Medicine & Health

464 items

arXiv Sep 25, 2026
The study proposes HierEM, a hierarchical expectation-maximization framework that treats each site's observed prostate lesion annotation as a noisy observation of a latent clean lesion mask, alternating between inferring a voxel-wise posterior over that latent mask and training a CNN with the posterior as a soft target while estimating site- and case-level sensitivity and specificity under a logistic-normal hierarchical prior; on three-site data it reaches per-site mean DSC of 29.50%–39.69% in pooled held-out evaluation and 27.91%–32.67% in leave-one-site-out generalization, with statistically significant improvements over comparison methods (p < 0.039) and interpretable per-site label-quality estimates (sensitivity α of 31.5%–47.3% at specificity β ≈ 0.99).
The study proposes HierEM, a hierarchical expectation-maximization framework that treats each site's observed prostate lesion annotation as a noisy observation of a latent clean lesion mask, alternating between inferring a voxel-wise posterior over that latent mask and training a CNN with the posterior as a soft target while estimating site- and case-level sensitivity and specificity under a logistic-normal hierarchical prior; on three-site data it reaches per-site mean DSC of 29.50%–39.69% in pooled held-out evaluation and 27.91%–32.67% in leave-one-site-out generalization, with statistically significant improvements over comparison methods (p < 0.039) and interpretable per-site label-quality estimates (sensitivity α of 31.5%–47.3% at specificity β ≈ 0.99).
HierEM treats each site's prostate lesion contour as a noisy view of a latent clean mask, lifting leave-one-site-out Dice to 27.91%–32.67% across three sites
The study proposes HierEM, a hierarchical expectation-maximization framework that treats each site's observed prostate lesion annotation as a noisy observation of a latent clean lesion mask, alternating between inferring a voxel-wise posterior over that latent mask and training a CNN with the posterior as a soft target while estimating site- and case-level sensitivity and specificity under a logistic-normal hierarchical prior; on three-site data it reaches per-site mean DSC of 29.50%–39.69% in pooled held-out evaluation and 27.91%–32.67% in leave-one-site-out generalization, with statistically significant improvements over comparison methods (p < 0.039) and interpretable per-site label-quality estimates (sensitivity α of 31.5%–47.3% at specificity β ≈ 0.99).
The study proposes HierEM, a hierarchical expectation-maximization framework that treats each site's observed prostate lesion annotation as a noisy observation of a latent clean lesion mask, alternating between inferring a voxel-wise posterior over that latent mask and training a CNN with the posterior as a soft target while estimating site- and case-level sensitivity and specificity under a logistic-normal hierarchical prior; on three-site data it reaches per-site mean DSC of 29.50%–39.69% in pooled held-out evaluation and 27.91%–32.67% in leave-one-site-out generalization, with statistically significant improvements over comparison methods (p < 0.039) and interpretable per-site label-quality estimates (sensitivity α of 31.5%–47.3% at specificity β ≈ 0.99).
arXiv Sep 25, 2026
The work proposes FermatSyn, which injects anatomical priors via LoRA+ fine-tuning of a frozen SAM2 vision encoder, preserves high-frequency lesion detail with HRDM and CIN, and builds an approximately isotropic receptive field through continuity-constrained Fermat spiral scanning inside a bidirectional Mamba; on SynthRAD2023 and merged BraTS (including BraTS-MEN and BraTS-MET) it surpasses compared methods on PSNR, SSIM, FID and 3D structural consistency, and segmentation models trained on its synthesized images show no significant difference from real-image training (p>0.05).
The work proposes FermatSyn, which injects anatomical priors via LoRA+ fine-tuning of a frozen SAM2 vision encoder, preserves high-frequency lesion detail with HRDM and CIN, and builds an approximately isotropic receptive field through continuity-constrained Fermat spiral scanning inside a bidirectional Mamba; on SynthRAD2023 and merged BraTS (including BraTS-MEN and BraTS-MET) it surpasses compared methods on PSNR, SSIM, FID and 3D structural consistency, and segmentation models trained on its synthesized images show no significant difference from real-image training (p>0.05).
FermatSyn combines SAM2 priors with Fermat-spiral Mamba scanning to synthesize missing medical modalities, topping four brain-imaging benchmarks while segmenters trained on its synthetic images match real-image training
The work proposes FermatSyn, which injects anatomical priors via LoRA+ fine-tuning of a frozen SAM2 vision encoder, preserves high-frequency lesion detail with HRDM and CIN, and builds an approximately isotropic receptive field through continuity-constrained Fermat spiral scanning inside a bidirectional Mamba; on SynthRAD2023 and merged BraTS (including BraTS-MEN and BraTS-MET) it surpasses compared methods on PSNR, SSIM, FID and 3D structural consistency, and segmentation models trained on its synthesized images show no significant difference from real-image training (p>0.05).
The work proposes FermatSyn, which injects anatomical priors via LoRA+ fine-tuning of a frozen SAM2 vision encoder, preserves high-frequency lesion detail with HRDM and CIN, and builds an approximately isotropic receptive field through continuity-constrained Fermat spiral scanning inside a bidirectional Mamba; on SynthRAD2023 and merged BraTS (including BraTS-MEN and BraTS-MET) it surpasses compared methods on PSNR, SSIM, FID and 3D structural consistency, and segmentation models trained on its synthesized images show no significant difference from real-image training (p>0.05).
arXiv Sep 25, 2026
The work proposes Brain Hierarchical Organization Learning (BrainHO), which uses learnable subgraph and graph tokens to aggregate brain regions bottom-up via hierarchical attention driven by node feature affinity, combined with a subgraph orthogonality constraint and a hierarchical consistency constraint; on ABIDE (N=1009, 516 ASD/493 healthy controls) and REST-meta-MDD (N=2380, 1276 MDD/1104 healthy controls) it attains the highest accuracy (69.68% and 64.71%) and sensitivity (73.11% and 67.43%) using only the static PCC connectivity matrix, while visualizing disease-related subgraphs that partly overlap predefined networks such as the SMN and DAN and partly span multiple predefined networks.
The work proposes Brain Hierarchical Organization Learning (BrainHO), which uses learnable subgraph and graph tokens to aggregate brain regions bottom-up via hierarchical attention driven by node feature affinity, combined with a subgraph orthogonality constraint and a hierarchical consistency constraint; on ABIDE (N=1009, 516 ASD/493 healthy controls) and REST-meta-MDD (N=2380, 1276 MDD/1104 healthy controls) it attains the highest accuracy (69.68% and 64.71%) and sensitivity (73.11% and 67.43%) using only the static PCC connectivity matrix, while visualizing disease-related subgraphs that partly overlap predefined networks such as the SMN and DAN and partly span multiple predefined networks.
BrainHO replaces fixed brain atlases with learnable subgraphs, reaching 69.68% accuracy on ABIDE from PCC input alone while localizing cross-network disease subgraphs
The work proposes Brain Hierarchical Organization Learning (BrainHO), which uses learnable subgraph and graph tokens to aggregate brain regions bottom-up via hierarchical attention driven by node feature affinity, combined with a subgraph orthogonality constraint and a hierarchical consistency constraint; on ABIDE (N=1009, 516 ASD/493 healthy controls) and REST-meta-MDD (N=2380, 1276 MDD/1104 healthy controls) it attains the highest accuracy (69.68% and 64.71%) and sensitivity (73.11% and 67.43%) using only the static PCC connectivity matrix, while visualizing disease-related subgraphs that partly overlap predefined networks such as the SMN and DAN and partly span multiple predefined networks.
The work proposes Brain Hierarchical Organization Learning (BrainHO), which uses learnable subgraph and graph tokens to aggregate brain regions bottom-up via hierarchical attention driven by node feature affinity, combined with a subgraph orthogonality constraint and a hierarchical consistency constraint; on ABIDE (N=1009, 516 ASD/493 healthy controls) and REST-meta-MDD (N=2380, 1276 MDD/1104 healthy controls) it attains the highest accuracy (69.68% and 64.71%) and sensitivity (73.11% and 67.43%) using only the static PCC connectivity matrix, while visualizing disease-related subgraphs that partly overlap predefined networks such as the SMN and DAN and partly span multiple predefined networks.
arXiv Sep 25, 2026
The work proposes KD-Brain, a prior-informed graph learning framework that injects disorder-specific semantic priors into the attention Query (Semantic-Conditioned Interaction) and aligns learned subnetwork interaction distributions with clinical priors via a KL-divergence Pathology-Consistent Constraint, achieving better performance than 12 baselines on the ABIDE (NYU) ASD task and single-center BD and MDD tasks while producing functional pathways and critical brain regions consistent with psychiatric pathophysiology.
The work proposes KD-Brain, a prior-informed graph learning framework that injects disorder-specific semantic priors into the attention Query (Semantic-Conditioned Interaction) and aligns learned subnetwork interaction distributions with clinical priors via a KL-divergence Pathology-Consistent Constraint, achieving better performance than 12 baselines on the ABIDE (NYU) ASD task and single-center BD and MDD tasks while producing functional pathways and critical brain regions consistent with psychiatric pathophysiology.
KD-Brain guides subnetwork interaction modeling with semantic priors and a pathology-consistent constraint, beating 12 baselines on ASD, BD, and MDD diagnosis while yielding interpretable functional pathways
The work proposes KD-Brain, a prior-informed graph learning framework that injects disorder-specific semantic priors into the attention Query (Semantic-Conditioned Interaction) and aligns learned subnetwork interaction distributions with clinical priors via a KL-divergence Pathology-Consistent Constraint, achieving better performance than 12 baselines on the ABIDE (NYU) ASD task and single-center BD and MDD tasks while producing functional pathways and critical brain regions consistent with psychiatric pathophysiology.
The work proposes KD-Brain, a prior-informed graph learning framework that injects disorder-specific semantic priors into the attention Query (Semantic-Conditioned Interaction) and aligns learned subnetwork interaction distributions with clinical priors via a KL-divergence Pathology-Consistent Constraint, achieving better performance than 12 baselines on the ABIDE (NYU) ASD task and single-center BD and MDD tasks while producing functional pathways and critical brain regions consistent with psychiatric pathophysiology.
bioRxiv (Cold Spring Harbor Laboratory) Sep 25, 2026
The study presents HARP (Human Cell Atlas Reference for PBMCs), an integrated atlas of ~9 million peripheral blood mononuclear cells (PBMCs) from >2,600 donors across 15 studies spanning four continents, neonates to 97 years, and health and diverse immune-related diseases; it develops optimized integration workflows with novel label-free metrics for integration quality, generates community-driven consensus annotations for 192 immune cell subsets including rare populations as low as 0.
The study presents HARP (Human Cell Atlas Reference for PBMCs), an integrated atlas of ~9 million peripheral blood mononuclear cells (PBMCs) from >2,600 donors across 15 studies spanning four continents, neonates to 97 years, and health and diverse immune-related diseases; it develops optimized integration workflows with novel label-free metrics for integration quality, generates community-driven consensus annotations for 192 immune cell subsets including rare populations as low as 0.
HARP integrates ~9 million PBMCs with 192 immune cell annotations and uncovers a sex dimorphism in prostaglandin signaling
The study presents HARP (Human Cell Atlas Reference for PBMCs), an integrated atlas of ~9 million peripheral blood mononuclear cells (PBMCs) from >2,600 donors across 15 studies spanning four continents, neonates to 97 years, and health and diverse immune-related diseases; it develops optimized integration workflows with novel label-free metrics for integration quality, generates community-driven consensus annotations for 192 immune cell subsets including rare populations as low as 0.
The study presents HARP (Human Cell Atlas Reference for PBMCs), an integrated atlas of ~9 million peripheral blood mononuclear cells (PBMCs) from >2,600 donors across 15 studies spanning four continents, neonates to 97 years, and health and diverse immune-related diseases; it develops optimized integration workflows with novel label-free metrics for integration quality, generates community-driven consensus annotations for 192 immune cell subsets including rare populations as low as 0.
arXiv Sep 25, 2026
Using MedRAX as the instantiated system, this work audits fairness in tool-using chest X-ray question-answering agents and proposes DUCX, a stage-wise decomposition that separates end-to-end bias into tool-exposure bias, tool-transition bias, and LLM reasoning bias; across five driver LLMs on CheXAgentBench and the curated MIMIC-FairnessVQA, demographic gaps persist end to end (equalized odds up to 20.79%, lowest fairness-utility tradeoff down to 28.65%), and subgroup disparities in tool usage, routing patterns, and reasoning traces are not predictable from end-to-end evaluation alone (for example, conditioned on segmentation-tool availability the subgroup utility gap reaches as high as 50%).
Using MedRAX as the instantiated system, this work audits fairness in tool-using chest X-ray question-answering agents and proposes DUCX, a stage-wise decomposition that separates end-to-end bias into tool-exposure bias, tool-transition bias, and LLM reasoning bias; across five driver LLMs on CheXAgentBench and the curated MIMIC-FairnessVQA, demographic gaps persist end to end (equalized odds up to 20.79%, lowest fairness-utility tradeoff down to 28.65%), and subgroup disparities in tool usage, routing patterns, and reasoning traces are not predictable from end-to-end evaluation alone (for example, conditioned on segmentation-tool availability the subgroup utility gap reaches as high as 50%).
DUCX decomposes chest X-ray agent unfairness into tool exposure, tool transition, and reasoning, finding gaps up to about 50% beyond end-to-end metrics
Using MedRAX as the instantiated system, this work audits fairness in tool-using chest X-ray question-answering agents and proposes DUCX, a stage-wise decomposition that separates end-to-end bias into tool-exposure bias, tool-transition bias, and LLM reasoning bias; across five driver LLMs on CheXAgentBench and the curated MIMIC-FairnessVQA, demographic gaps persist end to end (equalized odds up to 20.79%, lowest fairness-utility tradeoff down to 28.65%), and subgroup disparities in tool usage, routing patterns, and reasoning traces are not predictable from end-to-end evaluation alone (for example, conditioned on segmentation-tool availability the subgroup utility gap reaches as high as 50%).
Using MedRAX as the instantiated system, this work audits fairness in tool-using chest X-ray question-answering agents and proposes DUCX, a stage-wise decomposition that separates end-to-end bias into tool-exposure bias, tool-transition bias, and LLM reasoning bias; across five driver LLMs on CheXAgentBench and the curated MIMIC-FairnessVQA, demographic gaps persist end to end (equalized odds up to 20.79%, lowest fairness-utility tradeoff down to 28.65%), and subgroup disparities in tool usage, routing patterns, and reasoning traces are not predictable from end-to-end evaluation alone (for example, conditioned on segmentation-tool availability the subgroup utility gap reaches as high as 50%).
arXiv Sep 25, 2026
Training a unified multi-task model on ThyroidXL (11,635 images, 4,093 patients) and testing externally on DDTI (660 images), this work characterizes negative transfer between segmentation and TI-RADS malignancy classification under cross-center domain shift for a CNN (ResNet34) and a medical ViT (MedSAM), and proposes lightweight decoder-side adapters, MKGA and its residual variant ResMKGA, which refine multi-scale skip features with complementary receptive fields and apply semantic context-conditioned gating to suppress artifact-prone content; the adapters improve out-of-domain segmentation stability (ResNet34+MKGA external Dice 0.659, ResMKGA 0.671, versus 0.590 for the unfrozen baseline) and, in the CNN setting, significantly raise TI-RADS diagnostic accuracy (0.406 to 0.
Training a unified multi-task model on ThyroidXL (11,635 images, 4,093 patients) and testing externally on DDTI (660 images), this work characterizes negative transfer between segmentation and TI-RADS malignancy classification under cross-center domain shift for a CNN (ResNet34) and a medical ViT (MedSAM), and proposes lightweight decoder-side adapters, MKGA and its residual variant ResMKGA, which refine multi-scale skip features with complementary receptive fields and apply semantic context-conditioned gating to suppress artifact-prone content; the adapters improve out-of-domain segmentation stability (ResNet34+MKGA external Dice 0.659, ResMKGA 0.671, versus 0.590 for the unfrozen baseline) and, in the CNN setting, significantly raise TI-RADS diagnostic accuracy (0.406 to 0.
Decoder-side multi-kernel gated adapters raise CNN TI-RADS diagnostic accuracy from 0.406 to 0.632 and improve external segmentation Dice under cross-center thyroid ultrasound shift
Training a unified multi-task model on ThyroidXL (11,635 images, 4,093 patients) and testing externally on DDTI (660 images), this work characterizes negative transfer between segmentation and TI-RADS malignancy classification under cross-center domain shift for a CNN (ResNet34) and a medical ViT (MedSAM), and proposes lightweight decoder-side adapters, MKGA and its residual variant ResMKGA, which refine multi-scale skip features with complementary receptive fields and apply semantic context-conditioned gating to suppress artifact-prone content; the adapters improve out-of-domain segmentation stability (ResNet34+MKGA external Dice 0.659, ResMKGA 0.671, versus 0.590 for the unfrozen baseline) and, in the CNN setting, significantly raise TI-RADS diagnostic accuracy (0.406 to 0.
Training a unified multi-task model on ThyroidXL (11,635 images, 4,093 patients) and testing externally on DDTI (660 images), this work characterizes negative transfer between segmentation and TI-RADS malignancy classification under cross-center domain shift for a CNN (ResNet34) and a medical ViT (MedSAM), and proposes lightweight decoder-side adapters, MKGA and its residual variant ResMKGA, which refine multi-scale skip features with complementary receptive fields and apply semantic context-conditioned gating to suppress artifact-prone content; the adapters improve out-of-domain segmentation stability (ResNet34+MKGA external Dice 0.659, ResMKGA 0.671, versus 0.590 for the unfrozen baseline) and, in the CNN setting, significantly raise TI-RADS diagnostic accuracy (0.406 to 0.
arXiv Sep 25, 2026
The study introduces ICHOR, a self-supervised pretraining framework for ASL cerebral blood flow (CBF) maps based on 3D masked autoencoders (a ViT-Base encoder with a light decoder), pretrained on 11,405 ASL CBF scans from 14 studies spanning multiple sites and protocols, and evaluated on three diagnostic classification tasks plus one ASL CBF map quality prediction regression task, where it outperformed structural-MRI pretrained baselines BrainIAC, BrainSegFounder, and MedicalNet overall across all four tasks.
The study introduces ICHOR, a self-supervised pretraining framework for ASL cerebral blood flow (CBF) maps based on 3D masked autoencoders (a ViT-Base encoder with a light decoder), pretrained on 11,405 ASL CBF scans from 14 studies spanning multiple sites and protocols, and evaluated on three diagnostic classification tasks plus one ASL CBF map quality prediction regression task, where it outperformed structural-MRI pretrained baselines BrainIAC, BrainSegFounder, and MedicalNet overall across all four tasks.
ICHOR self-supervised pretraining on 11,405 ASL CBF scans outperforms structural-MRI pretrained baselines across four downstream tasks
The study introduces ICHOR, a self-supervised pretraining framework for ASL cerebral blood flow (CBF) maps based on 3D masked autoencoders (a ViT-Base encoder with a light decoder), pretrained on 11,405 ASL CBF scans from 14 studies spanning multiple sites and protocols, and evaluated on three diagnostic classification tasks plus one ASL CBF map quality prediction regression task, where it outperformed structural-MRI pretrained baselines BrainIAC, BrainSegFounder, and MedicalNet overall across all four tasks.
The study introduces ICHOR, a self-supervised pretraining framework for ASL cerebral blood flow (CBF) maps based on 3D masked autoencoders (a ViT-Base encoder with a light decoder), pretrained on 11,405 ASL CBF scans from 14 studies spanning multiple sites and protocols, and evaluated on three diagnostic classification tasks plus one ASL CBF map quality prediction regression task, where it outperformed structural-MRI pretrained baselines BrainIAC, BrainSegFounder, and MedicalNet overall across all four tasks.
arXiv Sep 25, 2026
The work proposes MAP-Diff, a multi-anchor guided diffusion framework for progressive 3D whole-body PET denoising that uses clinically observed intermediate-dose scans as trajectory anchors and enforces timestep-dependent supervision to regularize the reverse process toward dose-aligned intermediate states, with anchor timesteps calibrated by degradation matching between simulated diffusion corruption and real multi-dose PET pairs and a timestep-weighted anchor loss stabilizing stage-wise learning; at inference it requires only ultra-low-dose input while enabling progressive, dose-consistent intermediate restoration; on internal (Siemens Biograph Vision Quadra) and cross-scanner (United Imaging uEXPLORER) datasets, compared with 3D DDPM it improves internal PSNR from 42.48 dB to 43.
The work proposes MAP-Diff, a multi-anchor guided diffusion framework for progressive 3D whole-body PET denoising that uses clinically observed intermediate-dose scans as trajectory anchors and enforces timestep-dependent supervision to regularize the reverse process toward dose-aligned intermediate states, with anchor timesteps calibrated by degradation matching between simulated diffusion corruption and real multi-dose PET pairs and a timestep-weighted anchor loss stabilizing stage-wise learning; at inference it requires only ultra-low-dose input while enabling progressive, dose-consistent intermediate restoration; on internal (Siemens Biograph Vision Quadra) and cross-scanner (United Imaging uEXPLORER) datasets, compared with 3D DDPM it improves internal PSNR from 42.48 dB to 43.
MAP-Diff anchors diffusion reverse trajectories to clinical intermediate-dose scans, lifting whole-body low-dose PET denoising PSNR from 42.48 dB to 43.71 dB
The work proposes MAP-Diff, a multi-anchor guided diffusion framework for progressive 3D whole-body PET denoising that uses clinically observed intermediate-dose scans as trajectory anchors and enforces timestep-dependent supervision to regularize the reverse process toward dose-aligned intermediate states, with anchor timesteps calibrated by degradation matching between simulated diffusion corruption and real multi-dose PET pairs and a timestep-weighted anchor loss stabilizing stage-wise learning; at inference it requires only ultra-low-dose input while enabling progressive, dose-consistent intermediate restoration; on internal (Siemens Biograph Vision Quadra) and cross-scanner (United Imaging uEXPLORER) datasets, compared with 3D DDPM it improves internal PSNR from 42.48 dB to 43.
The work proposes MAP-Diff, a multi-anchor guided diffusion framework for progressive 3D whole-body PET denoising that uses clinically observed intermediate-dose scans as trajectory anchors and enforces timestep-dependent supervision to regularize the reverse process toward dose-aligned intermediate states, with anchor timesteps calibrated by degradation matching between simulated diffusion corruption and real multi-dose PET pairs and a timestep-weighted anchor loss stabilizing stage-wise learning; at inference it requires only ultra-low-dose input while enabling progressive, dose-consistent intermediate restoration; on internal (Siemens Biograph Vision Quadra) and cross-scanner (United Imaging uEXPLORER) datasets, compared with 3D DDPM it improves internal PSNR from 42.48 dB to 43.
arXiv Sep 25, 2026
The work proposes Gabor primitives for MRI reconstruction, modulating each Gaussian envelope with a complex exponential so its spectral support can be placed at an arbitrary k-space location, and designs a two-basis low-rank temporal model separating geometry and signal-intensity dynamics; on 99 Cartesian (R=12, R=16) and 102 radial (R≈23) cardiac cine acquisitions, Gabor primitives achieve the highest PSNR and SSIM in all settings, improving over Gaussian primitives by +1.11/+0.72/+0.86 dB and over PICS by +2.34 dB on radial data, with a parameter ratio ρ<0.5 and continuous-resolution evaluation enabling 4× super-resolution.
The work proposes Gabor primitives for MRI reconstruction, modulating each Gaussian envelope with a complex exponential so its spectral support can be placed at an arbitrary k-space location, and designs a two-basis low-rank temporal model separating geometry and signal-intensity dynamics; on 99 Cartesian (R=12, R=16) and 102 radial (R≈23) cardiac cine acquisitions, Gabor primitives achieve the highest PSNR and SSIM in all settings, improving over Gaussian primitives by +1.11/+0.72/+0.86 dB and over PICS by +2.34 dB on radial data, with a parameter ratio ρ<0.5 and continuous-resolution evaluation enabling 4× super-resolution.
Gabor primitives reconstruct accelerated cardiac cine MRI with higher PSNR than compressed sensing, Gaussian primitives, and hash-grid INR baselines on both Cartesian and radial trajectories
The work proposes Gabor primitives for MRI reconstruction, modulating each Gaussian envelope with a complex exponential so its spectral support can be placed at an arbitrary k-space location, and designs a two-basis low-rank temporal model separating geometry and signal-intensity dynamics; on 99 Cartesian (R=12, R=16) and 102 radial (R≈23) cardiac cine acquisitions, Gabor primitives achieve the highest PSNR and SSIM in all settings, improving over Gaussian primitives by +1.11/+0.72/+0.86 dB and over PICS by +2.34 dB on radial data, with a parameter ratio ρ<0.5 and continuous-resolution evaluation enabling 4× super-resolution.
The work proposes Gabor primitives for MRI reconstruction, modulating each Gaussian envelope with a complex exponential so its spectral support can be placed at an arbitrary k-space location, and designs a two-basis low-rank temporal model separating geometry and signal-intensity dynamics; on 99 Cartesian (R=12, R=16) and 102 radial (R≈23) cardiac cine acquisitions, Gabor primitives achieve the highest PSNR and SSIM in all settings, improving over Gaussian primitives by +1.11/+0.72/+0.86 dB and over PICS by +2.34 dB on radial data, with a parameter ratio ρ<0.5 and continuous-resolution evaluation enabling 4× super-resolution.

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