title: A novel approach to assess the treatment response using Gaussian random field in PET.
abstract: OBJECTIVE
  The assessment of early therapeutic response to anticancer therapy is vital for treatment planning and patient management in clinic. With the development of personal treatment plan, the early treatment response, especially before any anatomically apparent changes after treatment, becomes urgent need in clinic. Positron emission tomography (PET) imaging serves an important role in clinical oncology for tumor detection, staging, and therapy response assessment. Many studies on therapy response involve interpretation of differences between two PET images, usually in terms of standardized uptake values (SUVs). However, the quantitative accuracy of this measurement is limited. This work proposes a statistically robust approach for therapy response assessment based on Gaussian random field (GRF) to provide a statistically more meaningful scale to evaluate therapy effects.
  
  
  METHODS
  The authors propose a new criterion for therapeutic assessment by incorporating image noise into traditional SUV method. An analytical method based on the approximate expressions of the Fisher information matrix was applied to model the variance of individual pixels in reconstructed images. A zero mean unit variance GRF under the null hypothesis (no response to therapy) was obtained by normalizing each pixel of the post-therapy image with the mean and standard deviation of the pretherapy image. The performance of the proposed method was evaluated by Monte Carlo simulation, where XCAT phantoms (128(2) pixels) with lesions of various diameters (2-6 mm), multiple tumor-to-background contrasts (3-10), and different changes in intensity (6.25%-30%) were used. The receiver operating characteristic curves and the corresponding areas under the curve were computed for both the proposed method and the traditional methods whose figure of merit is the percentage change of SUVs. The formula for the false positive rate (FPR) estimation was developed for the proposed therapy response assessment utilizing local average method based on random field. The accuracy of the estimation was validated in terms of Euler distance and correlation coefficient.
  
  
  RESULTS
  It is shown that the performance of therapy response assessment is significantly improved by the introduction of variance with a higher area under the curve (97.3%) than SUVmean (91.4%) and SUVmax (82.0%). In addition, the FPR estimation serves as a good prediction for the specificity of the proposed method, consistent with simulation outcome with ∼1 correlation coefficient.
  
  
  CONCLUSIONS
  In this work, the authors developed a method to evaluate therapy response from PET images, which were modeled as Gaussian random field. The digital phantom simulations demonstrated that the proposed method achieved a large reduction in statistical variability through incorporating knowledge of the variance of the original Gaussian random field. The proposed method has the potential to enable prediction of early treatment response and shows promise for application to clinical practice. In future work, the authors will report on the robustness of the estimation theory for application to clinical practice of therapy response evaluation, which pertains to binary discrimination tasks at a fixed location in the image such as detection of small and weak lesion.
authors: Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China and Center for Advanced Medical Imaging Science, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114.
authors: Mengdie
authors: M
authors: Wang
authors: Center for Advanced Medical Imaging Science, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114.
authors: Ning
authors: N
authors: Guo
authors: Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China.
authors: Guangshu
authors: G
authors: Hu
authors: Center for Advanced Medical Imaging Science, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114 and Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115.
authors: Georges
authors: G
authors: El Fakhri
authors: Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China.
authors: Hui
authors: H
authors: Zhang
authors: Center for Advanced Medical Imaging Science, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114 and Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115.
authors: Quanzheng
authors: Q
authors: Li
mesh_terms: D007091:Image Processing, Computer-Assisted; D009010:Monte Carlo Method; D016011:Normal Distribution; D049268:Positron-Emission Tomography; D057285:Precision Medicine; D059629:Signal-To-Noise Ratio; D016896:Treatment Outcome