ArticleMagnetic resonance in medicine2021
Analysis of deep complex-valued convolutional neural networks for MRI reconstruction and phase-focused applications.
Article in Magnetic resonance in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 132 citations in OpenAlex.
- A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction.Sensors (Basel, Switzerland) · 2024Pooled it
- Complex Deep Neural Networks for Denoising Ultra-Fast Submillimeter T2*-weighted Imaging and Quantitative Susceptibility Mapping.European journal of radiology artificial intelligence · 2026Article
- Cross-Site Generalization of CNN-BasedNMR in biomedicine · 2026Article
- Jointly Learned 3D Non-Cartesian Sampling With Wave Encoding and Reconstruction for Neurovascular Phase Contrast MRI.Magnetic resonance in medicine · 2026Article
- Fat-water MRI separation using deep complex convolution network.Magma (New York, N.Y.) · 2025Article
- Image space formalism of convolutional neural networks for k-space interpolation.Magnetic resonance in medicine · 2025Article
- Insights on Scan-Specific Deep-Learning Strategies for Brain MRI Parallel Imaging Reconstruction.NMR in biomedicine · 2025Article
- Estimation of fatty acid composition in mammary adipose tissue using deep neural network with unsupervised training.Magnetic resonance in medicine · 2025Article
- Deep learning-based whole-brain BMagnetic resonance in medicine · 2025Article
- Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction.Magma (New York, N.Y.) · 2025Article
- Joint learning of nonlinear representation and projection for fast constrained MRSI reconstruction.Magnetic resonance in medicine · 2025Article
- MRI denoising with a non-blind deep complex-valued convolutional neural network.NMR in biomedicine · 2025Article
- Deep learning-based rapid image reconstruction and motion correction for high-resolution cartesian first-pass myocardial perfusion imaging at 3T.Magnetic resonance in medicine · 2024Article
- Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI.Magma (New York, N.Y.) · 2024Article
- Reproducible Research Practices in Magnetic Resonance Neuroimaging: A Review Informed by Advanced Language Models.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2024Review
- Compensating unknown speed of sound in learned fast 3D limited-view photoacoustic tomography.Photoacoustics · 2024Article
- High-resolution spiral real-time cardiac cine imaging with deep learning-based rapid image reconstruction and quantification.NMR in biomedicine · 2024Article
- Denoising diffusion MRI: Considerations and implications for analysis.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.PeerJ. Computer science · 2024Article
- Complexities of deep learning-based undersampled MR image reconstruction.European radiology experimental · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
purposeDeep learning has had success with MRI reconstruction, but previously published works use real-valued networks. The few works which have tried complex-valued networks have not fully assessed their impact on phase. Therefore, the purpose of this work is to fully investigate end-to-end complex-valued convolutional neural networks (CNNs) for accelerated MRI reconstruction and in several phase-based applications in comparison to 2-channel real-valued networks.
methodsSeveral complex-valued activation functions for MRI reconstruction were implemented, and their performance was compared. Complex-valued convolution was implemented and tested on an unrolled network architecture and a U-Net-based architecture over a wide range of network widths and depths with knee, body, and phase-contrast datasets.
resultsQuantitative and qualitative results demonstrated that complex-valued CNNs with complex-valued convolutions provided superior reconstructions compared to real-valued convolutions with the same number of trainable parameters for both an unrolled network architecture and a U-Net-based architecture, and for 3 different datasets. Complex-valued CNNs consistently had superior normalized RMS error, structural similarity index, and peak SNR compared to real-valued CNNs.
conclusionComplex-valued CNNs can enable superior accelerated MRI reconstruction and phase-based applications such as fat-water separation, and flow quantification compared to real-valued convolutional neural networks.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.