PET respiratory motion correction: quo vadis?

Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.

Ensemble of neural networks for 3D position estimation in monolithic PET detectors.

We propose an ensemble of multilayer feedforward neural networks to estimate the 3D position of photoelectric interactions in monolithic detectors. The ensemble is trained with data generated from optical Monte Carlo simulations only. The originality of our approach is to exploit simulations to obtain reference data, in combination with a variability reduction that the network ensembles offer, thus, removing the need of extensive per-detector calibration measurements. This procedure delivers an ensemble valid for any detector of the same design. We show the capability of the ensemble to solve the 3D positioning problem through testing four different detector designs with Monte Carlo data, measurements from physical detectors and reconstructed images from the MindView scanner. Network ensembles allow the detector to achieve a 2-2.4 mm FWHM, depending on its design, and the associated reconstructed images present improved SNR, CNR and SSIM when compared to those based on the MindView built-in positioning algorithm.

Simulation environment for the evaluation of 3D coronary tree reconstruction algorithms in rotational angiography.

We present a preliminary version of a simulation environment to evaluate the 3D reconstruction algorithms of the coronary arteries in rotational angiography. It includes the construction of a 3D dynamic model of the coronary tree from patient data, the modeling of the rotational angiography acquisition system to simulate different acquisition and gating strategies and the calculation of radiographic projections of the 3D model of coronary tree throughout several cardiac cycles.

Temporal tracking of coronaries in MSCTA by means of 3D geometrical moments.

An algorithm is proposed that perform a temporal tracking of the vessel central axis in a 3-D dynamic sequence in multi-slice computed tomography (MSCT). The approach is based on geometric moments and a local cylindrical approximation. The local characteristics of the vessel are estimated on the first volume of the sequence (position on the vessel central axis, local diameter, intravascular and background intensities), then used to track the vessel along the sequence. The correspondence between two volumes is solved through a region matching based on a criterion of minimal distance combining moment-based descriptors with intensity information. Preliminary results are presented on two sequences.

A multiscale tracking algorithm for the coronary extraction in MSCT angiography.

This paper deals with the extraction of the coronary network on dynamic volume sequences, acquired in multi-slice spiral computed tomography (MSCT). The proposed approach makes use of a tracking algorithm of the vascular structure, combining a 3D geometric moment operator with a multiscale Hessian filter to estimate the vessel central axis location, its local diameter and orientation. The method performs at the same time, a bifurcation detection to reconstitute the structure of the coronary network. The mean computation time to extract a coronary network is about 3 minutes using a P4-2.4G PC. Preliminary encouraging results are presented on one volume of a sequence.

A level set method for vessel segmentation in coronary angiography.

This paper presents a level set technique to extract the vascular structures in coronary angiography. It makes use of the Mumford-Shah functional to extract contours that are not necessary defined by gradient. A shape artery simulator was implemented to test and evaluate the detection method. Experimental results are presented on simulated data and real images successively.