Jose Mejia

ML-EM reconstruction model including total variation for low dose PET high resolution data

Data acquired through the PET system tend to be very noisy, partly due to low radiation doses. In this paper, a new reconstruction strategy based on a combination of the MLEM and total variation (TV) is presented. A comparision between the MLEM and MLEM-TV algorithms for three numbers of counts were done: (1) at 15 M counts; (2) at 35 M counts; and (3) at 55 M counts. The proposed method MLEM-TV can yield better result for image reconstruction, having a higher ability than the MLEM method to recover the spatial distribution of the counts, at low number of counts. Furthermore, an adaptive regularization parameters are embedded within the method. Experimental results, for the performance evaluation using PET simulated data, demostrate the efficiency of the MLEM-TV reconstruction method proposed, significantly improving the image quality and accuracy from the first iteration of the method, in comparison with that obtained using MLEM. For each reconstruction model under investigation, studies on the effects of image quality were addressed, using the SSIM index. Simulations were addressed using the small mouse MOBY phantom with SimSet.

Small animal PET image super-resolution using Tikhonov and modified total variation regularisation

ABSTRACT Positron emission tomography (PET) is an imaging procedure used mainly in the diagnosis and treatment of diseases. PET is also used in the preclinical research studies of small animals. However, researchers may have difficulty interpreting the particularly low-resolution images obtained via this procedure. This paper presents a new method of increasing the resolution of PET images through the use of super-resolution techniques. Aside from being resistant to the noise and other degradations that plague PET images, our proposed algorithm is also capable of preserving important structures (e.g. lesions). To this end, the proposed objective function includes a term based on the modified total variation model which allows the user to preserve texture and to deal with noise without incurring the artefacts that typically arise when the total variation norm is used. The present study shows the effectiveness of the method in recovering structures and details and indicates that, in most cases, it outperforms other state-of-the-art methods.

Noise Reduction in Small Animal PET Images Using a Variational Non-Convex Functional

Positron emission tomography (PET) imaging is widely used in nuclear medicine. However, data acquired by a PET system are generally contaminated with heavy noise, which often persists after image reconstruction. In this paper, a novel non-convex functional is introduced to suitably attenuate noise in PET images. The proposed functional contains a new regularization term defined as a convex combination of two terms: a robust function for border preserving and the L2 semi-norm. The combination coefficient depends on the gradient of the noisy image, so that it allows a selective smoothing of image regions according to their local characteristics. The proposed method has been qualitatively and quantitatively tested on both simulated and measured data, demonstrating its better performance against well-established methods for PET denoising.

Noise reduction in PET sinograms using non-local total variation regularization

Positron Emission Tomography is a technique of molecular imaging and provides information about biochemical process within the body of a patient, it is employed for diagnosis, staggering, and treatment planning. However, the resulting images have high noise levels that may cause difficulties for reading and interpreting the images by medical staff. For this reason, it is necessary to perform a denoising step to achieve better signal to noise ratio. In this paper, an approach is presented to denoise Positron Emission Tomography sinogram images using non-local total variation in the sinogram domain. The images are modeled in the sinogram domain using a Poisson noise model, it is proposed to adapt the SPIRAL algorithm to approximate the objective function to be minimized with separable quadratic functions to include the nonlocal total variation as a regularization term.

Hyperdense Middle Cerebral Artery Segmentation in Non Contrast Computed Tomography

The hyperdense middle cerebral artery (MCA) sign refers to focal increased density of the MCA in Non-Contrast Computed Tomography (NCCT) and is the earliest sign of acute ischemic stroke. In this paper, we present the implementation of a method that allows the automatic segmentation of the hyperdense MCA sign in NCCT pathological clinical cases, as a first phase in the development of a tool that will support the early detection of cerebral infarction. A fully automated algorithm was proposed for the delimitation of volumes of interest and the segmentation of the hyperdense MCA. Volumes of interest were defined according to the anatomical location of the suprasellar cistern, and features of the hyperdense MCA were extracted according to the Hounsfield Units and entropy. The segmentation was carried out using a model of region growing and active contours (snakes). The results show an accuracy of 96% (99% per slice) and a mean correlation of automatic versus manual segmentation of 94%.

Denoising of PET Images using NSCT and Quasi-Robust Potentials

In this paper we present an algorithm for the denoising of small animal positron emission images. The proposed algorithm combines a multiresolution transform with robust filtering of regions. The image is processed in the non-subsampled contourlet domain, taking advantage of the transform ability to capture geometric information of important structures like small lesions and borders between tissues. Additionally, in the transform domain, we proposed to apply quasi‑ robust potentials in order to reduce the noise on regions without borders, this is done by estimating an edge map and a set of image regions. Finally the inverse contourlet transform is applied to obtain a denoised image. Quality tests using the NEMA NU4 2008 phantom show that the proposed method reduces the noise in the image while at the same time the average count is preserved on each region. Comparisons with other methods, using a contrast analysis on a simulated lesion show the superiority of our approach to denoise and preserve small structures such as lesions.

Compress sensing for wireless sensor networks using gossip pairwise algorithm and optimization algorithms

For retrieving information in a wireless sensor network (WSN) from any node, each node must know the status of the entire network, leading to a high cost of energy in communication and information storage. In this paper, we evaluated a recovery method to request information from any node of a WSN with the use of compress sensing to compact the data to be stored and transmitted, and the use of a gossip pairwise algorithm to determine the network status. To recover the compressed information, two optimization algorithms are proposed and tested for convergence errors.