An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing

Abstract

Respiratory motion degrades anatomic position reproducibility and leads to issues affecting image acquisition, treatment planning, and radiation delivery. Four-dimensional (4D) computer tomography (CT) image acquisition can be used to measure the impact of organ motion and to explicitly account for respiratory motion during treatment planning and radiation delivery. Modern CT scanners can only scan a limited region of the body simultaneously and patients have to be scanned in segments consisting of multiple slices. A respiratory signal (spirometer signal or surface tracking) is used to reconstruct a 4D data set by sorting the CT scans according to the couch position and signal coherence with predefined respiratory phases. But artifacts can occur if there are no acquired data segments for exactly the same respiratory state for all couch positions. These artifacts are caused by device-dependent limitations of gantry rotation, image reconstruction times and by the variability of the patient's respiratory pattern. In this paper an optical flow based method for improved reconstruction of 4D CT data sets from multislice CT scans is presented. The optical flow between scans at neighboring respiratory states is estimated by a non-linear registration method. The calculated velocity field is then used to reconstruct a 4D CT data set by interpolating data at exactly the predefined respiratory phase. Our reconstruction method is compared with the usually used reconstruction based on amplitude sorting. The procedures described were applied to reconstruct 4D CT data sets for four cancer patients and a qualitative and quantitative evaluation of the optical flow based reconstruction method was performed. Evaluation results show a relevant reduction of reconstruction artifacts by our technique. The reconstructed 4D data sets were used to quantify organ displacements and to visualize the abdominothoracic organ motion.

Slipping Objects in Image Registration: Improved Motion Field Estimation with Direction-Dependent Regularization

“…Data sets are reconstructed using an optical flow based method [11]. Spatial resolution is 0.98 × 0.98 × 1.5 mm.…”

“…Due to this generation process and reconstruction artifacts in the CT data (comp. [11]), the segmentations often show inaccuracies and a non-smoothness, especially in z-direction.…”

An automated method for comparing motion artifacts in cine four‐dimensional computed tomography images

“…Strategies to reduce motion artifacts and improve 4D CT image clarity have been developed and investigated — for example, the use of audiovisual biofeedback to help improve the patient's respiratory regularity, 9 , 10 the application of different algorithms to improve retrospective sorting, 11 , 13 and postprocessing of data to improve image reconstruction (14) . However, most of these strategies rely on visual evaluation of the improvements and are, therefore, prone to human subjectivity.…”

Perspective de la plate-forme NEMOSIS dans le cadre d’une réduction de doses en imagerie

“…Dans cette optique de réduction des artefacts cinétiques, Ehrhardt et al (2007) proposent une méthode fondée sur les mouvements apparents pour l'amélioration de la reconstruction 4D des images. Pour cela, ils déterminent un champ de vecteurs à partir de deux scans proches de la phase souhaitée en utilisant un algorithme de recalage non-linéaire.…”