Projection‐based improvement of 3D reconstructions from motion‐impaired dental cone beam CT data

Abstract

Purpose Computed tomography (CT) and, in particular, cone beam CT (CBCT) have been increasingly used as a diagnostic tool in recent years. Patient motion during acquisition is common in CBCT due to long scan times. This results in degraded image quality and may potentially increase the number of retakes. Our aim was to develop a marker‐free iterative motion correction algorithm that works on the projection images and is suitable for local tomography. Methods We present an iterative motion correction algorithm that allows the patient's motion to be detected and taken into account during reconstruction. The core of our method is a fast GPU‐accelerated three‐dimensional reconstruction algorithm. Assuming rigid motion, motion correction is performed by minimizing a pixel‐wise cost function between all captured x‐ray images and parameterized projections of the reconstructed volume. Results Our method is marker‐free and requires only projection images. Furthermore, it can deal with local tomography data. We demonstrate the effectiveness of our approach on both simulated and real motion‐beset patient images. The results show that our new motion correction algorithm leads to accurate reconstructions with sharper edges, better contrasts and more detail. Conclusions The presented method allows for correction of patient motion with observable improvements in image quality compared to uncorrected reconstructions. Potentially, this may reduce the number of retakes caused by corrupted reconstructions due to patient movements.

Dental cone beam CT: An updated review

“…In recent years, iterative reconstruction algorithms have been increasingly applied due to the potential for image quality improvements and artefact reduction [17,18]. The iterative image reconstruction approach may also mitigate one of the most pertinent challenges in CBCT, which is sensitivity to motion artefacts due to the relatively long scan time [19], which is inevitable with typical CBCT C-arm type gantry design. Recently, artificial intelligence (AI) based deep-learning (DL) methods have been developed as the latest advancement in image reconstruction [20,21].…”

Correlation of objective image quality and working length measurements in different CBCT machines: An ex vivo study

“…Small voxel sizes increase this effect 16 . Whether novel approaches to correct for such errors 33 are effective remains unknown so far. It can be assumed that the task of accurately measuring an endodontic working length by means of CBCT data is more challenging in vivo than in our ex vivo setup.…”

Computed tomography with view angle estimation using uncertainty quantification

“…Alternatively, marker-free view angle estimation approaches that are based only on the measured CT data have been studied analytically for specific geometries in e.g., [2,7,9]. This has -along with a number of practical methods -led to a large body of work on estimating view angles and other scan parameters in CT such as [2,7,9,17,20,31,33]. These methods can be categorized into two groups: i) estimating the view angles directly from the CT data followed by a reconstruction, and ii) jointly estimating the view angles and image reconstruction.…”

“…In the standard models for CT the view angles of the set-up are assumed to be known exactly, but in practice they may only be known with a limited accuracy. For specific applications, such as nano and micro X-ray tomography [21,32] or motion impaired CT [17], this uncertainty in the view angles is important to take into account to improve the quality of the reconstruction. The goal in this paper is to study a new method for simultaneous image reconstruction and view angle estimation solely from measured CT data with no machine or object calibration.…”