Keyword search (4,163 papers available)

"Filtration" Keyword-tagged Publications:

Title Authors PubMed ID
1 Ultrasound and MRI-based evaluation of relationships between morphological and mechanical properties of the lower lumbar multifidus muscle in chronic low back pain Naghdi N; Masi S; Bertrand C; Rosenstein B; Cohen-Adad J; Rivaz H; Roy M; Fortin M; 40488869
HKAP
2 MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle McKay MJ; Weber KA; Wesselink EO; Smith ZA; Abbott R; Anderson DB; Ashton-James CE; Atyeo J; Beach AJ; Burns J; Clarke S; Collins NJ; Coppieters MW; Cornwall J; Crawford RJ; De Martino E; Dunn AG; Eyles JP; Feng HJ; Fortin M; Franettovich Smith MM; Galloway G; Gandomkar Z; Glastras S; Henderson LA; Hides JA; Hiller CE; Hilmer SN; Hoggarth MA; Kim B; Lal N; LaPorta L; Magnussen JS; Maloney S; March L; Nackley AG; O' Leary SP; Peolsson A; Perraton Z; Pool-Goudzwaard AL; Schnitzler M; Seitz AL; Semciw AI; Sheard PW; Smith AC; Snodgrass SJ; Sullivan J; Tran V; Valentin S; Walton DM; Wishart LR; Elliott JM; 39590726
HKAP
3 Morphological Changes of Deep Extensor Neck Muscles in Relation to the Maximum Level of Cord Compression and Canal Compromise in Patients With Degenerative Cervical Myelopathy Naghdi N; Elliott JM; Weber MH; Fehlings MG; Fortin M; 36289049
PERFORM
4 Paraspinal Muscle Changes in Individuals with and without Chronic Low Back Pain over a 4-Month Period: A Longitudinal MRI Study Anstruther M; Sean M; Tétreault P; Fortin M; 38541216
SOH
5 Thresholding approaches for estimating paraspinal muscle fat infiltration using T1- and T2-weighted MRI: Comparative analysis using water-fat MRI Ornowski J; Dziesinski L; Hess M; Krug R; Fortin M; Torres-Espin A; Majumdar S; Pedoia V; Bonnheim NB; Bailey JF; 38222819
HKAP
6 PILLAR: ParaspInaL muscLe segmentAtion pRoject - a comprehensive online resource to guide manual segmentation of paraspinal muscles from magnetic resonance imaging Anstruther M; Rossini B; Zhang T; Liang T; Xiao Y; Fortin M; 37996857
SOH
7 The Effects of Combined Motor Control and Isolated Extensor Strengthening versus General Exercise on Paraspinal Muscle Morphology, Composition, and Function in Patients with Chronic Low Back Pain: A Randomized Controlled Trial Fortin M; Rye M; Roussac A; Montpetit C; Burdick J; Naghdi N; Rosenstein B; Bertrand C; Macedo LG; Elliott JM; Dover G; DeMont R; Weber MH; Pepin V; 37762861
PERFORM
8 A Comparative Study of the Self-Cleaning and Filtration Performance of Suspension Plasma-Sprayed TiO2 Ultrafiltration and Microfiltration Membranes Alebrahim E; Moreau C; 37755172
ENCS
9 Assessment of the infiltration of water-in-oil emulsion into soil after spill incidents Qu Z; An C; Yue R; Bi H; Zhao S; 37414189
ENCS
10 Comparison of paraspinal muscle composition measurements using IDEAL fat-water and T2-weighted MR images Sara Masi 36997912
PERFORM
11 Removal of Nutrients from Water Using Biosurfactant Micellar-Enhanced Ultrafiltration Binte Rafiq Era S; Mulligan CN; 36838547
ENCS
12 A photo-Fenton nanocomposite ultrafiltration membrane for enhanced dye removal with self-cleaning properties Yue R; Raisi B; Rahmatinejad J; Ye Z; Barbeau B; Rahaman MS; 34273782
ENCS
13 Filtration for improving surface water quality of a eutrophic lake. Palakkeel Veetil D, Arriagada EC, Mulligan CN, Bhat S 33310244
ENCS
14 Removal of arsenic from water through ceramic filter modified by nano-CeO2: A cost-effective approach for remote areas. Yang X; Huang G; An C; Chen X; Shen J; Yin J; Song P; Xu Z; Li Y; 33182193
ENCS
15 How Effective Is the Filtration of 'KN95' Filtering Facepiece Respirators During the COVID-19 Pandemic? Brochot C, Saidi MN, Bahloul A 33125464
ENCS
16 Electrochemical efficacy of a carboxylated multiwalled carbon nanotube filter for the removal of ibuprofen from aqueous solutions under acidic conditions. Bakr AR, Rahaman MS 27035389
MASSSPEC
17 Relationship between cervical muscle morphology evaluated by MRI, cervical muscle strength and functional outcomes in patients with degenerative cervical myelopathy. Fortin M, Wilk N, Dobrescu O, Martel P, Santaguida C, Weber MH 30059855
PERFORM
18 Evaluation of an automated thresholding algorithm for the quantification of paraspinal muscle composition from MRI images. Fortin M, Omidyeganeh M, Battié MC, Ahmad O, Rivaz H 28532491
PERFORM
19 Association between paraspinal muscle morphology, clinical symptoms and functional status in patients with lumbar spinal stenosis. Fortin M, Lazáry À, Varga PP, Battié MC 28748488
PERFORM

 

Title:Thresholding approaches for estimating paraspinal muscle fat infiltration using T1- and T2-weighted MRI: Comparative analysis using water-fat MRI
Authors:Ornowski JDziesinski LHess MKrug RFortin MTorres-Espin AMajumdar SPedoia VBonnheim NBBailey JF
Link:https://pubmed.ncbi.nlm.nih.gov/38222819/
DOI:10.1002/jsp2.1301
Publication:JOR spine
Keywords:MRIfat infiltrationlow back painmuscle qualityparaspinal musclesthresholdingwater-fat MRI
PMID:38222819 Category: Date Added:2024-01-15
Dept Affiliation: HKAP
1 Department of Orthopaedic Surgery University of California San Francisco California USA.
2 Department of Radiology and Biomedical Imaging University of California San Francisco California USA.
3 Department of Health, Kinesiology, and Applied Physiology Concordia University Montreal Québec Canada.
4 School of Public Health Sciences Faculty of Health University of Waterloo Waterloo Ontario Canada.
5 Department of Physical Therapy University of Alberta Edmonton Alberta Canada.
6 Department of Neurological Surgery University of California San Francisco California USA.

Description:

Background: Paraspinal muscle fat infiltration is associated with spinal degeneration and low back pain, however, quantifying muscle fat using clinical magnetic resonance imaging (MRI) techniques continues to be a challenge. Advanced MRI techniques, including chemical-shift encoding (CSE) based water-fat MRI, enable accurate measurement of muscle fat, but such techniques are not widely available in routine clinical practice.

Methods: To facilitate assessment of paraspinal muscle fat using clinical imaging, we compared four thresholding approaches for estimating muscle fat fraction (FF) using T1- and T2-weighted images, with measurements from water-fat MRI as the ground truth: Gaussian thresholding, Otsu's method, K-mean clustering, and quadratic discriminant analysis. Pearson's correlation coefficients (r), mean absolute errors, and mean bias errors were calculated for FF estimates from T1- and T2-weighted MRI with water-fat MRI for the lumbar multifidus (MF), erector spinae (ES), quadratus lumborum (QL), and psoas (PS), and for all muscles combined.

Results: We found that for all muscles combined, FF measurements from T1- and T2-weighted images were strongly positively correlated with measurements from the water-fat images for all thresholding techniques (r = 0.70-0.86, p < 0.0001) and that variations in inter-muscle correlation strength were much greater than variations in inter-method correlation strength.

Conclusion: We conclude that muscle FF can be quantified using thresholded T1- and T2-weighted MRI images with relatively low bias and absolute error in relation to water-fat MRI, particularly in the MF and ES, and the choice of thresholding technique should depend on the muscle and clinical MRI sequence of interest.





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