8 research outputs found
Automated Cardiac Resting Phase Detection Targeted on the Right Coronary Artery
Static cardiac imaging such as late gadolinium enhancement, mapping, or 3-D
coronary angiography require prior information, e.g., the phase during a
cardiac cycle with least motion, called resting phase (RP). The purpose of this
work is to propose a fully automated framework that allows the detection of the
right coronary artery (RCA) RP within CINE series. The proposed prototype
system consists of three main steps. First, the localization of the regions of
interest (ROI) is performed. Second, the cropped ROI series are taken for
tracking motions over all time points. Third, the output motion values are used
to classify RPs. In this work, we focused on the detection of the area with the
outer edge of the cross-section of the RCA as our target. The proposed
framework was evaluated on 102 clinically acquired dataset at 1.5T and 3T. The
automatically classified RPs were compared with the reference RPs annotated
manually by a expert for testing the robustness and feasibility of the
framework. The predicted RCA RPs showed high agreement with the experts
annotated RPs with 92.7% accuracy, 90.5% sensitivity and 95.0% specificity for
the unseen study dataset. The mean absolute difference of the start and end RP
was 13.6 18.6 ms for the validation study dataset (n=102). In this work,
automated RP detection has been introduced by the proposed framework and
demonstrated feasibility, robustness, and applicability for static imaging
acquisitions.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) https://melba-journal.org/2023:00
Self-supervised MRI denoising: leveraging Stein’s unbiased risk estimator and spatially resolved noise maps
Abstract Thermal noise caused by the imaged object is an intrinsic limitation in magnetic resonance imaging (MRI), resulting in an impaired clinical value of the acquisitions. Recently, deep learning (DL)-based denoising methods achieved promising results by extracting complex feature representations from large data sets. Most approaches are trained in a supervised manner by directly mapping noisy to noise-free ground-truth data and, therefore, require extensive paired data sets, which can be expensive or infeasible to obtain for medical imaging applications. In this work, a DL-based denoising approach is investigated which operates on complex-valued reconstructed magnetic resonance (MR) images without noise-free target data. An extension of Stein’s unbiased risk estimator (SURE) and spatially resolved noise maps quantifying the noise level with pixel accuracy were employed during the training process. Competitive denoising performance was achieved compared to supervised training with mean squared error (MSE) despite optimizing the model without noise-free target images. The proposed DL-based method can be applied for MR image enhancement without requiring noise-free target data for training. Integrating the noise maps as an additional input channel further enables the regulation of the desired level of denoising to adjust to the preference of the radiologist