Browsing by Subject "SUV"
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Item Open Access Characterization of Gynecological Tumors using Texture Analysis in the Context of an 18F-FDG Adaptive PET Protocol(2015) Nawrocki, JeffIn radiation oncology, 18F-FDG Positron Emission Tomography (PET) is used for determining metabolic activity of cancers as well as delineating gross tumor volumes (GTV) for treatment planning. More recently, PET is being utilized for adaptive therapies for gynecological malignancies in which tumor response may be estimated and treatments adjusted during the course of radiation. In addition to treatment assessment, 18F-FDG PET has become a tool in the prediction of tumor response because of the derived Standard Uptake Value (SUV), a measure of the metabolic activity of a tumor. In this study, we seek to establish texture analysis as complimentary to SUV for predicting tumor response as well as understanding temporal changes during treatment in gynecological cancers. An additional experiment was performed studying the variability of texture features from baseline and intra-treatment PET scans due to reconstruction parameters in order to identify features that show statistically significant changes during treatment and that are independent of reconstruction parameters.
In this IRB approved clinical research study, 29 women with node positive gynecological malignancies visible on PET including cervical, endometrial, vulvar, and vaginal cancers are treated with radiation therapy. Prescribed dose varied between 45-50.4Gy, with a 55-70Gy boost to the PET positive nodes. A baseline, intra-treatment (between 30-36Gy), and post-treatment PET-CT were obtained with tumor response determined by a physician according to post-treatment RECIST. All volumes were re-contoured on the intra-treatment PET-CT. Primary GTVs were segmented both with the 40% SUVmax threshold method and a validated gradient-based contouring tool, PET Edge (MIM Software Inc., Cleveland, OH). A MATLAB Graphical User Interface (GUI) called Duke FIRE (Functional Imaging Research Environment) was developed for this study in order to calculate four mathematical algorithms representing the spatial distribution of pixels in an image: gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), gray level size zone matrix (GLSZM), and the neighborhood gray level difference matrix (NGLDM). Features representing characteristics of the image are derived from these texture matrices: 12 local features from the GLCM, 11 regional features from the GLRLM, 11 regional features from the GLSZM, and 5 local features from the NGLDM. Additionally, 6 global SUV histogram features including SUVmean, SUVmedian, SUVmax, skewness, kurtosis, and variance as well as metabolic volume (MV) and total lesion glycolysis (TLG) are extracted. The prognostic power of each baseline feature derived from both gradient-based and threshold segmentation methods was determined using the Wilcoxon rank-sum test. Receiver operating characteristic (ROC) curves were calculated to understand the sensitivity and specificity of baseline texture features compared to SUV metrics. Changes in features from baseline to intra-treatment PET-CT were determined using the Wilcoxon signed-rank test. A subset of 7 patient baseline and intra-treatment raw PET data was reconstructed 6 times using a TrueX+TOF algorithm on a Siemens Biograph mCT with varying iterations and Gaussian filter widths. Texture features were derived from the GTV as before. Texture features per patient were normalized to the respective clinical baseline value in order to limit variability to reconstruction parameters. Mean percent ranges of each feature at baseline and intra-treatment were determined and the change in features was compared using the Wilcoxon signed-rank test.
Of the 29 patients, there were 16 complete responders, 7 partial responders, and 6 non-responders. Comparing CR/PR vs. NR for the gradient-based GTVs, 7 texture values had a p < 0.05. The threshold GTVs yielded 4 texture features with p < 0.05. ROC and logistic regression was performed and texture features from both PET Edge and thresholding yielding a higher area under the curve (AUC) than SUV metrics. Features derived from PET Edge GTVs also showed higher AUCs than the threshold GTVs. From baseline to intra-treatment, 16 texture features changed with p < 0.05. Texture analysis of PET imaged gynecological tumors is considerably more powerful than SUV in early prognosis of tumor response, especially when using a gradient based method.
We then took the 16 texture features showing significant changes (p < 0.05) between baseline and intra-treatment PET scans in 29 patients and tested these against the subset of reconstructed features to determine if these changes were dependent upon the method in which the scans were reconstructed. A total of 13 features (including entropy, zone non-uniformity, and complexity) were found to be consistently different even when subjected to different means of reconstruction, however 3 of the 16 (inverse variance, run percentage, and zone percentage) were found to be dependent upon these reconstruction parameters. Texture features such as entropy, zone non-uniformity, and complexity are excellent candidates for future investigations of changes in texture analysis during radiation therapy of gynecological cancers. Caution should be taken with inverse variance, run percentage, and zone percentage due to their dependence upon reconstruction parameters.
This comprehensive work characterizes gynecological cancers using texture analysis in order to identify texture features that may be used for predicting tumor response as well as reflecting changes during treatment. It is the first study to our knowledge that utilizes all 4 texture matrices (GLCM, GLRLM, GLSZM, and NGLDM) and found 7 statistically significant features classifying responding and non-responding gynecological tumors: energy, entropy, max probability, zone gray level non uniformity, zone size non uniformity, contrast (NGLDM), and complexity. A novel method was implemented extending the NGLDM and its respective features to 3D space for this study. It is also the first study concluding that a semi-automatic gradient-based segmentation method results in more, stronger predictors than using a 40% SUVmax threshold method. Finally, this is the first study to examine variability of texture features with reconstruction parameters and to identify texture features as reliable and independent of reconstruction. In conclusion, texture analysis is a promising method of characterizing tumors visible on PET and should be considered for future studies.
Item Open Access Semi-Quantitative Metrics in Positron Emission Tomography(2010) Adams, MichaelThe 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.
Item Open Access Standardization of Small Lesion Contrast in PET Imaging(2014) Brookins, Drake ColeQuantitative 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.
Item Open Access SUV Analysis of F-18 FDG PET Imaging in the Vicinity of the Bladder(2012) Allen, ColleenPositron Emission Tomography with 18F-FDG can be used as a predictor for post-therapeutic tumor response in rectal and gynecologic cancers. An issue with assessing lesions in the vicinity of the bladder is the radioactivity that accumulates in the bladder due to constant filling. SUV analysis is used to discriminate between therapy responders and non-responders based on percentage change/threshold, but it is not yet known how much variability can be attributed to the bladder radioactivity. The purpose of this research is to understand the effects of bladder radioactivity on surrounding concentration measurements with different scanning and image reconstruction techniques.
Methods: ROI analysis was performed on 67 PET scans from DUMC. Typical values of bladder volume, radioactivity, radioactivity concentration and bladder-to-background uptake ratio were determined and incorporated into phantom studies. A bladder phantom insert was created for a 25 L torso phantom to explore effects of a bladder in the center of the FOV, on the edge of the FOV, and outside the FOV on the GE Discovery STE. The bladder insert was also used in a phantom study to assess the effects of different, realistic bladder radioactivity levels on surrounding 1-cm lesions on both the GE Discovery STE and GE Discovery 690. Background activity and in-air environments were explored, as well 2D, 3D and time-of-flight PET acquisitions with different image reconstruction techniques.
Results: The DSTE 2D PET data suffered large void artifacts that worsened as radioactivity in the bladder increased. The DSTE 3D PET data over-estimated background measurements up to 30 slices with the bladder outside the FOV. Concentration measurements within 4 cm distal to the bladder showed great variability and were generally recovered best with TOF PET or 3D PET with an increased number of iterations. Lesions greater than 4 cm distal to the bladder showed consistent recovery in both the 3D and TOF PET data.
Discussion: Radioactivity within the bladder has substantial effects on surrounding radioactivity concentration measurements. There are limits to the measurements of the radioactivity concentration values used in SUV analysis in the vicinity of the bladder.
A general theme is that more accurate results are produced with smaller amounts of radioactivity in the bladder. This validates the DUMC protocol that begins acquisition just below the pelvic area to reduce as much filling as possible and highlights the usefulness of patient voiding prior to scanning.
Item Open Access The Effects of PET Reconstruction Parameters on Radiotherapy Response Assessment and an Investigation of SUV-peak Sampling Parameters(2013) Rankine, Leith JohnPurpose: Our primary goal was to examine the effect of PET image reconstruction parameters on baseline and early-treatment FDG-PET/CT quantitative imaging. Early-treatment changes in tumor metabolism in primary tumor and nodes can potentially determine if the patient is responding to therapy, but this assessment can change based on the reconstruction parameters. We investigated the effect of the following reconstruction parameters: number of Ordered-Subset-Expectation-Maximization (OSEM) iterations, post-reconstruction smoothing, and quantitative metrics (SUV-max, SUV-mean, SUV-peak).
A concurrent investigation explored in detail the sampling parameters of SUV-peak by way of a Monte Carlo digital phantom study. SUV-peak was proposed as a compromise between SUV-max and SUV-mean, in hope to retain key attractive features of these two metrics (inter-physician independence of SUV-max, noise-averaging of SUV-mean) but reduce unwanted errors (noise dependence of SUV-max, contour-dependence of SUV-mean). Sampling parameters have vaguely been defined, in particular, the scanning resolution (i.e. 1 voxel, 1/2 voxel, 1/4 voxel, etc.) of the SUV-peak spherical ROI . We examined the role that partial-voxel scanning plays in tumor SUV recovery in both noise-free and realistic OS-EM noise environments.
Materials and Methods: The response assessment investigation involved 19 patients on an IRB-approved study who underwent 2 baseline PET scans (mean-separation = 11 days) prior to chemoradiotherapy (70 Gy, 2 Gy/fraction). An intra-treatment PET scan was performed early in the course of therapy (10-20 Gy, mean = 14 Gy). The images were reconstructed with varying OS-EM iterations (1-12) and Gaussian post-smoothing (0-7 mm). Patients were analyzed in two separate groups, distinguished by the PET/CT scanner used to acquire data: (1) GE Discovery STE; and (2) Siemens Biograph mCT. For each combination of iterations and smoothing, Bland-Altman analysis was applied to quantitative metrics (SUV-max, SUV-mean, SUV-peak) from the baseline scans to evaluate metabolic variability (repeatability, R = 1.96&sigma). The number and extent of early treatment changes that were significant, i.e., exceeding repeatability, was assessed.
An original SUV-peak algorithm was developed, which measures SUV-max and SUV-peak for as small as 1/32 voxel scanning. Two rounds of digital phantoms were generated for the SUV-peak investigation. First, 10,000 spherical tumors were generated at a random matrix location for each diameter 1-4 cm and smoothed with an isotropic Gaussian, FWHM = 0.8 cm, then evaluated using the SUV-peak algorithm. Next, realistic body-sized phantoms were generated with background activity, and 1,000 spherical tumors of activity 4 time the background for each diameter (1-4cm) were placed inside (8 tumors per phantom, location randomized within certain constraints). These images received realistic corrections in projection space for attenuation, spatial resolution, and noise, were reconstructed with an in-house OS-EM algorithm, and then assessed using the SUV-peak algorithm. The mean recovered activity above background and its coefficient of variation were calculated for all metrics for each tumor size, for both simulations. For the realistic noise simulation, various levels of Gaussian smoothing was applied post-reconstruction, the effects summarized in plots showing coefficient of variation vs. mean recovered activity above background - a comparison of the effectiveness of SUV-max and SUV-peak.
Results: For the GE Discovery STE 2D cases averaged over all metrics (SUV-max, SUV-mean, SUV-peak) and structures (GTV, LN), repeatability, R, improved with increasing smoothing and decreasing iterations. Individually, SUV-mean repeatability was less affected by the number of iterations, but demonstrated the same relationship with smoothing. SUV-mean outperformed SUV-max and SUV-peak with regards to the number of cases exceeding repeatability, N. Considering R, N, and the sum of relative metric change outside repeatability, &Omega, averaged over all metrics and all structures, and normalized, several combinations of reconstruction parameters produced five optimal combinations above set thresholds: 1 iteration with 0.1-3.0 mm smoothing; and 2 iterations with 2.0-3.0 mm smoothing. Current GE 2D reconstruction protocol for HN cases uses 2 iterations and 3.0 mm post-smoothing, which lies on the edge, but within these recommendations.
The relationship between repeatability and number of iterations for the 3D cases was more complex; SUV-max demonstrated the best repeatability with 2 iterations, with both SUV-mean and SUV-peak reaching the best repeatability with 4 iterations. The same dependence on smoothing was noted, i.e. increased smoothing gives lover (desirable) repeatability. SUV-mean once again outperformed SUV-max and SUV-peak with regards to the number of cases exceeding repeatability, N. The calculations of N and &Omega averaged over all metrics were limited severely by the low number of cases, damaging the statistical significance of the following recommendation. Three optimal combinations with averaged and normalized R, N, &Omega, above a set threshold are recommended as most effective reconstruction parameter combinations: 4 iterations with 2.0-4.0 mm smoothing. Current Siemens 3D reconstruction protocol for HN cases uses 4 iterations and 3.0 mm post-smoothing, which lies within these recommended parameters. However, no statistically significant conclusions could be drawn from this analysis for this scanner, and performing similar data analysis on a larger patient pool is proposed.
The minimum spherical tumor diameter required for full recovery was 3.0-3.5 cm for SUV-peak, and 2.5-3.0 cm for SUV-max. SUV-max was found to overestimate the recovered value of tumors by up to 46% (vs. 10% for SUV-peak); above the minimum diameter for full recovery, SUV-peak values were significantly closer to actual tumor activity. Considering only the realistic noise tumors, the coefficient of variation for SUV-max ranged from 5.5-17.7%, whereas for SUV-peak these values were lower, 2.7-13.2%. Partial-voxel scanning did not substantially affect the coefficient of variation (<0.2%). Comparison of coefficient of variation vs. mean recovered value demonstrated that SUV-max with additional Gaussian smoothing outperforms SUV-peak by up to 0.8% for 1 cm tumors and 0.2% for 4 cm tumors. Other tumor sizes showed little difference between the two metrics.
Conclusion: For patients scanned on the GE Discovery STE using the HN protocol (2D acquisition mode), images reconstructed for quantitative analysis may benefit from a low number of OS-EM iterations (≤ 2). Some post-reconstruction smoothing proved to be beneficial (1.0 mm ≤ FWHM ≤ 3.0 mm), however, over-smoothing for the sake of more qualitatively appealing images or improved image quality metric (e.g. SNR, CNR) may prove detrimental to quantitative response assessment analysis. Our results for the Siemens Biograph mCT using the HN protocol (3D acquisition mode) demonstrated favor towards 4 iterations and limited range of smoothing (2.0 mm ≤ FWHM ≤ 4.0 mm). These results are statistically limited, further cases are necessary for any conclusive recommendations on reconstruction parameters.
SUV-peak was shown to reduce uncertainties associated with quantitative PET image analysis when compared directly to SUV-max. Above the minimum tumor diameter required for full recovery, SUV-peak also provides a better estimate of the actual tumor activity. However, initial comparisons of SUV-peak and SUV-max over various levels of additional Gaussian smoothing found SUV-max more favorable. Partial-voxel scanning of SUV-peak did not reduce the metric's coefficient of variation in images with realistic noise. Therefore, a phantom investigation is proposed to compare SUV-peak and SUV-max of real scanned images with various levels of post-smoothing, which may conclusively eliminate the need for SUV-peak.