My Account | Careers | Downloads | Blog

PROJECT TITLE :

Use of a Tracer-Specific Deep Artificial Neural.Net to Denoise Dynamic PET Images

ABSTRACT:

The use of kinetic modeling (KM) on a voxel level in dynamic PET pictures frequently results in large amounts of noise, lowering the precision of parametric image analysis significantly. In this research, we look at how to denoise dynamic PET scans using Machine Learning and artificial neural networks. We used noisy and noise-free spatiotemporal image patches recovered from simulated images of [ 11 C]raclopride, a dopamine D2 receptor agonist, to train a deep denoising autoencoder (DAE). Traditional denoising techniques, such as temporal and spatial Gaussian smoothing, iterative spatiotemporal smoothing/deconvolution, and highly limited backprojection processing, are compared to the DAE-processed dynamic and parametric pictures (simulated and recorded) (HYPR). With temporal and 5.90 percent (14.50 percent) with spatial smoothing, 5.82 percent (16.21 percent) with smoothing/deconvolution, 5.49 percent (13.38 percent) with HYPR, and 3.52 percent (11.41 percent) with DAE, the simulated (acquired) parametric image non-uniformity was 7.75 percent (19.49 percent). In terms of the coefficient of variation of voxel values and the structural similarity index, the DAE delivered the best results. With temporal and 26.31 percent with spatial smoothing, 28.61 percent with smoothing/deconvolution, 27.63 percent with HYPR, and 14.8 percent with DAE, denoising-induced bias in the regional mean binding potential was 7.8% and 26.31 percent, respectively. Erroneous results were achieved when the test data did not match the training data. Our findings show that, as compared to traditional spatiotemporal denoising approaches, a deep DAE may significantly reduce voxel-level noise while introducing a similar or lower amount of bias. The higher DAE performance comes at the expense of lower generality and the need for more training data.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : A Convergent Image Fusion Algorithm Using Scene-Adapted Gaussian-Mixture-Based Denoising ABSTRACT: Inspired by contemporary PnP frameworks, we offer a new picture fusion technique. When a denoiser is used in PnP,
PROJECT TITLE : Statistical Nearest Neighbors for Image Denoising ABSTRACT: Non-local-means image denoising is based on processing a reference patch's neighbours. The algorithm's processing overhead can be reduced by using a small
PROJECT TITLE :External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising - 2018ABSTRACT:Most of existing image denoising ways learn image priors from either an external data or the noisy image itself
PROJECT TITLE :Dynamic, Fine-Grained Data Plane Monitoring With Monocle - 2018ABSTRACT:Ensuring network reliability is important for satisfying service-level objectives. However, diagnosing network anomalies during a timely fashion
PROJECT TITLE :EEG Signal Denoising based on Wavelet Transform using Xilinx System Generator - 2018ABSTRACT:Low amplitude EEG signal are simply affected by varied noise sources. This work presents de-noising methods based on the

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry