Evaluation of Iterative Denoising 3-Dimensional T2-Weighted Turbo Spin Echo for the Diagnosis of Deep Infiltrating Endometriosis

Florin M, Marie Florin, Alexis Vaussy, Vaussy A, Laurent Macron, Macron L, Marc Bazot, Bazot M, Alto Stemmer, Stemmer A, Pinar U, Ugo Pinar, Lamia Jarboui, Jarboui L
Investigative radiology · 2021 · vol. 56(10) , pp. 637–644 · doi:10.1097/rli.0000000000000786 · PMID:33813570 · W3141123521
Other OA: closed CC0 ⤵ 2 in-corpus citations
View on OpenAlex View on PubMed View at publisher
⚙ AI-generated summary by gemini-2.5-flash-lite, 2026-06-07 ⓘ

Iterative denoising improved 3D T2-weighted MRI quality and artifact reduction for diagnosing deep infiltrating endometriosis compared to non-denoised sequences, offering potential replacement for conventional 2D sequences with reduced scan time.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

Abstract

OBJECTIVES: The primary end point of this study was to evaluate the image quality and reliability of a highly accelerated 3-dimensional T2 turbo spin echo (3D-T2-TSE) sequence with prototype iterative denoising (ID) reconstruction compared with conventional 2D T2 sequences for the diagnosis of deep infiltrating endometriosis (DIE). The secondary end point was to demonstrate the 3D-T2-TSE sequence image quality improvement using ID reconstruction. MATERIAL AND METHODS: Patients were prospectively enrolled to our institution for pelvis magnetic resonance imaging because of a suspicion of endometriosis over a 4-month period. Both conventional 2D-T2 (sagittal, axial, coronal T2 oblique to the cervix) and 3D-T2-TSE sequences were performed with a scan time of 7 minutes 43 seconds and 4 minutes 58 seconds, respectively. Reconstructions with prototype ID (3D-T2-denoised) and without prototype ID (3D-T2) were generated inline at the end of the acquisition. Two radiologists independently evaluated the image quality of 3D-T2, 3D-T2-denoised, and 2D-T2 sequences. Diagnosis confidence of DIE was evaluated for both 3D-T2-denoised and 2D-T2 sequences. Intraobserver and interobserver agreements were calculated using Cohen κ coefficient. RESULTS: Ninety female patients were included. Both readers found that the ID algorithm significantly improved the image quality and decreased the artifacts of 3D-T2-denoised compared with 3D-T2 sequences (P < 0.001). A significant image quality improvement was found by 1 radiologist for 3D-T2-denoised compared with 2D-T2 sequences (P = 0.002), whereas the other reader evidenced no significant difference. The interobserver agreement of 3D-T2-denoised and 2D-T2 sequences was 0.84 (0.73-0.95) and 0.78 (0.65-0.9), respectively, for the diagnosis of DIE. Intraobserver agreement for readers 1 and 2 was 0.86 (0.79-1) and 0.83 (0.76-1), respectively. For all localization of DIE, interobserver and intraobserver agreements were either almost perfect or substantial for both 3D-T2-denoised and 2D-T2 sequences. CONCLUSIONS: Three-dimensional T2-denoised imaging is a promising tool to replace conventional 2D-T2 sequences, offering a significant scan time reduction without compromising image quality or diagnosis information for the assessment of DIE.

My notes (saved in your browser only)

Condition tags

endometriosisdie_deep_infiltrating

MeSH descriptors

Endometriosis Endometriosis Endometriosis Artifacts Artifacts Female Female Humans Humans Imaging, Three-Dimensional Imaging, Three-Dimensional Magnetic Resonance Imaging Magnetic Resonance Imaging Reproducibility of Results Reproducibility of Results

Citation neighborhood (2-hop)

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. Outer rings show 2-hop neighbours — papers reached through the immediate citers/citees. [ collapse to 1-hop ]

References (41)

Cited by (2)

Source provenance

europepmc
last seen: 2026-10-22T06:08:12.857683+00:00
openalex
last seen: 2026-06-10T17:14:06.276822+00:00
pubmed
last seen: 2026-10-08T21:30:40.998460+00:00
License: CC0 · commercial use OK