Post-acquisition standardization of positron emission tomography images

Purpose: Tissue radiotracer activity measured from positron emission tomography (PET) images is an important biomarker that is clinically utilized for diagnosis, staging, prognostication, and treatment response assessment in patients with cancer and other clinical disorders. Using PET image values to define a normal range of metabolic activity for quantification purposes is challenging due to variations in patient-related factors and technical factors. Although the formulation of standardized uptake value (SUV) has compensated for some of these variabilities, significant non-standardness still persists. We propose an image processing method to substantially mitigate these variabilities.

Methods: The standardization method is similar for activity concentration (AC) PET and SUV PET images with some differences and consists of two steps. The calibration step is performed only once for each of AC PET or SUV PET, employs a set of images of normal subjects, and requires a reference object, while the transformation step is executed for each patient image to be standardized. In the calibration step, a standardized scale is determined along with 3 key image intensity landmarks defined on it including the minimum percentile intensity $s_{\text{min}}$ , median intensity $s_m$ , and high percentile intensity $s_{\text{max}}.s_{\text{min}}$ and $s_m$ are estimated based on image intensities within the body region in the normal calibration image set. The optimal value of the maximum percentile $\beta$ corresponding to the intensity $s_{\text{max}}$ is estimated via an optimization process by using the reference object to optimally separate the highly variable high uptake values from the normal uptake intensities. In the transformation step, the first two landmarks-the minimum percentile intensity $p_{\alpha }\left(I\right)$ , and the median intensity $p_m\left(I\right)$ -are found for the given image $I$ for the body region, and the high percentile intensity $p_{\beta }\left(I\right)$ is determined corresponding to the optimally estimated high percentile value $\beta$ . Subsequently, intensities of $I$ are mapped to the standard scale piecewise linearly for different segments. We employ three strategies for evaluation and comparison with other standardization methods: (i) comparing coefficient of variation $\left({CV}_O\right)$ of mean intensity within test objects $O$ across different normal test subjects before and after standardization; (ii) comparing mean absolute difference ( ${\mathrm{MD}}_O$ ) of mean intensity within test objects $O$ across different subjects in repeat scans before and after standardization; (iii) comparing ${CV}_O$ of mean intensity across different normal subjects before and after standardization where the scans came from different brands of scanners.

Results: Our data set consisted of 84 FDG-PET/CT scans of the body torso including 38 normal subjects and two repeat-scans of 23 patients. We utilized one of two objects-liver and spleen-as a reference object and the other for testing. The proposed standardization method reduced ${CV}_O$ and ${\mathrm{MD}}_O$ by a factor of 3-8 in comparison to other standardization methods and no standardization. Upon standardization by our method, the image intensities (both for AC and SUV) from two different brands of scanners become statistically indistinguishable, while without standardization, they differ significantly and by a factor of 3-9.

Conclusions: The proposed method is automatic, outperforms current standardization methods, and effectively overcomes the residual variation left over in SUV and inter-scanner variations.