Super-Resolution Cine Image Enhancement for Fetal Cardiac Magnetic Resonance Imaging

STADNet: Spatial-Temporal Attention-Guided Dual-Path Network for cardiac cine MRI super-resolution

Cardiac cine magnetic resonance imaging (MRI) is a commonly used clinical tool for evaluating cardiac function and morphology. However, its diagnostic accuracy may be compromised by the low spatial resolution. Current methods for cine MRI super-resolution reconstruction still have limitations. They typically rely on 3D convolutional neural networks or recurrent neural networks, which may not effectively capture long-range or non-local features due to their limited receptive fields. Optical flow estimators are also commonly used to align neighboring frames, which may cause information loss and inaccurate motion estimation. Additionally, pre-warping strategies may involve interpolation, leading to potential loss of texture details and complicated anatomical structures. To overcome these challenges, we propose a novel Spatial-Temporal Attention-Guided Dual-Path Network (STADNet) for cardiac cine MRI super-resolution. We utilize transformers to model long-range dependencies in cardiac cine MR images and design a cross-frame attention module in the location-aware spatial path, which enhances the spatial details of the current frame by using complementary information from neighboring frames. We also introduce a recurrent flow-enhanced attention module in the motion-aware temporal path that exploits the correlation between cine MRI frames and extracts the motion information of the heart. Experimental results demonstrate that STADNet outperforms SOTA approaches and has significant potential for clinical practice.

Fetal Cardiac MRI Using Doppler US Gating: Emerging Technology and Clinical Implications

Fetal cardiac MRI using Doppler US gating is an emerging technique to support prenatal diagnosis of congenital heart disease and other cardiovascular abnormalities. Analogous to postnatal electrocardiographically gated cardiac MRI, this technique enables directly gated MRI of the fetal heart throughout the cardiac cycle, allowing for immediate data reconstruction and review of image quality. This review outlines the technical principles and challenges of cardiac MRI with Doppler US gating, such as loss of gating signal due to fetal movement. A practical workflow of patient preparation for the use of Doppler US-gated fetal cardiac MRI in clinical routine is provided. Currently applied MRI sequences (ie, cine or four-dimensional flow imaging), with special consideration of technical adaptations to the fetal heart, are summarized. The authors provide a literature review on the clinical benefits of Doppler US-gated fetal cardiac MRI for gaining additional diagnostic information on cardiovascular malformations and fetal hemodynamics. Finally, future perspectives of Doppler US-gated fetal cardiac MRI and further technical developments to reduce acquisition times and eliminate sources of artifacts are discussed. Keywords: MR Fetal, Ultrasound Doppler, Cardiac, Heart, Congenital, Obstetrics, Fetus Supplemental material is available for this article. © RSNA, 2024.

Deep learning denoising reconstruction for improved image quality in fetal cardiac cine MRI

Purpose

This study aims to evaluate deep learning (DL) denoising reconstructions for image quality improvement of Doppler ultrasound (DUS)-gated fetal cardiac MRI in congenital heart disease (CHD).

Methods

Twenty-five fetuses with CHD (mean gestational age: 35 ± 1 weeks) underwent fetal cardiac MRI at 3T. Cine imaging was acquired using a balanced steady-state free precession (bSSFP) sequence with Doppler ultrasound gating. Images were reconstructed using both compressed sensing (bSSFP CS) and a pre-trained convolutional neural network trained for DL denoising (bSSFP DL). Images were compared qualitatively based on a 5-point Likert scale (from 1 = non-diagnostic to 5 = excellent) and quantitatively by calculating the apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratio (aCNR). Diagnostic confidence was assessed for the atria, ventricles, foramen ovale, valves, great vessels, aortic arch, and pulmonary veins.

Results

Fetal cardiac cine MRI was successful in 23 fetuses (92%), with two studies excluded due to extensive fetal motion. The image quality of bSSFP DL cine reconstructions was rated superior to standard bSSFP CS cine images in terms of contrast [3 (interquartile range: 2-4) vs. 5 (4-5), P < 0.001] and endocardial edge definition [3 (2-4) vs. 4 (4-5), P < 0.001], while the extent of artifacts was found to be comparable [4 (3-4.75) vs. 4 (3-4), P = 0.40]. bSSFP DL images had higher aSNR and aCNR compared with the bSSFP CS images (aSNR: 13.4 ± 6.9 vs. 8.3 ± 3.6, P < 0.001; aCNR: 26.6 ± 15.8 vs. 14.4 ± 6.8, P < 0.001). Diagnostic confidence of the bSSFP DL images was superior for the evaluation of cardiovascular structures (e.g., atria and ventricles: P = 0.003).

Conclusion

DL image denoising provides superior quality for DUS-gated fetal cardiac cine imaging of CHD compared to standard CS image reconstruction.

Present and Future Innovations in AI and Cardiac MRI

Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and long acquisition protocols limit the specialty and restrain its impact on the practice of medicine. Artificial intelligence (AI)-the ability to mimic human intelligence in learning and performing tasks-will impact nearly all aspects of MRI. Deep learning (DL) primarily uses an artificial neural network to learn a specific task from example data sets. Self-driving scanners are increasingly available, where AI automatically controls cardiac image prescriptions. These scanners offer faster image collection with higher spatial and temporal resolution, eliminating the need for cardiac triggering or breath holding. In the future, fully automated inline image analysis will most likely provide all contour drawings and initial measurements to the reader. Advanced analysis using radiomic or DL features may provide new insights and information not typically extracted in the current analysis workflow. AI may further help integrate these features with clinical, genetic, wearable-device, and "omics" data to improve patient outcomes. This article presents an overview of AI and its application in cardiac MRI, including in image acquisition, reconstruction, and processing, and opportunities for more personalized cardiovascular care through extraction of novel imaging markers.

Integration of Prenatal Cardiovascular Magnetic Resonance Imaging in Congenital Heart Disease

Standard of care echocardiography can have limited diagnostic accuracy in certain cases of fetal congenital heart disease. Prenatal cardiovascular magnetic resonance (CMR) imaging has potential to provide additional anatomic imaging information, including excellent soft tissue images in multiple planes, improving prenatal diagnostics and in utero hemodynamic assessment. We conducted a literature review of fetal CMR, including its development and implementation into clinical practice, and compiled and analyzed the results. Our findings included the fact that technological and innovative approaches are required to overcome some of the challenges in fetal CMR, in part due to the dynamic nature of the fetal heart. A number of reconstruction algorithms and cardiac gating strategies have been developed over time to improve fetal CMR image quality, allowing unique investigations into fetal hemodynamics, oxygenation, and growth. Studies demonstrate that incorporating CMR in the prenatal arena influences postnatal clinical management. With further refinement and experience, fetal CMR in congenital heart disease continues to evolve and demonstrate ongoing potential as a complementary imaging modality to fetal echocardiography in the care of these patients.

Fetal 3D cardiovascular cine image acquisition using radial sampling and compressed sensing

PURPOSE

To explore a fetal 3D cardiovascular cine acquisition using a radial image acquisition and compressed-sensing reconstruction and compare image quality and scan time with conventional multislice 2D imaging.

METHODS

Volumetric fetal cardiac data were acquired in 26 volunteers using a radial 3D balanced SSFP pulse sequence. Cardiac gating was performed using a Doppler ultrasound device. Images were reconstructed using a parallel-imaging and compressed-sensing algorithm. Multiplanar reformatting to standard cardiac views was performed before image analysis. Clinical 2D images were used for comparison. Qualitative and quantitative image evaluation were performed by two experienced observers (scale: 1-4). Volumes, mass, and function were assessed.

RESULTS

Average scan time for the 3D imaging was 6 min, including one localizer. A 2D imaging stack covering the entire heart including localizer sequences took at least 6.5 min, depending on planning complexity. The 3D acquisition was successful in 7 of 26 subjects (27%). Overall image contrast and perceived resolution were lower in the 3D images. Nonetheless, the 3D images had, on average, a moderate cardiac diagnostic quality (median [range]: 3 [1-4]). Standard clinical 2D acquisitions had a high cardiac diagnostic quality (median [range]: 4 [3, 4]). Cardiac measurements were not different between 2D and 3D images (all p > 0.16).

CONCLUSION

The presented free-breathing whole-heart fetal 3D radial cine MRI acquisition and reconstruction method enables retrospective visualization of all cardiac views while keeping examination times short. This proof-of-concept work produced images with diagnostic quality, while at the same time reducing the planning complexity to a single localizer.