Practical experiences in the transfer of clinical protocols between CT scanners with different ATCM systems.

Automatic tube current modulation (ATCM) systems to aid in optimizing dose and image noise have become standard on computed tomography (CT) scanners over the last decade. ATCM systems of the main vendors modulate tube current in slightly different ways, with some using a control parameter related to image noise (e.g. Toshiba, GE) while others use a quality reference image mAs (e.g. Siemens). The translation of clinical protocols including ATCM operation between CT scanners from different manufacturers in order to obtain similar levels of image quality with optimized exposure variables has become an important issue. In this study, cylindrical phantoms of different sizes representing small, average and large patients, have been combined into one phantom, which has been scanned on Siemens, Toshiba and GE CT scanners with the full ranges of ATCM image quality settings. The volume weighted CT dose index (CTDIvol) and image noise over each section of the phantom were recorded for every setting. Relationships between the image quality level settings, and CTDIvol and measured image noise were analysed in order to investigate ATCM performance. Equations were developed from fits of the data to enable CTDIvol and image noise to be expressed in terms of the image quality parameters for different size phantoms on each scanner. The Siemens scanner protocol was chosen as the reference, as it avoided high doses for large patients, while allowing full modulation of tube current for patients of all sizes, and so was considered to provide optimized performance. The equations derived were used to equate the noise parameters on Toshiba and GE scanners to the quality reference mAs on the Siemens scanner, so that clinical protocols incorporating similar levels of optimization could be obtained on the three CT scanners.

Comparison of computed tomography dose index in polymethyl methacrylate and nylon dosimetry phantoms.

The use of computed tomography (CT) scanning has been growing steadily. Therefore, CT dose measurement is becoming increasingly important for patient protection and optimization. A phantom is an important tool for dose measurement. This paper focuses on the evaluation of a CT dosimetry phantom made from nylon, instead of the standard polymethyl methacrylate (PMMA), which is not readily available or is too expensive in some countries. Comparison between phantoms made from the two materials is made in terms of measurements of the CT dose indices (CTDI). These were measured for four different beam widths and kVp settings at the center and periphery in head and body phantoms made from both materials and weighted CTDIs (CTDIw) were calculated. CT numbers along the z-axis of the phantom were also measured at the center and four peripheral positions of each scanned slice to check phantom homogeneity. Results showed that values for the CTDIw measured in the nylon phantoms were slightly higher than those from the PMMA while CT numbers for nylon were lower than those of PMMA. This is because the mass attenuation coefficient of the nylon is higher. Nylon could be used as a substitute material for CT dosimetry phantom to enable measurements and adjustment factors are given which could be used to estimate PMMA values for making comparisons with displayed values.

Assessment of tumour size in PET/CT lung cancer studies: PET- and CT-based methods compared to pathology.

BACKGROUND: Positron emission tomography (PET) may be useful for defining the gross tumour volume for radiation treatment planning and for response monitoring of non-small cell lung cancer (NSCLC) patients. The purpose of this study was to compare tumour sizes obtained from CT- and various more commonly available PET-based tumour delineation methods to pathology findings. METHODS: Retrospective non-respiratory gated whole body [18F]-fluoro-2-deoxy-D-glucose PET/CT studies from 19 NSCLC patients were used. Several (semi-)automatic PET-based tumour delineation methods and manual CT-based delineation were used to assess the maximum tumour diameter. RESULTS: 50%, adaptive 41% threshold-based and contrast-oriented delineation methods showed good agreement with pathology after removing two outliers (R2=0.82). An absolute SUV threshold of 2.5 also showed a good agreement with pathology after the removal of 5 outliers (R2: 0.79), but showed a significant overestimation in the maximum diameter (19.8 mm, p<0.05). Adaptive 50%, relative threshold level and gradient-based methods did not show any outliers, provided only small, non-significant differences in maximum tumour diameter (<4.7 mm, p>0.10), and showed fair correlation (R2>0.62) with pathology. Although adaptive 70% threshold-based methods showed underestimation compared to pathology (36%), it provided the best precision (SD: 14%) together with good correlation (R2=0.81). Good correlation between CT delineation and pathology was observed (R2=0.77). However, CT delineation showed a significant overestimation compared with pathology (3.8 mm, p<0.05). CONCLUSIONS: PET-based tumour delineation methods provided tumour sizes in agreement with pathology and may therefore be useful to define the (metabolically most) active part of the tumour for radiotherapy and response monitoring purposes.

Effects of image characteristics on performance of tumor delineation methods: a test-retest assessment.

UNLABELLED: PET can be used to monitor response during chemotherapy and assess biologic target volumes for radiotherapy. Previous simulation studies have shown that the performance of various automatic or semiautomatic tumor delineation methods depends on image characteristics. The purpose of this study was to assess test-retest variability of tumor delineation methods, with emphasis on the effects of several image characteristics (e.g., resolution and contrast). METHODS: Baseline test-retest data from 19 non-small cell lung cancer patients were obtained using (18)F-FDG (n = 10) and 3'-deoxy-3'-(18)F-fluorothymidine ((18)F-FLT) (n = 9). Images were reconstructed with varying spatial resolution and contrast. Six different types of tumor delineation methods, based on various thresholds or on a gradient, were applied to all datasets. Test-retest variability of metabolic volume and standardized uptake value (SUV) was determined. RESULTS: For both tracers, size of metabolic volume and test-retest variability of both metabolic volume and SUV were affected by the image characteristics and tumor delineation method used. The median volume test-retest variability ranged from 8.3% to 23% and from 7.4% to 29% for (18)F-FDG and (18)F-FLT, respectively. For all image characteristics studied, larger differences (≤10-fold higher) were seen in test-retest variability of metabolic volume than in SUV. CONCLUSION: Test-retest variability of both metabolic volume and SUV varied with tumor delineation method, radiotracer, and image characteristics. The results indicate that a careful optimization of imaging and delineation method parameters is needed when metabolic volume is used, for example, as a response assessment parameter.

Impact of [¹8F]FDG PET imaging parameters on automatic tumour delineation: need for improved tumour delineation methodology.

PURPOSE: Delineation of tumour boundaries is important for quantification of [(18)F]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) studies and for definition of biological target volumes in radiotherapy. Several (semi-)automatic tumour delineation methods have been proposed, but these methods differ substantially in estimating tumour volume and their performance may be affected by imaging parameters. The main purpose of this study was to explore the performance dependence of various (semi-)automatic tumour delineation methods on different imaging parameters, i.e. reconstruction parameters, noise levels and tumour characteristics, and thereby the need for standardization or inter-institute calibration. METHODS: Six different types of delineation methods were evaluated by assessing accuracy and precision in estimating tumour volume from simulations and phantom experiments. The evaluated conditions were various tumour sizes, iterative reconstruction algorithm settings and image filtering, tumour to background ratios (TBR), noise levels and region growing initializations. RESULTS: The accuracy of all automatic delineation methods was influenced when imaging parameters were varied. The performance of all tumour delineation methods depends on variation of TBR, image resolution and image noise level, and to a lesser extent on number of iterations during image reconstruction or the initialization method of the region generation. For sphere sizes larger than 20 mm diameter a contrast-oriented method provided the most accurate results, on average, over all simulated conditions. For threshold-based methods the accuracy of tumour delineation improved after image denoising/filtering. CONCLUSION: The accuracy and precision of all studied tumour delineation methods was affected by physiological and imaging parameters. The latter illustrates the need for optimizing imaging parameters and/or for careful calibration and optimization of delineation methods.

Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies.

PURPOSE: Standardized uptake values (SUV) are commonly used for quantification of whole-body [(18)F]fluoro-2-deoxy-D: -glucose (FDG) positron emission tomography (PET) studies. Changes in SUV following therapy, however, only provide a proper measure of response in case of homogeneous FDG uptake in the tumour. The purpose of this study was therefore to implement and characterize a method that enables quantification of heterogeneity in tumour FDG uptake. METHODS: Cumulative SUV-volume histograms (CSH), describing % of total tumour volume above % threshold of maximum SUV (SUV(max)), were calculated. The area under a CSH curve (AUC) is a quantitative index of tumour uptake heterogeneity, with lower AUC corresponding to higher degrees of heterogeneity. Simulations of homogeneous and heterogeneous responses were performed to assess the value of AUC-CSH for measuring uptake and/or response heterogeneity. In addition, partial volume correction and image denoising was applied prior to calculating AUC-CSH. Finally, the method was applied to a number of human FDG scans. RESULTS: Partial volume correction and noise reduction improved CSH curves. Both simulations and clinical examples showed that AUC-CSH values corresponded with level of tumour heterogeneity and/or heterogeneity in response. In contrast, this correspondence was not seen with SUV(max) alone. The results indicate that the main advantage of AUC-CSH above other measures, such as 1/COV (coefficient of variation), is the possibility to measure or normalize AUC-CSH in different ways. CONCLUSION: AUC-CSH might be used as a quantitative index of heterogeneity in tracer uptake. In response monitoring studies it can be used to address heterogeneity in response.

Measuring response to therapy using FDG PET: semi-quantitative and full kinetic analysis.

PURPOSE: Imaging with positron emission tomography (PET) using (18)F-2-fluoro-2-deoxy-D: -glucose (FDG) plays an increasingly important role for response assessment in oncology. Several methods for quantifying FDG PET results exist. The goal of this study was to analyse and compare various semi-quantitative measures for response assessment with full kinetic analysis, specifically in assessment of novel therapies. METHODS: Baseline and response dynamic FDG studies from two different longitudinal studies (study A: seven subjects with lung cancer and study B: six subjects with gastrointestinal cancer) with targeted therapies were reviewed. Quantification of tumour uptake included full kinetic methods, i.e. nonlinear regression (NLR) and Patlak analyses, and simplified measures such as the simplified kinetic method (SKM) and standardized uptake value (SUV). An image-derived input function was used for NLR and Patlak analysis. RESULTS: There were 18 and 9 lesions defined for two response monitoring studies (A and B). In all cases there was excellent correlation between Patlak- and NLR-derived response (R (2) > 0.96). Percentage changes seen with SUV were significantly different from those seen with Patlak for both studies (p < 0.05). After correcting SUV for plasma glucose, SUV and Patlak responses became similar for study A, but large differences remained for study B. Further analysis revealed that differences in responses amongst methods in study B were primarily due to changes in the arterial input functions. CONCLUSION: Use of simplified methods for assessment of drug efficacy or treatment response may provide different results than those seen with full kinetic analysis.

Measurement of metabolic tumor volume: static versus dynamic FDG scans.

BACKGROUND: Metabolic tumor volume assessment using positron-emission tomography [PET] may be of interest for both target volume definition in radiotherapy and monitoring response to therapy. It has been reported, however, that metabolic volumes derived from images of metabolic rate of glucose (generated using Patlak analysis) are smaller than those derived from standardized uptake value [SUV] images. The purpose of this study was to systematically compare metabolic tumor volume assessments derived from SUV and Patlak images using a variety of (semi-)automatic tumor delineation methods in order to identify methods that can be used reliably on (whole body) SUV images. METHODS: Dynamic [18F]-fluoro-2-deoxy-D-glucose [FDG] PET data from 10 lung and 8 gastrointestinal cancer patients were analyzed retrospectively. Metabolic tumor volumes were derived from both Patlak and SUV images using five different types of tumor delineation methods, based on various thresholds or on a gradient. RESULTS: In general, most tumor delineation methods provided more outliers when metabolic volumes were derived from SUV images rather than Patlak images. Only gradient-based methods showed more outliers for Patlak-based tumor delineation. Median measured metabolic volumes derived from SUV images were larger than those derived from Patlak images (up to 59% difference) when using a fixed percentage threshold method. Tumor volumes agreed reasonably well (< 26% difference) when applying methods that take local signal-to-background ratio [SBR] into account. CONCLUSION: Large differences may exist in metabolic volumes derived from static and dynamic FDG image data. These differences depend strongly on the delineation method used. Delineation methods that correct for local SBR provide the most consistent results between SUV and Patlak images.