Edema-like marrow signal intensity: a narrative review with a pictorial essay

Maraghelli, Davide; Brandi, Maria Luisa; Cerinic, Marco Matucci; Peired, Anna Julie; Colagrande, Stefano

doi:10.1007/s00256-020-03632-4

Cited by 33 publications

(26 citation statements)

References 90 publications

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“…In children and adolescents, the normal skeletal maturation processes can influence the MRI signal in a similar way as pathology. Consequently, pathological and normal signal intensities and patterns can overlap [10][11][12], particularly at the knee [13].…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation of magnetic resonance bone marrow signal: a feasibility study

et al. 2022

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Background Manual assessment of bone marrow signal is time-consuming and requires meticulous standardisation to secure adequate precision of findings. Objective We examined the feasibility of using deep learning for automated segmentation of bone marrow signal in children and adolescents. Materials and methods We selected knee images from 95 whole-body MRI examinations of healthy individuals and of children with chronic non-bacterial osteomyelitis, ages 6–18 years, in a longitudinal prospective multi-centre study cohort. Bone marrow signal on T2-weighted Dixon water-only images was divided into three color-coded intensity-levels: 1 = slightly increased; 2 = mildly increased; 3 = moderately to highly increased, up to fluid-like signal. We trained a convolutional neural network on 85 examinations to perform bone marrow segmentation. Four readers manually segmented a test set of 10 examinations and calculated ground truth using simultaneous truth and performance level estimation (STAPLE). We evaluated model and rater performance through Dice similarity coefficient and in consensus. Results Consensus score of model performance showed acceptable results for all but one examination. Model performance and reader agreement had highest scores for level-1 signal (median Dice 0.68) and lowest scores for level-3 signal (median Dice 0.40), particularly in examinations where this signal was sparse. Conclusion It is feasible to develop a deep-learning-based model for automated segmentation of bone marrow signal in children and adolescents. Our model performed poorest for the highest signal intensity in examinations where this signal was sparse. Further improvement requires training on larger and more balanced datasets and validation against ground truth, which should be established by radiologists from several institutions in consensus.

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Section: Introductionmentioning

confidence: 99%

Automated segmentation of magnetic resonance bone marrow signal: a feasibility study

et al. 2022

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“…It is questionable as to whether the SIJ should ever be screened by MRI for sacroiliitis, and even if this were to be done, there is no evidence to support the use of the ASAS definition to determine 'screen test positivity'. Diagnostic MRI of inflammatory sacroiliitis and its differential diagnoses needs a careful assessment of structural lesions and the same standards as for other anatomical regions should also apply to the SIJ [38]. To establish a clinical diagnosis, the observations of osteitis and structural damage of the SIJ must be interpreted in the context of the clinical setting.…”

Section: "Asas Definition Of a Positive Mri" In Clinical Radiology Carementioning

confidence: 99%

MRI in axial spondyloarthritis: understanding an ‘ASAS-positive MRI’ and the ASAS classification criteria

2022

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In 2009, the Assessment of SpondyloArthritis international Society (ASAS) published a definition of ‘active sacroiliitis on magnetic resonance imaging (MRI) for classification of axial spondyloarthritis’. This new definition of an ‘ASAS-positive MRI’ was integral to new classification criteria for axial spondyloarthritis that were published in the same year. The ASAS MRI definition had the considerable advantage of simplicity and the definition gained popularity as guidance for interpreting MRI of the sacroiliac joints in clinical practice. However, classification criteria are not designed for use in clinical practice with the consequence that overreliance on the presence of bone marrow edema, which is the principal determinant of an ‘ASAS-positive MRI’, may result in a tendency to overcall inflammatory sacroiliitis in the clinical setting. This article aims to inform the reader about the rationale behind the ASAS definition of a positive MRI and ASAS classification criteria, their proper use in research and why they should not be used in clinical practice. The article also contains guidance for an updated imaging protocol and interpretation of images including typical imaging findings, differential diagnosis, and common pitfalls.

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“…Die Benennung als transiente Osteoporose ist insofern verwirrend, als dass bislang kein nachgewiesener pathophysiologischer Zusammenhang zur Systemerkrankung Osteoporose besteht. Es wird vereinzelt auch der Begriff „Edema-like Marrow Signal Intensity“ (ELMSI) verwendet, der eine abnorme Signalveränderung im MRT beschreibt – mit oder ohne bekannte Ursache 4 .…”

Section: Begriffsbestimmung Und äTiologieunclassified