Improved PET/CT Respiratory Motion Compensation by Incorporating Changes in Lung Density

Abstract

Positron emission tomography/computed tomography (PET/CT) lung imaging is highly sensitive to motion. Although several techniques exist to diminish motion artifacts, a few accounts for both tissue displacement and changes in density due to the compression and dilation of the lungs, which cause quantification errors. This article presents an experimental framework for joint activity image reconstruction and motion estimation in PET/CT, where the PET image and the motion are directly estimated from the raw data. Direct motion estimation methods for motion-compensated PET/CT are preferable as they require a single attenuation map only and result in optimal signal-to-noise ratio (SNR). Previous implementations, however, failed to address changes in density during respiration. We propose to account for such changes using the Jacobian determinant of the deformation fields. In a feasibility study, we demonstrate on a modified extended cardiac-torso (XCAT) phantom with breathing motion-where the lung density and activity vary-that our approach achieved better quantification in the lungs than conventional PET/CT joint activity image reconstruction and motion estimation that does not account for density changes. The proposed method resulted in lower bias and variance in the activity images, reduced mean relative activity error in the lung at the reference gate (−4.84% to −3.22%) and more realistic Jacobian determinant values.

[Retracted] Storage Method for Medical and Health Big Data Based on Distributed Sensor Network

“…Distributed technology is widely used in the field of storage with its advantages of low cost, high reliability, and large capacity, which provides a new idea for storing massive medical and health data. The technology stores, manages, and processes massive data in a distributed manner by connecting multiple common devices and supports the storage of unstructured data [4][5][6][7]. As a result, healthcare big data is usually stored in distributed file systems or nonrelational (NoSQL) databases, and the distributed parallel computing model improves the system data analysis to further optimize the query performance of the storage system.…”

Current Research Status of Respiratory Motion for Thorax and Abdominal Treatment: A Systematic Review

“…In 2020, E.C. Emond et al [ 79 ] proposed an experimental framework for combined PET/CT image reconstruction and motion estimation, in which PET images and motion are estimated directly from raw data. The change in volume is estimated by using the “Jacobian determinant” to calculate the deformation field, the problem of density changes during respiration can be taken into account, and the image registration that maintains quality can be directly applied to the joint estimation of PET active images and motion.…”

Long-term optical imaging of the spinal cord in awake, behaving animals

“…by using iterative multi-day cell extraction and alignment approaches to identify additional cells or those with weak activity on a given day (Tasci, 2020). For non-rigid motion correction, future improvements can be introduced, including deformation-based methods that are more computationally efficient and also mass-preserving (Emond et al, 2020). Such modifications will be critical to maintain numerical accuracy across frames and allow us to reduce the need to restrict motion correction to rostrocaudal oriented displacement fields.…”