Optical coherence tomography (OCT) is a promising non-invasive imaging technique that owns many biomedical applications. In this paper, a deep neural network is proposed for enhancing the spatial resolution of OCTimages. Different from the previous r...
As one of the most common adverse weather phenomena, haze has caused detrimental effects on many computer vision systems. To eliminate the effect of haze, in the field of image processing, image dehazing has been studied intensively, and many advance...
Due to the non-uniform illumination conditions, images captured by sensors often suffer from uneven brightness, low contrast and noise. In order to improve the quality of the image, in this paper, a multi-path interaction network is proposed to enhan...
BACKGROUND: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for noninvasive myocardial tissue characterization but requires intravenous contrast agent administration. It is highly desired to deve...
OBJECTIVE: To investigate the clinical feasibility of single-breath-hold (SBH) T2-weighted (T2WI) liver MRI with deep learning-based reconstruction in the evaluation of image quality and lesion delineation, compared with conventional multi-breath-hol...
AIM: To investigate machine learning based models combining clinical, radiomic, and molecular information to distinguish between early true progression (tPD) and pseudoprogression (psPD) in patients with glioblastoma.
OBJECTIVE: We examined whether artificial intelligence (AI) used with the novel digital image enhancement system modalities (CLARA+CHROMA, SPECTRA A, and SPECTRA B) could distinguish the cholesteatoma matrix, cholesteatoma debris, and normal middle e...
Background Digital subtraction angiography (DSA) generates an image by subtracting a mask image from a dynamic angiogram. However, patient movement-caused misregistration artifacts can result in unclear DSA images that interrupt procedures. Purpose T...