Dual‐rotation C‐arm cone‐beam computed tomography to increase low‐contrast detection

Abstract

Purpose: This paper investigates the capabilities of a dual-rotation C-arm cone-beam computed tomography (CBCT) framework to improve non-contrast-enhanced low-contrast detection for full volume or volume-of-interest (VOI) brain imaging. Method: The idea is to associate two C-arm short-scan rotational acquisitions (spins): one over the full detector field of view (FOV) at low dose, and one collimated to deliver a higher dose to the central densest parts of the head. The angular sampling performed by each spin is allowed to vary in terms of number of views and angular positions. Collimated data is truncated and does not contain measurement of the incoming X-ray intensities in air (air calibration). When targeting full volume reconstruction, the method is intended to act as a virtual bow-tie. When targeting VOI imaging, the method is intended to provide the minimum full detector FOV data that sufficiently corrects for truncation artifacts. A single dedicated iterative algorithm is described that handles all proposed sampling configurations despite truncation and absence of air calibration. Results: Full volume reconstruction of dual-rotation simulations and phantom acquisitions are shown to have increased low-contrast detection for less dose, with respect to a single-rotation acquisition. High CNR values were obtained on 1% inserts of the Catphan r 515 module in 0.94 mm thick slices. Image quality for VOI imaging was preserved from truncation artifacts even with less than 10 non-truncated views, without using the sparsity a priori common to such context. Conclusion: A flexible dual-rotation acquisition and reconstruction framework is proposed that has the potential to improve low-contrast detection in clinical C-arm brain soft-tissue imaging.

Convergence of proximal gradient algorithm in the presence of adjoint mismatch *

“…We also indicated the NMSE and the maximum absolute error (MAE), defined as max i∈{1,...,N} |x i − x i |, for all the reconstructed images when compared with the ground truth. Both FBP reconstructions suffer from various artifacts (peripheral bright-band artifacts, cupping, over-estimation of the values as shown in figure 7) [47,48,50], in contrast with the solutions provided by our regularized iterative approach. Furthermore, when parameter κ 2 is used, the reconstructed image obtained by PGA with the mismatched adjoint K is very similar to the image obtained without mismatch.…”

Clinical Micro-CT Empowered by Interior Tomography, Robotic Scanning, and Deep Learning

“…To facilitate accurate registration between the global scan data/images and local counterparts, an optical 3D surface scanner is used at the start of the local scan and it continues to monitor the head movement of the patient [11] during the local scan for motion compensation. This design integrates all relevant cutting-edge hardware and software elements, and systematically upgrades our earliest CMCT design published in 2005 [12] as well as other follow-up designs for similar purposes [13]- [18].…”