Positron Emission Tomography (PET) image enhancement using a gradient vector orientation based nonlinear diffusion filter (GVOF) for accurate quantitation of radioactivity concentration
To accurately quantify in vivo radiotracer uptake using Positron Emission Tomography (PET) is a challenging task due to low signal-to-noise ratio (SNR) and poor spatial resolution of PET camera along with the finite image sampling constraint. Furthermore, inter lesion variations of the SNR and contrast along with the variations in size of the lesion make the quantitation even more difficult. One of the ways to improve the quantitation is via post reconstruction filtering with Gaussian Filter (GF). Edge preserving Bilateral Filter (BF) and Nonlinear Diffusion Filter (NDF) are the alternatives to GF that can improve the SNR without degrading the image resolution. However, the performance of these edge preserving methods are only optimum for high count and low noise cases. A novel parameter free gradient vector orientation based nonlinear diffusion filter (GVOF) is proposed in this paper that is insensitive to statistical fluctuations (e. g., SNR, contrast, size etc.). GVOF method applied on the PET images collected with the NEMA phantom with varying levels of contrast and noise reveals that the GVOF method provides the highest SNR, CNR (contrast-to-noise ratio) and resolution compared to the original and other filtered images. The percentage bias in estimating the maximum activity representing SUVmax (Maximum Standardized Uptake Value) for the spheres with diameter > 2cm where the partial volume effects (PVE) is negligible is the lowest for the GVOF method. The GVOF method also improves the maximum intensity reproducibility. Robustness of the GVOF against variation in sizes, contrast levels and SNR makes it a suitable post filtering method for both accurate diagnosis and response assessment. Furthermore, its capability to provide accurate quantitative measurements irrespective of the SNR, it can also be effective in reduction of radioactivity dose.
Code (0)
등록된 구현이 없습니다.
Tasks
Image EnhancementSimilar Papers 제목 키워드 기반
PC-UNet: An Enforcing Poisson Statistics U-Net for Positron Emission Tomography Denoising
Positron Emission Tomography (PET) is crucial in medicine, but its clinical use is limited due to high signal-to-noise ratio doses increasing radiation exposure. Lowering doses increases Poisson noise, which current deno…
Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)
Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing image synthesis methods have problems in sy…
Computed Tomography (CT)Image GenerationMachine learning methods for accurate delineation of tumors in PET images
In oncology, Positron Emission Tomography imaging is widely used in diagnostics of cancer metastases, in monitoring of progress in course of the cancer treatment, and in planning radiotherapeutic interventions. Accurate …
BIG-bench Machine LearningFast PET Scan Tumor Segmentation using Superpixels, Principal Component Analysis and K-means Clustering
Positron Emission Tomography scan images are extensively used in radiotherapy planning, clinical diagnosis, assessment of growth and treatment of a tumor. These all rely on fidelity and speed of detection and delineation…
ClusteringSuperpixelsTumor SegmentationGLOW-FDG: Generalized cancer LesiOn Whole-body segmentation model for $^{18}$F-FDG-PET/CT
Whole-body fluorodeoxyglucose positron emission tomography combined with computed tomography is widely used in cancer care, but manual lesion delineation is slow, subjective, and difficult to scale. We present GLOW-FDG, …
Lesion Segmentation