A Novel Computed Tomography Image Reconstruction for Improving Visualization of Pulmonary Vasculature: Comparison Between Preprocessing and Postprocessing Images Using a Contrast Enhancement Boost Technique

Abstract

ObjectiveThis study aimed to evaluate chest computed tomography (CT) angiography image quality using the contrast enhancement (CE)–boost technique compared with conventional images.MethodsForty patients who underwent contrast-enhanced chest CT were included. Combined CT angiography images of the iodinated image obtained from the subtraction of nonenhanced CT images and CT angiography images were used to generate CE-boost images. Computed tomography attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for the right and left pulmonary arteries as the central and subsegmental arteries as peripheral vessels were assessed. Subjective image quality was rated on a 5-point scale by 2 radiologists. Image quality was assessed using a paired t test.ResultsComputed tomography attenuation in the main pulmonary artery was significantly higher for the CE-boost images (311.05 ± 91.94) than for the conventional images (221.25 ± 61.21, P < 0.001). Similarly, the CE-boost images resulted in significantly higher CT attenuation in the subsegmental arteries (right, 305.34 ± 90.13; left, 313.05 ± 97.21) than in the conventional images (right, 218.45 ± 63.16; left, 223.89 ± 74.27). The CE-boost technique demonstrated marked improvement in the visualization of the peripheral pulmonary artery without the administration of a higher iodine delivery rate. The mean SNR and CNR were also significantly higher in the central and peripheral vessels in the CE-boost images than in the conventional images (P < 0.001). In the subjective analysis, the image contrast and vascular contrast edge were significantly higher for the CE-boost images than for conventional images (P < 0.001).ConclusionsThe CE-boost technique increases not only the visualization of peripheral arteries by improving vascular attenuation but also the SNR and CNR.

Dark-Blood Computed Tomography Angiography Combined With Deep Learning Reconstruction for Cervical Artery Wall Imaging in Takayasu Arteritis

“…Additionally, because of the integrated denoising filter, the dark-blood technique maintained image noise even with the subtraction and addition operations. Our method is an extension of the contrast-enhancement boost (CE-boost) technique, which has traditionally been utilized to further increase enhancement on contrast-enhanced CT for various body parts [ 19 20 21 ]. The CE-boost technique was modified to generate dark-blood CT images in which the paired arterial and delayed-phases served as inputs instead of the conventional pair of unenhanced and enhanced images.…”

A comparative analysis of deep learning and hybrid iterative reconstruction algorithms with contrast-enhancement-boost post-processing on the image quality of indirect computed tomography venography of the lower extremities

“…The CE-boost technique shows promise in reducing the amount of contrast media and improving radiologists’ diagnostic confidence. Previous studies have demonstrated that the application of CE-boost significantly improves the detection of type II endoleaks following endovascular aortic aneurysm repair [ 7 ], shows efficacy in visualizing smaller diameter abdominal arteries and portal veins [ 9 , 10 ] and peripheral pulmonary arteries [ 8 ]. In the present research, the CE-boost images exhibited improved visibility of the veins compared to the original images.…”

“…Subsequently, a noise-reduced iodine image is obtained through a noise-reduction procedure applied to the subtracted iodine image. Finally, a CE-enhanced image is created by adding the noise-reduced image to the original contrast-enhanced image using an automated process of pixel alignment [ 8 – 10 ].…”