Evidence map›Paper›PMID 40799207›Full record

ReviewBJR open2025

Diffusion weighted imaging in musculoskeletal system: where are we now?

Sonal Saran, Avneesh Chhabra, Rajesh Botchu

Abstract readReview
In one paragraph

Review in BJR open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

3 authors.

Sonal SaranDepartment of Diagnostic and Interventional Radiology, AIIMS Rishikesh, Rishikesh, Uttarakhand, 249203, India.ORCID https://orcid.org/0000-0002-4912-1415
Avneesh ChhabraDepartment of Radiology, UT Southwestern Medical Center, Dallas, TX, 75390, United States.
Rajesh BotchuDepartment of Musculoskeletal Radiology, Royal Orthopedic Hospital, Birmingham , B31 2AP, United Kingdom.ORCID https://orcid.org/0000-0001-7998-2980

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diffusion-weighted imaging (DWI) is an advanced MRI technique that harnesses the movement of water molecules within tissues to assess and characterize a wide range of musculoskeletal disorders. By differentiating between isotropic and anisotropic diffusion, DWI provides critical insights into tissue integrity and pathology, proving instrumental in diagnosing conditions. Its sensitivity to changes in tissue microstructure is quantified through metrics like the apparent diffusion coefficient (ADC) and fractional anisotropy (FA). Advanced methodologies, including diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI), further enhance DWI's ability to evaluate complex tissue architectures, offering vital information on muscle, ligament, tendon, and cartilage health. DWI also excels in the assessment of soft tissue tumours, infections, and joint pathologies, enabling accurate differentiation between benign and malignant lesions and facilitating early detection of conditions like osteomyelitis. Additionally, DWI plays a crucial role in monitoring treatment responses, with ADC changes correlating to tumour necrosis and recurrence. Despite its advantages, DWI faces limitations, such as technical artefacts and challenges in interpretation that can impact diagnostic accuracy. This review explores the diverse applications of DWI and DTI in musculoskeletal imaging, highlighting their potential to improve diagnostic precision and clinical outcomes while addressing ongoing challenges in the field.

Indexed as

apparent diffusion coefficient (ADC)diffusion tensor imaging (DTI)diffusion-weighted imaging (DWI)musculoskeletal disorderstumour characterization

Identifiers

PMID40799207
PMCPMC12342750

What OpenQuestion holds

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Registered trials

None linked

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.