1024-pixel image matrix for chest CT – Impact on image quality of bronchial structures in phantoms and patients

Abstract

Objectives To compare objective and subjective image quality of bronchial structures between a 512pixel and a 1024-pixel image matrix for chest CT in phantoms and in patients. Materials and methods First, a two-size chest phantom was imaged at two radiation doses on a 192-slice CT scanner. Datasets were reconstructed with 512-, 768-, and 1024-pixel image matrices and a sharp reconstruction kernel (Bl64). Image sharpness and normalized noise power spectrum (nNPS) were quantified. Second, chest CT images of 100 patients were reconstructed with 512-and 1024-pixel matrices and two blinded readers independently assessed objective and subjective image quality. In each patient dataset, the highest number of visible bronchi was counted for each lobe of the right lung. A linear mixed effects model was applied in the phantom study and a Welch's t-test in the patient study. Results Objective image sharpness and image noise increased with increasing matrix size and were highest for the 1024-matrix in phantoms and patients (all, P<0.001). nNPS was comparable among the three matrices. Objective image noise was on average 16% higher for the 1024matrix compared to the 512-matrix in patients (P<0.0001). Subjective evaluation in patients yielded improved sharpness but increased image noise for the 1024-compared to the 512matrix (both, P<0.001). There was no significant difference between highest-order visible bronchi (P>0.07) and the overall bronchial image quality between the two matrices (P>0.22). Conclusion Our study demonstrated superior image sharpness and higher image noise for a 1024-compared to a 512-pixel matrix, while there was no significant difference in the depiction and subjective image quality of bronchial structures for chest CT.

Influence of CT Image Matrix Size and Kernel Type on the Assessment of HRCT in Patients with SSC-ILD

“…Similar to Euler et al, we attribute this difference in the impression of image noise to the fact that if a soft kernel is used, this is the limiting factor for the resolution [ 22 ].…”

“…Higher order bronchiole were detected in the middle lobe at a higher resolution but the image noise was roughly the same in both matrices. In another study by Euler et al, on the other hand, a hard kernel (Bl64) was used for a 512- and a 1024-pixel image matrix [ 22 ]. The results showed superior image sharpness and higher image noise for the 1024-pixel matrix and no difference in the subjective image quality or the depiction of bronchial structures.…”

“…In the study by Euler et al, the positive and negative characteristics of the 1024-pixel matrix balance each other out: better sharpness but also more noise (evaluated both subjectively and objectively) [ 22 ]. In contrast to our study, the FoV was kept constant, and a slightly harder kernel was used.…”

“…On the other hand, when using a conventional CT, spatial resolution depends on user-modifiable reconstruction parameters such as kernel and the size of the volumetric image pixel (VIP). This in turn depends on the field of view (FoV), the number of pixels of the pixel matrix and the slice thickness [ 22 ]. Using a large FoV of approximately 300–400 mm in a typical HRCT, the size of the pixel becomes relevant for a detailed spatial resolution, in order to reduce blurring or step artifacts.…”

A Pilot Study to Estimate the Impact of High Matrix Image Reconstruction on Chest Computed Tomography

“…A previous study using the same CT platform as this study demonstrated that high matrix image reconstruction showed no significant difference in subjective evaluation of the bronchial tree but higher image sharpness and image noise compared to standard image reconstruction [ 23 ] In studies investigating CT scanners utilizing a combination of smaller detector size (0.25 × 0.25 mm) and high matrix image reconstruction, the technique was beneficial in lung nodule evaluation, [ 24 , 25 ] peripheral airway assessment, [ 26 ] small pulmonary ossification detection, [ 27 ] quantitative emphysema measurement, [ 28 ] automatic bronchial segmentation, [ 29 ] and virtual bronchoscopy. [ 30 ] Although smaller detector size exploits the advantage of high matrix image reconstruction, it is uncertain how high matrix image reconstruction improves the performance of other diagnostic tasks when combined with conventional CT detector size.…”