Evidence map›Paper›PMID 41826431›Full record

ArticleScientific reports2026

Efficient cosine-windowed cross-correlation for intermediate deformable image registration.

Iman Aganj

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Iman AganjAthinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Harvard Medical School, 149, 13th St., Suite 2301, Boston, MA, 02129, USA. iaganj@mgh.harvard.edu.

Funding

Connectomic Biomarkers of Preclinical Alzheimer's Disease within Multi-Synaptic PathwaysRF1AG068261 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI AGANJ, IMAN · 2021 to 2021
$1.8M
Michael J. Fox Foundation for Parkinson's Research MJFF-021226NIA NIH HHS RF1 AG068261NIA NIH HHS RF1AG068261
6 · The paper itself

Abstract

Deformable image registration is commonly included in population and longitudinal medical imaging analysis pipelines. Initializing deformable registration with the results of affine registration, where global misalignments have been reduced, can improve overall registration accuracy. Affine registration, however, uses a limited linear transformation model that does not align nonlinear anatomical variations, such as those between pre- and post-operative images or across different individual anatomies. In this work, we introduce a new intermediate deformable image registration (IDIR) method that corrects large deformations via cosine-windowed cross-correlation, and provide an efficient implementation via the fast Fourier transform. We evaluate our general-purpose approach qualitatively and quantitatively on 2D bone X-ray images, 3D brain magnetic resonance images, and 3D abdominal computed tomography images, demonstrating its ability to align large nonlinear anatomical variations within a few iterations and its suitability for initializing standard deformable registration.

Indexed as

Cosine-windowed cross-correlationFast fourier transform (FFT)Intermediate deformable image registration (IDIR)

Identifiers

PMID41826431
PMCPMC12987980

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.