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Decroocq M, Frindel C, Rougé P, Ohta M, Lavoué G. Modeling and hexahedral meshing of cerebral arterial networks from centerlines. Med Image Anal 2023; 89:102912. [PMID: 37549612 DOI: 10.1016/j.media.2023.102912] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 06/12/2023] [Accepted: 07/25/2023] [Indexed: 08/09/2023]
Abstract
Computational fluid dynamics (CFD) simulation provides valuable information on blood flow from the vascular geometry. However, it requires extracting precise models of arteries from low-resolution medical images, which remains challenging. Centerline-based representation is widely used to model large vascular networks with small vessels, as it encodes both the geometric and topological information and facilitates manual editing. In this work, we propose an automatic method to generate a structured hexahedral mesh suitable for CFD directly from centerlines. We addressed both the modeling and meshing tasks. We proposed a vessel model based on penalized splines to overcome the limitations inherent to the centerline representation, such as noise and sparsity. The bifurcations are reconstructed using a parametric model based on the anatomy that we extended to planar n-furcations. Finally, we developed a method to produce a volume mesh with structured, hexahedral, and flow-oriented cells from the proposed vascular network model. The proposed method offers better robustness to the common defects of centerlines and increases the mesh quality compared to state-of-the-art methods. As it relies on centerlines alone, it can be applied to edit the vascular model effortlessly to study the impact of vascular geometry and topology on hemodynamics. We demonstrate the efficiency of our method by entirely meshing a dataset of 60 cerebral vascular networks. 92% of the vessels and 83% of the bifurcations were meshed without defects needing manual intervention, despite the challenging aspect of the input data. The source code is released publicly.
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Affiliation(s)
- Méghane Decroocq
- CREATIS, Université Lyon1, CNRS UMR5220, INSERM U1206, INSA-Lyon, 69621 Villeurbanne, France; LIRIS, CNRS UMR 5205, F-69621, France; ELyTMaX IRL3757, CNRS, INSA Lyon, Centrale Lyon, Université Claude Bernard Lyon 1, Tohoku University, 980-8577, Sendai, Japan; Institute of Fluid Science, Tohoku University, 2-1-1, Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan; Graduate School of Biomedical Engineering, Tohoku University, 6-6 Aramaki-aza-aoba, Aoba-ku, Sendai, Miyagi 980-8579, Japan
| | - Carole Frindel
- CREATIS, Université Lyon1, CNRS UMR5220, INSERM U1206, INSA-Lyon, 69621 Villeurbanne, France; Institute of Fluid Science, Tohoku University, 2-1-1, Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan.
| | - Pierre Rougé
- ELyTMaX IRL3757, CNRS, INSA Lyon, Centrale Lyon, Université Claude Bernard Lyon 1, Tohoku University, 980-8577, Sendai, Japan; Université de Reims Champagne Ardenne, CReSTIC, 51100 Reims, France
| | - Makoto Ohta
- ELyTMaX IRL3757, CNRS, INSA Lyon, Centrale Lyon, Université Claude Bernard Lyon 1, Tohoku University, 980-8577, Sendai, Japan; Institute of Fluid Science, Tohoku University, 2-1-1, Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan
| | - Guillaume Lavoué
- LIRIS, CNRS UMR 5205, F-69621, France; Ecole Centrale de Lyon, France
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Jing Y, Magnin IE, Frindel C. Monte Carlo simulation of water diffusion through cardiac tissue models. Med Eng Phys 2023; 120:104013. [PMID: 37673779 DOI: 10.1016/j.medengphy.2023.104013] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 05/13/2023] [Accepted: 06/22/2023] [Indexed: 09/08/2023]
Abstract
Monte Carlo diffusion simulations are commonly used to establish a reliable ground truth of tissue microstructure, including for the validation of diffusion-weighted MRI. However, selecting simulation parameters is challenging and affects validity and reproducibility. We conducted experiments to investigate critical conditions in Monte Carlo simulations, such as tissue representation complexity, simulated molecules, update duration, and compartment size. Results show significant changes in microstructure characteristics when parameters are altered, emphasizing the importance of careful control for a reliable ground truth.
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Affiliation(s)
- Yuhan Jing
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, 21 Avenue Jean Capelle, Lyon, 69621, France
| | - Isabelle E Magnin
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, 21 Avenue Jean Capelle, Lyon, 69621, France
| | - Carole Frindel
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, 21 Avenue Jean Capelle, Lyon, 69621, France.
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Mechtouff L, Debs N, Frindel C, Bani-Sadr A, Bochaton T, Paccalet A, Crola Da Silva C, Buisson M, Amaz C, Berthezene Y, Eker OF, Bouin M, de Bourguignon C, Mewton N, Ovize M, Bidaux G, Nighoghossian N, Cho TH. Association of Blood Biomarkers of Inflammation With Penumbra Consumption After Mechanical Thrombectomy in Patients With Acute Ischemic Stroke. Neurology 2022; 99:e2063-e2071. [PMID: 36316128 DOI: 10.1212/wnl.0000000000201038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 06/15/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The objective of this study was to assess the relationship between blood biomarkers of inflammation and lesion growth within the penumbra in acute ischemic stroke (AIS) patients treated with mechanical thrombectomy (MT). METHODS The HIBISCUS-STROKE cohort enrolled patients admitted in the Lyon Stroke Center for an anterior circulation AIS treated with MT after brain MRI assessment. Lesion growth within the penumbra was assessed on day 6 MRI using a voxel-based nonlinear coregistration method and dichotomized into low and high according to the median value. C-reactive protein, interleukin (IL)-6, IL-8, IL-10, monocyte chemoattractant protein-1, soluble tumor necrosis factor receptor I, soluble form suppression of tumorigenicity 2 (sST2), soluble P-selectin, vascular cellular adhesion molecule-1, and matrix metalloproteinase-9 were measured in sera at 4 time points within the first 48 hours. Reperfusion was considered as successful if Thrombolysis in Cerebral Infarction score was 2b/2c/3. A multiple logistic regression model was performed to detect any association between area under the curve (AUC) of these biomarkers within the first 48 hours and a high lesion growth within the penumbra. RESULTS Ninety patients were included. The median lesion growth within the penumbra was 2.3 (0.7-6.2) mL. On multivariable analysis, a high sST2 AUC (OR 3.77, 95% CI 1.36-10.46), a high baseline DWI volume (OR 3.65, 95% CI 1.32-10.12), and a lack of successful reperfusion (OR 0.19, 95% CI 0.04-0.92) were associated with a high lesion growth within the penumbra. When restricting analyses to patients with successful reperfusion (n = 76), a high sST2 AUC (OR 5.03, 95% CI 1.64-15.40), a high baseline DWI volume (OR 3.74, 95% CI 1.22-11.53), and a high penumbra volume (OR 3.25, 95% CI 1.10-9.57) remained associated with a high lesion growth within the penumbra. DISCUSSION High sST2 levels within the first 48 hours are associated with a high lesion growth within the penumbra.
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Affiliation(s)
- Laura Mechtouff
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France.
| | - Noelie Debs
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
| | - Carole Frindel
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
| | - Alexandre Bani-Sadr
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
| | - Thomas Bochaton
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
| | - Alexandre Paccalet
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
| | - Claire Crola Da Silva
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
| | - Marielle Buisson
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
| | - Camille Amaz
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
| | - Yves Berthezene
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
| | - Omer Faruk Eker
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
| | - Morgane Bouin
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
| | - Charles de Bourguignon
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
| | - Nathan Mewton
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
| | - Michel Ovize
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
| | - Gabriel Bidaux
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
| | - Norbert Nighoghossian
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
| | - Tae-Hee Cho
- From the Stroke Department (L.M., N.N., T.-H.C.), Hospices Civils de Lyon; Univ Lyon (L.M., T.B., A.P., C.C.D.S., M.O., G.B., N.N., T.-H.C.), CarMeN Laboratory, INSERM, INRA, University Lyon 1; CREATIS (N.D., C.F., Y.B.), CNRS UMR 5220, INSERM U1044, University Lyon 1; Neuroradiology Department (A.B.-S., Y.B., O.F.E.), Hospices Civils de Lyon; Cardiac Intensive Care Unit (T.B.), Hospices Civils de Lyon; Clinical Investigation Center (M.B., C.A., C.d.B., N.M., M.O.), INSERM 1407, Hospices Civils de Lyon; and Cellule Recherche Imagerie (M.B.), Hospices Civils de Lyon, France
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Dauleac C, Frindel C, Pélissou-Guyotat I, Nicolas C, Yeh FC, Fernandez-Miranda J, Cotton F, Jacquesson T. Full cervical cord tractography: A new method for clinical use. Front Neuroanat 2022; 16:993464. [PMID: 36237419 PMCID: PMC9550930 DOI: 10.3389/fnana.2022.993464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 09/06/2022] [Indexed: 12/02/2022] Open
Abstract
Despite recent improvements in diffusion-weighted imaging, spinal cord tractography is not used in routine clinical practice because of difficulties in reconstructing tractograms, with a pertinent tri-dimensional-rendering, in a long post-processing time. We propose a new full tractography approach to the cervical spinal cord without extensive manual filtering or multiple regions of interest seeding that could help neurosurgeons manage various spinal cord disorders. Four healthy volunteers and two patients with either cervical intramedullary tumors or spinal cord injuries were included. Diffusion-weighted images of the cervical spinal cord were acquired using a Philips 3 Tesla machine, 32 diffusion directions, 1,000 s/mm2b-value, 2 × 2 × 2 mm voxel size, reduced field-of-view (ZOOM), with two opposing phase-encoding directions. Distortion corrections were then achieved using the FSL software package, and tracking of the full cervical spinal cord was performed using the DSI Studio software (quantitative anisotropy-based deterministic algorithm). A unique region of avoidance was used to exclude everything that is not of the nervous system. Fiber tracking parameters used adaptative fractional anisotropy from 0.015 to 0.045, fiber length from 10 to 1,000 mm, and angular threshold of 90°. In all participants, a full cervical cord tractography was performed from the medulla to the C7 spine level. On a ventral view, the junction between the medulla and spinal cord was identified with its pyramidal bulging, and by an invagination corresponding to the median ventral sulcus. On a dorsal view, the fourth ventricle—superior, middle, and inferior cerebellar peduncles—was seen, as well as its floor and the obex; and gracile and cuneate tracts were recognized on each side of the dorsal median sulcus. In the case of the intramedullary tumor or spinal cord injury, the spinal tracts were seen to be displaced, and this helped to adjust the neurosurgical strategy. This new full tractography approach simplifies the tractography pipeline and provides a reliable 3D-rendering of the spinal cord that could help to adjust the neurosurgical strategy.
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Affiliation(s)
- Corentin Dauleac
- Service de Neurochirurgie, Hôpital neurologique et neurochirurgical Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France
- Laboratoire CREATIS, CNRS UMR5220, Inserm U1206, INSA-Lyon, Villeurbanne, France
- Université de Lyon I, Lyon, France
- *Correspondence: Corentin Dauleac
| | - Carole Frindel
- Laboratoire CREATIS, CNRS UMR5220, Inserm U1206, INSA-Lyon, Villeurbanne, France
- Université de Lyon I, Lyon, France
| | - Isabelle Pélissou-Guyotat
- Service de Neurochirurgie, Hôpital neurologique et neurochirurgical Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France
| | - Célia Nicolas
- Hospices Civils de Lyon, Centre Hospitalier de Lyon Sud, Service de Radiologie, Lyon, France
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Juan Fernandez-Miranda
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, United States
| | - François Cotton
- Laboratoire CREATIS, CNRS UMR5220, Inserm U1206, INSA-Lyon, Villeurbanne, France
- Université de Lyon I, Lyon, France
- Hospices Civils de Lyon, Centre Hospitalier de Lyon Sud, Service de Radiologie, Lyon, France
| | - Timothée Jacquesson
- Service de Neurochirurgie, Hôpital neurologique et neurochirurgical Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France
- Laboratoire CREATIS, CNRS UMR5220, Inserm U1206, INSA-Lyon, Villeurbanne, France
- Université de Lyon I, Lyon, France
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Ahmad A, Sala F, Paiè P, Candeo A, D'Annunzio S, Zippo A, Frindel C, Osellame R, Bragheri F, Bassi A, Rousseau D. On the robustness of machine learning algorithms toward microfluidic distortions for cell classification via on-chip fluorescence microscopy. Lab Chip 2022; 22:3453-3463. [PMID: 35946995 DOI: 10.1039/d2lc00482h] [Citation(s) in RCA: 11] [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] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Single-cell imaging and sorting are critical technologies in biology and clinical applications. The power of these technologies is increased when combined with microfluidics, fluorescence markers, and machine learning. However, this quest faces several challenges. One of these is the effect of the sample flow velocity on the classification performances. Indeed, cell flow speed affects the quality of image acquisition by increasing motion blur and decreasing the number of acquired frames per sample. We investigate how these visual distortions impact the final classification task in a real-world use-case of cancer cell screening, using a microfluidic platform in combination with light sheet fluorescence microscopy. We demonstrate, by analyzing both simulated and experimental data, that it is possible to achieve high flow speed and high accuracy in single-cell classification. We prove that it is possible to overcome the 3D slice variability of the acquired 3D volumes, by relying on their 2D sum z-projection transformation, to reach an efficient real time classification with an accuracy of 99.4% using a convolutional neural network with transfer learning from simulated data. Beyond this specific use-case, we provide a web platform to generate a synthetic dataset and to investigate the effect of flow speed on cell classification for any biological samples and a large variety of fluorescence microscopes (https://www.creatis.insa-lyon.fr/site7/en/MicroVIP).
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Affiliation(s)
- Ali Ahmad
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAE IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France.
- Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), CNRS UMR 5220 - INSERM U1206, Université Lyon 1, Insa de Lyon, Lyon, France
| | - Federico Sala
- Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Petra Paiè
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Alessia Candeo
- Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | | | | | - Carole Frindel
- Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), CNRS UMR 5220 - INSERM U1206, Université Lyon 1, Insa de Lyon, Lyon, France
| | - Roberto Osellame
- Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Francesca Bragheri
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Andrea Bassi
- Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - David Rousseau
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAE IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France.
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Decroocq M, Lavoue G, Ohta M, Frindel C. A Software to Visualize, Edit, Model and Mesh Vascular Networks. Annu Int Conf IEEE Eng Med Biol Soc 2022; 2022:2208-2214. [PMID: 36085963 DOI: 10.1109/embc48229.2022.9871365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Computational fluid dynamics (CFD) is a key tool for a wide range of research areas, beyond the computer science community. In particular, CFD is used in medicine to measure blood flow from patient specific models of arteries. In this field, the creation of accurate meshes remains the most challenging step, as it is based on the segmentation of medical images, a time-consuming task which often requires manual intervention by medical doctors. In this context, user-friendly, interactive softwares are valuable. They enable to spread the new advances in numerical treatment to the medical community and enrich them with the expert knowledge (e.g anatomical knowledge) of clinicians. In this work, we present a user interface dedicated to the meshing of vascular networks from centerlines. It allows for the 3D visualization and edition of input centerlines, which constitute a simplified, easy-to-manipulate representation of vascular networks. The surface of the artery can be reconstructed from the modified centerlines by an editable parametric model and then meshed with high quality hexahedral elements. At every step of the process, the network can be confronted with medical images with enhanced visualization. The software will be released publicly. Clinical relevance- This tool facilitates the manual extraction and editing of vascular networks by medical doctors. It opens the generation of hexahedral meshes for computational fluid dynamics studies to non-expert users.
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Hatami N, Cho TH, Mechtouff L, Eker OF, Rousseau D, Frindel C. CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients. Annu Int Conf IEEE Eng Med Biol Soc 2022; 2022:3430-3434. [PMID: 36085793 DOI: 10.1109/embc48229.2022.9871735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Clinical outcome prediction plays an important role in stroke patient management. From a machine learning point-of-view, one of the main challenges is dealing with heterogeneous data at patient admission, i.e. the image data which are multidimensional and the clinical data which are scalars. In this paper, a multimodal convolutional neural network - long short-term memory (CNN-LSTM) based ensemble model is proposed. For each MR image module, a dedicated network provides preliminary prediction of the clinical outcome using the modified Rankin scale (mRS). The final mRS score is obtained by merging the preliminary probabilities of each module dedicated to a specific type of MR image weighted by the clinical metadata, here age or the National Institutes of Health Stroke Scale (NIHSS). The experimental results demonstrate that the proposed model surpasses the baselines and offers an original way to automatically encode the spatio-temporal context of MR images in a deep learning architecture. The highest AUC (0.77) was achieved for the proposed model with NIHSS. Clinical Relevance- - We present the first deep learning approach predicting the clinical outcome of stroke patients treated by mechanical thrombectomy which integrates imaging data at the voxel level with key clinical metadata. Combining clinical and imaging data to evaluate the potential benefit from therapy closely mirrors the clinical decision process. Our promising results suggest our predictive model could assist in acute stroke management.
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Decroocq M, Des Ligneris M, Poquillon T, Vincent M, Aubert M, Jacquesson T, Frindel C. Automation of Cranial Nerve Tractography by Filtering Tractograms for Skull Base Surgery. Front Neuroimaging 2022; 1:838483. [PMID: 37555173 PMCID: PMC10406276 DOI: 10.3389/fnimg.2022.838483] [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] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 02/04/2022] [Indexed: 08/10/2023]
Abstract
Fiber tractography enables the in vivo reconstruction of white matter fibers in 3 dimensions using data collected by diffusion tensor imaging, thereby helping to understand functional neuroanatomy. In a pre-operative context, it provides essential information on the trajectory of fiber bundles of medical interest, such as cranial nerves. However, the optimization of tractography parameters is a time-consuming process and requires expert neuroanatomical knowledge, making the use of tractography difficult in clinical routine. Tractogram filtering is a method used to isolate the most relevant fibers. In this work, we propose to use filtering as a post-processing of tractography to avoid the manual optimization of tracking parameters and therefore making a step forward automation of tractography. To question the feasibility of automated tractography of cranial nerves, we perform an analysis of main cranial nerves on a series of patients with skull base tumors. A quantitative evaluation of the filtering performance of two state-of-the-art and a new entropy-based methods is carried out on the basis of reference tractograms produced by experts. Our approach proves to be more stable in the selection of the optimal filtering threshold and turns out to be interesting in terms of computational time complexity.
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Affiliation(s)
- Méghane Decroocq
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, INSERM, CREATIS UMR 5220, Lyon, France
| | - Morgane Des Ligneris
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, INSERM, CREATIS UMR 5220, Lyon, France
| | - Titouan Poquillon
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, INSERM, CREATIS UMR 5220, Lyon, France
| | - Maxime Vincent
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, INSERM, CREATIS UMR 5220, Lyon, France
| | - Manon Aubert
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, INSERM, CREATIS UMR 5220, Lyon, France
| | - Timothée Jacquesson
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, INSERM, CREATIS UMR 5220, Lyon, France
- Skull Base Multi-Disciplinary Unit, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France
| | - Carole Frindel
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, INSERM, CREATIS UMR 5220, Lyon, France
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Ahmad A, Frindel C, Rousseau D. Detecting Differences of Fluorescent Markers Distribution in Single Cell Microscopy: Textural or Pointillist Feature Space? Front Robot AI 2021; 7:39. [PMID: 33501207 PMCID: PMC7805927 DOI: 10.3389/frobt.2020.00039] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 03/09/2020] [Indexed: 12/22/2022] Open
Abstract
We consider the detection of change in spatial distribution of fluorescent markers inside cells imaged by single cell microscopy. Such problems are important in bioimaging since the density of these markers can reflect the healthy or pathological state of cells, the spatial organization of DNA, or cell cycle stage. With the new super-resolved microscopes and associated microfluidic devices, bio-markers can be detected in single cells individually or collectively as a texture depending on the quality of the microscope impulse response. In this work, we propose, via numerical simulations, to address detection of changes in spatial density or in spatial clustering with an individual (pointillist) or collective (textural) approach by comparing their performances according to the size of the impulse response of the microscope. Pointillist approaches show good performances for small impulse response sizes only, while all textural approaches are found to overcome pointillist approaches with small as well as with large impulse response sizes. These results are validated with real fluorescence microscopy images with conventional resolution. This, a priori non-intuitive result in the perspective of the quest of super-resolution, demonstrates that, for difference detection tasks in single cell microscopy, super-resolved microscopes may not be mandatory and that lower cost, sub-resolved, microscopes can be sufficient.
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Affiliation(s)
- Ali Ahmad
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes, UMR INRAE IRHS, Université d'Angers, Angers, France.,Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé, CNRS UMR 5220-INSERM U1206, Université Lyon 1, INSA de Lyon, Lyon, France
| | - Carole Frindel
- Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé, CNRS UMR 5220-INSERM U1206, Université Lyon 1, INSA de Lyon, Lyon, France
| | - David Rousseau
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes, UMR INRAE IRHS, Université d'Angers, Angers, France
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10
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Dauleac C, Bannier E, Cotton F, Frindel C. Effect of distortion corrections on the tractography quality in spinal cord diffusion-weighted imaging. Magn Reson Med 2021; 85:3241-3255. [PMID: 33475180 DOI: 10.1002/mrm.28665] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 12/03/2020] [Accepted: 12/10/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE To assess the impact of a different distortion correction (DC) method and patient geometry (sagittal balance) on the quality of spinal cord tractography rendering according to different tractography approaches. METHODS Forty-four adults free of spinal cord diseases underwent cervical diffusion-weighted imaging. The phase-encoding direction was head→foot. Sequence with opposed polarities (foot→head) was acquired to perform DC. Eddy-current, motion effects, and susceptibility artifact correction methods were used for DC, and two deterministic and one probabilistic tractography approaches were evaluated using MRtrix and DSI Studio tractography software. Fiber length and number of fibers were extracted to evaluate the quality of the tractography rendering. For each subject, cervical lordosis was measured to assess patient geometry. The angle between the main direction of the spinal cord and the orientation of the acquisition box were computed at each spine level to assess acquisition geometry and define an angle threshold for which a tractography of good quality is no longer possible. RESULTS There was a significant improvement in tractography quality after performing DC with susceptibility artifact correction using a deterministic approach based on tensor. Before DC, the angle threshold was defined at C6 (15.2°) compared with C7 (21.9°) after corrections, demonstrating the importance of spinal cord angulation for DC. CONCLUSION The impact of DC on tractography quality is greatly impacted by acquisition geometry. To obtain a good-quality tractography, we propose as a future perspective to adapt the acquisition geometry to that of the patient by automatically adjusting the acquisition box.
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Affiliation(s)
- Corentin Dauleac
- Department of Neurosurgery, Hôpital neurologique et neurochirurgical Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France.,Université de Lyon, Université Claude Bernard Lyon I, Lyon, France.,Laboratoire CREATIS, CNRS UMR5220, INSA-Lyon, Université de Lyon I, Inserm U1206, Lyon, France
| | - Elise Bannier
- Université de Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn, France.,Department of Radiology, CHU de Rennes, Rennes, France
| | - François Cotton
- Université de Lyon, Université Claude Bernard Lyon I, Lyon, France.,Laboratoire CREATIS, CNRS UMR5220, INSA-Lyon, Université de Lyon I, Inserm U1206, Lyon, France.,Department of Radiology, Centre Hospitalier de Lyon Sud, Hospices Civils de Lyon, Lyon, France
| | - Carole Frindel
- Université de Lyon, Université Claude Bernard Lyon I, Lyon, France.,Laboratoire CREATIS, CNRS UMR5220, INSA-Lyon, Université de Lyon I, Inserm U1206, Lyon, France
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Debs N, Cho TH, Rousseau D, Berthezène Y, Buisson M, Eker O, Mechtouff L, Nighoghossian N, Ovize M, Frindel C. Impact of the reperfusion status for predicting the final stroke infarct using deep learning. Neuroimage Clin 2020; 29:102548. [PMID: 33450521 PMCID: PMC7810765 DOI: 10.1016/j.nicl.2020.102548] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 12/15/2020] [Accepted: 12/20/2020] [Indexed: 01/10/2023]
Abstract
BACKGROUND Predictive maps of the final infarct may help therapeutic decisions in acute ischemic stroke patients. Our objectives were to assess whether integrating the reperfusion status into deep learning models would improve their performance, and to compare them to current clinical prediction methods. METHODS We trained and tested convolutional neural networks (CNNs) to predict the final infarct in acute ischemic stroke patients treated by thrombectomy in our center. When training the CNNs, non-reperfused patients from a non-thrombectomized cohort were added to the training set to increase the size of this group. Baseline diffusion and perfusion-weighted magnetic resonance imaging (MRI) were used as inputs, and the lesion segmented on day-6 MRI served as the ground truth for the final infarct. The cohort was dichotomized into two subsets, reperfused and non-reperfused patients, from which reperfusion status specific CNNs were developed and compared to one another, and to the clinically-used perfusion-diffusion mismatch model. Evaluation metrics included the Dice similarity coefficient (DSC), precision, recall, volumetric similarity, Hausdorff distance and area-under-the-curve (AUC). RESULTS We analyzed 109 patients, including 35 without reperfusion. The highest DSC were achieved in both reperfused and non-reperfused patients (DSC = 0.44 ± 0.25 and 0.47 ± 0.17, respectively) when using the corresponding reperfusion status-specific CNN. CNN-based models achieved higher DSC and AUC values compared to those of perfusion-diffusion mismatch models (reperfused patients: AUC = 0.87 ± 0.13 vs 0.79 ± 0.17, P < 0.001; non-reperfused patients: AUC = 0.81 ± 0.13 vs 0.73 ± 0.14, P < 0.01, in CNN vs perfusion-diffusion mismatch models, respectively). CONCLUSION The performance of deep learning models improved when the reperfusion status was incorporated in their training. CNN-based models outperformed the clinically-used perfusion-diffusion mismatch model. Comparing the predicted infarct in case of successful vs failed reperfusion may help in estimating the treatment effect and guiding therapeutic decisions in selected patients.
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Affiliation(s)
- Noëlie Debs
- CREATIS, CNRS, UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon, Villeurbanne, France.
| | - Tae-Hee Cho
- CREATIS, CNRS, UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon, Villeurbanne, France; Department of Vascular Neurology, Hospices Civils de Lyon, Lyon, France.
| | - David Rousseau
- LARIS, UMR IRHS INRA, Université d'Angers, Angers, France.
| | - Yves Berthezène
- CREATIS, CNRS, UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon, Villeurbanne, France; Department of Neuroradiology, Hospices Civils de Lyon, Lyon, France.
| | - Marielle Buisson
- Department of Cardiology, Clinical Investigation Center, CarMeN INSERM U1060, INRA U1397, INSA Lyon, Université Lyon 1, Hospices Civils de Lyon, Lyon, France.
| | - Omer Eker
- CREATIS, CNRS, UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon, Villeurbanne, France; Department of Neuroradiology, Hospices Civils de Lyon, Lyon, France.
| | - Laura Mechtouff
- Department of Vascular Neurology, Hospices Civils de Lyon, Lyon, France; Department of Cardiology, Clinical Investigation Center, CarMeN INSERM U1060, INRA U1397, INSA Lyon, Université Lyon 1, Hospices Civils de Lyon, Lyon, France.
| | - Norbert Nighoghossian
- CREATIS, CNRS, UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon, Villeurbanne, France; Department of Vascular Neurology, Hospices Civils de Lyon, Lyon, France.
| | - Michel Ovize
- Department of Neuroradiology, Hospices Civils de Lyon, Lyon, France.
| | - Carole Frindel
- CREATIS, CNRS, UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon, Villeurbanne, France.
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Dauleac C, Frindel C, Mertens P, Jacquesson T, Cotton F. Overcoming challenges of the human spinal cord tractography for routine clinical use: a review. Neuroradiology 2020; 62:1079-1094. [DOI: 10.1007/s00234-020-02442-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 04/16/2020] [Indexed: 02/06/2023]
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Leclerc P, Ray C, Mahieu-Williame L, Alston L, Frindel C, Brevet PF, Meyronet D, Guyotat J, Montcel B, Rousseau D. Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy. Sci Rep 2020; 10:1462. [PMID: 31996727 PMCID: PMC6989497 DOI: 10.1038/s41598-020-58299-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 01/06/2020] [Indexed: 12/22/2022] Open
Abstract
Gliomas are infiltrative brain tumors with a margin difficult to identify. 5-ALA induced PpIX fluorescence measurements are a clinical standard, but expert-based classification models still lack sensitivity and specificity. Here a fully automatic clustering method is proposed to discriminate glioma margin. This is obtained from spectroscopic fluorescent measurements acquired with a recently introduced intraoperative set up. We describe a data-driven selection of best spectral features and show how this improves results of margin prediction from healthy tissue by comparison with the standard biomarker-based prediction. This pilot study based on 10 patients and 50 samples shows promising results with a best performance of 77% of accuracy in healthy tissue prediction from margin tissue.
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Affiliation(s)
- Pierre Leclerc
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, F-69622, Villeurbanne, France, 10 Rue Ada Byron, 69622, Villeurbanne, France.,CREATIS, Univ Lyon, CNRS UMR5220, INSERM U1044, Université Claude Bernard Lyon1, INSA Lyon, Villeurbanne, France
| | - Cedric Ray
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, F-69622, Villeurbanne, France, 10 Rue Ada Byron, 69622, Villeurbanne, France
| | - Laurent Mahieu-Williame
- CREATIS, Univ Lyon, CNRS UMR5220, INSERM U1044, Université Claude Bernard Lyon1, INSA Lyon, Villeurbanne, France
| | - Laure Alston
- CREATIS, Univ Lyon, CNRS UMR5220, INSERM U1044, Université Claude Bernard Lyon1, INSA Lyon, Villeurbanne, France
| | - Carole Frindel
- CREATIS, Univ Lyon, CNRS UMR5220, INSERM U1044, Université Claude Bernard Lyon1, INSA Lyon, Villeurbanne, France
| | - Pierre-François Brevet
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, F-69622, Villeurbanne, France, 10 Rue Ada Byron, 69622, Villeurbanne, France
| | - David Meyronet
- Hospices Civils de Lyon, Centre de Pathologie et de Neuropathologie Est, Lyon, F-69003, France.,Cancer Research Centre of Lyon, Univ Lyon, INSERM U1052, CNRS UMR5286, Lyon, France, Université Claude Bernard Lyon 1, Lyon, France
| | - Jacques Guyotat
- Hospices Civils de Lyon, Centre de Pathologie et de Neuropathologie Est, Lyon, F-69003, France
| | - Bruno Montcel
- CREATIS, Univ Lyon, CNRS UMR5220, INSERM U1044, Université Claude Bernard Lyon1, INSA Lyon, Villeurbanne, France.
| | - David Rousseau
- CREATIS, Univ Lyon, CNRS UMR5220, INSERM U1044, Université Claude Bernard Lyon1, INSA Lyon, Villeurbanne, France.,Laboratoire Angevin de Recherche en Ingénierie des Systèmes, UMR INRA IRHS, Université d'Angers, 62 avenue Notre Dame du Lac, 49000, Angers, France
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14
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Debs N, Rasti P, Victor L, Cho TH, Frindel C, Rousseau D. Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke. Comput Biol Med 2019; 116:103579. [PMID: 31999557 DOI: 10.1016/j.compbiomed.2019.103579] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 12/04/2019] [Accepted: 12/08/2019] [Indexed: 11/16/2022]
Abstract
The problem of final tissue outcome prediction of acute ischemic stroke is assessed from physically realistic simulated perfusion magnetic resonance images. Different types of simulations with a focus on the arterial input function are discussed. These simulated perfusion magnetic resonance images are fed to convolutional neural network to predict real patients. Performances close to the state-of-the-art performances are obtained with a patient specific approach. This approach consists in training a model only from simulated images tuned to the arterial input function of a tested real patient. This demonstrates the added value of physically realistic simulated images to predict the final infarct from perfusion.
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Affiliation(s)
- Noëlie Debs
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât, Blaise Pascal, 7 Avenue Jean Capelle, 69621, Villeurbanne, France
| | - Pejman Rasti
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRA IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France
| | - Léon Victor
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât, Blaise Pascal, 7 Avenue Jean Capelle, 69621, Villeurbanne, France
| | - Tae-Hee Cho
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât, Blaise Pascal, 7 Avenue Jean Capelle, 69621, Villeurbanne, France
| | - Carole Frindel
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât, Blaise Pascal, 7 Avenue Jean Capelle, 69621, Villeurbanne, France
| | - David Rousseau
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRA IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France.
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Dauleac C, Frindel C, Cotton F, Pelissou-Guyotat I. Tractography-based surgical strategy for cavernoma of the conus medullaris: case illustration. J Neurosurg Spine 2019; 32:1-2. [PMID: 31783348 DOI: 10.3171/2019.10.spine19947] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 10/02/2019] [Indexed: 11/06/2022]
Affiliation(s)
- Corentin Dauleac
- 1Service de Neurochirurgie D, Hôpital neurologique et neurochirurgical Pierre Wertheimer, Hospices Civils de Lyon
- 2Université de Lyon, Université Claude Bernard Lyon I, Lyon
- 3Laboratoire CREATIS, CNRS UMR5220, Inserm U1206, INSA-Lyon, Université Lyon I, Lyon; and
| | - Carole Frindel
- 3Laboratoire CREATIS, CNRS UMR5220, Inserm U1206, INSA-Lyon, Université Lyon I, Lyon; and
| | - François Cotton
- 2Université de Lyon, Université Claude Bernard Lyon I, Lyon
- 3Laboratoire CREATIS, CNRS UMR5220, Inserm U1206, INSA-Lyon, Université Lyon I, Lyon; and
- 4Service de Radiologie, Centre Hospitalier de Lyon Sud, Hospices Civils de Lyon, France
| | - Isabelle Pelissou-Guyotat
- 1Service de Neurochirurgie D, Hôpital neurologique et neurochirurgical Pierre Wertheimer, Hospices Civils de Lyon
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Jacquesson T, Yeh FC, Panesar S, Barrios J, Attyé A, Frindel C, Cotton F, Gardner P, Jouanneau E, Fernandez-Miranda JC. Full tractography for detecting the position of cranial nerves in preoperative planning for skull base surgery: technical note. J Neurosurg 2019; 132:1642-1652. [PMID: 31003214 DOI: 10.3171/2019.1.jns182638] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 01/28/2019] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Diffusion imaging tractography has allowed the in vivo description of brain white matter. One of its applications is preoperative planning for brain tumor resection. Due to a limited spatial and angular resolution, it is difficult for fiber tracking to delineate fiber crossing areas and small-scale structures, in particular brainstem tracts and cranial nerves. New methods are being developed but these involve extensive multistep tractography pipelines including the patient-specific design of multiple regions of interest (ROIs). The authors propose a new practical full tractography method that could be implemented in routine presurgical planning for skull base surgery. METHODS A Philips MRI machine provided diffusion-weighted and anatomical sequences for 2 healthy volunteers and 2 skull base tumor patients. Tractography of the full brainstem, the cerebellum, and cranial nerves was performed using the software DSI Studio, generalized-q-sampling reconstruction, orientation distribution function (ODF) of fibers, and a quantitative anisotropy-based generalized deterministic algorithm. No ROI or extensive manual filtering of spurious fibers was used. Tractography rendering was displayed in a tridimensional space with directional color code. This approach was also tested on diffusion data from the Human Connectome Project (HCP) database. RESULTS The brainstem, the cerebellum, and the cisternal segments of most cranial nerves were depicted in all participants. In cases of skull base tumors, the tridimensional rendering permitted the visualization of the whole anatomical environment and cranial nerve displacement, thus helping the surgical strategy. CONCLUSIONS As opposed to classical ROI-based methods, this novel full tractography approach could enable routine enhanced surgical planning or brain imaging for skull base tumors.
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Affiliation(s)
- Timothee Jacquesson
- 1Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.,2Skull Base Multi-Disciplinary Unit, Department of Neurosurgery B, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon.,3CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1
| | - Fang-Chang Yeh
- 1Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Sandip Panesar
- 4Department of Neurosurgery, Stanford University Medical Center, Stanford, California
| | - Jessica Barrios
- 1Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Arnaud Attyé
- 5Department of Neuroradiology and MRI, Grenoble University Hospital, Grenoble, France; and
| | - Carole Frindel
- 3CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1
| | - Francois Cotton
- 3CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1.,6Department of Radiology, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon
| | - Paul Gardner
- 1Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Emmanuel Jouanneau
- 2Skull Base Multi-Disciplinary Unit, Department of Neurosurgery B, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon
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Jacquesson T, Cotton F, Attyé A, Zaouche S, Tringali S, Bosc J, Robinson P, Jouanneau E, Frindel C. Probabilistic Tractography to Predict the Position of Cranial Nerves Displaced by Skull Base Tumors: Value for Surgical Strategy Through a Case Series of 62 Patients. Neurosurgery 2018; 85:E125-E136. [DOI: 10.1093/neuros/nyy538] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Accepted: 10/14/2018] [Indexed: 11/14/2022] Open
Affiliation(s)
- Timothée Jacquesson
- Skull Base Multi-disciplinary Unit, Department of Neurosurgery B, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France
- Department of Anatomy, University of Lyon 1, Lyon, France
- CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1, Lyon, France
| | - Francois Cotton
- CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1, Lyon, France
- Department of Radiology, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon, France
| | - Arnaud Attyé
- Department of Radiology, Grenoble University Hospital, Grenoble, France
| | - Sandra Zaouche
- Department of ENT Surgery, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon, France
| | - Stéphane Tringali
- Department of ENT Surgery, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon, France
| | - Justine Bosc
- CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1, Lyon, France
| | - Philip Robinson
- Department of Clinical Research and Innovation, Hospices Civils de Lyon, Lyon, France
| | - Emmanuel Jouanneau
- Skull Base Multi-disciplinary Unit, Department of Neurosurgery B, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France
| | - Carole Frindel
- CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1, Lyon, France
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Giacalone M, Rasti P, Debs N, Frindel C, Cho TH, Grenier E, Rousseau D. Local spatio-temporal encoding of raw perfusion MRI for the prediction of final lesion in stroke. Med Image Anal 2018; 50:117-126. [PMID: 30268970 DOI: 10.1016/j.media.2018.08.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 07/28/2018] [Accepted: 08/31/2018] [Indexed: 10/28/2022]
Abstract
We address the medical image analysis issue of predicting the final lesion in stroke from early perfusion magnetic resonance imaging. The classical processing approach for the dynamical perfusion images consists in a temporal deconvolution to improve the temporal signals associated with each voxel before performing prediction. We demonstrate here the value of exploiting directly the raw perfusion data by encoding the local environment of each voxel as a spatio-temporal texture, with an observation scale larger than the voxel. As a first illustration for this approach, the textures are characterized with local binary patterns and the classification is performed using a standard support vector machine (SVM). This simple machine learning classification scheme demonstrates results with 95% accuracy on average while working only raw perfusion data. We discuss the influence of the observation scale and evaluate the interest of using post-processed perfusion data with this approach.
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Affiliation(s)
- Mathilde Giacalone
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât. Blaise Pascal, 7 avenue Jean Capelle, Villeurbanne 69621, France
| | - Pejman Rasti
- LARIS, UMR IRHS INRA, Université d'Angers 62 avenue Notre Dame du Lac, Angers 49000, France
| | - Noelie Debs
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât. Blaise Pascal, 7 avenue Jean Capelle, Villeurbanne 69621, France
| | - Carole Frindel
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât. Blaise Pascal, 7 avenue Jean Capelle, Villeurbanne 69621, France
| | - Tae-Hee Cho
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât. Blaise Pascal, 7 avenue Jean Capelle, Villeurbanne 69621, France
| | - Emmanuel Grenier
- ENS-Lyon, UMR CNRS 5669 'UMPA', and INRIA Alpes, project NUMED, Lyon F-69364, France
| | - David Rousseau
- LARIS, UMR IRHS INRA, Université d'Angers 62 avenue Notre Dame du Lac, Angers 49000, France.
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Jacquesson T, Frindel C, Kocevar G, Berhouma M, Jouanneau E, Attyé A, Cotton F. Overcoming Challenges of Cranial Nerve Tractography: A Targeted Review. Neurosurgery 2018; 84:313-325. [PMID: 30010992 DOI: 10.1093/neuros/nyy229] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Accepted: 05/01/2018] [Indexed: 11/14/2022] Open
Affiliation(s)
- Timothée Jacquesson
- Skull Base Multi-disciplinary Unit, Department of Neurosurgery B, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France
- Department of Anatomy, University of Lyon 1, Lyon, France
- CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1, Lyon, France
| | - Carole Frindel
- CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1, Lyon, France
| | - Gabriel Kocevar
- CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1, Lyon, France
| | - Moncef Berhouma
- Skull Base Multi-disciplinary Unit, Department of Neurosurgery B, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France
- CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1, Lyon, France
| | - Emmanuel Jouanneau
- Skull Base Multi-disciplinary Unit, Department of Neurosurgery B, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France
| | - Arnaud Attyé
- Department of Radiology, Grenoble University Hospital, Grenoble, France
| | - Francois Cotton
- CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1, Lyon, France
- Department of Radiology, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon, France
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Jacquesson T, Frindel C, Cotton F. Diffusion Tensor Imaging Tractography Detecting Isolated Oculomotor Nerve Damage After Traumatic Brain Injury. World Neurosurg 2017; 100:707.e5-707.e7. [PMID: 28153623 DOI: 10.1016/j.wneu.2017.01.082] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Revised: 01/17/2017] [Accepted: 01/19/2017] [Indexed: 11/24/2022]
Abstract
A 24-year-old woman was hit by a bus and suffered an isolated complete oculomotor nerve palsy. Computed tomography scan did not show a skull base fracture. T2*-weighted magnetic resonance imaging revealed petechial cerebral hemorrhages sparing the brainstem. T2 constructive interference in steady state suggested a partial sectioning of the left oculomotor nerve just before entering the superior orbital fissure. Diffusion tensor imaging fiber tractography confirmed a sharp arrest of the left oculomotor nerve. This recent imaging technique could be of interest to assess white fiber damage and help make a diagnosis or prognosis.
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Affiliation(s)
- Timothée Jacquesson
- Department of Neurosurgery B, Skull Base Multi-disciplinary Unit, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France; Department of Anatomy, University of Lyon 1, Lyon, France; CREATIS Laboratory, CNRS UMR 5220 - INSERM U1044, Villeurbanne, France.
| | - Carole Frindel
- CREATIS Laboratory, CNRS UMR 5220 - INSERM U1044, Villeurbanne, France
| | - Francois Cotton
- CREATIS Laboratory, CNRS UMR 5220 - INSERM U1044, Villeurbanne, France; Department of Radiology, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon, France
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Abstract
OBJECTIVE Our goal is to develop a robust global tractography method for cardiac diffusion imaging. METHODS A graph is stretched over the whole myocardium to represent the fiber structure, and the solutions are minima of a graph energy measuring the fidelity to the data along with the fiber density and curvature. The optimization is performed by a variant of simulated annealing that offers increased design freedom without sacrificing theoretical convergence guarantees. RESULTS Numerical experiments on synthetic and real data demonstrate the capability of our tractography algorithm to deal with low angular resolution, highly noisy data. In particular, our algorithm outperforms the Bayesian model-based algorithm of Reisert et al. (NeuroImage, vol. 54, no. 2, 2011) and the graph-based algorithm of Frindel et al. (Magn. Reson. Med., vol. 64, no. 4, 2010) at the noise levels typical of in vivo imaging. CONCLUSION The proposed algorithm avoids the drawbacks of local techniques and is very robust to noise, which makes it a promising tool for in vivo diffusion imaging of moving organs. SIGNIFICANCE Our approach is global in terms of both the fiber structure representation and the minimization problem. It also allows us to adjust the trajectory density by simply changing the vertex-lattice spacing in the graph model, a desirable feature for multiresolution tractography analysis.
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Giacalone M, Frindel C, Robini M, Cervenansky F, Grenier E, Rousseau D. Robustness of spatio-temporal regularization in perfusion MRI deconvolution: An application to acute ischemic stroke. Magn Reson Med 2016; 78:1981-1990. [PMID: 28019027 DOI: 10.1002/mrm.26573] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 11/16/2016] [Accepted: 11/16/2016] [Indexed: 12/23/2022]
Abstract
PURPOSE The robustness of a recently introduced globally convergent deconvolution algorithm with temporal and edge-preserving spatial regularization for the deconvolution of dynamic susceptibility contrast perfusion magnetic resonance imaging is assessed in the context of ischemic stroke. THEORY AND METHODS Ischemic tissues are not randomly distributed in the brain but form a spatially organized entity. The addition of a spatial regularization term allows to take into account this spatial organization contrarily to the sole temporal regularization approach which processes each voxel independently. The robustness of the spatial regularization in relation to shape variability, hemodynamic variability in tissues, noise in the magnetic resonance imaging apparatus, and uncertainty on the arterial input function selected for the deconvolution is addressed via an original in silico validation approach. RESULTS The deconvolution algorithm proved robust to the different sources of variability, outperforming temporal Tikhonov regularization in most realistic conditions considered. The limiting factor is the proper estimation of the arterial input function. CONCLUSION This study quantified the robustness of a spatio-temporal approach for dynamic susceptibility contrast-magnetic resonance imaging deconvolution via a new simulator. This simulator, now accessible online, is of wide applicability for the validation of any deconvolution algorithm. Magn Reson Med 78:1981-1990, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Mathilde Giacalone
- University of Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, LYON, F69006, France
| | - Carole Frindel
- University of Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, LYON, F69006, France
| | - Marc Robini
- University of Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, LYON, F69006, France
| | - Frédéric Cervenansky
- University of Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, LYON, F69006, France
| | - Emmanuel Grenier
- ENS-Lyon, UCB Lyon, Inria, NUMED, CNRS, UMPA UMR 5669, LYON, F69007, France
| | - David Rousseau
- University of Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1206, LYON, F69006, France
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Rositi H, Frindel C, Wiart M, Langer M, Olivier C, Peyrin F, Rousseau D. Computer vision tools to optimize reconstruction parameters in x-ray in-line phase tomography. Phys Med Biol 2016; 59:7767-75. [PMID: 25419867 DOI: 10.1088/0031-9155/59/24/7767] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
In this article, a set of three computer vision tools, including scale invariant feature transform (SIFT), a measure of focus, and a measure based on tractography are demonstrated to be useful in replacing the eye of the expert in the optimization of the reconstruction parameters in x-ray in-line phase tomography. We demonstrate how these computer vision tools can be used to inject priors on the shape and scale of the object to be reconstructed. This is illustrated with the Paganin single intensity image phase retrieval algorithm in heterogeneous soft tissues of biomedical interest, where the selection of the reconstruction parameters was previously made from visual inspection or physical assumptions on the composition of the sample.
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Affiliation(s)
- H Rositi
- Université de Lyon, Laboratoire CREATIS, CNRS UMR5220, INSERM U1044, Université Lyon 1, INSA-Lyon, 69621 Villeurbanne, France
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Frindel C, Rouanet A, Giacalone M, Cho TH, Østergaard L, Fiehler J, Pedraza S, Baron JC, Wiart M, Berthezène Y, Nighoghossian N, Rousseau D. Validity of shape as a predictive biomarker of final infarct volume in acute ischemic stroke. Stroke 2015; 46:976-81. [PMID: 25744520 DOI: 10.1161/strokeaha.114.008046] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE This study examines whether lesion shape documented on magnetic resonance diffusion-weighted imaging during acute stroke improves the prediction of the final infarct volume compared with lesion volume only. METHODS Diffusion-weighted imaging data and clinical information were retrospectively reviewed in 110 consecutive patients who underwent (n=67) or not (n=43) thrombolytic therapy for acute ischemic stroke. Three-dimensional shape analysis was performed on admission diffusion-weighted imaging data and 5 shape descriptors were developed. Final infarct volume was measured on T2-fluid-attenuated inversion recovery imaging data performed 30 days after stroke. RESULTS Shape analysis of acute ischemic lesion and more specifically the ratio of the bounding box volume to the lesion volume before thrombolytic treatment improved the prediction of the final infarct for patients undergoing thrombolysis (R(2)=0.86 in model with volume; R(2)=0.98 in model with volume and shape). CONCLUSIONS Our findings suggest that lesion shape contains important predictive information and reflects important environmental factors that might determine the progression of ischemia from the core.
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Affiliation(s)
- Carole Frindel
- From Université de Lyon, CREATIS, CNRS UMR5220, INSERM 1044, Université Lyon 1, INSA Lyon, Villeurbanne, France (C.F., A.R., M.G., T.-H.C., M.W., Y.B., N.N., D.R.); Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Århus University, Århus, Denmark (L.Ø.); Departments of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany (J.F.); Department of Radiology, Girona Biomedical Research Institute, Hospital Universitari de Girona Doctor Josep Trueta, Girona, Spain (S.P.); and INSERM U894, Université Paris Descartes, Sorbonne Paris Cité, Paris, France (J.-C.B.)
| | - Anaïs Rouanet
- From Université de Lyon, CREATIS, CNRS UMR5220, INSERM 1044, Université Lyon 1, INSA Lyon, Villeurbanne, France (C.F., A.R., M.G., T.-H.C., M.W., Y.B., N.N., D.R.); Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Århus University, Århus, Denmark (L.Ø.); Departments of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany (J.F.); Department of Radiology, Girona Biomedical Research Institute, Hospital Universitari de Girona Doctor Josep Trueta, Girona, Spain (S.P.); and INSERM U894, Université Paris Descartes, Sorbonne Paris Cité, Paris, France (J.-C.B.)
| | - Mathilde Giacalone
- From Université de Lyon, CREATIS, CNRS UMR5220, INSERM 1044, Université Lyon 1, INSA Lyon, Villeurbanne, France (C.F., A.R., M.G., T.-H.C., M.W., Y.B., N.N., D.R.); Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Århus University, Århus, Denmark (L.Ø.); Departments of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany (J.F.); Department of Radiology, Girona Biomedical Research Institute, Hospital Universitari de Girona Doctor Josep Trueta, Girona, Spain (S.P.); and INSERM U894, Université Paris Descartes, Sorbonne Paris Cité, Paris, France (J.-C.B.)
| | - Tae-Hee Cho
- From Université de Lyon, CREATIS, CNRS UMR5220, INSERM 1044, Université Lyon 1, INSA Lyon, Villeurbanne, France (C.F., A.R., M.G., T.-H.C., M.W., Y.B., N.N., D.R.); Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Århus University, Århus, Denmark (L.Ø.); Departments of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany (J.F.); Department of Radiology, Girona Biomedical Research Institute, Hospital Universitari de Girona Doctor Josep Trueta, Girona, Spain (S.P.); and INSERM U894, Université Paris Descartes, Sorbonne Paris Cité, Paris, France (J.-C.B.)
| | - Leif Østergaard
- From Université de Lyon, CREATIS, CNRS UMR5220, INSERM 1044, Université Lyon 1, INSA Lyon, Villeurbanne, France (C.F., A.R., M.G., T.-H.C., M.W., Y.B., N.N., D.R.); Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Århus University, Århus, Denmark (L.Ø.); Departments of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany (J.F.); Department of Radiology, Girona Biomedical Research Institute, Hospital Universitari de Girona Doctor Josep Trueta, Girona, Spain (S.P.); and INSERM U894, Université Paris Descartes, Sorbonne Paris Cité, Paris, France (J.-C.B.)
| | - Jens Fiehler
- From Université de Lyon, CREATIS, CNRS UMR5220, INSERM 1044, Université Lyon 1, INSA Lyon, Villeurbanne, France (C.F., A.R., M.G., T.-H.C., M.W., Y.B., N.N., D.R.); Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Århus University, Århus, Denmark (L.Ø.); Departments of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany (J.F.); Department of Radiology, Girona Biomedical Research Institute, Hospital Universitari de Girona Doctor Josep Trueta, Girona, Spain (S.P.); and INSERM U894, Université Paris Descartes, Sorbonne Paris Cité, Paris, France (J.-C.B.)
| | - Salvador Pedraza
- From Université de Lyon, CREATIS, CNRS UMR5220, INSERM 1044, Université Lyon 1, INSA Lyon, Villeurbanne, France (C.F., A.R., M.G., T.-H.C., M.W., Y.B., N.N., D.R.); Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Århus University, Århus, Denmark (L.Ø.); Departments of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany (J.F.); Department of Radiology, Girona Biomedical Research Institute, Hospital Universitari de Girona Doctor Josep Trueta, Girona, Spain (S.P.); and INSERM U894, Université Paris Descartes, Sorbonne Paris Cité, Paris, France (J.-C.B.)
| | - Jean-Claude Baron
- From Université de Lyon, CREATIS, CNRS UMR5220, INSERM 1044, Université Lyon 1, INSA Lyon, Villeurbanne, France (C.F., A.R., M.G., T.-H.C., M.W., Y.B., N.N., D.R.); Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Århus University, Århus, Denmark (L.Ø.); Departments of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany (J.F.); Department of Radiology, Girona Biomedical Research Institute, Hospital Universitari de Girona Doctor Josep Trueta, Girona, Spain (S.P.); and INSERM U894, Université Paris Descartes, Sorbonne Paris Cité, Paris, France (J.-C.B.)
| | - Marlène Wiart
- From Université de Lyon, CREATIS, CNRS UMR5220, INSERM 1044, Université Lyon 1, INSA Lyon, Villeurbanne, France (C.F., A.R., M.G., T.-H.C., M.W., Y.B., N.N., D.R.); Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Århus University, Århus, Denmark (L.Ø.); Departments of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany (J.F.); Department of Radiology, Girona Biomedical Research Institute, Hospital Universitari de Girona Doctor Josep Trueta, Girona, Spain (S.P.); and INSERM U894, Université Paris Descartes, Sorbonne Paris Cité, Paris, France (J.-C.B.)
| | - Yves Berthezène
- From Université de Lyon, CREATIS, CNRS UMR5220, INSERM 1044, Université Lyon 1, INSA Lyon, Villeurbanne, France (C.F., A.R., M.G., T.-H.C., M.W., Y.B., N.N., D.R.); Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Århus University, Århus, Denmark (L.Ø.); Departments of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany (J.F.); Department of Radiology, Girona Biomedical Research Institute, Hospital Universitari de Girona Doctor Josep Trueta, Girona, Spain (S.P.); and INSERM U894, Université Paris Descartes, Sorbonne Paris Cité, Paris, France (J.-C.B.)
| | - Norbert Nighoghossian
- From Université de Lyon, CREATIS, CNRS UMR5220, INSERM 1044, Université Lyon 1, INSA Lyon, Villeurbanne, France (C.F., A.R., M.G., T.-H.C., M.W., Y.B., N.N., D.R.); Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Århus University, Århus, Denmark (L.Ø.); Departments of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany (J.F.); Department of Radiology, Girona Biomedical Research Institute, Hospital Universitari de Girona Doctor Josep Trueta, Girona, Spain (S.P.); and INSERM U894, Université Paris Descartes, Sorbonne Paris Cité, Paris, France (J.-C.B.)
| | - David Rousseau
- From Université de Lyon, CREATIS, CNRS UMR5220, INSERM 1044, Université Lyon 1, INSA Lyon, Villeurbanne, France (C.F., A.R., M.G., T.-H.C., M.W., Y.B., N.N., D.R.); Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Århus University, Århus, Denmark (L.Ø.); Departments of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany (J.F.); Department of Radiology, Girona Biomedical Research Institute, Hospital Universitari de Girona Doctor Josep Trueta, Girona, Spain (S.P.); and INSERM U894, Université Paris Descartes, Sorbonne Paris Cité, Paris, France (J.-C.B.).
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Berner L, Rositi H, Vadcard F, Bolbos R, Langlois JB, Parola S, Rousseau D, Nighoghossian N, Frindel C, Berthezène Y, Wiart M. Caractérisation en IRM d un nouveau produit de contraste bimodal pour l’imagerie cérébrale in vivo : premiers résultats expérimentaux chez la souris. J Neuroradiol 2014. [DOI: 10.1016/j.neurad.2014.01.101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Frindel C, Robini MC, Rousseau D. A 3-D spatio-temporal deconvolution approach for MR perfusion in the brain. Med Image Anal 2014; 18:144-60. [DOI: 10.1016/j.media.2013.10.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2013] [Revised: 09/12/2013] [Accepted: 10/07/2013] [Indexed: 11/26/2022]
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Rositi H, Frindel C, Langer M, Wiart M, Olivier C, Peyrin F, Rousseau D. Information-based analysis of X-ray in-line phase tomography with application to the detection of iron oxide nanoparticles in the brain. Opt Express 2013; 21:27185-27196. [PMID: 24216942 DOI: 10.1364/oe.21.027185] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The study analyzes noise in X-ray in-line phase tomography in a biomedical context. The impact of noise on detection of iron oxide nanoparticles in mouse brain is assessed. The part of the noise due to the imaging system and the part due to biology are quantitatively expressed in a Neyman Pearson detection strategy with two models of noise. This represents a practical extension of previous work on noise in phase-contrast X-ray imaging which focused on the theoretical expression of the signal-to-noise ratio in mono-dimensional phantoms, taking account of the statistical noise of the imaging system only. We also report the impact of the phase retrieval step on detection performance. Taken together, this constitutes a general methodology of practical interest for quantitative extraction of information from X-ray in-line phase tomography, and is also relevant to assessment of contrast agents with a blob-like signature in high resolution imaging.
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Frindel C, Robini M, Schaerer J, Croisille P, Zhu YM. A graph-based approach for automatic cardiac tractography. Magn Reson Med 2010; 64:1215-29. [DOI: 10.1002/mrm.22443] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Frindel C, Robini M, Croisille P, Zhu YM. Comparison of regularization methods for human cardiac diffusion tensor MRI. Med Image Anal 2009; 13:405-18. [DOI: 10.1016/j.media.2009.01.002] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2008] [Revised: 01/08/2009] [Accepted: 01/09/2009] [Indexed: 11/15/2022]
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Frindel C, Robini M, Rapacchi S, Stephant E, Zhu YM, Croisille P. Towards in vivo diffusion tensor MRI on human heart using edge-preserving regularization. ACTA ACUST UNITED AC 2008; 2007:6008-11. [PMID: 18003383 DOI: 10.1109/iembs.2007.4353717] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
We investigate the noise sensitivity in various Diffusion Tensor MRI acquisition protocols in sixteen human ex vivo hearts. In particular, we compare the accuracy of protocols with various numbers of excitations and diffusion sensitizing directions for estimating the principal diffusion directions in the myocardium. It is observed that noise sensitivity decreases as the number of excitations and the number of sensitizing directions increase (and hence as the acquisition time increases). To reduce the effects of noise and to improve the results obtained with a smaller number of excitations and/or a smaller number of sensitizing directions, we introduce a 3-D edge-preserving regularization method operating on diffusion weighted images. It allows to maintain the quality of the principal diffusion direction field while minimizing the acquisition time, which is a necessary step for in vivo diffusion tensor MR imaging of the human heart.
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Affiliation(s)
- Carole Frindel
- CREATIS (UMR CNRS 5220 and INSERM U630), INSA de Lyon, 69621 Villeurbanne Cedex, France.
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Gagnon DG, Frindel C, Lapointe JY. Voltage-clamp fluorometry in the local environment of the C255-C511 disulfide bridge of the Na+/glucose cotransporter. Biophys J 2007; 92:2403-11. [PMID: 17208964 PMCID: PMC1864846 DOI: 10.1529/biophysj.106.097964] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [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: 11/18/2022] Open
Abstract
We recently identified a functionally important disulfide bridge between C255 and C511 of the human Na+/glucose cotransporter SGLT1. In this study, voltage-clamp fluorometry was used to characterize the fluorescence of four different dyes attached to C255 and C511 under various ionic and substrate/inhibitor conditions. State-dependent fluorescence changes (DeltaF) were observed when TMR5M or TMR6M dyes were attached to C255 and C511 or when Alexa488 was bound to C511. TMR5M-C511 was extremely sensitive to membrane potential (Vm) and to external Na+ and alphaMG (a nonmetabolizable glucose analog) concentrations. A progressive increase in alphaMG concentration drastically changed the maximal voltage-dependent DeltaF and produced a positive shift in the midpoint of the DeltaF-Vm curve. By determining specific fluorescence intensity for each state of the cotransporter, our steady-state fluorescence data could be reproduced using the rate constants previously proposed for a five-state kinetic model exclusively derived from electrophysiological measurements. Our results bring an independent support to the proposed kinetic model and show that the binding of alphaMG substrate significantly modifies the environment of C255 and C511.
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Affiliation(s)
- Dominique G Gagnon
- Groupe d'étude des protéines membranaires (GEPROM), Université de Montréal, Montréal, Québec, Canada
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Abstract
When measuring Na(+)/glucose cotransporter (SGLT1) activity in Xenopus oocytes with the two-electrode voltage-clamp technique, pre-steady-state currents dissipate completely in the presence of saturating alpha-methyl-glucose (alphaMG, a nonhydrolyzable glucose analog) concentrations. In sharp contrast, two SGLT1 mutants (C255A and C511A) that lack a recently identified disulfide bridge express the pre-steady-state currents in the presence of alphaMG. The dose-dependent effects of alphaMG on pre-steady-state currents were studied for wild-type (wt) SGLT1 and for the two mutants. Increases in alphaMG concentration reduced the total transferred charge (partially for the mutants, totally for wt SGLT1), shifted the transferred charge versus membrane potential (Q-V) curve toward positive potentials, and significantly modified the time constants of the pre-steady-state currents. A five-state kinetic model is proposed to quantitatively explain the effect of alphaMG on pre-steady-state currents. This analysis reveals that the reorientation of free transporter is the slowest step for wt SGLT1 either in the presence or in the absence of alphaMG. In contrast, the conformational change of the fully loaded mutant transporters constitutes their rate-limiting step in the presence of substrate and explains the persistence of pre-steady-state currents in this situation.
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Affiliation(s)
- Dominique G Gagnon
- Groupe d'étude des protéines membranaires, Université de Montréal, Montreal, Quebec, Canada
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