1
|
Daudé P, Roussel T, Troalen T, Viout P, Hernando D, Guye M, Kober F, Confort Gouny S, Bernard M, Rapacchi S. Comparative review of algorithms and methods for chemical-shift-encoded quantitative fat-water imaging. Magn Reson Med 2024; 91:741-759. [PMID: 37814776 DOI: 10.1002/mrm.29860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 08/19/2023] [Accepted: 08/21/2023] [Indexed: 10/11/2023]
Abstract
PURPOSE To propose a standardized comparison between state-of-the-art open-source fat-water separation algorithms for proton density fat fraction (PDFF) andR 2 * $$ {R}_2^{\ast } $$ quantification using an open-source multi-language toolbox. METHODS Eight recent open-source fat-water separation algorithms were compared in silico, in vitro, and in vivo. Multi-echo data were synthesized with varying fat-fractions, B0 off-resonance, SNR and TEs. Experimental evaluation was conducted using calibrated fat-water phantoms acquired at 3T and multi-site open-source phantoms data. Algorithms' performances were observed on challenging in vivo datasets at 3T. Finally, reconstruction algorithms were investigated with different fat spectra to evaluate the importance of the fat model. RESULTS In silico and in vitro results proved most algorithms to be not sensitive to fat-water swaps andB 0 $$ {\mathrm{B}}_0 $$ offsets with five or more echoes. However, two methods remained inaccurate even with seven echoes and SNR = 50, and two other algorithms' precision depended on the echo spacing scheme (p < 0.05). The remaining four algorithms provided reliable performances with limits of agreement under 2% for PDFF and 6 s-1 forR 2 * $$ {R}_2^{\ast } $$ . The choice of fat spectrum model influenced quantification of PDFF mildly (<2% bias) and ofR 2 * $$ {R}_2^{\ast } $$ more severely, with errors up to 20 s-1 . CONCLUSION In promoting standardized comparisons of MRI-based fat and iron quantification using chemical-shift encoded multi-echo methods, this benchmark work has revealed some discrepancies between recent approaches for PDFF andR 2 * $$ {R}_2^{\ast } $$ mapping. Explicit choices and parameterization of the fat-water algorithm appear necessary for reproducibility. This open-source toolbox further enables the user to optimize acquisition parameters by predicting algorithms' margins of errors.
Collapse
Affiliation(s)
- Pierre Daudé
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
- Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Tangi Roussel
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | | | - Patrick Viout
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Diego Hernando
- Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Maxime Guye
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Frank Kober
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Sylviane Confort Gouny
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Monique Bernard
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Stanislas Rapacchi
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| |
Collapse
|
2
|
Daudé P, Ancel P, Confort Gouny S, Jacquier A, Kober F, Dutour A, Bernard M, Gaborit B, Rapacchi S. Deep-Learning Segmentation of Epicardial Adipose Tissue Using Four-Chamber Cardiac Magnetic Resonance Imaging. Diagnostics (Basel) 2022; 12:126. [PMID: 35054297 PMCID: PMC8774679 DOI: 10.3390/diagnostics12010126] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 12/29/2021] [Accepted: 12/29/2021] [Indexed: 12/24/2022] Open
Abstract
In magnetic resonance imaging (MRI), epicardial adipose tissue (EAT) overload remains often overlooked due to tedious manual contouring in images. Automated four-chamber EAT area quantification was proposed, leveraging deep-learning segmentation using multi-frame fully convolutional networks (FCN). The investigation involved 100 subjects-comprising healthy, obese, and diabetic patients-who underwent 3T cardiac cine MRI, optimized U-Net and FCN (noted FCNB) were trained on three consecutive cine frames for segmentation of central frame using dice loss. Networks were trained using 4-fold cross-validation (n = 80) and evaluated on an independent dataset (n = 20). Segmentation performances were compared to inter-intra observer bias with dice (DSC) and relative surface error (RSE). Both systole and diastole four-chamber area were correlated with total EAT volume (r = 0.77 and 0.74 respectively). Networks' performances were equivalent to inter-observers' bias (EAT: DSCInter = 0.76, DSCU-Net = 0.77, DSCFCNB = 0.76). U-net outperformed (p < 0.0001) FCNB on all metrics. Eventually, proposed multi-frame U-Net provided automated EAT area quantification with a 14.2% precision for the clinically relevant upper three quarters of EAT area range, scaling patients' risk of EAT overload with 70% accuracy. Exploiting multi-frame U-Net in standard cine provided automated EAT quantification over a wide range of EAT quantities. The method is made available to the community through a FSLeyes plugin.
Collapse
Affiliation(s)
- Pierre Daudé
- Aix-Marseille Univ, CNRS, CRMBM, 13005 Marseille, France; (S.C.G.); (A.J.); (F.K.); (M.B.)
- APHM, Hôpital Universitaire Timone, CEMEREM, 13385 Marseille, France
| | - Patricia Ancel
- Department of Radiology, APHM, La Timone Hospital, 13005 Marseille, France;
- Aix-Marseille Univ, INSERM, INRAE, C2VN, 13005 Marseille, France; (A.D.); (B.G.)
| | - Sylviane Confort Gouny
- Aix-Marseille Univ, CNRS, CRMBM, 13005 Marseille, France; (S.C.G.); (A.J.); (F.K.); (M.B.)
- APHM, Hôpital Universitaire Timone, CEMEREM, 13385 Marseille, France
| | - Alexis Jacquier
- Aix-Marseille Univ, CNRS, CRMBM, 13005 Marseille, France; (S.C.G.); (A.J.); (F.K.); (M.B.)
- APHM, Hôpital Universitaire Timone, CEMEREM, 13385 Marseille, France
- Department of Radiology, APHM, La Timone Hospital, 13005 Marseille, France;
| | - Frank Kober
- Aix-Marseille Univ, CNRS, CRMBM, 13005 Marseille, France; (S.C.G.); (A.J.); (F.K.); (M.B.)
- APHM, Hôpital Universitaire Timone, CEMEREM, 13385 Marseille, France
| | - Anne Dutour
- Aix-Marseille Univ, INSERM, INRAE, C2VN, 13005 Marseille, France; (A.D.); (B.G.)
- Department of Endocrinology, Metabolic Diseases and Nutrition, Pôle ENDO, APHM, Hôpital Nord, Chemin Des Bourrely, 13005 Marseille, France
| | - Monique Bernard
- Aix-Marseille Univ, CNRS, CRMBM, 13005 Marseille, France; (S.C.G.); (A.J.); (F.K.); (M.B.)
- APHM, Hôpital Universitaire Timone, CEMEREM, 13385 Marseille, France
| | - Bénédicte Gaborit
- Aix-Marseille Univ, INSERM, INRAE, C2VN, 13005 Marseille, France; (A.D.); (B.G.)
- Department of Endocrinology, Metabolic Diseases and Nutrition, Pôle ENDO, APHM, Hôpital Nord, Chemin Des Bourrely, 13005 Marseille, France
| | - Stanislas Rapacchi
- Aix-Marseille Univ, CNRS, CRMBM, 13005 Marseille, France; (S.C.G.); (A.J.); (F.K.); (M.B.)
- APHM, Hôpital Universitaire Timone, CEMEREM, 13385 Marseille, France
| |
Collapse
|
3
|
Girard N, Gouny SC, Viola A, Le Fur Y, Viout P, Chaumoitre K, D'Ercole C, Gire C, Figarella-Branger D, Cozzone PJ. Assessment of normal fetal brain maturation in utero by proton magnetic resonance spectroscopy. Magn Reson Med 2007; 56:768-75. [PMID: 16964617 DOI: 10.1002/mrm.21017] [Citation(s) in RCA: 93] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Cerebral maturation in the normal human fetal brain was investigated by in utero localized proton MR spectroscopy ((1)H MRS). Fifty-eight subjects at 22-39 weeks of gestational age (GA) were explored. A combination of anterior body phased-array coils (four elements) and posterior spinal coils (two to three elements) was used. Four sequences were performed (point-resolved spectroscopy (PRESS) sequence with short and long TEs (30 and 135 ms), with and without water saturation). A significant reduction in myo-inositol (myo-Ins) and choline (Cho) levels, and an increase in N-acetylaspartate (NAA) and creatine (Cr) content were observed with progressing age. A new finding is the detection of NAA as early as 22 weeks of GA. This result is probably related to the fact that oligodendrocytes (whether mature or not) express NAA, as demonstrated by in vitro studies. Cho and myo-inositol were the predominant resonances from 22 to 30 weeks and decreased gradually, probably reflecting the variations in substrate needed for membrane synthesis and myelination. The normal MRS data for the second trimester of gestation (when fetal MRI is usually performed) reported here can help determine whether brain metabolism is altered or not, especially when subtle anatomic changes are observed on conventional images.
Collapse
Affiliation(s)
- Nadine Girard
- Service de Neuroradiologie, Assistance Publique-Hôpitaux de Marseille, Hôpital la Timone, Université de la Méditerranée, Marseille, France.
| | | | | | | | | | | | | | | | | | | |
Collapse
|