. The unique quantification method devised, which uses image quality plots to generate gain factors in terms of equivalent non-TOF scan time, was successfully implemented and yielded relatively consistent results. The new phantom insert developed to mimic lower-contrast regions present in human abdominal images was successfully imaged, showing a 1.3× to 4.2× overall gain in equivalent scan time across all phantom sizes.

Trends were observed in several aspects of these results that may subjugate TOF quality gain even further. Cold areas recover better than hot lesions, as expected, but low-contrast areas show varying levels of TOF improvement, and tend to lie between those demonstrated for hot and cold regions.

Finally, similar results were found when considering background variability and replicate variability noise measures, which can be considered further validation of the image quality results.