Publications
1015 results found
Taylor TRP, Menten MJ, Rueckert D, et al., 2023, The role of the retinal vasculature in age-related macular degeneration: a spotlight on OCTA, EYE, ISSN: 0950-222X
Cruz G, Hammernik K, Kuestner T, et al., 2023, Single-heartbeat cardiac cine imaging via jointly regularized non-rigid motion corrected reconstruction, NMR in Biomedicine, Vol: 36, Pages: 1-16, ISSN: 0952-3480
PURPOSE: Develop a novel approach for 2D breath-hold cardiac cine from a single heartbeat, by combining cardiac motion corrected reconstructions and non-rigidly aligned patch-based regularization. METHODS: Conventional cardiac cine imaging is obtained via motion resolved reconstructions of data acquired over multiple heartbeats. Here, we achieve single-heartbeat cine imaging by incorporating non-rigid cardiac motion correction into the reconstruction of each cardiac phase, in conjunction with a motion-aligned patch-based regularization. The proposed Motion Corrected CINE (MC-CINE) incorporates all acquired data into the reconstruction of each (motion corrected) cardiac phase, resulting in a better posed problem than motion resolved approaches. MC-CINE was compared to iterative SENSE and XD-GRASP in fourteen healthy subjects in terms of image sharpness, reader scoring (1-5 range) and reader ranking (1-9 range) of image quality, and single-slice left ventricular assessment. RESULTS: MC-CINE was significantly superior to both iterative SENSE and XD-GRASP using 20, 2 and 1 heartbeat(s). Iterative SENSE, XD-GRASP and MC-CINE achieved sharpness of 74%, 74% and 82% using 20 heartbeats, and 53%, 66% and 82% with 1 heartbeat, respectively. Corresponding results for reader scores were 4.0, 4.7 and 4.9, with 20 heartbeats, and 1.1, 3.0 and 3.9 with 1 heartbeat. Corresponding results for reader rankings were 5.3, 7.3 and 8.6 with 20 heartbeats, and 1.0, 3.2 and 5.4 with 1 heartbeat. MC-CINE using a single heartbeat presented non-significant differences in image quality to iterative SENSE with 20 heartbeats. MC-CINE and XD-GRASP at one heartbeat both presented a non-significant negative bias of <2% in ejection fraction relative to the reference iterative SENSE. CONCLUSION: The proposed MC-CINE significantly improves image quality relative to iterative SENSE and XD-GRASP, enabling 2D cine from a single heartbeat.
Al-Jibury E, King JWD, Guo Y, et al., 2023, A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks, Nature Communications, Vol: 14, ISSN: 2041-1723
The organisation of the genome in nuclear space is an important frontier of biology. Chromosome conformation capture methods such as Hi-C and Micro-C produce genome-wide chromatin contact maps that provide rich data containing quantitative and qualitative information about genome architecture. Most conventional approaches to genome-wide chromosome conformation capture data are limited to the analysis of pre-defined features, and may therefore miss important biological information. One constraint is that biologically important features can be masked by high levels of technical noise in the data. Here we introduce a replicate-based method for deep learning from chromatin conformation contact maps. Using a Siamese network configuration our approach learns to distinguish technical noise from biological variation and outperforms image similarity metrics across a range of biological systems. The features extracted from Hi-C maps after perturbation of cohesin and CTCF reflect the distinct biological functions of cohesin and CTCF in the formation of domains and boundaries, respectively. The learnt distance metrics are biologically meaningful, as they mirror the density of cohesin and CTCF binding. These properties make our method a powerful tool for the exploration of chromosome conformation capture data, such as Hi-C capture Hi-C, and Micro-C.
Meiser P, Knolle MA, Hirschberger A, et al., 2023, A distinct stimulatory cDC1 subpopulation amplifies CD8+T cell responses in tumors for protective anti-cancer immunity, CANCER CELL, Vol: 41, Pages: 1498-+, ISSN: 1535-6108
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Woodrow RE, Winzeck S, Luppi A, et al., 2023, Acute thalamic connectivity precedes chronic post-concussive symptoms in mild traumatic brain injury, BRAIN, Vol: 146, Pages: 3484-3499, ISSN: 0006-8950
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- Citations: 2
Anders P, Traber GL, Pfau M, et al., 2023, Comparison of Novel Volumetric Microperimetry Metrics in Intermediate Age-Related Macular Degeneration: PINNACLE Study Report 3., Transl Vis Sci Technol, Vol: 12
PURPOSE: To investigate and compare novel volumetric microperimetry (MP)-derived metrics in intermediate age-related macular degeneration (iAMD), as current MP metrics show high variability and low sensitivity. METHODS: This is a cross-sectional analysis of microperimetry baseline data from the multicenter, prospective PINNACLE study (ClinicalTrials.gov NCT04269304). The Visual Field Modeling and Analysis (VFMA) software and an open-source implementation (OSI) were applied to calculate MP-derived hill-of-vison (HOV) surface plots and the total volume (VTOT) beneath the plots. Bland-Altman plots were used for methodologic comparison, and the association of retinal sensitivity metrics with explanatory variables was tested with mixed-effects models. RESULTS: In total, 247 eyes of 189 participants (75 ± 7.3 years) were included in the analysis. The VTOT output of VFMA and OSI exhibited a significant difference (P < 0.0001). VFMA yielded slightly higher coefficients of determination than OSI and mean sensitivity (MS) in univariable and multivariable modeling, for example, in association with low-luminance visual acuity (LLVA) (marginal R2/conditional R2: VFMA 0.171/0.771, OSI 0.162/0.765, MS 0.133/0.755). In the multivariable analysis, LLVA was the only demonstrable predictor of VFMA VTOT (t-value, P-value: -7.5, <0.001) and MS (-6.5, <0.001). CONCLUSIONS: The HOV-derived metric of VTOT exhibits favorable characteristics compared to MS in evaluating retinal sensitivity. The output of VFMA and OSI is not exactly interchangeable in this cross-sectional analysis. Longitudinal analysis is necessary to assess their performance in ability-to-detect change. TRANSLATIONAL RELEVANCE: This study explores new volumetric MP endpoints for future application in therapeutic trials in iAMD and reports specific characteristics of the available HOV software applications.
Mikolić A, Steyerberg EW, Polinder S, et al., 2023, Prognostic Models for Global Functional Outcome and Post-Concussion Symptoms Following Mild Traumatic Brain Injury: A Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Study., J Neurotrauma, Vol: 40, Pages: 1651-1670
After mild traumatic brain injury (mTBI), a substantial proportion of individuals do not fully recover on the Glasgow Outcome Scale Extended (GOSE) or experience persistent post-concussion symptoms (PPCS). We aimed to develop prognostic models for the GOSE and PPCS at 6 months after mTBI and to assess the prognostic value of different categories of predictors (clinical variables; questionnaires; computed tomography [CT]; blood biomarkers). From the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study, we included participants aged 16 or older with Glasgow Coma Score (GCS) 13-15. We used ordinal logistic regression to model the relationship between predictors and the GOSE, and linear regression to model the relationship between predictors and the Rivermead Post-concussion Symptoms Questionnaire (RPQ) total score. First, we studied a pre-specified Core model. Next, we extended the Core model with other clinical and sociodemographic variables available at presentation (Clinical model). The Clinical model was then extended with variables assessed before discharge from hospital: early post-concussion symptoms, CT variables, biomarkers, or all three categories (extended models). In a subset of patients mostly discharged home from the emergency department, the Clinical model was extended with 2-3-week post-concussion and mental health symptoms. Predictors were selected based on Akaike's Information Criterion. Performance of ordinal models was expressed as a concordance index (C) and performance of linear models as proportion of variance explained (R2). Bootstrap validation was used to correct for optimism. We included 2376 mTBI patients with 6-month GOSE and 1605 patients with 6-month RPQ. The Core and Clinical models for GOSE showed moderate discrimination (C = 0.68 95% confidence interval 0.68 to 0.70 and C = 0.70[0.69 to 0.71], respectively) and injury severity was the strongest predictor. The
Sitaru S, Oueslati T, Schielein MC, et al., 2023, [Automatische Körperteil-Identifikation in dermatologischen klinischen Bildern durch maschinelles Lernen]., J Dtsch Dermatol Ges, Vol: 21, Pages: 863-871
Sitaru S, Oueslati T, Schielein MC, et al., 2023, Automatic body part identification in real-world clinical dermatological images using machine learning, JOURNAL DER DEUTSCHEN DERMATOLOGISCHEN GESELLSCHAFT, Vol: 21, Pages: 863-869, ISSN: 1610-0379
Usynin D, Rueckert D, Kaissis G, 2023, Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks, ACM TRANSACTIONS ON PRIVACY AND SECURITY, Vol: 26, ISSN: 2471-2566
Foellmer B, Williams MCC, Dey D, et al., 2023, Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries, NATURE REVIEWS CARDIOLOGY, ISSN: 1759-5002
Karolis VR, Fitzgibbon SP, Cordero-Grande L, et al., 2023, Maturational networks of human fetal brain activity reveal emerging connectivity patterns prior to ex-utero exposure, COMMUNICATIONS BIOLOGY, Vol: 6
Mueller TT, Paetzold JC, Prabhakar C, et al., 2023, Differentially Private Graph Neural Networks for Whole-Graph Classification, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol: 45, Pages: 7308-7318, ISSN: 0162-8828
Sutton J, Menten MJ, Riedl S, et al., 2023, Correction: Developing and validating a multivariable prediction model which predicts progression of intermediate to late age-related macular degeneration-the PINNACLE trial protocol, Eye, Vol: 37, Pages: 1-1, ISSN: 0950-222X
Paetzold JCC, Lux L, Kreitner L, et al., 2023, Geometric deep learning for disease classification in OCTA images, Annual Meeting of the Association-for-Research-in-Vision-and-Ophthalmology (ARVO), Publisher: ASSOC RESEARCH VISION OPHTHALMOLOGY INC, ISSN: 0146-0404
Chakravarty A, Emre T, Leingang O, et al., 2023, Self-supervised machine learning for individual prediction of conversion to neovascular AMD in PINNACLE study, Annual Meeting of the Association-for-Research-in-Vision-and-Ophthalmology (ARVO), Publisher: ASSOC RESEARCH VISION OPHTHALMOLOGY INC, ISSN: 0146-0404
Menten M, Kreitner L, Paetzold J, et al., 2023, Synthetic data facilitates deep-learning-based segmentation of OCT angiography images without human annotations, Annual Meeting of the Association-for-Research-in-Vision-and-Ophthalmology (ARVO), Publisher: ASSOC RESEARCH VISION OPHTHALMOLOGY INC, ISSN: 0146-0404
Anders P, Traber G, Pfau M, et al., 2023, Regional Variation of Retinal Sensitivity in Intermediate AMD in the PINNACLE study, Annual Meeting of the Association-for-Research-in-Vision-and-Ophthalmology (ARVO), Publisher: ASSOC RESEARCH VISION OPHTHALMOLOGY INC, ISSN: 0146-0404
Williams LZJ, Fitzgibbon SP, Bozek J, et al., 2023, Structural and functional asymmetry of the neonatal cerebral cortex, NATURE HUMAN BEHAVIOUR, Vol: 7, Pages: 942-955, ISSN: 2397-3374
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Fayed AE, Menten MJ, Kreitner L, et al., 2023, Retinal Vasculature of Different Diameters and Plexuses Exhibit Distinct Vulnerability to Varying Stages of Diabetic Retinopathy, Annual Meeting of the Association-for-Research-in-Vision-and-Ophthalmology (ARVO), Publisher: ASSOC RESEARCH VISION OPHTHALMOLOGY INC, ISSN: 0146-0404
Holland R, Leingang O, Hagag AM, et al., 2023, Deep-learning-based clustering of OCT images for automated biomarker discovery in age-related macular degeneration, Annual Meeting of the Association-for-Research-in-Vision-and-Ophthalmology (ARVO), Publisher: ASSOC RESEARCH VISION OPHTHALMOLOGY INC, ISSN: 0146-0404
Cullen H, Dimitrakopoulou K, Patel H, et al., 2023, Common genetic variability associated with years of education and cognitive performance predicts language outcomes at two, 55th European-Society-of-Human-Genetics (ESHG) Conference, Publisher: SPRINGERNATURE, Pages: 339-339, ISSN: 1018-4813
Fenn-Moltu S, Fitzgibbon SP, Ciarrusta J, et al., 2023, Development of neonatal brain functional centrality and alterations associated with preterm birth, Cerebral Cortex, Vol: 33, Pages: 5585-5596, ISSN: 1047-3211
Formation of the functional connectome in early life underpins future learning and behavior. However, our understanding of how the functional organization of brain regions into interconnected hubs (centrality) matures in the early postnatal period is limited, especially in response to factors associated with adverse neurodevelopmental outcomes such as preterm birth. We characterized voxel-wise functional centrality (weighted degree) in 366 neonates from the Developing Human Connectome Project. We tested the hypothesis that functional centrality matures with age at scan in term-born babies and is disrupted by preterm birth. Finally, we asked whether neonatal functional centrality predicts general neurodevelopmental outcomes at 18 months. We report an age-related increase in functional centrality predominantly within visual regions and a decrease within the motor and auditory regions in term-born infants. Preterm-born infants scanned at term equivalent age had higher functional centrality predominantly within visual regions and lower measures in motor regions. Functional centrality was not related to outcome at 18 months old. Thus, preterm birth appears to affect functional centrality in regions undergoing substantial development during the perinatal period. Our work raises the question of whether these alterations are adaptive or disruptive and whether they predict neurodevelopmental characteristics that are more subtle or emerge later in life.
Sutton J, Menten MJ, Riedl S, et al., 2023, Developing and validating a multivariable prediction model which predicts progression of intermediate to late age-related macular degeneration-the PINNACLE trial protocol, Eye, Vol: 37, Pages: 1275-1283, ISSN: 0950-222X
AimsAge-related macular degeneration (AMD) is characterised by a progressive loss of central vision. Intermediate AMD is a risk factor for progression to advanced stages categorised as geographic atrophy (GA) and neovascular AMD. However, rates of progression to advanced stages vary between individuals. Recent advances in imaging and computing technologies have enabled deep phenotyping of intermediate AMD. The aim of this project is to utilise machine learning (ML) and advanced statistical modelling as an innovative approach to discover novel features and accurately quantify markers of pathological retinal ageing that can individualise progression to advanced AMD.MethodsThe PINNACLE study consists of both retrospective and prospective parts. In the retrospective part, more than 400,000 optical coherent tomography (OCT) images collected from four University Teaching Hospitals and the UK Biobank Population Study are being pooled, centrally stored and pre-processed. With this large dataset featuring eyes with AMD at various stages and healthy controls, we aim to identify imaging biomarkers for disease progression for intermediate AMD via supervised and unsupervised ML. The prospective study part will firstly characterise the progression of intermediate AMD in patients followed between one and three years; secondly, it will validate the utility of biomarkers identified in the retrospective cohort as predictors of progression towards late AMD. Patients aged 55–90 years old with intermediate AMD in at least one eye will be recruited across multiple sites in UK, Austria and Switzerland for visual function tests, multimodal retinal imaging and genotyping. Imaging will be repeated every four months to identify early focal signs of deterioration on spectral-domain optical coherence tomography (OCT) by human graders. A focal event triggers more frequent follow-up with visual function and imaging tests. The primary outcome is the sensitivity and specificity of the OCT ima
Hinterwimmer F, Lazic I, Langer S, et al., 2023, Prediction of complications and surgery duration in primary TKA with high accuracy using machine learning with arthroplasty-specific data., Knee Surg Sports Traumatol Arthrosc, Vol: 31, Pages: 1323-1333
PURPOSE: The number of primary total knee arthroplasties (TKA) is expected to rise constantly. For patients and healthcare providers, the early identification of risk factors therefore becomes increasingly fundamental in the context of precision medicine. Others have already investigated the detection of risk factors by conducting literature reviews and applying conventional statistical methods. Since the prediction of events has been moderately accurate, a more comprehensive approach is needed. Machine learning (ML) algorithms have had ample success in many disciplines. However, these methods have not yet had a significant impact in orthopaedic research. The selection of a data source as well as the inclusion of relevant parameters is of utmost importance in this context. In this study, a standardized approach for ML in TKA to predict complications during surgery and an irregular surgery duration using data from two German arthroplasty-specific registries was evaluated. METHODS: The dataset is based on two initiatives of the German Society for Orthopaedics and Orthopaedic Surgery. A problem statement and initial parameters were defined. After screening, cleaning and preparation of these datasets, 864 cases of primary TKA (2016-2019) were gathered. The XGBoost algorithm was chosen and applied with a hyperparameter search, a cross validation and a loss weighting to cope with class imbalance. For final evaluation, several metrics (accuracy, sensitivity, specificity, AUC) were calculated. RESULTS: An accuracy of 92.0%, sensitivity of 34.8%, specificity of 95.8%, and AUC of 78.0% were achieved for predicting complications in primary TKA and 93.4%, 74.0%, 96.3%, and 91.6% for predicting irregular surgery duration, respectively. While traditional statistics (correlation coefficient) could not find any relevant correlation between any two parameters, the feature importance revealed several non-linear outcomes. CONCLUSION: In this study, a feasible ML model to predict outco
Dahan S, Fawaz A, Suliman MA, et al., 2023, The Multiscale Surface Vision Transformer., ArXiv
Surface meshes are a favoured domain for representing structural and functional information on the human cortex, but their complex topology and geometry pose significant challenges for deep learning analysis. While Transformers have excelled as domain-agnostic architectures for sequence-to-sequence learning, notably for structures where the translation of the convolution operation is non-trivial, the quadratic cost of the self-attention operation remains an obstacle for many dense prediction tasks. Inspired by some of the latest advances in hierarchical modelling with vision transformers, we introduce the Multiscale Surface Vision Transformer (MS-SiT) as a backbone architecture for surface deep learning. The self-attention mechanism is applied within local-mesh-windows to allow for high-resolution sampling of the underlying data, while a shifted-window strategy improves the sharing of information between windows. Neighbouring patches are successively merged, allowing the MS-SiT to learn hierarchical representations suitable for any prediction task. Results demonstrate that the MS-SiT outperforms existing surface deep learning methods for neonatal phenotyping prediction tasks using the Developing Human Connectome Project (dHCP) dataset. Furthermore, building the MS-SiT backbone into a U-shaped architecture for surface segmentation demonstrates competitive results on cortical parcellation using the UK Biobank (UKB) and manually-annotated MindBoggle datasets. Code and trained models are publicly available at https://github.com/metrics-lab/surface-vision-transformers.
Hinterwimmer F, Consalvo S, Wilhelm N, et al., 2023, SAM-X: sorting algorithm for musculoskeletal x-ray radiography., Eur Radiol, Vol: 33, Pages: 1537-1544
OBJECTIVE: To develop a two-phased deep learning sorting algorithm for post-X-ray image acquisition in order to facilitate large musculoskeletal image datasets according to their anatomical entity. METHODS: In total, 42,608 unstructured and pseudonymized radiographs were retrieved from the PACS of a musculoskeletal tumor center. In phase 1, imaging data were sorted into 1000 clusters by a self-supervised model. A human-in-the-loop radiologist assigned weak, semantic labels to all clusters and clusters with the same label were merged. Three hundred thirty-two non-musculoskeletal clusters were discarded. In phase 2, the initial model was modified by "injecting" the identified labels into the self-supervised model to train a classifier. To provide statistical significance, data split and cross-validation were applied. The hold-out test set consisted of 50% external data. To gain insight into the model's predictions, Grad-CAMs were calculated. RESULTS: The self-supervised clustering resulted in a high normalized mutual information of 0.930. The expert radiologist identified 28 musculoskeletal clusters. The modified model achieved a classification accuracy of 96.2% and 96.6% for validation and hold-out test data for predicting the top class, respectively. When considering the top two predicted class labels, an accuracy of 99.7% and 99.6% was accomplished. Grad-CAMs as well as final cluster results underlined the robustness of the proposed method by showing that it focused on similar image regions a human would have considered for categorizing images. CONCLUSION: For efficient dataset building, we propose an accurate deep learning sorting algorithm for classifying radiographs according to their anatomical entity in the assessment of musculoskeletal diseases. KEY POINTS: • Classification of large radiograph datasets according to their anatomical entity. • Paramount importance of structuring vast amounts of retrospective data for modern deep learning app
Menten MJ, Holland R, Leingang O, et al., 2023, Exploring healthy retinal aging with deep learning, Ophthalmology Science, Vol: 3, Pages: 1-10, ISSN: 2666-9145
PurposeTo study the individual course of retinal changes caused by healthy aging using deep learning.DesignRetrospective analysis of a large data set of retinal OCT images.ParticipantsA total of 85 709 adults between the age of 40 and 75 years of whom OCT images were acquired in the scope of the UK Biobank population study.MethodsWe created a counterfactual generative adversarial network (GAN), a type of neural network that learns from cross-sectional, retrospective data. It then synthesizes high-resolution counterfactual OCT images and longitudinal time series. These counterfactuals allow visualization and analysis of hypothetical scenarios in which certain characteristics of the imaged subject, such as age or sex, are altered, whereas other attributes, crucially the subject’s identity and image acquisition settings, remain fixed.Main Outcome MeasuresUsing our counterfactual GAN, we investigated subject-specific changes in the retinal layer structure as a function of age and sex. In particular, we measured changes in the retinal nerve fiber layer (RNFL), combined ganglion cell layer plus inner plexiform layer (GCIPL), inner nuclear layer to the inner boundary of the retinal pigment epithelium (INL-RPE), and retinal pigment epithelium (RPE).ResultsOur counterfactual GAN is able to smoothly visualize the individual course of retinal aging. Across all counterfactual images, the RNFL, GCIPL, INL-RPE, and RPE changed by −0.1 μm ± 0.1 μm, −0.5 μm ± 0.2 μm, −0.2 μm ± 0.1 μm, and 0.1 μm ± 0.1 μm, respectively, per decade of age. These results agree well with previous studies based on the same cohort from the UK Biobank population study. Beyond population-wide average measures, our counterfactual GAN allows us to explore whether the retinal layers of a given eye will increase in thickness, decrease in thickness, or stagnate as a subject ages.ConclusionThis study demonstrates how counterfactual GANs
Ma Q, Li L, Robinson EC, et al., 2023, CortexODE: learning cortical surface reconstruction by neural ODEs, IEEE Transactions on Medical Imaging, Vol: 42, Pages: 430-443, ISSN: 0278-0062
We present CortexODE, a deep learning framework for cortical surface reconstruction. CortexODE leverages neural ordinary differential equations (ODEs) to deform an input surface into a target shape by learning a diffeomorphic flow. The trajectories of the points on the surface are modeled as ODEs, where the derivatives of their coordinates are parameterized via a learnable Lipschitz-continuous deformation network. This provides theoretical guarantees for the prevention of self-intersections. CortexODE can be integrated to an automatic learning-based pipeline, which reconstructs cortical surfaces efficiently in less than 5 seconds. The pipeline utilizes a 3D U-Net to predict a white matter segmentation from brain Magnetic Resonance Imaging (MRI) scans, and further generates a signed distance function that represents an initial surface. Fast topology correction is introduced to guarantee homeomorphism to a sphere. Following the isosurface extraction step, two CortexODE models are trained to deform the initial surface to white matter and pial surfaces respectively. The proposed pipeline is evaluated on large-scale neuroimage datasets in various age groups including neonates (25-45 weeks), young adults (22-36 years) and elderly subjects (55-90 years). Our experiments demonstrate that the CortexODE-based pipeline can achieve less than 0.2mm average geometric error while being orders of magnitude faster compared to conventional processing pipelines.
Huang W, Li HB, Pan J, et al., 2023, Neural Implicit k-Space for Binning-Free Non-Cartesian Cardiac MR Imaging, Pages: 548-560, ISSN: 0302-9743
In this work, we propose a novel image reconstruction framework that directly learns a neural implicit representation in k-space for ECG-triggered non-Cartesian Cardiac Magnetic Resonance Imaging (CMR). While existing methods bin acquired data from neighboring time points to reconstruct one phase of the cardiac motion, our framework allows for a continuous, binning-free, and subject-specific k-space representation. We assign a unique coordinate that consists of time, coil index, and frequency domain location to each sampled k-space point. We then learn the subject-specific mapping from these unique coordinates to k-space intensities using a multi-layer perceptron with frequency domain regularization. During inference, we obtain a complete k-space for Cartesian coordinates and an arbitrary temporal resolution. A simple inverse Fourier transform recovers the image, eliminating the need for density compensation and costly non-uniform Fourier transforms for non-Cartesian data. This novel imaging framework was tested on 42 radially sampled datasets from 6 subjects. The proposed method outperforms other techniques qualitatively and quantitatively using data from four and one heartbeat(s) and 30 cardiac phases. Our results for one heartbeat reconstruction of 50 cardiac phases show improved artifact removal and spatio-temporal resolution, leveraging the potential for real-time CMR. (Code available: https://github.com/wenqihuang/NIK_MRI ).
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