Deep learning reconstruction allows for usage of contrast agent of lower concentration for coronary CTA than filtered back projection and hybrid iterative reconstruction

Abstract

Background The demand for homogeneous and higher vascular contrast enhancement is critical to provide an appropriate interpretation of abnormal vascular findings in coronary computed tomography angiography (CTA). Purpose To evaluate the effect of various contrast media concentrations (Iohexol-370, Iohexol-300, Iohexol-240) and image reconstructions (filtered back projection [FBP], hybrid iterative reconstruction [IR], and deep learning reconstruction [DLR]) on coronary CTA. Material and Methods A total of 63 patients referred for coronary CTA between July and October 2021 were enrolled in this prospective study, and they randomly received one of three contrast media. CTA images were reconstructed with FBP, hybrid IR, and DLR. The CT attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated for all three images. The images were subjectively evaluated by two radiologists in terms of overall image quality, artifacts, image noise, and vessel wall delineation on a 5-point Likert scale. Results The application of DLR resulted in significantly lower image noise; higher CT attenuation, SNR, and CNR; and better subjective analysis among the three different concentrations of contrast media groups ( P < 0.001). There was no significant difference in the CT attenuation of the left ventricle ( P = 0.089) and coronary arteries ( P = 0.072) between hybrid IR at Iohexol-300 and DLR at Iohexol-240. Furthermore, application of DLR to the Iohexol-240 significantly improved SNR and CNR; it achieved higher subjective scores compared with hybrid IR at Iohexol-300 ( P < 0.001). Conclusion We suggest that using DLR with Iohexol-240 contrast media is preferable to hybrid IR with Iohexol-300 contrast media in coronary CTA.

Impact of a reduced iodine load with deep learning reconstruction on abdominal MDCT

“…Only 2 papers have used the same vendor as the one we used (Canon Medical Systems). [ 31 , 32 ] A possible explanation for this apparent disagreement with the results between all published papers could be the type of vendor used, with its specific DLR algorithm. In the DLR, the quality of the training target determines the performance of the output.…”

“…A recent study on coronary CTA [ 32 ] with the same vendor as our study investigated the influence of 3 different image reconstructions (FBR, hybrid IR, and DLR) with different contrast media iodine concentrations (Iohexol 370, Iohexol-300, Iohexol 240). They found that using DLR with the lowest concentration (Iohexol 240) yielded comparable attenuation to hybrid IR with Iohexol 300, with a higher SNR, CNR, and image quality for DLR.…”

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

“…Recently, deep learning reconstruction (DLR) [ 10 ] based on convolutional neural networks (CNNs) was proposed to further enhance the spatial resolution and diagnostic performance without affecting the noise texture. Previous studies demonstrated the benefits of DLR on coronary CT angiography [ 11 12 13 14 ], abdominal contrast-enhanced dual-energy CT [ 15 ], and brain CTA [ 16 ]. Here, we have extended the application of the DLR algorithm to dark-blood CTA to evaluate vessel wall imaging in the head and neck region.…”