Evidence map›Paper›PMID 39578321›Full record

SynthesisJournal of imaging informatics in medicine2025

A Systematic Review on the Use of Registration-Based Change Tracking Methods in Longitudinal Radiological Images.

Jeeho E Im, Muhammed Khalifa, Adriana V Gregory, Bradley J Erickson, Timothy L Kline

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of imaging informatics in medicine, 2025. 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

5 authors.

Jeeho E ImDepartment of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
Muhammed KhalifaDepartment of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
Adriana V GregoryDepartment of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
Bradley J EricksonDepartment of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
Timothy L KlineDepartment of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA. Kline.Timothy@mayo.edu.ORCID http://orcid.org/0000-0002-7917-9853

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Registration is the process of spatially and/or temporally aligning different images. It is a critical tool that can facilitate the automatic tracking of pathological changes detected in radiological images and align images captured by different imaging systems and/or those acquired using different acquisition parameters. The longitudinal analysis of clinical changes has a significant role in helping clinicians evaluate disease progression and determine the most suitable course of treatment for patients. This study provides a comprehensive review of the role registration-based approaches play in automated change tracking in radiological imaging and explores the three types of registration approaches which include rigid, affine, and nonrigid registration, as well as methods of detecting and quantifying changes in registered longitudinal images: the intensity-based approach and the deformation-based approach. After providing an overview and background, we highlight the clinical applications of these methods, specifically focusing on computed tomography (CT) and magnetic resonance imaging (MRI) in tumors and multiple sclerosis (MS), two of the most heavily studied areas in automated change tracking. We conclude with a discussion and recommendation for future directions.

Indexed as

Image Processing, Computer-AssistedAlgorithmsDisease ProgressionHumansLongitudinal StudiesMagnetic Resonance ImagingMultiple SclerosisNeoplasmsTomography, X-Ray ComputedChange trackingDeformable registrationImage registrationLongitudinalMachine learningSubtraction

Identifiers

PMID39578321
PMCPMC12343390

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Registered trials

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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.