KEYWORDS: Positron emission tomography, 3D modeling, Convolution, Animal model studies, Signal to noise ratio, Scanners, Imaging systems, Computer programming, 3D image processing, Veins
As a mainstay of metabolic studies, Positron Emission Tomography (PET) has aroused remarkable attention in the clinical arena and the translational realm. The amount of radiotracer dosage is amongst the major problems in PET imaging, creating ongoing challenges for both the clinical community and the preclinical researchers. In quest of generating diagnostic quality PET images in extremely low-dose conditions, several deep-learning(DL)-inspired methods have sprung up in human imaging over the past few years. Propelled by the successful application of DL techniques in human studies and the unique advantages of deep neural networks in learning specific features, we have investigated a fully 3D U-Netlike model which enables reconstructing standard-dose PET dataset directly from its low-dose equivalent. We verified the performance of the method both in mice and rat PET scans through calculating image evaluation metrics such as RMSE, PSNR, and SSIM. Our measurements revealed that the proposed method could provide high-quality PET scans with improved noise properties in low-dose rodent studies.
Significance: Photoacoustic imaging (PAI) has been greatly developed in a broad range of diagnostic applications. The efficiency of light to sound conversion in PAI is limited by the ubiquitous noise arising from the tissue background, leading to a low signal-to-noise ratio (SNR), and thus a poor quality of images. Frame averaging has been widely used to reduce the noise; however, it compromises the temporal resolution of PAI.
Aim: We propose an approach for photoacoustic (PA) signal denoising based on a combination of low-pass filtering and sparse coding (LPFSC).
Approach: LPFSC method is based on the fact that PA signal can be modeled as the sum of low frequency and sparse components, which allows for the reduction of noise levels using a hybrid alternating direction method of multipliers in an optimization process.
Results: LPFSC method was evaluated using in-silico and experimental phantoms. The results show a 26% improvement in the peak SNR of PA signal compared to the averaging method for in-silico data. On average, LPFSC method offers a 63% improvement in the image contrast-to-noise ratio and a 33% improvement in the structural similarity index compared to the averaging method for objects located at three different depths, ranging from 10 to 20 mm, in a porcine tissue phantom.
Conclusions: The proposed method is an effective tool for PA signal denoising, whereas it ultimately improves the quality of reconstructed images, especially at higher depths, without limiting the image acquisition speed.
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