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Julia A. Schnabel

7 papers hereh-index 336 citations21 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3
  • last author4

Across the 7 of 7 papers where every author was matched, so the position is known.

fields
  • eess.IV6
  • cs.CV1
same name
  • Julia A. Schnabel — 12 papers, h 6
  • Julia A. Schnabel — 9 papers, h 6
  • Julia A. Schnabel — 5 papers, h 4
  • Julia A. Schnabel — 4 papers, h 2
  • Julia A. Schnabel — 4 papers, h 1
  • Julia A. Schnabel — 3 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedPISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI

4 citations · 6 across the 7 of their papers we have counts for

collaborators
Showing 2026Show all

3 papers · 1 filter

cs.CV2026

CUPA-T2*: Covariance-Aware Uncertainty Propagation and Alignment for T2* Mapping in Accelerated MRI

Gideon N. L. Rouwendaal, Natascha Niessen, Hannah Eichhorn +3

Quantitative T2* maps have strong potential for biomarker discovery but are limited by long scan times, rendering them impractical in clinical settings. Significant acceleration ca…

eess.IV2026

Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction

Veronika Spieker, Wenqi Huang, Cemre Ariyurek +5

Reliable quantitative analysis of dynamic contrast-enhanced MRI requires high-quality spatiotemporal reconstructions at high undersampling rates. Scan-specific reconstructions usin…

eess.IV2026

Gabor Primitives for Accelerated Cardiac Cine MRI Reconstruction

Wenqi Huang, Veronika Spieker, Nil Stolt-Ansó +6

Accelerated cardiac cine MRI requires reconstructing spatiotemporal images from highly undersampled k-space data. Implicit neural representations (INRs) enable scan-specific recons…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.