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

AI-driven 3D bioprinting for regenerative medicine: From bench to bedside

In recent decades, 3D bioprinting has garnered significant research attention due to its ability to manipulate biomaterials and cells to create complex structures precisely. However, due to technological and cost constraints, the clinical translation of 3D bioprinted products (BPPs) from bench to bedside has been hindered by challenges in terms of personalization of design and scaling up of production. Recently, the emerging applications of artificial intelligence (AI) technologies have significantly improved the performance of 3D bioprinting. However, the existing literature remains deficient in a methodological exploration of AI technologies' potential to overcome these challenges in advancing 3D bioprinting toward clinical application. This paper aims to present a systematic methodology for AI-driven 3D bioprinting, structured within the theoretical framework of Quality by Design (QbD). This paper commences by introducing the QbD theory into 3D bioprinting, followed by summarizing the technology roadmap of AI integration in 3D bioprinting, including multi-scale and multi-modal sensing, data-driven design, and in-line process control. This paper further describes specific AI applications in 3D bioprinting's key elements, including bioink formulation, model structure, printing process, and function regulation. Finally, the paper discusses current prospects and challenges associated with AI technologies to further advance the clinical translation of 3D bioprinting.

Reliability of 4D Flow MRI for Investigation of Fetal Cardiovascular Hemodynamics in the Third Trimester

Purpose To provide reference values for four-dimensional (4D) flow MRI in healthy fetuses and evaluate reliability of fetal 4D flow MRI hemodynamics in third trimester fetuses with normal cardiovascular development or suspected coarctation of the aorta (CoA). Materials and Methods Pregnant patients with healthy fetuses or fetuses with echocardiographic concern for CoA were prospectively recruited between May 2021 and October 2023. Doppler US-gated fetal 4D flow MRI was performed at 3 T. Repeated 4D flow (time permitting) and two-dimensional (2D) phase contrast (PC) MRI data were acquired. Net flow was quantified, and the reliability of 4D flow measurement was evaluated by using precision across adjacent measurement planes, internal consistency based on conservation of mass, comparison of net flow from 4D flow MRI versus 2D PC MRI, and repeatability of 4D flow from separate acquisitions. Results Data were obtained in 34 pregnant participants (mean maternal age, 33 years ± 5 [SD]; mean gestational age, 35 weeks ± 2; n = 22 healthy fetuses and 12 fetuses with suspected CoA). Precision was high across all vascular segments (mean within-subject coefficient of variation = 7%). For mass conservation, there was an average difference of 19% ± 12 between ductus arteriosus plus isthmus flow versus descending aorta flow (r = 0.76). Net flow measured with 4D flow MRI correlated with that measured with 2D PC MRI (r = 0.51) but was underestimated relative to 2D PC MRI by approximately 34%. Hemodynamic parameters quantified from repeated 4D flow acquisitions had good agreement, with an intraclass correlation coefficient of 0.94 between test and retest data. Conclusion Hemodynamic measurements derived from fetal 4D flow MRI were reliable, showing good internal consistency, precision, and repeatability; however, as expected, 4D flow MRI underestimated absolute blood flow relative to 2D PC MRI. Keywords: Fetal MRI, Cardiac, Aorta, Hemodynamics/Flow Dynamics, Pulmonary Arteries Supplemental material is available for this article. © RSNA, 2024.

The super-resolution reconstruction in diffusion-weighted imaging of preoperative rectal MR using generative adversarial network (GAN): Image quality and T-stage assessment

AIMS

To assess the feasibility of using a generative adversarial network (GAN) to improve diffusion-weighted imaging (DWI) resolution in rectal MR scans for rectal carcinoma (RC), and to evaluate both the image quality and the diagnostic utility of super-resolution DWI (SR-DWI) in T stage assessment.

MATERIALS AND METHODS

In this retrospective investigation, a total of 291 patients diagnosed with RC during the period spanning May 2018 to December 2021 were included. The generated SR-DWI was evaluated against the original DWI using multi-scale structural similarity and peak signal-to-noise ratio. Two radiologists scored the SR-DWI and original DWI using a 4-point Likert scale in image quality. Moreover, both radiologists independently evaluated the T category staging based on T2WI and SR-DWI. Interobserver agreement was assessed using Cohen's kappa.

RESULTS

The PSRN and MS-SSIM values of SR-DWI (4 ×) were significantly higher compared to those of SR-DWI (16 ×). Regarding the details of anatomic structures and overall image quality parameters, both radiologists exhibited a preference for SR DWI with 16 × enlargement over SR DWI with 4 × enlargement, yielding significantly superior ratings (both p < 0.001). The T-staging accuracy rates of SR-DWI (16 ×) performed by radiologist 1 and radiologist 2 were significantly superior to those achieved with T2WI (0.621 vs. 0.768, p = 0.027; 0.653 vs 0.810, p = 0.014).

CONCLUSIONS

Our study demonstrates that the adapted super-resolution approach can significantly improve the overall image quality and details of anatomic structure of DWI in rectal MR. And SR-DWI offer better diagnostic accuracy in RC T staging when compared with T2WI.

Late gadolinium enhancement cardiovascular magnetic resonance with generative artificial intelligence

BACKGROUND

Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging enables imaging of scar/fibrosis and is a cornerstone of most CMR imaging protocols. CMR imaging can benefit from image acceleration; however, image acceleration in LGE remains challenging due to its limited signal-to-noise ratio. In this study, we sought to evaluate a rapid two-dimensional (2D) LGE imaging protocol using a generative artificial intelligence (AI) algorithm with inline reconstruction.

METHODS

A generative AI-based image enhancement was used to improve the sharpness of 2D LGE images acquired with low spatial resolution in the phase-encode direction. The generative AI model is an image enhancement technique built on the enhanced super-resolution generative adversarial network. The model was trained using balanced steady-state free-precession cine images, readily used for LGE without additional training. The model was implemented inline, allowing the reconstruction of images on the scanner console. We prospectively enrolled 100 patients (55 ± 14 years, 72 males) referred for clinical CMR at 3T. We collected three sets of LGE images in each subject, with in-plane spatial resolutions of 1.5 × 1.5-3-6 mm2. The generative AI model enhanced in-plane resolution to 1.5 × 1.5 mm2 from the low-resolution counterparts. Images were compared using a blur metric, quantifying the perceived image sharpness (0 = sharpest, 1 = blurriest). LGE image sharpness (using a 5-point scale) was assessed by three independent readers.

RESULTS

The scan times for the three imaging sets were 15 ± 3, 9 ± 2, and 6 ± 1 s, with inline generative AI-based images reconstructed time of ∼37 ms. The generative AI-based model improved visual image sharpness, resulting in lower blur metric compared to low-resolution counterparts (AI-enhanced from 1.5 × 3 mm2 resolution: 0.3 ± 0.03 vs 0.35 ± 0.03, P < 0.01). Meanwhile, AI-enhanced images from 1.5 × 3 mm2 resolution and original LGE images showed similar blur metric (0.30 ± 0.03 vs 0.31 ± 0.03, P = 1.0) Additionally, there was an overall 18% improvement in image sharpness between AI-enhanced images from 1.5 × 3 mm2 resolution and original LGE images in the subjective blurriness score (P < 0.01).

CONCLUSION

The generative AI-based model enhances the image quality of 2D LGE images while reducing the scan time and preserving imaging sharpness. Further evaluation in a large cohort is needed to assess the clinical utility of AI-enhanced LGE images for scar evaluation, as this proof-of-concept study does not provide evidence of an impact on diagnosis.

Accelerated chemical shift encoded cardiovascular magnetic resonance imaging with use of a resolution enhancement network

BACKGROUND

Cardiovascular magnetic resonance (CMR) chemical shift encoding (CSE) enables myocardial fat imaging. We sought to develop a deep learning network (fast chemical shift encoding [FastCSE]) to accelerate CSE.

METHODS

FastCSE was built on a super-resolution generative adversarial network extended to enhance complex-valued image sharpness. FastCSE enhances each echo image independently before water-fat separation. FastCSE was trained with retrospectively identified cines from 1519 patients (56 ± 16 years; 866 men) referred for clinical 3T CMR. In a prospective study of 16 participants (58 ± 19 years; 7 females) and 5 healthy individuals (32 ± 17 years; 5 females), dual-echo CSE images were collected with 1.5 × 1.5 mm2, 2.5 × 1.5 mm2, and 3.8 × 1.9 mm2 resolution using generalized autocalibrating partially parallel acquisition (GRAPPA). FastCSE was applied to images collected with resolution of 2.5 × 1.5 mm2 and 3.8 × 1.9 mm2 to restore sharpness. Fat images obtained from two-point Dixon reconstruction were evaluated using a quantitative blur metric and analyzed with a five-way analysis of variance.

RESULTS

FastCSE successfully reconstructed CSE images inline. FastCSE acquisition, with a resolution of 2.5 × 1.5 mm2 and 3.8 × 1.9 mm2, reduced the number of breath-holds without impacting visualization of fat by approximately 1.5-fold and 3-fold compared to GRAPPA acquisition with a resolution of 1.5 × 1.5 mm2, from 3.0 ± 0.8 breath-holds to 2.0 ± 0.2 and 1.1 ± 0.4 breath-holds, respectively. FastCSE improved image sharpness and removed ringing artifacts in GRAPPA fat images acquired with a resolution of 2.5 × 1.5 mm2 (0.32 ± 0.03 vs 0.35 ± 0.04, P < 0.001) and 3.8 × 1.9 mm2 (0.32 ± 0.03 vs 0.43 ± 0.06, P < 0.001). Blurring in FastCSE images was similar to blurring in images with 1.5 × 1.5 mm2 resolution (0.32 ± 0.03 vs 0.31 ± 0.03, P = 0.57; 0.32 ± 0.03 vs 0.31 ± 0.03, P = 0.66).

CONCLUSION

We showed that a deep learning-accelerated CSE technique based on complex-valued resolution enhancement can reduce the number of breath-holds in CSE imaging without impacting the visualization of fat. FastCSE showed similar image sharpness compared to a standardized parallel imaging method.

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.

Ngo L, J. Manning W, Nezafat R. Accelerated Cardiac MRI Cine with Use of Resolution Enhancement Generative Adversarial Inline Neural Network

Background Cardiac cine can benefit from deep learning-based image reconstruction to reduce scan time and/or increase spatial and temporal resolution. Purpose To develop and evaluate a deep learning model that can be combined with parallel imaging or compressed sensing (CS). Materials and Methods The deep learning model was built on the enhanced super-resolution generative adversarial inline neural network, trained with use of retrospectively identified cine images and evaluated in participants prospectively enrolled from September 2021 to September 2022. The model was applied to breath-hold electrocardiography (ECG)-gated segmented and free-breathing real-time cine images collected with reduced spatial resolution with use of generalized autocalibrating partially parallel acquisitions (GRAPPA) or CS. The deep learning model subsequently restored spatial resolution. For comparison, GRAPPA-accelerated cine images were collected. Diagnostic quality and artifacts were evaluated by two readers with use of Likert scales and compared with use of Wilcoxon signed-rank tests. Agreement for left ventricle (LV) function, volume, and strain was assessed with Bland-Altman analysis. Results The deep learning model was trained on 1616 patients (mean age ± SD, 56 years ± 16; 920 men) and evaluated in 181 individuals, 126 patients (mean age, 57 years ± 16; 77 men) and 55 healthy subjects (mean age, 27 years ± 10; 15 men). In breath-hold ECG-gated segmented cine and free-breathing real-time cine, the deep learning model and GRAPPA showed similar diagnostic quality scores (2.9 vs 2.9, P = .41, deep learning vs GRAPPA) and artifact score (4.4 vs 4.3, P = .55, deep learning vs GRAPPA). Deep learning acquired more sections per breath-hold than GRAPPA (3.1 vs one section, P < .001). In free-breathing real-time cine, the deep learning showed a similar diagnostic quality score (2.9 vs 2.9, P = .21, deep learning vs GRAPPA) and lower artifact score (3.9 vs 4.3, P < .001, deep learning vs GRAPPA). For both sequences, the deep learning model showed excellent agreement for LV parameters, with near-zero mean differences and narrow limits of agreement compared with GRAPPA. Conclusion Deep learning-accelerated cardiac cine showed similarly accurate quantification of cardiac function, volume, and strain to a standardized parallel imaging method. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Vannier and Wang in this issue.

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.