Functional Volume Segmentation Research Articles

PVA cryogel test objects for image-based quantification and functional volume segmentation in nuclear medicine

Efforts to characterise the factors that affect the accuracy of image based quantification in nuclear medicine imaging and the development of methods to correct for them are often based in the first instance on phantom experiments using radioactive test objects. Poly-vinyl alcohol (PVA) cryogel is a tissue-equivalent material that sees widespread use as a medical imaging phantom material in other imaging modalities but is not in common use in nuclear medicine. We present a simple method to manufacture radioactive PVA cryogel test objects using moulds designed and made in house. Six spherical test objects were produced with diameters ranging from 0.5 to 4 cm without the need for an outer layer of attenuating material. The test objects were made using 10% PVA solution mixed with 177Lu to give an activity concentration of approximately 0.3 MBq ml−1. The mixture was injected into the moulds and frozen at approximately −20 °C for approximately 14 h then allowed to thaw for approximately 6 h. SPECT/CT images of the test objects were acquired both in air and with the test objects inserted into a torso phantom. The images obtained demonstrate the feasibility of our method for the production of anatomically relevant phantoms for testing image quantification and functional volume segmentation regimes.

Characterization of PET/CT images using texture analysis: the past, the present… any future?

After seminal papers over the period 2009-2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.

TH‐C‐WAB‐05: Blind Functional Volume Segmentation in PET

Purpose: To propose a blind PET functional volume segmentation method that does not need any prior calibrations. Methods: The partial volume effect (PVE) in PET imaging was considered as a forward problem that convolves the original target object with the point spread function (PSF) of the PET scanner, and made a PET image blurred. We showed that the optimal threshold for accurate object localization in a PET image is a function of the ratio of the object size to the PSF standard variance. We defined a new quantity, the optimal volume ratio (OAR), which represents the ratio of the segmented volume when using the optimal threshold to the whole target volume with greater activity than the background. The OAR can be expressed as a function of the optimal threshold, and was then used to derive the optimal threshold through a sequence of region growing algorithms with different thresholds. The new method avoids solving the ill‐conditioned inverse problem, and is fully blind to the PET scanner. The proposed method was tested on PET phantom with known object location and extension, and its robustness with respect to source‐to‐background ratio (SBR) and object size was evaluated. Results: At all SBR levels and for all object sizes, the new blind segment method performed similarly to existing methods with prior calibrations, and outperformed existing methods that do not take into account PVE. In addition, the new blind segment method is robust to the changes of SBR level and object size. Conclusion: The results showed that the proposed blind method did not need any prior calibrations and performed well for the functional volume segmentation in PET.

Incorporating Quantitative Single Photon Emission Computed Tomography into Radiation Therapy Treatment Planning for Lung Cancer: Impact of Attenuation and Scatter Correction on the Single Photon Emission Computed Tomography–Weighted Mean Dose and Functional Lung Segmentation

To assess the impact of attenuation and scatter corrections on the calculation of single photon emission computed tomography (SPECT)-weighted mean dose (SWMD) and functional volume segmentation as applied to radiation therapy treatment planning for lung cancer. Nine patients with lung cancer underwent a SPECT lung perfusion scan. For each scan, four image sets were reconstructed using the ordered subsets expectation maximization method with attenuation and scatter corrections ranging from none to a most comprehensive combination of attenuation corrections and direct scatter modeling. Functional volumes were segmented in each reconstructed image using 10%, 20%, …, 90% of maximum SPECT intensity as a threshold. Systematic effects of SPECT reconstruction methods on treatment planning using functional volume were studied by calculating size and spatial agreements of functional volumes, and V(20) for functional volume from actual treatment plans. The SWMD was calculated for radiation beams with a variety of possible gantry angles and field sizes. Functional volume segmentation is sensitive to the particular method of SPECT reconstruction used. Large variations in functional volumes, as high as >50%, were observed in SPECT images reconstructed with different attenuation/scatter corrections. However, SWMD was less sensitive to the type of scatter corrections. SWMD was consistent within 2% for all reconstructions as long as computed tomography-based attenuation correction was used. When using perfusion SPECT images during treatment planning optimization/evaluation, the SWMD may be the preferred figure of merit, as it is less affected by reconstruction technique, compared with threshold-based functional volume segmentation.

Accurate Automatic Delineation of Heterogeneous Functional Volumes in Positron Emission Tomography for Oncology Applications

Accurate contouring of positron emission tomography (PET) functional volumes is now considered crucial in image-guided radiotherapy and other oncology applications because the use of functional imaging allows for biological target definition. In addition, the definition of variable uptake regions within the tumor itself may facilitate dose painting for dosimetry optimization. Current state-of-the-art algorithms for functional volume segmentation use adaptive thresholding. We developed an approach called fuzzy locally adaptive Bayesian (FLAB), validated on homogeneous objects, and then improved it by allowing the use of up to three tumor classes for the delineation of inhomogeneous tumors (3-FLAB). Simulated and real tumors with histology data containing homogeneous and heterogeneous activity distributions were used to assess the algorithm's accuracy. The new 3-FLAB algorithm is able to extract the overall tumor from the background tissues and delineate variable uptake regions within the tumors, with higher accuracy and robustness compared with adaptive threshold (T(bckg)) and fuzzy C-means (FCM). 3-FLAB performed with a mean classification error of less than 9% +/- 8% on the simulated tumors, whereas binary-only implementation led to errors of 15% +/- 11%. T(bckg) and FCM led to mean errors of 20% +/- 12% and 17% +/- 14%, respectively. 3-FLAB also led to more robust estimation of the maximum diameters of tumors with histology measurements, with <6% standard deviation, whereas binary FLAB, T(bckg) and FCM lead to 10%, 12%, and 13%, respectively. These encouraging results warrant further investigation in future studies that will investigate the impact of 3-FLAB in radiotherapy treatment planning, diagnosis, and therapy response evaluation.

Sci-Thurs PM: Planning-07: Impact of Quantitative SPECT Corrections on SPECT-Weighted Mean Dose and Functional Lung Volume Segmentation as Applied in Functional Sparing RT Planning

Purpose: To compare functional lung volume segmentation and Single Photon Emission Computed Tomography (SPECT) weighted mean dose (SWMD) approaches used in SPECT guided treatment planning studies for patients with lung cancer. Methods and Materials: Nine lung cancer patients were consented to have a perfusion SPECT scan with 99mTc-macroaggregated albumin. Four image sets were reconstructed from each scan: one using a vendor provided ordered subsets expectation maximization (OSEM) algorithm and three quantitative SPECT reconstructions using OSEM methods with different types of attenuation and scatter corrections. SWMDs were calculated for open field with different sizes and gantry angles. Functional lung volumes were segmented in each reconstructed image using 10, 20, …, 90% of maximum SPECT intensity as a threshold. Results: Image reconstruction accuracy and thus functional lung volume segmentation are affected by several factors, such as attenuation and scatter correction, resolution recovery method and number of iterations used in reconstruction. Large differences in functional volumes (more than 50%) were found between images obtained from the four reconstructions. In contrast, the SWMD calculation produced consistent results for all SPECT reconstructions which included attenuation correction, regardless of whether or not scatter correction was used and the number of iterations. Conclusion: Functional volume segmentation is sensitive to the type of attenuation and scatter correction and number of iterations. In contrast, for images reconstructed with attenuation correction, the SWMD calculation produces consistent results and appears to be a more robust choice to be used in future studies incorporating SPECT into treatment planning.

SU-FF-J-136: The Impact of Attenuation and Scatter Correction On the SPECT Guided Radiation Therapy for Lung Cancer Patients: Comparison of SPECT Weighted Mean Dose and Functional Lung Segmentation

Purpose: To investigate the impact of different types of image reconstruction and attenuation/scatter (A/S) correction on the calculation of dosimetric indices proposed to be used for Single Photon Emission Computed Tomography (SPECT) guided dose escalation in lung cancer patients. Methods and Materials: SPECT lung perfusion scans were obtained for nine lung cancer patients using 99mTc-macroaggregated albumin. Four image sets were reconstructed from each scan: one using a vendor provided ordered subsets expectation maximization (OSEM) algorithm, two quantitative SPECT reconstructions using OSEM methods with different types of A/S corrections and the fourth an OSEM reconstruction without any A/S correction. SPECT weighted mean dose (SWMD), dose function histogram, and functional lung volume have been calculated from dose distributions and regional perfusion maps. To investigate the dependence of SWMDs on gantry angle, twelve equally spaced co-planar open field radiation beams delivering the same MU were centered on the PTV. Three field sizes, 5×5, 7.5×7.5 and 10×10cm2 were considered. SWMDs were calculated for each field and reconstruction. Functional lung volumes were segmented in each reconstruction using 10, 20, …, 90% of maximum SPECT uptake as a threshold. Results: SWMDs calculated from reconstructions without A/S correction showed more than 5% average difference compared to those with corrections. With A/S corrections, more consistent SWMDs were found in all the three OSEM reconstructions (average difference ∼2%). However, a large variation was observed between segmented functional lung volumes and the V20 of these volumes in all four reconstructions. The difference between the volumes reached over 50% regardless of whether A/S correction was applied in the reconstruction. Conclusion: Functional volume segmentation is sensitive to the type of A/S correction. In contrast, SPECT weighted mean dose calculation produces more consistent results and appears to be a more robust choice for clinical outcome analysis and SPECT guided treatment planning.