1
|
Chanra V, Chudzinska A, Braniewska N, Silski B, Holst B, Sauvigny T, Stodieck S, Pelzl S, House PM. Development and prospective clinical validation of a convolutional neural network for automated detection and segmentation of focal cortical dysplasias. Epilepsy Res 2024; 202:107357. [PMID: 38582073 DOI: 10.1016/j.eplepsyres.2024.107357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/28/2024] [Accepted: 04/01/2024] [Indexed: 04/08/2024]
Abstract
PURPOSE Focal cortical dysplasias (FCDs) are a leading cause of drug-resistant epilepsy. Early detection and resection of FCDs have favorable prognostic implications for postoperative seizure freedom. Despite advancements in imaging methods, FCD detection remains challenging. House et al. (2021) introduced a convolutional neural network (CNN) for automated FCD detection and segmentation, achieving a sensitivity of 77.8%. However, its clinical applicability was limited due to a low specificity of 5.5%. The objective of this study was to improve the CNN's performance through data-driven training and algorithm optimization, followed by a prospective validation on daily-routine MRIs. MATERIAL AND METHODS A dataset of 300 3 T MRIs from daily clinical practice, including 3D T1 and FLAIR sequences, was prospectively compiled. The MRIs were visually evaluated by two neuroradiologists and underwent morphometric assessment by two epileptologists. The dataset included 30 FCD cases (11 female, mean age: 28.1 ± 10.1 years) and a control group of 150 normal cases (97 female, mean age: 32.8 ± 14.9 years), along with 120 non-FCD pathological cases (64 female, mean age: 38.4 ± 18.4 years). The dataset was divided into three subsets, each analyzed by the CNN. Subsequently, the CNN underwent a two-phase-training process, incorporating subset MRIs and expert-labeled FCD maps. This training employed both classical and continual learning techniques. The CNN's performance was validated by comparing the baseline model with the trained models at two training levels. RESULTS In prospective validation, the best model trained using continual learning achieved a sensitivity of 90.0%, specificity of 70.0%, and accuracy of 72.0%, with an average of 0.41 false positive clusters detected per MRI. For FCD segmentation, an average Dice coefficient of 0.56 was attained. The model's performance improved in each training phase while maintaining a high level of sensitivity. Continual learning outperformed classical learning in this regard. CONCLUSIONS Our study presents a promising CNN for FCD detection and segmentation, exhibiting both high sensitivity and specificity. Furthermore, the model demonstrates continuous improvement with the inclusion of more clinical MRI data. We consider our CNN a valuable tool for automated, examiner-independent FCD detection in daily clinical practice, potentially addressing the underutilization of epilepsy surgery in drug-resistant focal epilepsy and thereby improving patient outcomes.
Collapse
Affiliation(s)
- Vicky Chanra
- Hamburg Epilepsy Center, Protestant Hospital Alsterdorf, Department of Neurology and Epileptology, Hamburg, Germany
| | | | | | | | - Brigitte Holst
- University Hospital Hamburg-Eppendorf, Department of Neuroradiology, Hamburg, Germany
| | - Thomas Sauvigny
- University Hospital Hamburg-Eppendorf, Department of Neurosurgery, Hamburg, Germany
| | - Stefan Stodieck
- Hamburg Epilepsy Center, Protestant Hospital Alsterdorf, Department of Neurology and Epileptology, Hamburg, Germany
| | | | - Patrick M House
- Hamburg Epilepsy Center, Protestant Hospital Alsterdorf, Department of Neurology and Epileptology, Hamburg, Germany; theBlue.ai GmbH, Hamburg, Germany; Epileptologicum Hamburg, Specialist's Practice for Epileptology, Hamburg, Germany.
| |
Collapse
|
2
|
Bapst B, Massire A, Mauconduit F, Gras V, Boulant N, Dufour J, Bodini B, Stankoff B, Luciani A, Vignaud A. Pushing MP2RAGE boundaries: Ultimate time-efficient parameterization combined with exhaustive T 1 synthetic contrasts. Magn Reson Med 2024; 91:1608-1624. [PMID: 38102807 DOI: 10.1002/mrm.29948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 11/09/2023] [Accepted: 11/12/2023] [Indexed: 12/17/2023]
Abstract
PURPOSE MP2RAGE parameter optimization is redefined to allow more time-efficient MR acquisitions, whereas the T1 -based synthetic imaging framework is used to obtain on-demand T1 -weighted contrasts. Our aim was to validate this concept on healthy volunteers and patients with multiple sclerosis, using plug-and-play parallel-transmission brain imaging at 7 T. METHODS A "time-efficient" MP2RAGE sequence was designed with optimized parameters including TI and TR set as small as possible. Extended phase graph formalism was used to set flip-angle values to maximize the gray-to-white-matter contrast-to-noise ratio (CNR). Several synthetic contrasts (UNI, EDGE, FGATIR, FLAWSMIN , FLAWSHCO ) were generated online based on the acquired T1 maps. Experimental validation was performed on 4 healthy volunteers at various spatial resolutions. Clinical applicability was evaluated on 6 patients with multiple sclerosis, scanned with both time-efficient and conventional MP2RAGE parameterizations. RESULTS The proposed time-efficient MP2RAGE protocols reduced acquisition time by 40%, 30%, and 19% for brain imaging at (1 mm)3 , (0.80 mm)3 and (0.65 mm)3 , respectively, when compared with conventional parameterizations. They also provided all synthetic contrasts and comparable contrast-to-noise ratio on UNI images. The flexibility in parameter selection allowed us to obtain a whole-brain (0.45 mm)3 acquisition in 19 min 56 s. On patients with multiple sclerosis, a (0.67 mm)3 time-efficient acquisition enhanced cortical lesion visualization compared with a conventional (0.80 mm)3 protocol, while decreasing the scan time by 15%. CONCLUSION The proposed optimization, associated with T1 -based synthetic contrasts, enabled substantial decrease of the acquisition time or higher spatial resolution scans for a given time budget, while generating all typical brain contrasts derived from MP2RAGE.
Collapse
Affiliation(s)
- Blanche Bapst
- University of Paris-Saclay, CEA, CNRS, BAOBAB, NeuroSpin, Gif-sur-Yvette, France
- Department of Neuroradiology, AP-HP, Henri Mondor University Hospital, Créteil, France
- EA 4391, Université Paris Est Créteil, Créteil, France
| | | | - Franck Mauconduit
- University of Paris-Saclay, CEA, CNRS, BAOBAB, NeuroSpin, Gif-sur-Yvette, France
| | - Vincent Gras
- University of Paris-Saclay, CEA, CNRS, BAOBAB, NeuroSpin, Gif-sur-Yvette, France
| | - Nicolas Boulant
- University of Paris-Saclay, CEA, CNRS, BAOBAB, NeuroSpin, Gif-sur-Yvette, France
| | - Juliette Dufour
- Sorbonne Université, Paris Brain Institute, ICM, CNRS, Inserm, Paris, France
| | - Benedetta Bodini
- Sorbonne Université, Paris Brain Institute, ICM, CNRS, Inserm, Paris, France
| | - Bruno Stankoff
- Sorbonne Université, Paris Brain Institute, ICM, CNRS, Inserm, Paris, France
| | - Alain Luciani
- Department of Medical Imaging, Henri Mondor University Hospital, Créteil, France
| | - Alexandre Vignaud
- University of Paris-Saclay, CEA, CNRS, BAOBAB, NeuroSpin, Gif-sur-Yvette, France
| |
Collapse
|
3
|
Beaumont J, Fripp J, Raniga P, Acosta O, Ferre JC, McMahon K, Trinder J, Kober T, Gambarota G. Multi T1-weighted contrast imaging and T1 mapping with compressed sensing FLAWS at 3 T. MAGMA (NEW YORK, N.Y.) 2023; 36:823-836. [PMID: 36847989 DOI: 10.1007/s10334-023-01071-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/26/2023] [Accepted: 02/10/2023] [Indexed: 03/01/2023]
Abstract
OBJECTIVE The Fluid And White matter Suppression (FLAWS) MRI sequence provides multiple T1-weighted contrasts of the brain in a single acquisition. However, the FLAWS acquisition time is approximately 8 min with a standard GRAPPA 3 acceleration factor at 3 T. This study aims at reducing the FLAWS acquisition time by providing a new sequence optimization based on a Cartesian phyllotaxis k-space undersampling and a compressed sensing (CS) reconstruction. This study also aims at showing that T1 mapping can be performed with FLAWS at 3 T. MATERIALS AND METHODS The CS FLAWS parameters were determined using a method based on a profit function maximization under constraints. The FLAWS optimization and T1 mapping were assessed with in-silico, in-vitro and in-vivo (10 healthy volunteers) experiments conducted at 3 T. RESULTS In-silico, in-vitro and in-vivo experiments showed that the proposed CS FLAWS optimization allows the acquisition time of a 1 mm-isotropic full-brain scan to be reduced from [Formula: see text] to [Formula: see text] without decreasing image quality. In addition, these experiments demonstrate that T1 mapping can be performed with FLAWS at 3 T. DISCUSSION The results obtained in this study suggest that the recent advances in FLAWS imaging allow to perform multiple T1-weighted contrast imaging and T1 mapping in a single [Formula: see text] sequence acquisition.
Collapse
Affiliation(s)
- Jeremy Beaumont
- Univ Rennes, CRLCC Eugene Marquis, Inserm, LTSI-UMR1099, LTSI, Campus de Beaulieu, Université de Rennes 1, 35042, Rennes, France.
- The Australian e-Health Research Centre, CSIRO, Brisbane, QLD, Australia.
| | - Jurgen Fripp
- The Australian e-Health Research Centre, CSIRO, Brisbane, QLD, Australia
| | - Parnesh Raniga
- The Australian e-Health Research Centre, CSIRO, Brisbane, QLD, Australia
| | - Oscar Acosta
- Univ Rennes, CRLCC Eugene Marquis, Inserm, LTSI-UMR1099, LTSI, Campus de Beaulieu, Université de Rennes 1, 35042, Rennes, France
| | - Jean-Christophe Ferre
- Univ Rennes, Inria, CNRS, Inserm, IRISA, EMPENN ERL U-1228, Rennes, France
- Department of Neuroradiology, CHU Rennes, Rennes, France
| | - Katie McMahon
- School of Clinical Sciences, Centre for Biomedical Technologies, Queensland University of Technology (QUT), Brisbane, QLD, Australia
- Herston Imaging Research Facility, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
| | - Julie Trinder
- The Australian e-Health Research Centre, CSIRO, Brisbane, QLD, Australia
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Giulio Gambarota
- Univ Rennes, CRLCC Eugene Marquis, Inserm, LTSI-UMR1099, LTSI, Campus de Beaulieu, Université de Rennes 1, 35042, Rennes, France
| |
Collapse
|
4
|
Urbach H, Scheiwe C, Shah MJ, Nakagawa JM, Heers M, San Antonio-Arce MV, Altenmueller DM, Schulze-Bonhage A, Huppertz HJ, Demerath T, Doostkam S. Diagnostic Accuracy of Epilepsy-dedicated MRI with Post-processing. Clin Neuroradiol 2023; 33:709-719. [PMID: 36856785 PMCID: PMC10449992 DOI: 10.1007/s00062-023-01265-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 01/17/2023] [Indexed: 03/02/2023]
Abstract
PURPOSE To evaluate the diagnostic accuracy of epilepsy-dedicated 3 Tesla MRI including post-processing by correlating MRI, histopathology, and postsurgical seizure outcomes. METHODS 3 Tesla-MRI including a magnetization-prepared two rapid acquisition gradient echo (MP2RAGE) sequence for post-processing using the morphometric analysis program MAP was acquired in 116 consecutive patients with drug-resistant focal epilepsy undergoing resection surgery. The MRI, histopathology reports and postsurgical seizure outcomes were recorded from the patient's charts. RESULTS The MRI and histopathology were concordant in 101 and discordant in 15 patients, 3 no hippocampal sclerosis/gliosis only lesions were missed on MRI and 1 of 28 focal cortical dysplasia (FCD) type II associated with a glial scar was considered a glial scar only on MRI. In another five patients, MRI was suggestive of FCD, the histopathology was uneventful but patients were seizure-free following surgery. The MRI and histopathology were concordant in 20 of 21 glioneuronal tumors, 6 cavernomas, and 7 glial scars. Histopathology was negative in 10 patients with temporal lobe epilepsy, 4 of them had anteroinferior meningoencephaloceles. Engel class IA outcome was reached in 71% of patients. CONCLUSION The proposed MRI protocol is highly accurate. No hippocampal sclerosis/gliosis only lesions are typically MRI negative. Small MRI positive FCD can be histopathologically missed, most likely due to sampling errors resulting from insufficient harvesting of tissue.
Collapse
Affiliation(s)
- Horst Urbach
- Dept. of Neuroradiology, Medical Center, University of Freiburg, Breisacher Str. 64, 79106, Freiburg, Germany.
| | - Christian Scheiwe
- Dept. of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany
| | - Muskesh J Shah
- Dept. of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany
| | - Julia M Nakagawa
- Dept. of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany
| | - Marcel Heers
- Dept. of Epileptology, Medical Center, University of Freiburg, Freiburg, Germany
| | | | | | | | | | - Theo Demerath
- Dept. of Neuroradiology, Medical Center, University of Freiburg, Breisacher Str. 64, 79106, Freiburg, Germany
| | - Soroush Doostkam
- Dept. of Neuropathology, Medical Center, University of Freiburg, Freiburg, Germany
| |
Collapse
|
5
|
Dokumacı AS, Aitken FR, Sedlacik J, Bridgen P, Tomi‐Tricot R, Mooiweer R, Vecchiato K, Wilkinson T, Casella C, Giles S, Hajnal JV, Malik SJ, O'Muircheartaigh J, Carmichael DW. Simultaneous Optimization of MP2RAGE T 1 -weighted (UNI) and FLuid And White matter Suppression (FLAWS) brain images at 7T using Extended Phase Graph (EPG) Simulations. Magn Reson Med 2023; 89:937-950. [PMID: 36352772 PMCID: PMC10100108 DOI: 10.1002/mrm.29479] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 09/16/2022] [Accepted: 09/16/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE The MP2RAGE sequence is typically optimized for either T1 -weighted uniform image (UNI) or gray matter-dominant fluid and white matter suppression (FLAWS) contrast images. Here, the purpose was to optimize an MP2RAGE protocol at 7 Tesla to provide UNI and FLAWS images simultaneously in a clinically applicable acquisition time at <0.7 mm isotropic resolution. METHODS Using the extended phase graph formalism, the signal evolution of the MP2RAGE sequence was simulated incorporating T2 relaxation, diffusion, RF spoiling, and B1 + variability. Flip angles and TI were optimized at different TRs (TRMP2RAGE ) to produce an optimal contrast-to-noise ratio for UNI and FLAWS images. Simulation results were validated by comparison to MP2RAGE brain scans of 5 healthy subjects, and a final protocol at TRMP2RAGE = 4000 ms was applied in 19 subjects aged 8-62 years with and without epilepsy. RESULTS FLAWS contrast images could be obtained while maintaining >85% of the optimal UNI contrast-to-noise ratio. Using TI1 /TI2 /TRMP2RAGE of 650/2280/4000 ms, 6/8 partial Fourier in the inner phase-encoding direction, and GRAPPA factor = 4 in the other, images with 0.65 mm isotropic resolution were produced in <7.5 min. The contrast-to-noise ratio was around 20% smaller at TRMP2RAGE = 4000 ms compared to that at TRMP2RAGE = 5000 ms; however, the 20% shorter duration makes TRMP2RAGE = 4000 ms a good candidate for clinical applications example, pediatrics. CONCLUSION FLAWS and UNI images could be obtained in a single scan with 0.65 mm isotropic resolution, providing a set of high-contrast images and full brain coverage in a clinically applicable scan time. Images with excellent anatomical detail were demonstrated over a wide age range using the optimized parameter set.
Collapse
Affiliation(s)
- Ayşe Sıla Dokumacı
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Fraser R. Aitken
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Jan Sedlacik
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
- Radiology DepartmentGreat Ormond Street Hospital for ChildrenLondonUnited Kingdom
| | - Pip Bridgen
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Raphael Tomi‐Tricot
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
- MR Research CollaborationsSiemens Healthcare LimitedCamberleyUnited Kingdom
| | - Ronald Mooiweer
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- MR Research CollaborationsSiemens Healthcare LimitedCamberleyUnited Kingdom
| | - Katy Vecchiato
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
- Department of Forensic and Neurodevelopmental SciencesInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging Sciences, King's College LondonLondonUnited Kingdom
| | - Tom Wilkinson
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Chiara Casella
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging Sciences, King's College LondonLondonUnited Kingdom
| | - Sharon Giles
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Joseph V. Hajnal
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Shaihan J. Malik
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Jonathan O'Muircheartaigh
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
- Department of Forensic and Neurodevelopmental SciencesInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging Sciences, King's College LondonLondonUnited Kingdom
- MRC Centre for Neurodevelopmental Disorders, King's College LondonLondonUnited Kingdom
| | - David W. Carmichael
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| |
Collapse
|
6
|
Hou XX, Feng HX, Xu B, Li ZS, Lu YL, Zhao HM, Wang MX, Shi QR, Gui Q, Wu GH, Shen MQ, Zhu W, Xu QR, Dong XF, Cheng QZ, Zhang JB, Ding ZL. A tract-based spatial statistics study of white matter integrity in epilepsy. Am J Transl Res 2022; 14:8980-8990. [PMID: 36628222 PMCID: PMC9827316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 11/25/2022] [Indexed: 01/12/2023]
Abstract
OBJECTIVE To explore the changes of cerebral white matter diffusion tensor in epilepsy. METHODS This study was a retrospective study based on diffusion tensor imaging (DTI). Twenty-six epileptic patients and 42 normal controls matched for sex, age and handedness were enrolled in our research. Based on the method of tract-based spatial statistics (TBSS), we analyzed the changes of each relevant parameter index of DTI in white matter of the brain in all subjects, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). RESULTS In comparison with the control group, epileptic patients had decreased FA and elevated MD, AD, and RD in the anterior thalamic radiation, corticospinal tract, forceps major, forceps minor, cingulum, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus and uncinate fasciculus (P < 0.05). CONCLUSION Widespread white matter integrity was observed in epileptic patients, which may be the structural basis for the development of affective disorders, impaired cognition, and motor abnormalities.
Collapse
Affiliation(s)
- Xiao-Xia Hou
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Hong-Xuan Feng
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Bo Xu
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Zhi-Sen Li
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Yan-Li Lu
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Hui-Min Zhao
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Mei-Xia Wang
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Qian-Ru Shi
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Qian Gui
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Guan-Hui Wu
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Ming-Qiang Shen
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Wei Zhu
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Qin-Rong Xu
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Xiao-Feng Dong
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Qing-Zhang Cheng
- Department of Neurology, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Ji-Bin Zhang
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Zhi-Liang Ding
- Department of Neurosurgery, The Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| |
Collapse
|
7
|
Müller J, La Rosa F, Beaumont J, Tsagkas C, Rahmanzadeh R, Weigel M, Bach Cuadra M, Gambarota G, Granziera C. Fluid and White Matter Suppression: New Sensitive 3 T Magnetic Resonance Imaging Contrasts for Cortical Lesion Detection in Multiple Sclerosis. Invest Radiol 2022; 57:592-600. [PMID: 35510874 PMCID: PMC10184808 DOI: 10.1097/rli.0000000000000877] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Accepted: 02/26/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Cortical lesions are common in multiple sclerosis (MS), but their visualization is challenging on conventional magnetic resonance imaging. The uniform image derived from magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE uni ) detects cortical lesions with a similar rate as the criterion standard sequence, double inversion recovery. Fluid and white matter suppression (FLAWS) provides multiple reconstructed contrasts acquired during a single acquisition. These contrasts include FLAWS minimum image (FLAWS min ), which provides an exquisite sensitivity to the gray matter signal and therefore may facilitate cortical lesion identification, as well as high contrast FLAWS (FLAWS hco ), which gives a contrast that is similar to one of MP2RAGE uni . In this study, we compared the manual detection rate of cortical lesions on MP2RAGE uni , FLAWS min , and FLAWS hco in MS patients. Furthermore, we assessed whether the combined detection rate on FLAWS min and FLAWS hco was superior to MP2RAGE uni for cortical lesions identification. Last, we compared quantitative T1 maps (qT1) provided by both MP2RAGE and FLAWS in MS lesions. MATERIALS AND METHODS We included 30 relapsing-remitting MS patients who underwent MP2RAGE and FLAWS magnetic resonance imaging with isotropic spatial resolution of 1 mm at 3 T. Cortical lesions were manually segmented by consensus of 3 trained raters and classified as intracortical or leukocortical lesions on (1) MP2RAGE uniform/flat images, (2) FLAWS min , and (3) FLAWS hco . In addition, segmented lesions on FLAWS min and FLAWS hco were merged to produce a union lesion map (FLAWS min + hco ). Number and volume of all cortical, intracortical, and leukocortical lesions were compared among MP2RAGE uni , FLAWS min , and FLAWS hco using Friedman test and between MP2RAGE uni and FLAWS min + hco using Wilcoxon signed rank test. The FLAWS T1 maps were then compared with the reference MP2RAGE T1 maps using relative differences in percentage. In an exploratory analysis, individual cortical lesion counts of the 3 raters were compared, and interrater variability was quantified using Fleiss ϰ. RESULTS In total, 633 segmentations were made on the 3 contrasts, corresponding to 355 cortical lesions. The median number and volume of single cortical, intracortical, and leukocortical lesions were comparable among MP2RAGE uni , FLAWS min , and FLAWS hco . In patients with cortical lesions (22/30), median cumulative lesion volume was larger on FLAWS min (587 μL; IQR, 1405 μL) than on MP2RAGE uni (490 μL; IQR, 990 μL; P = 0.04), whereas there was no difference between FLAWS min and FLAWS hco , or FLAWS hco and MP2RAGE uni . FLAWS min + hco showed significantly greater numbers of cortical (median, 4.5; IQR, 15) and leukocortical (median, 3.5; IQR, 12) lesions than MP2RAGE uni (median, 3; IQR, 10; median, 2.5; IQR, 7; both P < 0.001). Interrater agreement was moderate on MP2RAGE uni (ϰ = 0.582) and FLAWS hco (ϰ = 0.584), but substantial on FLAWS min (ϰ = 0.614). qT1 in lesions was similar between MP2RAGE and FLAWS. CONCLUSIONS Cortical lesions identification in FLAWS min and FLAWS hco was comparable to MP2RAGE uni . The combination of FLAWS min and FLAWS hco allowed to identify a higher number of cortical lesions than MP2RAGE uni , whereas qT1 maps did not differ between the 2 acquisition schemes.
Collapse
Affiliation(s)
- Jannis Müller
- From the Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel
| | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Jeremy Beaumont
- Univ Rennes, Inserm, LTSI-UMR1099, Rennes, France
- The Australian e-Health Research Centre, CSIRO, Brisbane, Australia
| | - Charidimos Tsagkas
- From the Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel
| | - Reza Rahmanzadeh
- From the Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel
| | - Matthias Weigel
- From the Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel
- Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Radiology Department, Lausanne University and University Hospital, Lausanne, Switzerland
| | | | - Cristina Granziera
- From the Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel
| |
Collapse
|
8
|
MRI of focal cortical dysplasia. Neuroradiology 2021; 64:443-452. [PMID: 34839379 PMCID: PMC8850246 DOI: 10.1007/s00234-021-02865-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 11/17/2021] [Indexed: 11/09/2022]
Abstract
Focal cortical dysplasia (FCD) are histopathologically categorized in ILAE type I to III. Mild malformations of cortical development (mMCD) including those with oligodendroglial hyperplasia (MOGHE) are to be integrated into this classification yet. Only FCD type II have distinctive MRI and molecular genetics alterations so far. Subtle FCD including FCD type II located in the depth of a sulcus are often overlooked requiring the use of dedicated sequences (MP2RAGE, FLAWS, EDGE) and/or voxel (VBM)- or surface-based (SBM) postprocessing. The added value of 7 Tesla MRI has to be proven yet.
Collapse
|
9
|
Holthausen H, Coras R, Tang Y, Bai L, Wang I, Pieper T, Kudernatsch M, Hartlieb T, Staudt M, Winkler P, Hofer W, Jabari S, Kobow K, Blumcke I. Multilobar unilateral hypoplasia with emphasis on the posterior quadrant and severe epilepsy in children with FCD ILAE Type 1A. Epilepsia 2021; 63:42-60. [PMID: 34741301 DOI: 10.1111/epi.17114] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 10/08/2021] [Accepted: 10/18/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVE Focal cortical dysplasia (FCD) Type 1 and its three subtypes have yet not been fully characterized at the clinical, anatomopathological, and molecular level (International League Against Epilepsy [ILAE] FCD classification from 2011). We aimed to describe the clinical phenotype of patients with histopathologically confirmed FCD1A obtained from a single epilepsy center between 2002 and 2016. METHODS Medical records were retrieved from the hospital's archive. Results from electroencephalography (EEG) video recordings, neuroimaging, and histopathology were reevaluated. Magnetic resonance imaging (MRI) post-processing was retrospectively performed in nine patients. DNA methylation studies were carried out from archival surgical brain tissue in 11 patients. RESULTS Nineteen children with a histopathological diagnosis of FCD1A were included. The average onset of epilepsy was 0.9 years (range 0.2-10 years). All children had severe cognitive impairment and one third had mild motor deficits, yet fine finger movements were preserved in all patients. All patients had daily seizures, being drug resistant from disease onset. Interictal electroencephalography revealed bilateral multi-regional epileptiform discharges. Interictal status epilepticus was observed in 8 and countless subclinical seizures in 11 patients. Regional continuous irregular slow waves were of higher lateralizing and localizing yield than spikes. Posterior background rhythms were normal in 16 of 19 children. Neuroimaging showed unilateral multilobar hypoplasia and increased T2-FLAIR signals of the white matter in 18 of 19 patients. All children underwent tailored multilobar resections, with seizure freedom achieved in 47% (Engel class I). There was no case with frontal involvement without involvement of the posterior quadrant by MRI and histopathology. DNA methylation profiling distinguished FCD1A samples from all other epilepsy specimens and controls. SIGNIFICANCE We identified a cohort of young children with drug resistance from seizure onset, bad EEG with posterior emphasis, lack of any focal neurological deficits but severe cognitive impairment, subtle hypoplasia of the epileptogenic area on MRI, and histopathologically defined and molecularly confirmed by DNA methylation analysis as FCD ILAE Type 1A.
Collapse
Affiliation(s)
- Hans Holthausen
- Center for Pediatric Neurology, Neurorehabilitation, and Epileptology, Schoen-Clinic, Vogtareuth, Germany
| | - Roland Coras
- Department of Neuropathology, University Hospitals Erlangen, Erlangen, Germany
| | - Yingying Tang
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.,Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, USA
| | - Lily Bai
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, USA
| | - Irene Wang
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, USA
| | - Tom Pieper
- Center for Pediatric Neurology, Neurorehabilitation, and Epileptology, Schoen-Clinic, Vogtareuth, Germany
| | - Manfred Kudernatsch
- Center for Pediatric Neurology, Neurorehabilitation, and Epileptology, Schoen-Clinic, Vogtareuth, Germany.,Paracelsus Private Medical University, Salzburg, Austria
| | - Till Hartlieb
- Center for Pediatric Neurology, Neurorehabilitation, and Epileptology, Schoen-Clinic, Vogtareuth, Germany.,Paracelsus Private Medical University, Salzburg, Austria
| | - Martin Staudt
- Center for Pediatric Neurology, Neurorehabilitation, and Epileptology, Schoen-Clinic, Vogtareuth, Germany
| | - Peter Winkler
- Center for Pediatric Neurology, Neurorehabilitation, and Epileptology, Schoen-Clinic, Vogtareuth, Germany
| | - Wiebke Hofer
- Center for Pediatric Neurology, Neurorehabilitation, and Epileptology, Schoen-Clinic, Vogtareuth, Germany
| | - Samir Jabari
- Department of Neuropathology, University Hospitals Erlangen, Erlangen, Germany
| | - Katja Kobow
- Department of Neuropathology, University Hospitals Erlangen, Erlangen, Germany
| | - Ingmar Blumcke
- Department of Neuropathology, University Hospitals Erlangen, Erlangen, Germany.,Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, USA
| |
Collapse
|
10
|
Saute RL, Peixoto-Santos JE, Velasco TR, Leite JP. Improving surgical outcome with electric source imaging and high field magnetic resonance imaging. Seizure 2021; 90:145-154. [PMID: 33608134 DOI: 10.1016/j.seizure.2021.02.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/26/2021] [Accepted: 02/04/2021] [Indexed: 12/14/2022] Open
Abstract
While most patients with focal epilepsy present with clear structural abnormalities on standard, 1.5 or 3 T MRI, some patients are MRI-negative. For those, quantitative MRI techniques, such as volumetry, voxel-based morphometry, and relaxation time measurements can aid in finding the epileptogenic focus. High-field MRI, just recently approved for clinical use by the FDA, increases the resolution and, in several publications, was shown to improve the detection of focal cortical dysplasias and mild cortical malformations. For those cases without any tissue abnormality in neuroimaging, even at 7 T, scalp EEG alone is insufficient to delimitate the epileptogenic zone. They may benefit from the use of high-density EEG, in which the increased number of electrodes helps improve spatial sampling. The spatial resolution of even low-density EEG can benefit from electric source imaging techniques, which map the source of the recorded abnormal activity, such as interictal epileptiform discharges, focal slowing, and ictal rhythm. These EEG techniques help localize the irritative, functional deficit, and seizure-onset zone, to better estimate the epileptogenic zone. Combining those technologies allows several drug-resistant cases to be submitted to surgery, increasing the odds of seizure freedom and providing a must needed hope for patients with epilepsy.
Collapse
Affiliation(s)
- Ricardo Lutzky Saute
- Department of Neurosciences and Behavioral Sciences, Ribeirao Preto Medical School, University of Sao Paulo, Brazil
| | - Jose Eduardo Peixoto-Santos
- Discipline of Neuroscience, Department of Neurology and Neurosurgery, Paulista School of Medicine, Unifesp, Brazil
| | - Tonicarlo R Velasco
- Department of Neurosciences and Behavioral Sciences, Ribeirao Preto Medical School, University of Sao Paulo, Brazil
| | - Joao Pereira Leite
- Department of Neurosciences and Behavioral Sciences, Ribeirao Preto Medical School, University of Sao Paulo, Brazil.
| |
Collapse
|
11
|
Sone D. Making the Invisible Visible: Advanced Neuroimaging Techniques in Focal Epilepsy. Front Neurosci 2021; 15:699176. [PMID: 34385902 PMCID: PMC8353251 DOI: 10.3389/fnins.2021.699176] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/28/2021] [Indexed: 12/30/2022] Open
Abstract
It has been a clinically important, long-standing challenge to accurately localize epileptogenic focus in drug-resistant focal epilepsy because more intensive intervention to the detected focus, including resection neurosurgery, can provide significant seizure reduction. In addition to neurophysiological examinations, neuroimaging plays a crucial role in the detection of focus by providing morphological and neuroanatomical information. On the other hand, epileptogenic lesions in the brain may sometimes show only subtle or even invisible abnormalities on conventional MRI sequences, and thus, efforts have been made for better visualization and improved detection of the focus lesions. Recent advance in neuroimaging has been attracting attention because of the potentials to better visualize the epileptogenic lesions as well as provide novel information about the pathophysiology of epilepsy. While the progress of newer neuroimaging techniques, including the non-Gaussian diffusion model and arterial spin labeling, could non-invasively detect decreased neurite parameters or hypoperfusion within the focus lesions, advances in analytic technology may also provide usefulness for both focus detection and understanding of epilepsy. There has been an increasing number of clinical and experimental applications of machine learning and network analysis in the field of epilepsy. This review article will shed light on recent advances in neuroimaging for focal epilepsy, including both technical progress of images and newer analytical methodologies and discuss about the potential usefulness in clinical practice.
Collapse
Affiliation(s)
- Daichi Sone
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan.,Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom
| |
Collapse
|
12
|
Li X, Yu T, Ren Z, Wang X, Yan J, Chen X, Yan X, Wang W, Xing Y, Zhang X, Zhang H, Loh HH, Zhang G, Yang X. Localization of the Epileptogenic Zone by Multimodal Neuroimaging and High-Frequency Oscillation. Front Hum Neurosci 2021; 15:677840. [PMID: 34168546 PMCID: PMC8217465 DOI: 10.3389/fnhum.2021.677840] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 04/23/2021] [Indexed: 11/29/2022] Open
Abstract
Accurate localization of the epileptogenic zone (EZ) is a key factor to obtain good surgical outcome for refractory epilepsy patients. However, no technique, so far, can precisely locate the EZ, and there are barely any reports on the combined application of multiple technologies to improve the localization accuracy of the EZ. In this study, we aimed to explore the use of a multimodal method combining PET-MRI, fluid and white matter suppression (FLAWS)—a novel MRI sequence, and high-frequency oscillation (HFO) automated analysis to delineate EZ. We retrospectively collected 15 patients with refractory epilepsy who underwent surgery and used the above three methods to detect abnormal brain areas of all patients. We compared the PET-MRI, FLAWS, and HFO results with traditional methods to evaluate their diagnostic value. The sensitivities, specificities of locating the EZ, and marking extent removed versus not removed [RatioChann(ev)] of each method were compared with surgical outcome. We also tested the possibility of using different combinations to locate the EZ. The marked areas in every patient established using each method were also compared to determine the correlations among the three methods. The results showed that PET-MRI, FLAWS, and HFOs can provide more information about potential epileptic areas than traditional methods. When detecting the EZs, the sensitivities of PET-MRI, FLAWS, and HFOs were 68.75, 53.85, and 87.50%, and the specificities were 80.00, 33.33, and 100.00%. The RatioChann(ev) of HFO-marked contacts was significantly higher in patients with good outcome than those with poor outcome (p< 0.05). When intracranial electrodes covered all the abnormal areas indicated by neuroimaging with the overlapping EZs being completely removed referred to HFO analysis, patients could reach seizure-free (p < 0.01). The periphery of the lesion marked by neuroimaging may be epileptic, but not every lesion contributes to seizures. Therefore, approaches in multimodality can detect EZ more accurately, and HFO analysis may help in defining real epileptic areas that may be missed in the neuroimaging results. The implantation of intracranial electrodes guided by non-invasive PET-MRI and FLAWS findings as well as HFO analysis would be an optimized multimodal approach for locating EZ.
Collapse
Affiliation(s)
- Xiaonan Li
- Laboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Ministry of Science and Technology, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China.,Xuanwu Hospital, Capital Medical University, Beijing, China.,Bioland Laboratory, Guangzhou, China
| | - Tao Yu
- Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Zhiwei Ren
- Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xueyuan Wang
- Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jiaqing Yan
- College of Electrical and Control Engineering, North China University of Technology, Beijing, China
| | - Xin Chen
- Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xiaoming Yan
- Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Wei Wang
- Laboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Ministry of Science and Technology, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China.,Xuanwu Hospital, Capital Medical University, Beijing, China.,Bioland Laboratory, Guangzhou, China
| | - Yue Xing
- Laboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Ministry of Science and Technology, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China.,Xuanwu Hospital, Capital Medical University, Beijing, China.,Bioland Laboratory, Guangzhou, China
| | | | | | | | - Guojun Zhang
- Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xiaofeng Yang
- Laboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Ministry of Science and Technology, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China.,Xuanwu Hospital, Capital Medical University, Beijing, China.,Bioland Laboratory, Guangzhou, China
| |
Collapse
|
13
|
Sun K, Yu T, Yang D, Ren Z, Qiao L, Ni D, Wang X, Zhao Y, Chen X, Xiang J, Chen N, Gao R, Yang K, Lin Y, Kober T, Zhang G. Fluid and White Matter Suppression Imaging and Voxel-Based Morphometric Analysis in Conventional Magnetic Resonance Imaging-Negative Epilepsy. Front Neurol 2021; 12:651592. [PMID: 33995250 PMCID: PMC8116947 DOI: 10.3389/fneur.2021.651592] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 03/18/2021] [Indexed: 01/23/2023] Open
Abstract
Purpose: Delineation of subtle lesions in magnetic resonance imaging (MRI)-negative patients is of great importance in preoperative epilepsy evaluation. The aim of our study was to explore the diagnostic value of the novel fluid and white matter suppression (FLAWS) sequence in comparison with a voxel-based MRI postprocessing morphometric analysis program (MAP) in a consecutive cohort of non-lesional patients. Methods: Surgical candidates with a negative finding on an official neuroradiology report were enrolled. High-resolution FLAWS image and MAP maps generated based on high-resolution three-dimensional (3D) T1 image were visually inspected for each patient. The findings of FLAWS or MAP-positive (FLAWS/MAP+) regions were compared with the surgical resection cavity in correlation with surgical outcome and pathology. Results: Forty-five patients were enrolled; the pathological examination revealed focal cortical dysplasia (FCD) in 32 patients and other findings in 13 patients. The positive rate, sensitivity, and specificity were 48.9%, 0.43, and 0.87, respectively, for FLAWS and 64.4%, 0.57, and 0.8, respectively, for MAP. Concordance between surgical resection and FLAWS+ or MAP+ regions was significantly associated with a seizure-free outcome (FLAWS: p = 0.002; MAP: p = 0.0003). A positive finding in FLAWS and MAP together with abnormalities in the same gyrus (FLAWS–MAP gyral+) was detected in 31.1% of patients. FLAWS+ only and MAP+ only were found in 7 (15.5%) and 14 (31.1%) patients, respectively. Conclusions: FLAWS showed a promising value for identifying subtle epileptogenic lesions and can be used as a complement to current MAP in patients with MRI-negative epilepsy.
Collapse
Affiliation(s)
- Ke Sun
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Tao Yu
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Dongju Yang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Zhiwei Ren
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Liang Qiao
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Duanyu Ni
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xueyuan Wang
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yongxiang Zhao
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xin Chen
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jing Xiang
- Department of Neurology, MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Nan Chen
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Runshi Gao
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Kun Yang
- Department of Evidence-Based Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yicong Lin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Guojun Zhang
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
14
|
House PM, Kopelyan M, Braniewska N, Silski B, Chudzinska A, Holst B, Sauvigny T, Martens T, Stodieck S, Pelzl S. Automated detection and segmentation of focal cortical dysplasias (FCDs) with artificial intelligence: Presentation of a novel convolutional neural network and its prospective clinical validation. Epilepsy Res 2021; 172:106594. [PMID: 33677163 DOI: 10.1016/j.eplepsyres.2021.106594] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 02/10/2021] [Accepted: 02/20/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE Focal cortical dysplasias (FCDs) represent one of the most frequent causes of pharmaco-resistant focal epilepsies. Despite improved clinical imaging methods over the past years, FCD detection remains challenging, as FCDs vary in location, size, and shape and commonly blend into surrounding tissues without clear definable boundaries. We developed a novel convolutional neural network for FCD detection and segmentation and validated it prospectively on daily-routine MRIs. MATERIAL AND METHODS The neural network was trained on 201 T1 and FLAIR 3 T MRI volume sequences of 158 patients with mainly FCDs, regardless of type, and 7 focal PMG. Non-FCD/PMG MRIs, drawn from 100 normal MRIs and 50 MRIs with non-FCD/PMG pathologies, were added to the training. We applied the algorithm prospectively on 100 consecutive MRIs of patients with focal epilepsy from daily clinical practice. The results were compared with corresponding neuroradiological reports and morphometric MRI analyses evaluated by an experienced epileptologist. RESULTS Best training results reached a sensitivity (recall) of 70.1 % and a precision of 54.3 % for detecting FCDs. Applied on the daily-routine MRIs, 7 out of 9 FCDs were detected and segmented correctly with a sensitivity of 77.8 % and a specificity of 5.5 %. The results of conventional visual analyses were 33.3 % and 94.5 %, respectively (3/9 FCDs detected); the results of morphometric analyses with overall epileptologic evaluation were both 100 % (9/9 FCDs detected) and thus served as reference. CONCLUSION We developed a 3D convolutional neural network with autoencoder regularization for FCD detection and segmentation. Our algorithm employs the largest FCD training dataset to date with various types of FCDs and some focal PMG. It provided a higher sensitivity in detecting FCDs than conventional visual analyses. Despite its low specificity, the number of false positively predicted lesions per MRI was lower than with morphometric analysis. We consider our algorithm already useful for FCD pre-screening in everyday clinical practice.
Collapse
Affiliation(s)
- Patrick M House
- Hamburg Epilepsy Center, Protestant Hospital Alsterdorf, Department of Neurology and Epileptology, Hamburg, Germany.
| | | | | | | | | | - Brigitte Holst
- University Hospital Hamburg-Eppendorf, Department of Neuroradiology, Hamburg, Germany
| | - Thomas Sauvigny
- University Hospital Hamburg-Eppendorf, Department of Neurosurgery, Hamburg, Germany
| | - Tobias Martens
- University Hospital Hamburg-Eppendorf, Department of Neurosurgery, Hamburg, Germany; Asklepios Klinikum St. Georg, Department of Neurosurgery, Hamburg, Germany
| | - Stefan Stodieck
- Hamburg Epilepsy Center, Protestant Hospital Alsterdorf, Department of Neurology and Epileptology, Hamburg, Germany
| | | |
Collapse
|
15
|
Sun K, Ren Z, Yang D, Wang X, Yu T, Ni D, Qiao L, Xu C, Gao R, Lin Y, Zhang X, Shang K, Chen X, Wang Y, Zhang G. Voxel-based morphometric MRI post-processing and PET/MRI co-registration reveal subtle abnormalities in cingulate epilepsy. Epilepsy Res 2021; 171:106568. [PMID: 33610065 DOI: 10.1016/j.eplepsyres.2021.106568] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 01/14/2021] [Accepted: 02/01/2021] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Diagnostic challenges exist in the presurgical evaluation of patients with magnetic resonance imaging (MRI) negative cingulate epilepsy (CE) because of the heterogeneity in clinical semiology and lack of localizing findings on scalp electroencephalographic (EEG) recordings. We aimed to examine the neuroimaging characteristics in a consecutive cohort of patients with MRI-negative CE with a focus on two image post-processing methods, including the MRI post-processing morphometric analysis program (MAP) and 18F-fluorodeoxyglucose-positron emission tomography-MRI (PET/MRI) co-registration. METHODS Included in this retrospective study were patients with MRI-negative CE who met the following criteria: negative on preoperative MRI, invasive EEG (iEEG) confirmed cingulate gyrus-onset seizures, surgical resection of the cingulate gyrus with/without adjacent cortex, and seizure-free for more than 12 months. MAP and PET/MRI co-registration were performed and investigated by comparison to ictal intracranial EEG findings. Other characteristics obtained from scalp EEG, magnetoencephalography (MEG), iEEG, and pathological study were also reported. RESULTS Ten patients were included, of which eight were diagnosed with anterior CE, one with middle CE, and one with posterior CE. The semiology included fear, embarrassment, vocalization, ictal pouting, asymmetric tonic posture, hypermotor, and automatism. Scalp EEG revealed unilateral or bilateral frontal-temporal onset. MEG localized the dipoles correctly in one patient (1/10). MAP detected subtle abnormalities in regions concordant with iEEG onset in seven patients (7/10) while PET/MRI co-registration revealed focal concordant hypometabolism in five patients (5/10). Combining MAP with PET/MRI co-registration improved the detection rate to 90 % in this cohort. The pathology was focal cortical dysplasia (FCD), including FCD type IIA in three, type IIB in three, and type I in four. CONCLUSION MAP and PET/MRI co-registration show promising results in identifying subtle FCD abnormalities in CE with negative results on conventional MRI, which can be otherwise challenging. More importantly, a combination of MRI post-processing and PET/MRI co-registration can greatly improve the identification of epileptic abnormalities, which can be used as surgical target. MAP and PET/MRI co-registration should be incorporated into the routine presurgical evaluation.
Collapse
Affiliation(s)
- Ke Sun
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Zhiwei Ren
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Dongju Yang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xueyuan Wang
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Tao Yu
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Duanyu Ni
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Liang Qiao
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Cuiping Xu
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Runshi Gao
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yicong Lin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xiating Zhang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Kun Shang
- Department of Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xin Chen
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yajie Wang
- Department of Pathology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Guojun Zhang
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
| |
Collapse
|
16
|
Massire A, Seiler C, Troalen T, Girard OM, Lehmann P, Brun G, Bartoli A, Audoin B, Bartolomei F, Pelletier J, Callot V, Kober T, Ranjeva JP, Guye M. T1-Based Synthetic Magnetic Resonance Contrasts Improve Multiple Sclerosis and Focal Epilepsy Imaging at 7 T. Invest Radiol 2021; 56:127-133. [PMID: 32852445 DOI: 10.1097/rli.0000000000000718] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Ultra-high field magnetic resonance imaging (MRI) (≥7 T) is a unique opportunity to improve the clinical diagnosis of brain pathologies, such as multiple sclerosis or focal epilepsy. However, several shortcomings of 7 T MRI, such as radiofrequency field inhomogeneities, could degrade image quality and hinder radiological interpretation. To address these challenges, an original synthetic MRI method based on T1 mapping achieved with the magnetization-prepared 2 rapid acquisition gradient echo (MP2RAGE) sequence was developed. The radiological quality of on-demand T1-based contrasts generated by this technique was evaluated in multiple sclerosis and focal epilepsy imaging at 7 T. MATERIALS AND METHODS This retrospective study was carried out from October 2017 to September 2019 and included 21 patients with different phenotypes of multiple sclerosis and 35 patients with focal epilepsy who underwent MRI brain examinations using a whole-body investigative 7 T magnetic resonance system. The quality of 2 proposed synthetic contrast images were assessed and compared with conventional images acquired at 7 T using the MP2RAGE sequence by 4 radiologists, evaluating 3 qualitative criteria: signal homogeneity, contrast intensity, and lesion visualization. Statistical analyses were performed on reported quality scores using Wilcoxon rank tests and further multiple comparisons tests. Intraobserver and interobserver reliabilities were calculated as well. RESULTS Radiological quality scores were reported higher for synthetic images when compared with original images, regardless of contrast, pathologies, or raters considered, with significant differences found for all 3 criteria (P < 0.0001, Wilcoxon rank test). None of the 4 radiologists ever rated a synthetic image "markedly worse" than an original image. Synthetic images were rated slightly less satisfying for only 3 epileptic patients, without precluding lesion identification. CONCLUSION T1-based synthetic MRI with the MP2RAGE sequence provided on-demand contrasts and high-quality images to the radiologist, facilitating lesion visualization in multiple sclerosis and focal epilepsy, while reducing the magnetic resonance examination total duration by removing an additional sequence.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | - Fabrice Bartolomei
- Pôle de Neurosciences Cliniques, Service de Neurophysiologie, APHM, Hôpital de la Timone, Marseille, France
| | | | | | | | | | | |
Collapse
|
17
|
Beaumont J, Gambarota G, Saint-Jalmes H, Acosta O, Ferré JC, Raniga P, Fripp J. High-resolution multi-T 1 -weighted contrast and T 1 mapping with low B 1 >+ sensitivity using the fluid and white matter suppression (FLAWS) sequence at 7T. Magn Reson Med 2020; 85:1364-1378. [PMID: 32989788 DOI: 10.1002/mrm.28517] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 08/21/2020] [Accepted: 08/23/2020] [Indexed: 11/09/2022]
Abstract
PURPOSE To demonstrate that fluid and white matter suppression (FLAWS) imaging can be used for high-resolution T1 mapping with low transmitted bias field ( B 1 + ) sensitivity at 7T. METHODS The FLAWS sequence was optimized for 0.8-mm isotropic resolution imaging. The theoretical accuracy and precision of the FLAWS T1 mapping was compared with the one of the magnetization-prepared two rapid gradient echoes (MP2RAGE) sequence optimized for low B 1 + sensitivity. FLAWS images were acquired at 7T on six healthy volunteers (21 to 48 years old; two women). MP2RAGE and saturation-prepared with two rapid gradient echoes (SA2RAGE) datasets were also acquired to obtain T1 mapping references and B 1 + maps. The contrast-to-noise ratio (CNR) between brain tissues was measured in the FLAWS-hco and MP2RAGE-uni images. The Pearson correlation was measured between the MP2RAGE and FLAWS T1 maps. The effect of B 1 + on FLAWS T1 mapping was assessed using the Pearson correlation. RESULTS The FLAWS-hco images were characterized by a higher brain tissue CNR ( CNR WM / GM = 5.5 , CNR WM / CSF = 14.7 , CNR GM / CSF = 10.3 ) than the MP2RAGE-uni images ( CNR WM / GM = 4.9 , CNR WM / CSF = 6.6 , CNR GM / CSF = 3.7 ). The theoretical accuracy and precision of the FLAWS T1 mapping ( acc = 91.9 % ; prec = 90.2 % ) were in agreement with those provided by the MP2RAGE T1 mapping ( acc = 90.0 % ; prec = 86.8 % ). A good agreement was found between in vivo T1 values measured with the MP2RAGE and FLAWS sequences (r = 0.91). A weak correlation was found between the FLAWS T1 map and the B 1 + map within cortical gray matter and white matter segmentations ( r WM = - 0.026 ; r GM = 0.081 ). CONCLUSION The results from this study suggest that FLAWS is a good candidate for high-resolution T1 -weighted imaging and T1 mapping at the field strength of 7T.
Collapse
Affiliation(s)
- Jérémy Beaumont
- Univ Rennes, CRLCC Eugene Marquis, Inserm, LTSI-UMR1099, Rennes, France.,The Australian e-Health Research Centre, CSIRO, Brisbane, Australia
| | - Giulio Gambarota
- Univ Rennes, CRLCC Eugene Marquis, Inserm, LTSI-UMR1099, Rennes, France
| | | | - Oscar Acosta
- Univ Rennes, CRLCC Eugene Marquis, Inserm, LTSI-UMR1099, Rennes, France
| | - Jean-Christophe Ferré
- Univ Rennes, Inria, CNRS, Inserm, IRISA, EMPENN ERL U-1228, Rennes, France.,CHU Rennes, Department of Neuroradiology, Rennes, France
| | - Parnesh Raniga
- The Australian e-Health Research Centre, CSIRO, Brisbane, Australia
| | - Jurgen Fripp
- The Australian e-Health Research Centre, CSIRO, Brisbane, Australia
| |
Collapse
|
18
|
Middlebrooks EH, Lin C, Westerhold E, Okromelidze L, Vibhute P, Grewal SS, Gupta V. Improved detection of focal cortical dysplasia using a novel 3D imaging sequence: Edge-Enhancing Gradient Echo (3D-EDGE) MRI. NEUROIMAGE-CLINICAL 2020; 28:102449. [PMID: 33032066 PMCID: PMC7552096 DOI: 10.1016/j.nicl.2020.102449] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 09/18/2020] [Accepted: 09/22/2020] [Indexed: 11/29/2022]
Abstract
Detection of focal cortical dysplasia remains a substantial challenge in radiology. 3D-EDGE is a novel MR method to directly image abnormalities of gray-white boundary. 3D-EDGE had a significantly higher contrast for FCD than FLAIR and MP2RAGE.
Epilepsy is a common neurological disorder with focal cortical dysplasia (FCD) being one of the most common lesional causes. Detection of FCD by MRI is a major determinant of surgical outcome. Evolution of MRI sequences and hardware has greatly increased the detection rate of FCD, but these gains have largely been related to the more visible Type IIb FCD, with Type I and IIa remaining elusive. While most sequence improvements have relied on increasing contrast between gray and white matter, we propose a novel imaging approach, 3D Edge-Enhancing Gradient Echo (3D-EDGE), to directly image the gray-white boundary. By acquiring images at an inversion time where gray and white matter have equal signal but opposite phases, voxels with a mixture of gray and white matter (e.g., at the gray-white boundary) will have cancellation of longitudinal magnetization producing a thin area of signal void at the normal boundary. By creating greater sensitivity for minor changes in T1 relaxation, microarchitectural abnormalities present in FCD produce greater contrast than on other common MRI sequences. 3D-EDGE had a significantly greater contrast ratio between lesion and white matter for FCD compared to MP2RAGE (98% vs 17%; p = 0.0006) and FLAIR (98% vs 19%; p = 0.0006), which highlights its potential to improve outcomes in epilepsy. We present a discussion of the framework for 3D-EDGE, optimization strategies, and analysis of a series of FCDs to highlight the benefit of 3D-EDGE in FCD detection compared to commonly used sequences in epilepsy.
Collapse
Affiliation(s)
- Erik H Middlebrooks
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA; Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, USA.
| | - Chen Lin
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | | | | | | | | | - Vivek Gupta
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| |
Collapse
|
19
|
Demerath T, Rubensdörfer L, Schwarzwald R, Schulze-Bonhage A, Altenmüller DM, Kaller C, Kober T, Huppertz HJ, Urbach H. Morphometric MRI Analysis: Improved Detection of Focal Cortical Dysplasia Using the MP2RAGE Sequence. AJNR Am J Neuroradiol 2020; 41:1009-1014. [PMID: 32499249 DOI: 10.3174/ajnr.a6579] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 03/24/2020] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Focal cortical dysplasias are the most common resected epileptogenic lesions in children and the third most common lesion in adults, but they are often subtle and frequently overlooked on MR imaging. The purpose of this study was to evaluate whether MP2RAGE-based morphometric MR imaging analysis is superior to MPRAGE-based analysis in the detection of focal cortical dysplasia. MATERIALS AND METHODS MPRAGE and MP2RAGE datasets were acquired in a consecutive series of 640 patients with epilepsy. Datasets were postprocessed using the Morphometric Analysis Program to generate morphometric z score maps such as junction, extension, and thickness images based on both MPRAGE and MP2RAGE images. Focal cortical dysplasia lesions were manually segmented in the junction images, and volumes and mean z scores of the lesions were measured. RESULTS Of 21 focal cortical dysplasias discovered, all were clearly visible on MP2RAGE junction images, whereas 2 were not visible on MPRAGE junction images. In all except 4 patients, the volume of the focal cortical dysplasia was larger and mean lesion z scores were higher on MP2RAGE junction images compared with the MPRAGE-based images (P = .005, P = .013). CONCLUSIONS In this study, MP2RAGE-based morphometric analysis created clearer output maps with larger lesion volumes and higher z scores than the MPRAGE-based analysis. This new approach may improve the detection of subtle, otherwise overlooked focal cortical dysplasia.
Collapse
Affiliation(s)
- T Demerath
- From the Departments of Neuroradiology (T.D., L.R., R.S., C.K., H.U.)
| | - L Rubensdörfer
- From the Departments of Neuroradiology (T.D., L.R., R.S., C.K., H.U.)
| | - R Schwarzwald
- From the Departments of Neuroradiology (T.D., L.R., R.S., C.K., H.U.)
| | - A Schulze-Bonhage
- Epileptology (A.S.-B., D.-M.A.), Faculty of Medicine, Medical Center-University of Freiburg, Freiburg, Germany
| | - D-M Altenmüller
- Epileptology (A.S.-B., D.-M.A.), Faculty of Medicine, Medical Center-University of Freiburg, Freiburg, Germany
| | - C Kaller
- From the Departments of Neuroradiology (T.D., L.R., R.S., C.K., H.U.)
| | - T Kober
- Advanced Clinical Imaging Technology (T.K.), Siemens Healthcare AG, Lausanne, Switzerland
| | - H-J Huppertz
- Swiss Epilepsy Clinic (H.-J.H.), Klinik Lengg AG, Zurich, Switzerland
| | - H Urbach
- From the Departments of Neuroradiology (T.D., L.R., R.S., C.K., H.U.)
| |
Collapse
|
20
|
|
21
|
Beaumont J, Saint-Jalmes H, Acosta O, Kober T, Tanner M, Ferré JC, Salvado O, Fripp J, Gambarota G. Multi T1-weighted contrast MRI with fluid and white matter suppression at 1.5 T. Magn Reson Imaging 2019; 63:217-225. [PMID: 31425812 DOI: 10.1016/j.mri.2019.08.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 08/11/2019] [Accepted: 08/15/2019] [Indexed: 11/16/2022]
Abstract
INTRODUCTION The fluid and white matter suppression sequence (FLAWS) provides two T1-weighted co-registered datasets: a white matter (WM) suppressed contrast (FLAWS1) and a cerebrospinal fluid (CSF) suppressed contrast (FLAWS2). FLAWS has the potential to improve the contrast of the subcortical brain regions that are important for Deep Brain Stimulation surgery planning. However, to date FLAWS has not been optimized for 1.5 T. In this study, the FLAWS sequence was optimized for use at 1.5 T. In addition, the contrast-enhancement properties of FLAWS image combinations were investigated using two voxel-wise FLAWS combined images: the division (FLAWS-div) and the high contrast (FLAWS-hc) image. METHODS FLAWS sequence parameters were optimized for 1.5 T imaging using an approach based on the use of a profit function under constraints for brain tissue signal and contrast maximization. MR experiments were performed on eleven healthy volunteers (age 18-30). Contrast (CN) and contrast to noise ratio (CNR) between brain tissues were measured in each volunteer. Furthermore, a qualitative assessment was performed to ensure that the separation between the internal globus pallidus (GPi) and the external globus pallidus (GPe) is identifiable in FLAWS1. RESULTS The optimized set of sequence parameters for FLAWS at 1.5 T provided contrasts similar to those obtained in a previous study at 3 T. The separation between the GPi and the GPe was clearly identified in FLAWS1. The CN of FLAWS-hc was higher than that of FLAWS1 and FLAWS2, but was not different from the CN of FLAWS-div. The CNR of FLAWS-hc was higher than that of FLAWS-div. CONCLUSION Both qualitative and quantitative assessments validated the optimization of the FLAWS sequence at 1.5 T. Quantitative assessments also showed that FLAWS-hc provides an enhanced contrast compared to FLAWS1 and FLAWS2, with a higher CNR than FLAWS-div.
Collapse
Affiliation(s)
- J Beaumont
- Univ Rennes, CLCC Eugène Marquis, Inserm, LTSI-UMR1099, F-35000 Rennes, France; CSIRO, the Australian eHealth Research Centre, Herston, Queensland, Australia.
| | - H Saint-Jalmes
- Univ Rennes, CLCC Eugène Marquis, Inserm, LTSI-UMR1099, F-35000 Rennes, France
| | - O Acosta
- Univ Rennes, CLCC Eugène Marquis, Inserm, LTSI-UMR1099, F-35000 Rennes, France
| | - T Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, University Hospital (CHUV), Lausanne, Switzerland; LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - M Tanner
- Invicro, A Konica Minolta Company, London, UK
| | - J C Ferré
- Univ Rennes, Inria, CNRS, INSERM, IRISA, VISAGES ERL U-1228, F-35000 Rennes, France; CHU Rennes, Department of Neuroradiology, F-35033 Rennes, France
| | - O Salvado
- CSIRO, Data61, Herston, Queensland, Australia
| | - J Fripp
- CSIRO, the Australian eHealth Research Centre, Herston, Queensland, Australia
| | - G Gambarota
- Univ Rennes, CLCC Eugène Marquis, Inserm, LTSI-UMR1099, F-35000 Rennes, France
| |
Collapse
|