Paper Citation Record · LEDGER

An unsupervised method for MRI recovery: Deep image prior with structured sparsity

As of 14 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2501.01482.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.01482 v3

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Pith citing papers itemized under the disclosed page cap.

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Outbound references

Observation 378fa020-f0cc-4f0d-80df-b2496017dad5 · outbound

This paper cites Low-rank and adaptive sparse signal (LASSI) models for highly accelerated dynamic imaging.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Low-rank and adaptive sparse signal (LASSI) models for highly accelerated dynamic imaging

Reference 1

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Observation baf780a5-5742-41b1-a393-e09f75ea8422 · outbound

This paper cites SENSE: sensitivity encoding for fast MRI.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity SENSE: sensitivity encoding for fast MRI

Reference 2

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Observation e9a62fe1-fe95-44ef-aee9-39a0c2eebfe6 · outbound

This paper cites General- ized autocalibrating partially parallel acqui- sitions (GRAPPA).

An unsupervised method for MRI recovery: Deep image prior with structured sparsity General- ized autocalibrating partially parallel acqui- sitions (GRAPPA)

Reference 3

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This paper cites ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA

Reference 4

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Observation 5016c732-620f-4553-98cc-dec1bb28cd00 · outbound

This paper cites Sparse MRI: The application of compressed sensing for rapid MR imaging.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Sparse MRI: The application of compressed sensing for rapid MR imaging

Reference 5

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Observation 5d29c584-39b7-4f96-bf49-77aa7f66df96 · outbound

This paper cites Low-rank plus sparse matrix decomposition for acceler- ated dynamic MRI with separation of back- ground and dynamic components.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Low-rank plus sparse matrix decomposition for acceler- ated dynamic MRI with separation of back- ground and dynamic components

Reference 6

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Observation eb316974-1292-49e6-a824-3c6944483e7e · outbound

This paper cites fastMRI: An open dataset and benchmarks for accelerated MRI.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity fastMRI: An open dataset and benchmarks for accelerated MRI

Reference 7

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This paper cites Deep learning for undersampled MRI recon- struction.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Deep learning for undersampled MRI recon- struction

Reference 8

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Observation d90b66cf-ef8c-4989-b157-90acf9dff019 · outbound

This paper cites OCMR (v1.0)–open- access multi-coil k-space dataset for cardio- vascular magnetic resonance imaging.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity OCMR (v1.0)–open- access multi-coil k-space dataset for cardio- vascular magnetic resonance imaging

Reference 9

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Observation 87712a54-5bdc-4132-b56e-7425c01a67be · outbound

This paper cites Learn- ing a variational network for reconstruction of accelerated MRI data.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Learn- ing a variational network for reconstruction of accelerated MRI data

Reference 10

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Observation d94b7b5e-aff1-40a2-ab00-632913b9be78 · outbound

This paper cites MoDL: Model-based deep learning architecture for inverse problems.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity MoDL: Model-based deep learning architecture for inverse problems

Reference 11

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Observation f8abb4f8-5a0b-436e-b56d-f2a82c6c3d93 · outbound

This paper cites Plug-and-play priors for model based reconstruction.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Plug-and-play priors for model based reconstruction

Reference 12

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Observation 4bf8fba7-0510-464c-be86-3ab8fb35c6d9 · outbound

This paper cites Plug-and- play methods for magnetic resonance imag- ing: Using denoisers for image recovery.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Plug-and- play methods for magnetic resonance imag- ing: Using denoisers for image recovery

Reference 13

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Observation 73b3c571-81ac-49b6-9d4e-426ce1cd44ee · outbound

This paper cites Deep image prior.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Deep image prior

Reference 14

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Observation f6a9d75d-361d-4b50-bbaf-54d2b6b60edb · outbound

This paper cites Deep decoder: Con- cise image representations from untrained non-convolutional networks.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Deep decoder: Con- cise image representations from untrained non-convolutional networks

Reference 15

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Observation 8d25edf8-e3d3-4400-a980-cafb9ef7014d · outbound

This paper cites The spectral bias of the deep image prior.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity The spectral bias of the deep image prior

Reference 16

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Observation fc3e1635-f27e-4039-83cf-553a9499dafa · outbound

This paper cites Early stopping for deep image prior.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Early stopping for deep image prior

Reference 17

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Observation 753eb5a3-40bd-4554-8985-cdd76971cc22 · outbound

This paper cites Robust Self-Guided Deep Image Prior.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Robust Self-Guided Deep Image Prior

Reference 18

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Observation 97d5fda2-6eb6-4758-adbe-1bb571034080 · outbound

This paper cites Physics- driven deep learning for computational mag- netic resonance imaging: Combining physics 15 and machine learning for improved medical imaging.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Physics- driven deep learning for computational mag- netic resonance imaging: Combining physics 15 and machine learning for improved medical imaging

Reference 19

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This paper cites Deep learning for accelerated and robust MRI reconstruction.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Deep learning for accelerated and robust MRI reconstruction

Reference 20

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Observation 219ec55f-eac9-4ab2-98dd-b50ffeb86e18 · outbound

This paper cites Time-dependent deep image prior for dynamic MRI.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Time-dependent deep image prior for dynamic MRI

Reference 21

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Observation 41f0d03d-a2b7-4a20-8479-7c7f6c6b3390 · outbound

This paper cites Dynamic imaging using a deep generative SToRM (Gen-SToRM) model.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Dynamic imaging using a deep generative SToRM (Gen-SToRM) model

Reference 22

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This paper cites Dynamic imaging using deep bi-linear unsupervised representation (DEBLUR).

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Dynamic imaging using deep bi-linear unsupervised representation (DEBLUR)

Reference 23

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Observation 13c43f0f-fe5a-4d7b-98d9-bc1b45dc8fec · outbound

This paper cites A low-rank deep image prior reconstruction for free- breathing ungated spiral functional CMR at 0.55 T and 1.5 T.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity A low-rank deep image prior reconstruction for free- breathing ungated spiral functional CMR at 0.55 T and 1.5 T

Reference 24

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This paper cites Dynamic imaging using motion-compensated smoothness regulariza- tion on manifolds (MoCo-SToRM).

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Dynamic imaging using motion-compensated smoothness regulariza- tion on manifolds (MoCo-SToRM)

Reference 25

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Observation e8eee86d-c9a2-4ce2-8582-bf8d0dc777ca · outbound

This paper cites Implementation and validation of a three- dimensional cardiac motion estimation net- work.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Implementation and validation of a three- dimensional cardiac motion estimation net- work

Reference 26

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Observation cc68fcd9-6882-41f9-bc94-52b7c68f4a39 · outbound

This paper cites A bidirectional registration neural network for cardiac motion tracking using cine MRI images.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity A bidirectional registration neural network for cardiac motion tracking using cine MRI images

Reference 27

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This paper cites End- to-end deep learning of non-rigid groupwise registration and reconstruction of dynamic MRI.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity End- to-end deep learning of non-rigid groupwise registration and reconstruction of dynamic MRI

Reference 28

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Observation 5b594a84-8f69-4609-863b-dddce480b023 · outbound

This paper cites Attention-aware non-rigid image registration for accelerated MR imaging.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Attention-aware non-rigid image registration for accelerated MR imaging

Reference 29

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Observation 1fb2cdf4-2108-45a1-8fba-dbde070c61c1 · outbound

This paper cites Deep Image prior with StruCtUred Sparsity (DISCUS) for dynamic MRI reconstruction.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Deep Image prior with StruCtUred Sparsity (DISCUS) for dynamic MRI reconstruction

Reference 30

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Observation 9cacae76-43d3-4b57-9782-8731e84a30a5 · outbound

This paper cites Model selection and esti- mation in regression with grouped variables.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Model selection and esti- mation in regression with grouped variables

Reference 31

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This paper cites Free-breathing, motion-corrected late gadolinium enhance- ment is robust and extends risk stratification to vulnerable patients.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Free-breathing, motion-corrected late gadolinium enhance- ment is robust and extends risk stratification to vulnerable patients

Reference 32

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Observation 225f5dda-282e-4a62-bed7-989ac2bb3f1d · outbound

This paper cites Dynamic MRI using smoothness regularization on manifolds (SToRM).

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Dynamic MRI using smoothness regularization on manifolds (SToRM)

Reference 33

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This paper cites MRXCAT: Realistic numerical phantoms for cardiovascular magnetic resonance.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity MRXCAT: Realistic numerical phantoms for cardiovascular magnetic resonance

Reference 34

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This paper cites Analytical model for the approx- imation of hysteresis loop and its application to the scanning tunneling microscope.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Analytical model for the approx- imation of hysteresis loop and its application to the scanning tunneling microscope

Reference 35

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Observation 09fa202f-6de0-4662-93f0-81733eae1e1c · outbound

This paper cites Technical Report (v1.0)--Pseudo-random Cartesian Sampling for Dynamic MRI.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Technical Report (v1.0)--Pseudo-random Cartesian Sampling for Dynamic MRI

Reference 36

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This paper cites Phase-sensitive inversion recov- ery for detecting myocardial infarction using gadolinium-delayed hyperenhance- ment.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Phase-sensitive inversion recov- ery for detecting myocardial infarction using gadolinium-delayed hyperenhance- ment

Reference 37

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Observation 983cc901-c8fa-4734-92d9-78e09ec09434 · outbound

This paper cites Array compression for MRI with large coil arrays.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Array compression for MRI with large coil arrays

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:35:18.594693Z

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This paper cites Surface Coil Intensity Correction for MRI.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Surface Coil Intensity Correction for MRI

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:35:18.577772Z

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Observation 5354c878-edc5-4b22-95fe-57d673730b2e · outbound

This paper cites Motion cor- rection for myocardial T1 mapping using image registration with synthetic image esti- mation.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Motion cor- rection for myocardial T1 mapping using image registration with synthetic image esti- mation

Reference 40

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Observation f18863c5-b342-418a-9961-17fec70494aa · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

An unsupervised method for MRI recovery: Deep image prior with structured sparsity Image quality assessment: from error visibility to structural similarity

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:35:18.545195Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

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