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Fisch L, Zumdick S, Barkhau C, Emden D, Ernsting J, Leenings R, Sarink K, Winter NR, Risse B, Dannlowski U, Hahn T. deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks. Comput Biol Med 2024; 179:108845. [PMID: 39002314 DOI: 10.1016/j.compbiomed.2024.108845] [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: 03/01/2024] [Revised: 07/01/2024] [Accepted: 07/02/2024] [Indexed: 07/15/2024]
Abstract
BACKGROUND Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research fields in which deep learning had the biggest impact in recent years. Consequently, traditional brain extraction methods are now being replaced by deep learning-based methods. METHOD Here, we used a unique dataset compilation comprising 7837 T1-weighted (T1w) MR images from 191 different OpenNeuro datasets in combination with advanced deep learning methods to build a fast, high-precision brain extraction tool called deepbet. RESULTS deepbet sets a novel state-of-the-art performance during cross-dataset validation with a median Dice score (DSC) of 99.0 on unseen datasets, outperforming the current best performing deep learning (DSC=97.9) and classic (DSC=96.5) methods. While current methods are more sensitive to outliers, deepbet achieves a Dice score of >97.4 across all 7837 images from 191 different datasets. This robustness was additionally tested in 5 external datasets, which included challenging clinical MR images. During visual exploration of each method's output which resulted in the lowest Dice score, major errors could be found for all of the tested tools except deepbet. Finally, deepbet uses a compute efficient variant of the UNet architecture, which accelerates brain extraction by a factor of ≈10 compared to current methods, enabling the processing of one image in ≈2 s on low level hardware. CONCLUSIONS In conclusion, deepbet demonstrates superior performance and reliability in brain extraction across a wide range of T1w MR images of adults, outperforming existing top tools. Its high minimal Dice score and minimal objective errors, even in challenging conditions, validate deepbet as a highly dependable tool for accurate brain extraction. deepbet can be conveniently installed via "pip install deepbet" and is publicly accessible at https://github.com/wwu-mmll/deepbet.
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Affiliation(s)
- Lukas Fisch
- University of Münster, Institute for Translational Psychiatry, Münster, Germany.
| | - Stefan Zumdick
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Carlotta Barkhau
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Daniel Emden
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Jan Ernsting
- University of Münster, Institute for Translational Psychiatry, Münster, Germany; Department of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Ramona Leenings
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Kelvin Sarink
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Nils R Winter
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Benjamin Risse
- Department of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Udo Dannlowski
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Tim Hahn
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
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Kataike VM, Desmond PM, Steward C, Mitchell PJ, Davey C, Yassi N, Bivard A, Parsons MW, Campbell BCV, Ng F, Venkatraman V. Iron changes within infarct tissue in ischemic stroke patients after successful reperfusion quantified using QSM. Neuroradiology 2024:10.1007/s00234-024-03444-6. [PMID: 39172165 DOI: 10.1007/s00234-024-03444-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Accepted: 08/11/2024] [Indexed: 08/23/2024]
Abstract
PURPOSE For nearly half of patients who undergo Endovascular Thrombectomy following ischemic stroke, successful recanalisation does not guarantee a good outcome. Understanding the underlying tissue changes in the infarct tissue with the help of biomarkers specific to ischemic stroke could offer valuable insights for better treatment and patient management decisions. Using quantitative susceptibility mapping (QSM) MRI to measure cerebral iron concentration, this study aims to track the progression of iron within the infarct lesion after successful reperfusion. METHODS In a prospective study of 87 ischemic stroke patients, successfully reperfused patients underwent MRI scans at 24-to-72 h and 3 months after reperfusion. QSM maps were generated from gradient-echo MRI images. QSM values, measured in parts per billion (ppb), were extracted from ROIs defining the infarct and mirror homolog in the contralateral hemisphere and were compared cross-sectionally and longitudinally. RESULTS QSM values in the infarct ROIs matched those of the contralateral ROIs at 24-to-72 h, expressed as median (interquartile range) ppb [0.71(-7.67-10.09) vs. 2.20(-10.50-14.05) ppb, p = 0.55], but were higher at 3 months [10.68(-2.30-21.10) vs. -1.27(-12.98-9.82) ppb, p < 0.001]. The infarct QSM values at 3 months were significantly higher than those at 24-to-72 h [10.41(-2.50-18.27) ppb vs. 1.68(-10.36-12.25) ppb, p < 0.001]. Infarct QSM at 24-to-72 h and patient outcome measured at three months did not demonstrate a significant association. CONCLUSION Following successful endovascular reperfusion, iron concentration in infarct tissue, as measured by QSM increases over time compared to that in healthy tissue. However, its significance warrants further investigation.
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Affiliation(s)
| | - Patricia M Desmond
- Department of Radiology, The University of Melbourne, Parkville, VIC, 3050, Australia
- Department of Medical Imaging, The Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Christopher Steward
- Department of Radiology, The University of Melbourne, Parkville, VIC, 3050, Australia
- Department of Medical Imaging, The Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Peter J Mitchell
- Department of Radiology, The University of Melbourne, Parkville, VIC, 3050, Australia
- Department of Medical Imaging, The Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Christian Davey
- Statistical Consulting Centre, School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC, Australia
| | - Nawaf Yassi
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria, Australia
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Andrew Bivard
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria, Australia
| | - Mark W Parsons
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
- Department of Neurology, University of New South Wales Southwestern Sydney Clinical School, Ingham Institute for Applied Medical Research, Liverpool Hospital, Sydney, New South Wales, Australia
| | - Bruce C V Campbell
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria, Australia
- Department of Neurology, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Felix Ng
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria, Australia
- Department of Neurology, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Vijay Venkatraman
- Department of Radiology, The University of Melbourne, Parkville, VIC, 3050, Australia
- Department of Medical Imaging, The Royal Melbourne Hospital, Parkville, VIC, Australia
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Lohrke F, Madai VI, Kossen T, Aydin OU, Behland J, Hilbert A, Mutke MA, Bendszus M, Sobesky J, Frey D. Perfusion parameter map generation from TOF-MRA in stroke using generative adversarial networks. Neuroimage 2024; 298:120770. [PMID: 39117094 DOI: 10.1016/j.neuroimage.2024.120770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 06/28/2024] [Accepted: 08/02/2024] [Indexed: 08/10/2024] Open
Abstract
PURPOSE To generate perfusion parameter maps from Time-of-flight magnetic resonance angiography (TOF-MRA) images using artificial intelligence to provide an alternative to traditional perfusion imaging techniques. MATERIALS AND METHODS This retrospective study included a total of 272 patients with cerebrovascular diseases; 200 with acute stroke (from 2010 to 2018), and 72 with steno-occlusive disease (from 2011 to 2014). For each patient the TOF MRA image and the corresponding Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) were retrieved from the datasets. The authors propose an adapted generative adversarial network (GAN) architecture, 3D pix2pix GAN, that generates common perfusion maps (CBF, CBV, MTT, TTP, Tmax) from TOF-MRA images. The performance was evaluated by the structural similarity index measure (SSIM). For a subset of 20 patients from the acute stroke dataset, the Dice coefficient was calculated to measure the overlap between the generated and real hypoperfused lesions with a time-to-maximum (Tmax) > 6 s. RESULTS The GAN model exhibited high visual overlap and performance for all perfusion maps in both datasets: acute stroke (mean SSIM 0.88-0.92, mean PSNR 28.48-30.89, mean MAE 0.02-0.04 and mean NRMSE 0.14-0.37) and steno-occlusive disease patients (mean SSIM 0.83-0.98, mean PSNR 23.62-38.21, mean MAE 0.01-0.05 and mean NRMSE 0.03-0.15). For the overlap analysis for lesions with Tmax>6 s, the median Dice coefficient was 0.49. CONCLUSION Our AI model can successfully generate perfusion parameter maps from TOF-MRA images, paving the way for a non-invasive alternative for assessing cerebral hemodynamics in cerebrovascular disease patients. This method could impact the stratification of patients with cerebrovascular diseases. Our results warrant more extensive refinement and validation of the method.
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Affiliation(s)
- Felix Lohrke
- CLAIM - Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Germany
| | - Vince Istvan Madai
- QUEST Center for Transforming Biomedical Research, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Germany; School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, United Kingdom
| | - Tabea Kossen
- CLAIM - Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Germany
| | - Orhun Utku Aydin
- CLAIM - Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Germany
| | - Jonas Behland
- CLAIM - Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Germany
| | - Adam Hilbert
- CLAIM - Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Germany
| | | | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jan Sobesky
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany; Johanna-Etienne-Hospital, Neuss, Germany
| | - Dietmar Frey
- CLAIM - Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Germany; Department of Neurosurgery, Charité Universitätsmedizin Berlin, Germany.
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Li N, Ding S, Liu Z, Ye W, Liu P, Jing J, Jiang Y, Zhao X, Liu T. A Deep Learning-Based Framework for Predicting Intracerebral Hemorrhage Hematoma Expansion Using Head Non-contrast CT Scan. Acad Radiol 2024:S1076-6332(24)00472-0. [PMID: 39107191 DOI: 10.1016/j.acra.2024.07.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 07/11/2024] [Accepted: 07/21/2024] [Indexed: 08/09/2024]
Abstract
RATIONALE AND OBJECTIVES Hematoma expansion (HE) in intracerebral hemorrhage (ICH) is a critical factor affecting patient outcomes, yet effective clinical tools for predicting HE are currently lacking. We aim to develop a fully automated framework based on deep learning for predicting HE using only clinical non-contrast CT (NCCT) scans. MATERIALS AND METHODS A large retrospective dataset (n = 2484) was collected from 84 centers, while a prospective dataset (n = 500) was obtained from 26 additional centers. Baseline NCCT scans and follow-up NCCT scans were conducted within 6 h and 48 h from symptom onset, respectively. HE was defined as a volume increase of more than 6 mL on the follow-up NCCT. The retrospective dataset was divided into a training set (n = 1876) and a validation set (n = 608) by patient inclusion time. A two-stage framework was trained to predict HE, and its performance was evaluated on both the validation and prospective sets. Receiver operating characteristics area under the curve (AUC), sensitivity, and specificity were leveraged. RESULTS Our two-stage framework achieved an AUC of 0.760 (95% CI 0.724-0.799) on the retrospective validation set and 0.806 (95% CI 0.750-0.859) on the prospective set, outperforming the commonly used BAT score, which had AUCs of 0.582 and 0.699, respectively. CONCLUSION Our framework can automatically and robustly identify ICH patients at high risk of HE using admission head NCCT scans, providing more accurate predictions than the BAT score.
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Affiliation(s)
- Na Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (N.L., J.J., X.Z.); China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (N.L., W.Y., J.J., Y.J., X.Z.)
| | - Shaodong Ding
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China (S.D., Z.L., T.L.)
| | - Ziyang Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China (S.D., Z.L., T.L.)
| | - Wanxing Ye
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (N.L., W.Y., J.J., Y.J., X.Z.)
| | - Pan Liu
- Medical Big Data Research Center, Chinese PLA General Hospital, Beijing 100853, China (P.L.)
| | - Jing Jing
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (N.L., J.J., X.Z.); China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (N.L., W.Y., J.J., Y.J., X.Z.)
| | - Yong Jiang
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (N.L., W.Y., J.J., Y.J., X.Z.)
| | - Xingquan Zhao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (N.L., J.J., X.Z.); China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (N.L., W.Y., J.J., Y.J., X.Z.)
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China (S.D., Z.L., T.L.).
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Harting I, Garbade SF, Roosendaal SD, Fels-Palesandro H, Raudonat C, Mohr A, Wolf NI. Age-appropriate or delayed myelination? Scoring myelination in routine clinical MRI. Eur J Paediatr Neurol 2024; 52:59-66. [PMID: 39098096 DOI: 10.1016/j.ejpn.2024.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 06/29/2024] [Accepted: 07/24/2024] [Indexed: 08/06/2024]
Abstract
BACKGROUND Assessment of myelination is a core issue in paediatric neuroimaging and can be challenging, particularly in settings without dedicated paediatric neuroradiologists. Deep learning models have recently been shown to be able to estimate myelination age in children with normal MRI, but currently lack validation for patients with myelination delay and implementation including pre-processing suitable for local imaging is not trivial. Standardized myelination scores, which have been successfully used as biomarkers for myelination in hypomyelinating diseases, rely on visual, semiquantitative scoring of myelination on routine clinical MRI and may offer an easy-to-use alternative for assessment of myelination. METHODS Myelination was scored in 13 anatomic sites (items) on conventional T2w and T1w images in controls (n = 253, 0-2 years). Items for the score were selected based on inter-rater variability, practicability of scoring, and importance for correctly identifying validation scans. RESULTS The resulting myelination score consisting of 7 T2- and 5 T1-items delineated myelination from term-equivalent to advanced, incomplete myelination which 50 % and 99 % of controls had reached by 19.1 and 32.7 months, respectively. It correctly identified 20/20 new control MRIs and 40/43 with myelination delay, missing one patient with borderline myelination delay at 8.6 months and 2 patients with incomplete T2-myelination of subcortical temporopolar white matter at 28 and 34 months. CONCLUSIONS The proposed myelination score provides an easy to use, standardized, and versatile tool to delineate myelination normally occurring during the first 1.5 years of life.
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Affiliation(s)
- Inga Harting
- Department of Neuroradiology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
| | - Sven F Garbade
- Center for Pediatric and Adolescent Medicine, Division of Pediatric Neurology and Metabolic Medicine, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany
| | - Stefan D Roosendaal
- Department of Radiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Hannah Fels-Palesandro
- Department of Neuroradiology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany; Clinical Cooperation Unit Translational Radiation Oncology, Deutsches Forschungszentrum (DKFZ), Heidelberg, Germany
| | - Clara Raudonat
- Department of Neuroradiology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany
| | - Alexander Mohr
- Department of Neuroradiology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany
| | - Nicole I Wolf
- Department of Child Neurology, Amsterdam Leukodystrophy Center, Emma Children's Hospital, and Amsterdam Neuroscience, Cellular & Molecular Mechanisms, Amsterdam University Medical Center, Amsterdam, the Netherlands
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6
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Chen Y, Hong H, Nazeri A, Markus HS, Luo X. Cerebrospinal fluid-based spatial statistics: towards quantitative analysis of cerebrospinal fluid pseudodiffusivity. Fluids Barriers CNS 2024; 21:59. [PMID: 39026214 PMCID: PMC11256588 DOI: 10.1186/s12987-024-00559-z] [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: 02/14/2024] [Accepted: 06/29/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND Cerebrospinal fluid (CSF) circulation is essential in removing metabolic wastes from the brain and is an integral component of the glymphatic system. Abnormal CSF circulation is implicated in neurodegenerative diseases. Low b-value magnetic resonance imaging quantifies the variance of CSF motion, or pseudodiffusivity. However, few studies have investigated the relationship between the spatial patterns of CSF pseudodiffusivity and cognition. METHODS We introduced a novel technique, CSF-based spatial statistics (CBSS), to automatically quantify CSF pseudodiffusivity in each sulcus, cistern and ventricle. Using cortical regions as landmarks, we segmented each CSF region. We retrospectively analyzed a cohort of 93 participants with varying degrees of cognitive impairment. RESULTS We identified two groups of CSF regions whose pseudodiffusivity profiles were correlated with each other: one group displaying higher pseudodiffusivity and near large arteries and the other group displaying lower pseudodiffusivity and away from the large arteries. The pseudodiffusivity in the third ventricle positively correlated with short-term memory (standardized slope of linear regression = 0.38, adjusted p < 0.001) and long-term memory (slope = 0.37, adjusted p = 0.005). Fine mapping along the ventricles revealed that the pseudodiffusivity in the region closest to the start of the third ventricle demonstrated the highest correlation with cognitive performance. CONCLUSIONS CBSS enabled quantitative spatial analysis of CSF pseudodiffusivity and suggested the third ventricle pseudodiffusivity as a potential biomarker of cognitive impairment.
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Affiliation(s)
- Yutong Chen
- Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK
| | - Hui Hong
- Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China
| | - Arash Nazeri
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Hugh S Markus
- Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK
| | - Xiao Luo
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China.
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Czeibert K, Nagy G, Csörgő T, Donkó T, Petneházy Ö, Csóka Á, Garamszegi LZ, Kolm N, Kubinyi E. High-resolution computed tomographic (HRCT) image series from 413 canid and 18 felid skulls. Sci Data 2024; 11:753. [PMID: 39013883 PMCID: PMC11252271 DOI: 10.1038/s41597-024-03572-x] [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: 02/14/2024] [Accepted: 06/24/2024] [Indexed: 07/18/2024] Open
Abstract
Computed tomography (CT) is a non-invasive, three-dimensional imaging tool used in medical imaging, forensic science, industry and engineering, anthropology, and archaeology. The current study used high-resolution medical CT scanning of 431 animal skulls, including 399 dog skulls from 152 breeds, 14 cat skulls from 9 breeds, 14 skulls from 8 wild canid species (gray wolf, golden jackal, coyote, maned wolf, bush dog, red fox, Fennec fox, bat-eared fox), and 4 skulls from 4 wild felid species (wildcat, leopard, serval, caracal). This comprehensive and unique collection of CT image series of skulls can provide a solid foundation not only for comparative anatomical and evolutionary studies but also for the advancement of veterinary education, virtual surgery planning, and the facilitation of training in sophisticated machine learning methodologies.
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Affiliation(s)
- Kalman Czeibert
- Department of Ethology, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
- LimesVet Ltd., Budapest, Hungary
| | - Gergely Nagy
- Institute of Ecology and Botany, HUN-REN Centre for Ecological Research, Vácrátót, Hungary
| | - Tibor Csörgő
- Department of Genetics, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
| | | | - Örs Petneházy
- Medicopus Nonprofit Ltd., Kaposvár, Hungary
- Hungarian University of Agriculture- and Life Sciences, Institute of Physiology and Nutrition, Department of Physiology and Animal Health, Kaposvár Campus, Kaposvár, Hungary
| | - Ádám Csóka
- Medicopus Nonprofit Ltd., Kaposvár, Hungary
- Hungarian University of Agriculture- and Life Sciences, Institute of Physiology and Nutrition, Department of Physiology and Animal Health, Kaposvár Campus, Kaposvár, Hungary
| | | | - Niclas Kolm
- Department of Zoology, Stockholm University, Stockholm, Sweden
| | - Eniko Kubinyi
- Department of Ethology, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary.
- MTA-ELTE Lendület "Momentum" Companion Animal Research Group, Budapest, Hungary.
- ELTE NAP Canine Brain Research Group, Budapest, Hungary.
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Faghihpirayesh R, Karimi D, Erdoğmuş D, Gholipour A. Fetal-BET: Brain Extraction Tool for Fetal MRI. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:551-562. [PMID: 39157057 PMCID: PMC11329220 DOI: 10.1109/ojemb.2024.3426969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 05/09/2024] [Accepted: 07/07/2024] [Indexed: 08/20/2024] Open
Abstract
Goal: In this study, we address the critical challenge of fetal brain extraction from MRI sequences. Fetal MRI has played a crucial role in prenatal neurodevelopmental studies and in advancing our knowledge of fetal brain development in-utero. Fetal brain extraction is a necessary first step in most computational fetal brain MRI pipelines. However, it poses significant challenges due to 1) non-standard fetal head positioning, 2) fetal movements during examination, and 3) vastly heterogeneous appearance of the developing fetal brain and the neighboring fetal and maternal anatomy across gestation, and with various sequences and scanning conditions. Development of a machine learning method to effectively address this task requires a large and rich labeled dataset that has not been previously available. Currently, there is no method for accurate fetal brain extraction on various fetal MRI sequences. Methods: In this work, we first built a large annotated dataset of approximately 72,000 2D fetal brain MRI images. Our dataset covers the three common MRI sequences including T2-weighted, diffusion-weighted, and functional MRI acquired with different scanners. These data include images of normal and pathological brains. Using this dataset, we developed and validated deep learning methods, by exploiting the power of the U-Net style architectures, the attention mechanism, feature learning across multiple MRI modalities, and data augmentation for fast, accurate, and generalizable automatic fetal brain extraction. Results: Evaluations on independent test data, including data available from other centers, show that our method achieves accurate brain extraction on heterogeneous test data acquired with different scanners, on pathological brains, and at various gestational stages. Conclusions:By leveraging rich information from diverse multi-modality fetal MRI data, our proposed deep learning solution enables precise delineation of the fetal brain on various fetal MRI sequences. The robustness of our deep learning model underscores its potential utility for fetal brain imaging.
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Affiliation(s)
- Razieh Faghihpirayesh
- Electrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA
- Radiology DepartmentBoston Children's Hospital, and Harvard Medical SchoolBostonMA02115USA
| | - Davood Karimi
- Radiology DepartmentBoston Children's Hospital, and Harvard Medical SchoolBostonMA02115USA
| | - Deniz Erdoğmuş
- Electrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA
| | - Ali Gholipour
- Radiology DepartmentBoston Children's Hospital, and Harvard Medical SchoolBostonMA02115USA
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Xue C, Kowshik SS, Lteif D, Puducheri S, Jasodanand VH, Zhou OT, Walia AS, Guney OB, Zhang JD, Pham ST, Kaliaev A, Andreu-Arasa VC, Dwyer BC, Farris CW, Hao H, Kedar S, Mian AZ, Murman DL, O'Shea SA, Paul AB, Rohatgi S, Saint-Hilaire MH, Sartor EA, Setty BN, Small JE, Swaminathan A, Taraschenko O, Yuan J, Zhou Y, Zhu S, Karjadi C, Alvin Ang TF, Bargal SA, Plummer BA, Poston KL, Ahangaran M, Au R, Kolachalama VB. AI-based differential diagnosis of dementia etiologies on multimodal data. Nat Med 2024:10.1038/s41591-024-03118-z. [PMID: 38965435 DOI: 10.1038/s41591-024-03118-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 06/06/2024] [Indexed: 07/06/2024]
Abstract
Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care.
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Affiliation(s)
- Chonghua Xue
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Electrical & Computer Engineering, Boston University, Boston, MA, USA
| | - Sahana S Kowshik
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA
| | - Diala Lteif
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Computer Science, Boston University, Boston, MA, USA
| | - Shreyas Puducheri
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Varuna H Jasodanand
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Olivia T Zhou
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Anika S Walia
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Osman B Guney
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Electrical & Computer Engineering, Boston University, Boston, MA, USA
| | - J Diana Zhang
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- School of Chemistry, University of New South Wales, Sydney, Australia
| | - Serena T Pham
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Artem Kaliaev
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - V Carlota Andreu-Arasa
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Brigid C Dwyer
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Chad W Farris
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Honglin Hao
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Sachin Kedar
- Departments of Neurology & Ophthalmology, Emory University School of Medicine, Atlanta, GA, USA
| | - Asim Z Mian
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Daniel L Murman
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Sarah A O'Shea
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron B Paul
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Saurabh Rohatgi
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Emmett A Sartor
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Bindu N Setty
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Juan E Small
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA
| | | | - Olga Taraschenko
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Jing Yuan
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yan Zhou
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Shuhan Zhu
- Department of Neurology, Brigham & Women's Hospital, Boston, MA, USA
| | - Cody Karjadi
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Ting Fang Alvin Ang
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Sarah A Bargal
- Department of Computer Science, Georgetown University, Washington, DC, USA
| | - Bryan A Plummer
- Department of Computer Science, Boston University, Boston, MA, USA
| | | | - Meysam Ahangaran
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Rhoda Au
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Boston University Alzheimer's Disease Research Center, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Vijaya B Kolachalama
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
- Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA.
- Department of Computer Science, Boston University, Boston, MA, USA.
- Boston University Alzheimer's Disease Research Center, Boston, MA, USA.
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10
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Ratkunas V, Misiulis E, Lapinskiene I, Skarbalius G, Navakas R, Dziugys A, Barkauskiene A, Preiksaitis A, Serpytis M, Rocka S, Lukosevicius S, Iesmantas T, Alzbutas R, Sengupta J, Petkus V. Cerebrospinal fluid volume as an early radiological factor for clinical course prediction after aneurysmal subarachnoid hemorrhage. A pilot study. Eur J Radiol 2024; 176:111483. [PMID: 38705051 DOI: 10.1016/j.ejrad.2024.111483] [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: 02/13/2024] [Revised: 03/29/2024] [Accepted: 04/27/2024] [Indexed: 05/07/2024]
Abstract
BACKGROUND The pathological mechanisms following aneurysmal subarachnoid hemorrhage (SAH) are poorly understood. Limited clinical evidence exists on the association between cerebrospinal fluid (CSF) volume and the risk of delayed cerebral ischemia (DCI) or cerebral vasospasm (CV). In this study, we raised the hypothesis that the amount of CSF or its ratio to hemorrhage blood volume, as determined from non-contrast Computed Tomography (NCCT) images taken on admission, could be a significant predictor for CV and DCI. METHODS The pilot study included a retrospective analysis of NCCT scans of 49 SAH patients taken shortly after an aneurysm rupture (33 males, 16 females, mean age 56.4 ± 15 years). The SynthStrip and Slicer3D software tools were used to extract radiological factors - CSF, brain, and hemorrhage volumes from the NCCT images. The "pure" CSF volume (VCSF) was estimated in the range of [-15, 15] Hounsfield units (HU). RESULTS VCSF was negatively associated with the risk of CV occurrence (p = 0.0049) and DCI (p = 0.0069), but was not associated with patients' outcomes. The hemorrhage volume (VSAH) was positively associated with an unfavorable outcome (p = 0.0032) but was not associated with CV/DCI. The ratio VSAH/VCSF was positively associated with, both, DCI (p = 0.031) and unfavorable outcome (p = 0.002). The CSF volume normalized by the brain volume showed the highest characteristics for DCI prediction (AUC = 0.791, sensitivity = 0.80, specificity = 0.812) and CV prediction (AUC = 0.769, sensitivity = 0.812, specificity = 0.70). CONCLUSION It was demonstrated that "pure" CSF volume retrieved from the initial NCCT images of SAH patients (including CV, Non-CV, DCI, Non-DCI groups) is a more significant predictor of DCI and CV compared to other routinely used radiological biomarkers. VCSF could be used to predict clinical course as well as to personalize the management of SAH patients. Larger multicenter clinical trials should be performed to test the added value of the proposed methodology.
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Affiliation(s)
- Vytenis Ratkunas
- Department of Radiology, Lithuanian University of Health Sciences, Eiveniu st. 2, Kaunas 50009, Lithuania
| | - Edgaras Misiulis
- Laboratory of Heat-Equipment Research and Testing, Lithuanian Energy Institute, Breslaujos st. 3, Kaunas 44403, Lithuania.
| | - Indre Lapinskiene
- Faculty of Medicine, Vilnius University, M. K. Ciurlionio st. 21, Vilnius 03101, Lithuania
| | - Gediminas Skarbalius
- Laboratory of Heat-Equipment Research and Testing, Lithuanian Energy Institute, Breslaujos st. 3, Kaunas 44403, Lithuania
| | - Robertas Navakas
- Laboratory of Heat-Equipment Research and Testing, Lithuanian Energy Institute, Breslaujos st. 3, Kaunas 44403, Lithuania
| | - Algis Dziugys
- Laboratory of Heat-Equipment Research and Testing, Lithuanian Energy Institute, Breslaujos st. 3, Kaunas 44403, Lithuania
| | - Alina Barkauskiene
- Center for Radiology and Nuclear Medicine, Vilnius University Hospital Santaros Klinikos, Santariskiu st. 2, Vilnius 08661, Lithuania
| | - Aidanas Preiksaitis
- Faculty of Medicine, Vilnius University, M. K. Ciurlionio st. 21, Vilnius 03101, Lithuania
| | - Mindaugas Serpytis
- Faculty of Medicine, Vilnius University, M. K. Ciurlionio st. 21, Vilnius 03101, Lithuania
| | - Saulius Rocka
- Faculty of Medicine, Vilnius University, M. K. Ciurlionio st. 21, Vilnius 03101, Lithuania
| | - Saulius Lukosevicius
- Department of Radiology, Lithuanian University of Health Sciences, Eiveniu st. 2, Kaunas 50009, Lithuania
| | - Tomas Iesmantas
- Kaunas University of Technology, K. Donelaičio st. 73, Kaunas 44249, Lithuania
| | - Robertas Alzbutas
- Kaunas University of Technology, K. Donelaičio st. 73, Kaunas 44249, Lithuania
| | - Jewel Sengupta
- Kaunas University of Technology, K. Donelaičio st. 73, Kaunas 44249, Lithuania
| | - Vytautas Petkus
- Kaunas University of Technology, K. Donelaičio st. 73, Kaunas 44249, Lithuania
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11
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Alushaj E, Hemachandra D, Ganjavi H, Seergobin KN, Sharma M, Kashgari A, Barr J, Reisman W, Khan AR, MacDonald PA. Increased mean diffusivity of the caudal motor SNc identifies patients with REM sleep behaviour disorder and Parkinson's disease. NPJ Parkinsons Dis 2024; 10:128. [PMID: 38951528 PMCID: PMC11217278 DOI: 10.1038/s41531-024-00731-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 05/30/2024] [Indexed: 07/03/2024] Open
Abstract
Idiopathic rapid eye movement sleep behaviour disorder (iRBD)-a Parkinson's disease (PD) prodrome-might exhibit neural changes similar to those in PD. Substantia nigra pars compacta (SNc) degeneration underlies motor symptoms of PD. In iRBD and early PD (ePD), we measured diffusion MRI (dMRI) in the caudal motor SNc, which overlaps the nigrosome-1-the earliest-degenerating dopaminergic neurons in PD-and in the striatum. Nineteen iRBD, 26 ePD (1.7 ± 0.03 years), and 46 age-matched healthy controls (HCs) were scanned at Western University, and 47 iRBD, 115 ePD (0.9 ± 0.01 years), and 56 HCs were scanned through the Parkinson's Progression Markers Initiative, using 3T MRI. We segmented the SNc and striatum into subregions using automated probabilistic tractography to the cortex. We measured mean diffusivity (MD) and fractional anisotropy (FA) along white-matter bundles and subregional surfaces. We performed group-level and classification analyses. Increased caudal motor SNc surface MD was the only iRBD-HCs and ePD-HCs difference replicating across datasets (padj < 0.05). No iRBD-ePD differences emerged. Caudal motor SNc surface MD classified patient groups from HCs at the single-subject level with good-to-excellent balanced accuracy in an independent sample (0.91 iRBD and 0.86 iRBD and ePD combined), compared to fair performance for total SNc surface MD (0.72 iRBD and ePD). Caudal motor SNc surface MD correlated significantly with MDS-UPDRS-III scores in ePD patients. Using dMRI and automated segmentation, we detected changes suggesting altered microstructural integrity in iRBD and ePD in the nigrostriatal subregion known to degenerate first in PD. Surface MD of the caudal motor SNc presents a potential measure for inclusion in neuroimaging biomarkers of iRBD and PD.
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Affiliation(s)
- Erind Alushaj
- Department of Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Western Institute for Neuroscience, Western University, London, ON, Canada
| | - Dimuthu Hemachandra
- Robarts Research Institute, Western University, London, ON, Canada
- School of Biomedical Engineering, Western University, London, ON, Canada
| | - Hooman Ganjavi
- Department of Psychiatry, Western University, London, ON, Canada
| | - Ken N Seergobin
- Western Institute for Neuroscience, Western University, London, ON, Canada
| | - Manas Sharma
- Department of Radiology, Western University, London, ON, Canada
- Department of Clinical Neurological Sciences, Western University, London, ON, Canada
| | - Alia Kashgari
- Department of Medicine, Respirology Division, Western University, London, ON, Canada
| | - Jennifer Barr
- Department of Psychiatry, Western University, London, ON, Canada
| | - William Reisman
- Department of Medicine, Respirology Division, Western University, London, ON, Canada
| | - Ali R Khan
- Robarts Research Institute, Western University, London, ON, Canada
- Department of Medical Biophysics, Western University, London, ON, Canada
| | - Penny A MacDonald
- Western Institute for Neuroscience, Western University, London, ON, Canada.
- Department of Clinical Neurological Sciences, Western University, London, ON, Canada.
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12
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Hoffmann M, Hoopes A, Greve DN, Fischl B, Dalca AV. Anatomy-aware and acquisition-agnostic joint registration with SynthMorph. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2024; 2:1-33. [PMID: 39015335 PMCID: PMC11247402 DOI: 10.1162/imag_a_00197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 04/27/2024] [Accepted: 05/21/2024] [Indexed: 07/18/2024]
Abstract
Affine image registration is a cornerstone of medical-image analysis. While classical algorithms can achieve excellent accuracy, they solve a time-consuming optimization for every image pair. Deep-learning (DL) methods learn a function that maps an image pair to an output transform. Evaluating the function is fast, but capturing large transforms can be challenging, and networks tend to struggle if a test-image characteristic shifts from the training domain, such as the resolution. Most affine methods are agnostic to the anatomy the user wishes to align, meaning the registration will be inaccurate if algorithms consider all structures in the image. We address these shortcomings with SynthMorph, a fast, symmetric, diffeomorphic, and easy-to-use DL tool for joint affine-deformable registration of any brain image without preprocessing. First, we leverage a strategy that trains networks with widely varying images synthesized from label maps, yielding robust performance across acquisition specifics unseen at training. Second, we optimize the spatial overlap of select anatomical labels. This enables networks to distinguish anatomy of interest from irrelevant structures, removing the need for preprocessing that excludes content which would impinge on anatomy-specific registration. Third, we combine the affine model with a deformable hypernetwork that lets users choose the optimal deformation-field regularity for their specific data, at registration time, in a fraction of the time required by classical methods. This framework is applicable to learning anatomy-aware, acquisition-agnostic registration of any anatomy with any architecture, as long as label maps are available for training. We analyze how competing architectures learn affine transforms and compare state-of-the-art registration tools across an extremely diverse set of neuroimaging data, aiming to truly capture the behavior of methods in the real world. SynthMorph demonstrates high accuracy and is available at https://w3id.org/synthmorph, as a single complete end-to-end solution for registration of brain magnetic resonance imaging (MRI) data.
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Affiliation(s)
- Malte Hoffmann
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Andrew Hoopes
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Douglas N. Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Adrian V. Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
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13
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Mitew S, Yeow LY, Ho CL, Bhanu PKN, Nickalls OJ. PyFaceWipe: a new defacing tool for almost any MRI contrast. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01170-x. [PMID: 38904745 DOI: 10.1007/s10334-024-01170-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 05/25/2024] [Accepted: 05/28/2024] [Indexed: 06/22/2024]
Abstract
RATIONALE AND OBJECTIVES Defacing research MRI brain scans is often a mandatory step. With current defacing software, there are issues with Windows compatibility and researcher doubt regarding the adequacy of preservation of brain voxels in non-T1w scans. To address this, we developed PyFaceWipe, a multiplatform software for multiple MRI contrasts, which was evaluated based on its anonymisation ability and effect on downstream processing. MATERIALS AND METHODS Multiple MRI brain scan contrasts from the OASIS-3 dataset were defaced with PyFaceWipe and PyDeface and manually assessed for brain voxel preservation, remnant facial features and effect on automated face detection. Original and PyFaceWipe-defaced data from locally acquired T1w structural scans underwent volumetry with FastSurfer and brain atlas generation with ANTS. RESULTS 214 MRI scans of several contrasts from OASIS-3 were successfully processed with both PyFaceWipe and PyDeface. PyFaceWipe maintained complete brain voxel preservation in all tested contrasts except ASL (45%) and DWI (90%), and PyDeface in all tested contrasts except ASL (95%), BOLD (25%), DWI (40%) and T2* (25%). Manual review of PyFaceWipe showed no failures of facial feature removal. Pinna removal was less successful (6% of T1 scans showed residual complete pinna). PyDeface achieved 5.1% failure rate. Automated detection found no faces in PyFaceWipe-defaced scans, 19 faces in PyDeface scans compared with 78 from the 224 original scans. Brain atlas generation showed no significant difference between atlases created from original and defaced data in both young adulthood and late elderly cohorts. Structural volumetry dice scores were ≥ 0.98 for all structures except for grey matter which had 0.93. PyFaceWipe output was identical across the tested operating systems. CONCLUSION PyFaceWipe is a promising multiplatform defacing tool, demonstrating excellent brain voxel preservation and competitive defacing in multiple MRI contrasts, performing favourably against PyDeface. ASL, BOLD, DWI and T2* scans did not produce recognisable 3D renders and hence should not require defacing. Structural volumetry dice scores (≥ 0.98) were higher than previously published FreeSurfer results, except for grey matter which were comparable. The effect is measurable and care should be exercised during studies. ANTS atlas creation showed no significant effect from PyFaceWipe defacing.
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Affiliation(s)
- Stanislaw Mitew
- Department of Radiology, Sengkang General Hospital, Singhealth, 110 Sengkang E Way, Singapore, 544886, Singapore
| | - Ling Yun Yeow
- Clinical Data Analytics & Radiomics Group, Bioinformatics Institute, Agency for Science, Technology and Research, Singapore, 30 Biopolis St, Matrix, Singapore, 138671, Singapore
| | - Chi Long Ho
- Department of Radiology, Sengkang General Hospital, Singhealth, 110 Sengkang E Way, Singapore, 544886, Singapore
| | - Prakash K N Bhanu
- Clinical Data Analytics & Radiomics Group, Bioinformatics Institute, Agency for Science, Technology and Research, Singapore, 30 Biopolis St, Matrix, Singapore, 138671, Singapore
| | - Oliver James Nickalls
- Department of Radiology, Sengkang General Hospital, Singhealth, 110 Sengkang E Way, Singapore, 544886, Singapore.
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14
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Jansen MG, Zwiers MP, Marques JP, Chan KS, Amelink JS, Altgassen M, Oosterman JM, Norris DG. The Advanced BRain Imaging on ageing and Memory (ABRIM) data collection: Study design, data processing, and rationale. PLoS One 2024; 19:e0306006. [PMID: 38905233 PMCID: PMC11192316 DOI: 10.1371/journal.pone.0306006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 06/07/2024] [Indexed: 06/23/2024] Open
Abstract
To understand the neurocognitive mechanisms that underlie heterogeneity in cognitive ageing, recent scientific efforts have led to a growing public availability of imaging cohort data. The Advanced BRain Imaging on ageing and Memory (ABRIM) project aims to add to these existing datasets by taking an adult lifespan approach to provide a cross-sectional, normative database with a particular focus on connectivity, myelinization and iron content of the brain in concurrence with cognitive functioning, mechanisms of reserve, and sleep-wake rhythms. ABRIM freely shares MRI and behavioural data from 295 participants between 18-80 years, stratified by age decade and sex (median age 52, IQR 36-66, 53.20% females). The ABRIM MRI collection consists of both the raw and pre-processed structural and functional MRI data to facilitate data usage among both expert and non-expert users. The ABRIM behavioural collection includes measures of cognitive functioning (i.e., global cognition, processing speed, executive functions, and memory), proxy measures of cognitive reserve (e.g., educational attainment, verbal intelligence, and occupational complexity), and various self-reported questionnaires (e.g., on depressive symptoms, pain, and the use of memory strategies in daily life and during a memory task). In a sub-sample (n = 120), we recorded sleep-wake rhythms using an actigraphy device (Actiwatch 2, Philips Respironics) for a period of 7 consecutive days. Here, we provide an in-depth description of our study protocol, pre-processing pipelines, and data availability. ABRIM provides a cross-sectional database on healthy participants throughout the adult lifespan, including numerous parameters relevant to improve our understanding of cognitive ageing. Therefore, ABRIM enables researchers to model the advanced imaging parameters and cognitive topologies as a function of age, identify the normal range of values of such parameters, and to further investigate the diverse mechanisms of reserve and resilience.
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Affiliation(s)
- Michelle G. Jansen
- Donders Centre for Cognition, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Marcel P. Zwiers
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Jose P. Marques
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Kwok-Shing Chan
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Jitse S. Amelink
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Radboud University, Nijmegen, the Netherlands
| | - Mareike Altgassen
- Department of Psychology, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Joukje M. Oosterman
- Donders Centre for Cognition, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - David G. Norris
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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15
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Taylor PA, Glen DR, Chen G, Cox RW, Hanayik T, Rorden C, Nielson DM, Rajendra JK, Reynolds RC. A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth and interactive QC with afni_proc.py and more. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.586976. [PMID: 38585923 PMCID: PMC10996659 DOI: 10.1101/2024.03.27.586976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically under-discussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g., alignment and motion correction) to regression modeling (correct stimuli, no collinearity, valid fits, enough degrees of freedom, etc.) for each subject. There are a wide variety of features to verify throughout any single subject processing pipeline, both quantitatively and qualitatively. We present several FMRI preprocessing QC features available in the AFNI toolbox, many of which are automatically generated by the pipeline-creation tool, afni_proc.py. These items include: a modular HTML document that covers full single subject processing from the raw data through statistical modeling; several review scripts in the results directory of processed data; and command line tools for identifying subjects with one or more quantitative properties across a group (such as triaging warnings, making exclusion criteria or creating informational tables). The HTML itself contains several buttons that efficiently facilitate interactive investigations into the data, when deeper checks are needed beyond the systematic images. The pages are linkable, so that users can evaluate individual items across a group, for increased sensitivity to differences (e.g., in alignment or regression modeling images). Finally, the QC document contains rating buttons for each "QC block", as well as comment fields for each, to facilitate both saving and sharing the evaluations. This increases the specificity of QC, as well as its shareability, as these files can be shared with others and potentially uploaded into repositories, promoting transparency and open science. We describe the features and applications of these QC tools for FMRI.
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Affiliation(s)
- Paul A Taylor
- Scientific and Statistical Computing Core, NIMH, NIH, USA
| | - Daniel R Glen
- Scientific and Statistical Computing Core, NIMH, NIH, USA
| | - Gang Chen
- Scientific and Statistical Computing Core, NIMH, NIH, USA
| | - Robert W Cox
- Scientific and Statistical Computing Core, NIMH, NIH, USA
| | - Taylor Hanayik
- Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, UK
| | - Chris Rorden
- Department of Psychology, University of South Carolina, USA
- McCausland Center for Brain Imaging, University of South Carolina, USA
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16
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Hildesheim FE, Ophey A, Zumbansen A, Funck T, Schuster T, Jamison KW, Kuceyeski A, Thiel A. Predicting Language Function Post-Stroke: A Model-Based Structural Connectivity Approach. Neurorehabil Neural Repair 2024; 38:447-459. [PMID: 38602161 PMCID: PMC11097606 DOI: 10.1177/15459683241245410] [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] [Indexed: 04/12/2024]
Abstract
BACKGROUND The prediction of post-stroke language function is essential for the development of individualized treatment plans based on the personal recovery potential of aphasic stroke patients. OBJECTIVE To establish a framework for integrating information on connectivity disruption of the language network based on routinely collected clinical magnetic resonance (MR) images into Random Forest modeling to predict post-stroke language function. METHODS Language function was assessed in 76 stroke patients from the Non-Invasive Repeated Therapeutic Stimulation for Aphasia Recovery trial, using the Token Test (TT), Boston Naming Test (BNT), and Semantic Verbal Fluency (sVF) Test as primary outcome measures. Individual infarct masks were superimposed onto a diffusion tensor imaging tractogram reference set to calculate Change in Connectivity scores of language-relevant gray matter regions as estimates of structural connectivity disruption. Multivariable Random Forest models were derived to predict language function. RESULTS Random Forest models explained moderate to high amount of variance at baseline and follow-up for the TT (62.7% and 76.2%), BNT (47.0% and 84.3%), and sVF (52.2% and 61.1%). Initial language function and non-verbal cognitive ability were the most important variables to predict language function. Connectivity disruption explained additional variance, resulting in a prediction error increase of up to 12.8% with variable omission. Left middle temporal gyrus (12.8%) and supramarginal gyrus (9.8%) were identified as among the most important network nodes. CONCLUSION Connectivity disruption of the language network adds predictive value beyond lesion volume, initial language function, and non-verbal cognitive ability. Obtaining information on connectivity disruption based on routine clinical MR images constitutes a significant advancement toward practical clinical application.
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Affiliation(s)
- Franziska E. Hildesheim
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada
- Department of Neurology & Neurosurgery, McGill University, Montréal, QC, Canada
- Canadian Platform for Trials in Non-Invasive Brain Stimulation (CanStim), Montréal, QC, Canada
| | - Anja Ophey
- Department of Medical Psychology | Neuropsychology and Gender Studies, Center for Neuropsychological Diagnostics and Intervention, University Hospital Cologne, Medical Faculty of the University of Cologne, Cologne, Germany
| | - Anna Zumbansen
- School of Rehabilitation Sciences, University of Ottawa, Ottawa, ON, Canada
- Music and Health Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Thomas Funck
- Institute of Neurosciences and Medicine INM-1, Research Centre Jülich, Jülich, Germany
| | - Tibor Schuster
- Department of Family Medicine, McGill University, Montréal, QC, Canada
| | - Keith W. Jamison
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Alexander Thiel
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada
- Department of Neurology & Neurosurgery, McGill University, Montréal, QC, Canada
- Canadian Platform for Trials in Non-Invasive Brain Stimulation (CanStim), Montréal, QC, Canada
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17
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Donnay C, Okar SV, Tsagkas C, Gaitán MI, Poorman M, Reich DS, Nair G. Super resolution using sparse sampling at portable ultra-low field MR. Front Neurol 2024; 15:1330203. [PMID: 38854960 PMCID: PMC11157107 DOI: 10.3389/fneur.2024.1330203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 05/03/2024] [Indexed: 06/11/2024] Open
Abstract
Ultra-low field (ULF) magnetic resonance imaging (MRI) holds the potential to make MRI more accessible, given its cost-effectiveness, reduced power requirements, and portability. However, signal-to-noise ratio (SNR) drops with field strength, necessitating imaging with lower resolution and longer scan times. This study introduces a novel Fourier-based Super Resolution (FouSR) approach, designed to enhance the resolution of ULF MRI images with minimal increase in total scan time. FouSR combines spatial frequencies from two orthogonal ULF images of anisotropic resolution to create an isotropic T2-weighted fluid-attenuated inversion recovery (FLAIR) image. We hypothesized that FouSR could effectively recover information from under-sampled slice directions, thereby improving the delineation of multiple sclerosis (MS) lesions and other significant anatomical features. Importantly, the FouSR algorithm can be implemented on the scanner with changes to the k-space trajectory. Paired ULF (Hyperfine SWOOP, 0.064 tesla) and high field (Siemens, Skyra, 3 Tesla) FLAIR scans were collected on the same day from a phantom and a cohort of 10 participants with MS or suspected MS (6 female; mean ± SD age: 44.1 ± 4.1). ULF scans were acquired along both coronal and axial planes, featuring an in-plane resolution of 1.7 mm × 1.7 mm with a slice thickness of 5 mm. FouSR was evaluated against registered ULF coronal and axial scans, their average (ULF average) and a gold standard SR (ANTs SR). FouSR exhibited higher SNR (47.96 ± 12.6) compared to ULF coronal (36.7 ± 12.2) and higher lesion conspicuity (0.12 ± 0.06) compared to ULF axial (0.13 ± 0.07) but did not exhibit any significant differences contrast-to-noise-ratio (CNR) compared to other methods in patient scans. However, FouSR demonstrated superior image sharpness (0.025 ± 0.0040) compared to all other techniques (ULF coronal 0.021 ± 0.0037, q = 5.9, p-adj. = 0.011; ULF axial 0.018 ± 0.0026, q = 11.1, p-adj. = 0.0001; ULF average 0.019 ± 0.0034, q = 24.2, p-adj. < 0.0001) and higher lesion sharpness (-0.97 ± 0.31) when compared to the ULF average (-1.02 ± 0.37, t(543) = -10.174, p = <0.0001). Average blinded qualitative assessment by three experienced MS neurologists showed no significant difference in WML and sulci or gyri visualization between FouSR and other methods. FouSR can, in principle, be implemented on the scanner to produce clinically useful FLAIR images at higher resolution on the fly, providing a valuable tool for visualizing lesions and other anatomical structures in MS.
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Affiliation(s)
- Corinne Donnay
- Translational Neuroradiology Section, National Institutes of Health, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Serhat V. Okar
- Translational Neuroradiology Section, National Institutes of Health, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
| | - Charidimos Tsagkas
- Translational Neuroradiology Section, National Institutes of Health, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
| | - María I. Gaitán
- Translational Neuroradiology Section, National Institutes of Health, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
| | | | - Daniel S. Reich
- Translational Neuroradiology Section, National Institutes of Health, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
| | - Govind Nair
- Quantitative MRI Core, National Institutes of Health, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
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18
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Jafrasteh B, Lubián-Gutiérrez M, Lubián-López SP, Benavente-Fernández I. Enhanced Spatial Fuzzy C-Means Algorithm for Brain Tissue Segmentation in T1 Images. Neuroinformatics 2024:10.1007/s12021-024-09661-x. [PMID: 38656595 DOI: 10.1007/s12021-024-09661-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/15/2024] [Indexed: 04/26/2024]
Abstract
Magnetic Resonance Imaging (MRI) plays an important role in neurology, particularly in the precise segmentation of brain tissues. Accurate segmentation is crucial for diagnosing brain injuries and neurodegenerative conditions. We introduce an Enhanced Spatial Fuzzy C-means (esFCM) algorithm for 3D T1 MRI segmentation to three tissues, i.e. White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). The esFCM employs a weighted least square algorithm utilizing the Structural Similarity Index (SSIM) for polynomial bias field correction. It also takes advantage of the information from the membership function of the last iteration to compute neighborhood impact. This strategic refinement enhances the algorithm's adaptability to complex image structures, effectively addressing challenges such as intensity irregularities and contributing to heightened segmentation accuracy. We compare the segmentation accuracy of esFCM against four variants of FCM, Gaussian Mixture Model (GMM) and FSL and ANTs algorithms using four various dataset, employing three measurement criteria. Comparative assessments underscore esFCM's superior performance, particularly in scenarios involving added noise and bias fields.The obtained results emphasize the significant potential of the proposed method in the segmentation of MRI images.
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Affiliation(s)
- Bahram Jafrasteh
- Biomedical Research and Innovation Institute of Cádiz (INiBICA) Research Unit, Puerta del Mar University Hospital, Cádiz, 11008, Spain.
| | - Manuel Lubián-Gutiérrez
- Biomedical Research and Innovation Institute of Cádiz (INiBICA) Research Unit, Puerta del Mar University Hospital, Cádiz, 11008, Spain
- Division of Neonatology, Department of Paediatrics, Puerta del Mar University Hospital, Cádiz, 11008, Spain
| | - Simón Pedro Lubián-López
- Biomedical Research and Innovation Institute of Cádiz (INiBICA) Research Unit, Puerta del Mar University Hospital, Cádiz, 11008, Spain
- Division of Neonatology, Department of Paediatrics, Puerta del Mar University Hospital, Cádiz, 11008, Spain
| | - Isabel Benavente-Fernández
- Biomedical Research and Innovation Institute of Cádiz (INiBICA) Research Unit, Puerta del Mar University Hospital, Cádiz, 11008, Spain
- Division of Neonatology, Department of Paediatrics, Puerta del Mar University Hospital, Cádiz, 11008, Spain
- Area of Paediatrics, Department of Child and Mother Health and Radiology, Medical School, University of Cádiz, Cádiz, 11003, Spain
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19
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Tubiolo PN, Williams JC, Van Snellenberg JX. Characterization and Mitigation of a Simultaneous Multi-Slice fMRI Artifact: Multiband Artifact Regression in Simultaneous Slices. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.25.573210. [PMID: 38234755 PMCID: PMC10793397 DOI: 10.1101/2023.12.25.573210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Simultaneous multi-slice (multiband) acceleration in fMRI has become widespread, but may be affected by novel forms of signal artifact. Here, we demonstrate a previously unreported artifact manifesting as a shared signal between simultaneously acquired slices in all resting-state and task-based multiband fMRI datasets we investigated, including publicly available consortium data from the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) Study. We propose Multiband Artifact Regression in Simultaneous Slices (MARSS), a regression-based detection and correction technique that successfully mitigates this shared signal in unprocessed data. We demonstrate that the signal isolated by MARSS correction is likely non-neural, appearing stronger in neurovasculature than grey matter. Additionally, we evaluate MARSS both against and in tandem with sICA+FIX denoising, which is implemented in HCP resting-state data, to show that MARSS mitigates residual artifact signal that is not modeled by sICA+FIX. MARSS correction leads to study-wide increases in signal-to-noise ratio, decreases in cortical coefficient of variation, and mitigation of systematic artefactual spatial patterns in participant-level task betas. Finally, MARSS correction has substantive effects on second-level t-statistics in analyses of task-evoked activation. We recommend that investigators apply MARSS to multiband fMRI datasets with moderate or higher acceleration factors, in combination with established denoising methods.
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Affiliation(s)
- Philip N. Tubiolo
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- Department of Psychiatry and Behavioral Health, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY 11794
| | - John C. Williams
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- Department of Psychiatry and Behavioral Health, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY 11794
| | - Jared X. Van Snellenberg
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- Department of Psychiatry and Behavioral Health, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY 11794
- Department of Psychology, Stony Brook University, Stony Brook, NY 11794
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20
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Assländer J, Mao A, Marchetto E, Beck ES, La Rosa F, Charlson RW, Shepherd TM, Flassbeck S. Unconstrained quantitative magnetization transfer imaging: disentangling T1 of the free and semi-solid spin pools. ARXIV 2024:arXiv:2301.08394v3. [PMID: 36713253 PMCID: PMC9882584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Since the inception of magnetization transfer (MT) imaging, it has been widely assumed that Henkelman's two spin pools have similar longitudinal relaxation times, which motivated many researchers to constrain them to each other. However, several recent publications reported a T 1 s of the semi-solid spin pool that is much shorter than T 1 f of the free pool. While these studies tailored experiments for robust proofs-of-concept, we here aim to quantify the disentangled relaxation processes on a voxel-by-voxel basis in a clinical imaging setting, i.e., with an effective resolution of 1.24mm isotropic and full brain coverage in 12min. To this end, we optimized a hybrid-state pulse sequence for mapping the parameters of an unconstrained MT model. We scanned four people with relapsing-remitting multiple sclerosis (MS) and four healthy controls with this pulse sequence and estimated T 1 f ≈ 1.84 s and T 1 s ≈ 0.34 s in healthy white matter. Our results confirm the reports that T 1 s ≪ T 1 f and we argue that this finding identifies MT as an inherent driver of longitudinal relaxation in brain tissue. Moreover, we estimated a fractional size of the semi-solid spin pool of m 0 s ≈ 0.212 , which is larger than previously assumed. An analysis of T 1 f in normal-appearing white matter revealed statistically significant differences between individuals with MS and controls.
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Affiliation(s)
- Jakob Assländer
- Center for Biomedical Imaging, Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
- Center for Advanced Imaging Innovation and Research (CAI R), Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
| | - Andrew Mao
- Center for Biomedical Imaging, Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
- Center for Advanced Imaging Innovation and Research (CAI R), Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
- Vilcek Institute of Graduate Biomedical Sciences, New York University School of Medicine, 550 1st Avenue, New York, 10016, NY, USA
| | - Elisa Marchetto
- Center for Biomedical Imaging, Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
- Center for Advanced Imaging Innovation and Research (CAI R), Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
| | - Erin S Beck
- Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Department of Neurology, Icahn School of Medicine at Mount Sinai, 5 East 98th Street, New York, 10029, NY, USA
| | - Francesco La Rosa
- Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Department of Neurology, Icahn School of Medicine at Mount Sinai, 5 East 98th Street, New York, 10029, NY, USA
| | - Robert W Charlson
- Department of Neurology, New York University School of Medicine, 240 E 38th Street, New York, 10016, NY, USA
| | - Timothy M Shepherd
- Center for Biomedical Imaging, Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
| | - Sebastian Flassbeck
- Center for Biomedical Imaging, Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
- Center for Advanced Imaging Innovation and Research (CAI R), Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
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21
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Xue C, Kowshik SS, Lteif D, Puducheri S, Jasodanand VH, Zhou OT, Walia AS, Guney OB, Zhang JD, Pham ST, Kaliaev A, Andreu-Arasa VC, Dwyer BC, Farris CW, Hao H, Kedar S, Mian AZ, Murman DL, O’Shea SA, Paul AB, Rohatgi S, Saint-Hilaire MH, Sartor EA, Setty BN, Small JE, Swaminathan A, Taraschenko O, Yuan J, Zhou Y, Zhu S, Karjadi C, Ang TFA, Bargal SA, Plummer BA, Poston KL, Ahangaran M, Au R, Kolachalama VB. AI-based differential diagnosis of dementia etiologies on multimodal data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.08.24302531. [PMID: 38585870 PMCID: PMC10996713 DOI: 10.1101/2024.02.08.24302531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an AI model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a micro-averaged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the micro-averaged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in various clinical settings and drug trials, with promising implications for person-level management.
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Affiliation(s)
- Chonghua Xue
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Electrical & Computer Engineering, Boston University, MA, USA
| | - Sahana S. Kowshik
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Faculty of Computing & Data Sciences, Boston University, MA, USA
| | - Diala Lteif
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Computer Science, Boston University, MA, USA
| | - Shreyas Puducheri
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Varuna H. Jasodanand
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Olivia T. Zhou
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Anika S. Walia
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Osman B. Guney
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Electrical & Computer Engineering, Boston University, MA, USA
| | - J. Diana Zhang
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- School of Chemistry, University of New South Wales, Sydney, Australia
| | - Serena T. Pham
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Artem Kaliaev
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - V. Carlota Andreu-Arasa
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Brigid C. Dwyer
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Chad W. Farris
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Honglin Hao
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Sachin Kedar
- Departments of Neurology & Ophthalmology, Emory University School of Medicine, Atlanta, GA, USA
| | - Asim Z. Mian
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Daniel L. Murman
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Sarah A. O’Shea
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron B. Paul
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Saurabh Rohatgi
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Emmett A. Sartor
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Bindu N. Setty
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Juan E. Small
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA
| | | | - Olga Taraschenko
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Jing Yuan
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yan Zhou
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Shuhan Zhu
- Department of Neurology, Brigham & Women’s Hospital, Boston, MA, USA
| | - Cody Karjadi
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Ting Fang Alvin Ang
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Sarah A. Bargal
- Department of Computer Science, Georgetown University, Washington DC, USA
| | | | | | - Meysam Ahangaran
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Rhoda Au
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Boston University Alzheimer’s Disease Research Center, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Vijaya B. Kolachalama
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Faculty of Computing & Data Sciences, Boston University, MA, USA
- Department of Computer Science, Boston University, MA, USA
- Boston University Alzheimer’s Disease Research Center, Boston, MA, USA
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22
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Phillips JS, Adluru N, Chung MK, Radhakrishnan H, Olm CA, Cook PA, Gee JC, Cousins KAQ, Arezoumandan S, Wolk DA, McMillan CT, Grossman M, Irwin DJ. Greater white matter degeneration and lower structural connectivity in non-amnestic vs. amnestic Alzheimer's disease. Front Neurosci 2024; 18:1353306. [PMID: 38567286 PMCID: PMC10986184 DOI: 10.3389/fnins.2024.1353306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Multimodal evidence indicates Alzheimer's disease (AD) is characterized by early white matter (WM) changes that precede overt cognitive impairment. WM changes have overwhelmingly been investigated in typical, amnestic mild cognitive impairment and AD; fewer studies have addressed WM change in atypical, non-amnestic syndromes. We hypothesized each non-amnestic AD syndrome would exhibit WM differences from amnestic and other non-amnestic syndromes. Materials and methods Participants included 45 cognitively normal (CN) individuals; 41 amnestic AD patients; and 67 patients with non-amnestic AD syndromes including logopenic-variant primary progressive aphasia (lvPPA, n = 32), posterior cortical atrophy (PCA, n = 17), behavioral variant AD (bvAD, n = 10), and corticobasal syndrome (CBS, n = 8). All had T1-weighted MRI and 30-direction diffusion-weighted imaging (DWI). We performed whole-brain deterministic tractography between 148 cortical and subcortical regions; connection strength was quantified by tractwise mean generalized fractional anisotropy. Regression models assessed effects of group and phenotype as well as associations with grey matter volume. Topological analyses assessed differences in persistent homology (numbers of graph components and cycles). Additionally, we tested associations of topological metrics with global cognition, disease duration, and DWI microstructural metrics. Results Both amnestic and non-amnestic patients exhibited lower WM connection strength than CN participants in corpus callosum, cingulum, and inferior and superior longitudinal fasciculi. Overall, non-amnestic patients had more WM disease than amnestic patients. LvPPA patients had left-lateralized WM degeneration; PCA patients had reductions in connections to bilateral posterior parietal, occipital, and temporal areas. Topological analysis showed the non-amnestic but not the amnestic group had more connected components than controls, indicating persistently lower connectivity. Longer disease duration and cognitive impairment were associated with more connected components and fewer cycles in individuals' brain graphs. Discussion We have previously reported syndromic differences in GM degeneration and tau accumulation between AD syndromes; here we find corresponding differences in WM tracts connecting syndrome-specific epicenters. Determining the reasons for selective WM degeneration in non-amnestic AD is a research priority that will require integration of knowledge from neuroimaging, biomarker, autopsy, and functional genetic studies. Furthermore, longitudinal studies to determine the chronology of WM vs. GM degeneration will be key to assessing evidence for WM-mediated tau spread.
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Affiliation(s)
- Jeffrey S. Phillips
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Moo K. Chung
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Hamsanandini Radhakrishnan
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Christopher A. Olm
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Philip A. Cook
- Penn Image Computing and Science Laboratory, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - James C. Gee
- Penn Image Computing and Science Laboratory, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Katheryn A. Q. Cousins
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Sanaz Arezoumandan
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Penn Memory Center, University of Pennsylvania Health System, Philadelphia, PA, United States
| | - Corey T. McMillan
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - David J. Irwin
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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23
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Gi Y, Oh G, Jo Y, Lim H, Ko Y, Hong J, Lee E, Park S, Kwak T, Kim S, Yoon M. Study of multistep Dense U-Net-based automatic segmentation for head MRI scans. Med Phys 2024; 51:2230-2238. [PMID: 37956307 DOI: 10.1002/mp.16824] [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: 05/21/2023] [Revised: 09/25/2023] [Accepted: 10/22/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Despite extensive efforts to obtain accurate segmentation of magnetic resonance imaging (MRI) scans of a head, it remains challenging primarily due to variations in intensity distribution, which depend on the equipment and parameters used. PURPOSE The goal of this study is to evaluate the effectiveness of an automatic segmentation method for head MRI scans using a multistep Dense U-Net (MDU-Net) architecture. METHODS The MDU-Net-based method comprises two steps. The first step is to segment the scalp, skull, and whole brain from head MRI scans using a convolutional neural network (CNN). In the first step, a hybrid network is used to combine 2.5D Dense U-Net and 3D Dense U-Net structure. This hybrid network acquires logits in three orthogonal planes (axial, coronal, and sagittal) using 2.5D Dense U-Nets and fuses them by averaging. The resultant fused probability map with head MRI scans then serves as the input to a 3D Dense U-Net. In this process, different ratios of active contour loss and focal loss are applied. The second step is to segment the cerebrospinal fluid (CSF), white matter, and gray matter from extracted brain MRI scans using CNNs. In the second step, the histogram of the extracted brain MRI scans is standardized and then a 2.5D Dense U-Net is used to further segment the brain's specific tissues using the focal loss. A dataset of 100 head MRI scans from an OASIS-3 dataset was used for training, internal validation, and testing, with ratios of 80%, 10%, and 10%, respectively. Using the proposed approach, we segmented the head MRI scans into five areas (scalp, skull, CSF, white matter, and gray matter) and evaluated the segmentation results using the Dice similarity coefficient (DSC) score, Hausdorff distance (HD), and the average symmetric surface distance (ASSD) as evaluation metrics. We compared these results with those obtained using the Res-U-Net, Dense U-Net, U-Net++, Swin-Unet, and H-Dense U-Net models. RESULTS The MDU-Net model showed DSC values of 0.933, 0.830, 0.833, 0.953, and 0.917 in the scalp, skull, CSF, white matter, and gray matter, respectively. The corresponding HD values were 2.37, 2.89, 2.13, 1.52, and 1.53 mm, respectively. The ASSD values were 0.50, 1.63, 1.28, 0.26, and 0.27 mm, respectively. Comparing these results with other models revealed that the MDU-Net model demonstrated the best performance in terms of the DSC values for the scalp, CSF, white matter, and gray matter. When compared with the H-Dense U-Net model, which showed the highest performance among the other models, the MDU-Net model showed substantial improvements in the HD view, particularly in the gray matter region, with a difference of approximately 9%. In addition, in terms of the ASSD, the MDU-Net model outperformed the H-Dense U-Net model, showing an approximately 7% improvements in the white matter and approximately 9% improvements in the gray matter. CONCLUSION Compared with existing models in terms of DSC, HD, and ASSD, the proposed MDU-Net model demonstrated the best performance on average and showed its potential to enhance the accuracy of automatic segmentation for head MRI scans.
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Affiliation(s)
- Yongha Gi
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
| | - Geon Oh
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
| | - Yunhui Jo
- Institute of Global Health Technology (IGHT), Korea University, Seoul, Republic of Korea
| | - Hyeongjin Lim
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
| | - Yousun Ko
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
| | - Jinyoung Hong
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
| | - Eunjun Lee
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
| | - Sangmin Park
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
- Field Cure Ltd., Seoul, Republic of Korea
| | - Taemin Kwak
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
- Field Cure Ltd., Seoul, Republic of Korea
| | - Sangcheol Kim
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
- Field Cure Ltd., Seoul, Republic of Korea
| | - Myonggeun Yoon
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
- Field Cure Ltd., Seoul, Republic of Korea
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Wan S, Lu W, Fu Y, Wang M, Liu K, Chen S, Chen W, Wang Y, Wu J, Leng X, Fiehler J, Siddiqui AH, Guan S, Xiang J. Automated ASPECTS calculation may equal the performance of experienced clinicians: a machine learning study based on a large cohort. Eur Radiol 2024; 34:1624-1634. [PMID: 37658137 DOI: 10.1007/s00330-023-10053-z] [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: 11/11/2022] [Revised: 05/15/2023] [Accepted: 06/22/2023] [Indexed: 09/03/2023]
Abstract
OBJECTIVES The Alberta Stroke Program Early CT Score (ASPECTS) is a semi-quantitative method to evaluate the severity of early ischemic change on non-contrast computed tomography (NCCT) in patients with acute ischemic stroke (AIS). In this work, we propose an automated ASPECTS method based on large cohort of data and machine learning. METHODS For this study, we collected 3626 NCCT cases from multiple centers and annotated directly on this dataset by neurologists. Based on image analysis and machine learning methods, we constructed a two-stage machine learning model. The validity and reliability of this automated ASPECTS method were tested on an independent external validation set of 300 cases. Statistical analyses on the total ASPECTS, dichotomized ASPECTS, and region-level ASPECTS were presented. RESULTS On an independent external validation set of 300 cases, for the total ASPECTS results, the intraclass correlation coefficient between automated ASPECTS and expert-rated was 0.842. The agreement between ASPECTS threshold of ≥ 6 versus < 6 using a dichotomized method was moderate (κ = 0.438, 0.391-0.477), and the detection rate (sensitivity) was 86.5% for patients with ASPECTS threshold of ≥ 6. Compared with the results of previous studies, our method achieved a slight lead in sensitivity (67.8%) and AUC (0.845), with comparable accuracy (78.9%) and specificity (81.2%). CONCLUSION The proposed automated ASPECTS method driven by a large cohort of NCCT images performed equally well compared with expert-rated ASPECTS. This work further demonstrates the validity and reliability of automated ASPECTS evaluation method. CLINICAL RELEVANCE STATEMENT The automated ASPECTS method proposed by this study may help AIS patients to receive rapid intervention, but should not be used as a stand-alone diagnostic basis. KEY POINTS NCCT-based manual ASPECTS scores were poorly consistent. Machine learning can automate the ASPECTS scoring process. Machine learning model design based on large cohort data can effectively improve the consistency of ASPECTS scores.
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Affiliation(s)
- Shu Wan
- Brain Center, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wei Lu
- ArteryFlow Technology Co., Ltd., Hangzhou, China
| | - Yu Fu
- Department of Neurointervention Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ming Wang
- Brain Center, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Kaizheng Liu
- ArteryFlow Technology Co., Ltd., Hangzhou, China
| | - Sijing Chen
- Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Wubiao Chen
- Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Yang Wang
- Department of Neurosurgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Jun Wu
- Department of Neurology, Qingtian County People's Hospital, Lishui, China
| | | | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Adnan H Siddiqui
- Departments of Neurosurgery and Radiology, University at Buffalo, Buffalo, NY, USA
| | - Sheng Guan
- Department of Neurointervention Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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Kelley W, Ngo N, Dalca AV, Fischl B, Zöllei L, Hoffmann M. BOOSTING SKULL-STRIPPING PERFORMANCE FOR PEDIATRIC BRAIN IMAGES. ARXIV 2024:arXiv:2402.16634v1. [PMID: 38463507 PMCID: PMC10925384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing. However, the broad range of neuroanatomical variation in the developing brain, combined with additional challenges such as high motion levels, as well as shoulder and chest signal in the images, leaves many adult-specific tools ill-suited for pediatric skull-stripping. Building on an existing framework for robust and accurate skull-stripping, we propose developmental SynthStrip (d-SynthStrip), a skull-stripping model tailored to pediatric images. This framework exposes networks to highly variable images synthesized from label maps. Our model substantially outperforms pediatric baselines across scan types and age cohorts. In addition, the <1-minute runtime of our tool compares favorably to the fastest baselines. We distribute our model at https://w3id.org/synthstrip.
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Affiliation(s)
- William Kelley
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Nathan Ngo
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Adrian V Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Computer Science & Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Malte Hoffmann
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA
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Park JS, Fadnavis S, Garyfallidis E. Multi-scale V-net architecture with deep feature CRF layers for brain extraction. COMMUNICATIONS MEDICINE 2024; 4:29. [PMID: 38396078 PMCID: PMC10891085 DOI: 10.1038/s43856-024-00452-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Brain extraction is a computational necessity for researchers using brain imaging data. However, the complex structure of the interfaces between the brain, meninges and human skull have not allowed a highly robust solution to emerge. While previous methods have used machine learning with structural and geometric priors in mind, with the development of Deep Learning (DL), there has been an increase in Neural Network based methods. Most proposed DL models focus on improving the training data despite the clear gap between groups in the amount and quality of accessible training data between. METHODS We propose an architecture we call Efficient V-net with Additional Conditional Random Field Layers (EVAC+). EVAC+ has 3 major characteristics: (1) a smart augmentation strategy that improves training efficiency, (2) a unique way of using a Conditional Random Fields Recurrent Layer that improves accuracy and (3) an additional loss function that fine-tunes the segmentation output. We compare our model to state-of-the-art non-DL and DL methods. RESULTS Results show that even with limited training resources, EVAC+ outperforms in most cases, achieving a high and stable Dice Coefficient and Jaccard Index along with a desirable lower Surface (Hausdorff) Distance. More importantly, our approach accurately segmented clinical and pediatric data, despite the fact that the training dataset only contains healthy adults. CONCLUSIONS Ultimately, our model provides a reliable way of accurately reducing segmentation errors in complex multi-tissue interfacing areas of the brain. We expect our method, which is publicly available and open-source, to be beneficial to a wide range of researchers.
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Affiliation(s)
- Jong Sung Park
- Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, USA.
| | - Shreyas Fadnavis
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Huang W, Dong X, Zhao T, Kucikova L, Fu A, Shu N. DCP: A pipeline toolbox for diffusion connectome. Hum Brain Mapp 2024; 45:e26626. [PMID: 38375916 PMCID: PMC10877999 DOI: 10.1002/hbm.26626] [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: 06/08/2023] [Revised: 12/29/2023] [Accepted: 02/02/2024] [Indexed: 02/21/2024] Open
Abstract
The brain structural network derived from diffusion magnetic resonance imaging (dMRI) reflects the white matter connections between brain regions, which can quantitatively describe the anatomical connection pattern of the entire brain. The development of structural brain connectome leads to the emergence of a large number of dMRI processing packages and network analysis toolboxes. However, the fully automated network analysis based on dMRI data remains challenging. In this study, we developed a cross-platform MATLAB toolbox named "Diffusion Connectome Pipeline" (DCP) for automatically constructing brain structural networks and calculating topological attributes of the networks. The toolbox integrates a few developed packages, including FSL, Diffusion Toolkit, SPM, Camino, MRtrix3, and MRIcron. It can process raw dMRI data collected from any number of participants, and it is also compatible with preprocessed files from public datasets such as HCP and UK Biobank. Moreover, a friendly graphical user interface allows users to configure their processing pipeline without any programming. To prove the capacity and validity of the DCP, two tests were conducted with using DCP. The results showed that DCP can reproduce the findings in our previous studies. However, there are some limitations of DCP, such as relying on MATLAB and being unable to fixel-based metrics weighted network. Despite these limitations, overall, the DCP software provides a standardized, fully automated computational workflow for white matter network construction and analysis, which is beneficial for advancing future human brain connectomics application research.
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Affiliation(s)
- Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
- School of Systems Science, Beijing Normal UniversityBeijingPR China
- Department of NeuroscienceSheffield Institute for Translational Neuroscience, Medical School and Insigneo Institute for in Silico Medicine, University of SheffieldSheffieldUK
| | - Xinyi Dong
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
| | - Ludmila Kucikova
- Department of NeuroscienceSheffield Institute for Translational Neuroscience, Medical School and Insigneo Institute for in Silico Medicine, University of SheffieldSheffieldUK
| | - Anguo Fu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
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Hammer Y, Najjar W, Kahanov L, Joskowicz L, Shoshan Y. Two is better than one: longitudinal detection and volumetric evaluation of brain metastases after Stereotactic Radiosurgery with a deep learning pipeline. J Neurooncol 2024; 166:547-555. [PMID: 38300389 PMCID: PMC10876809 DOI: 10.1007/s11060-024-04580-y] [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: 12/14/2023] [Accepted: 01/18/2024] [Indexed: 02/02/2024]
Abstract
PURPOSE Close MRI surveillance of patients with brain metastases following Stereotactic Radiosurgery (SRS) treatment is essential for assessing treatment response and the current disease status in the brain. This follow-up necessitates the comparison of target lesion sizes in pre- (prior) and post-SRS treatment (current) T1W-Gad MRI scans. Our aim was to evaluate SimU-Net, a novel deep-learning model for the detection and volumetric analysis of brain metastases and their temporal changes in paired prior and current scans. METHODS SimU-Net is a simultaneous multi-channel 3D U-Net model trained on pairs of registered prior and current scans of a patient. We evaluated its performance on 271 pairs of T1W-Gad MRI scans from 226 patients who underwent SRS. An expert oncological neurosurgeon manually delineated 1,889 brain metastases in all the MRI scans (1,368 with diameters > 5 mm, 834 > 10 mm). The SimU-Net model was trained/validated on 205 pairs from 169 patients (1,360 metastases) and tested on 66 pairs from 57 patients (529 metastases). The results were then compared to the ground truth delineations. RESULTS SimU-Net yielded a mean (std) detection precision and recall of 1.00±0.00 and 0.99±0.06 for metastases > 10 mm, 0.90±0.22 and 0.97±0.12 for metastases > 5 mm of, and 0.76±0.27 and 0.94±0.16 for metastases of all sizes. It improves lesion detection precision by 8% for all metastases sizes and by 12.5% for metastases < 10 mm with respect to standalone 3D U-Net. The segmentation Dice scores were 0.90±0.10, 0.89±0.10 and 0.89±0.10 for the above metastases sizes, all above the observer variability of 0.80±0.13. CONCLUSION Automated detection and volumetric quantification of brain metastases following SRS have the potential to enhance the assessment of treatment response and alleviate the clinician workload.
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Affiliation(s)
- Yonny Hammer
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Edmond. J. Safra Campus, Givat Ram, 9190401, Jerusalem, Israel
| | - Wenad Najjar
- Department of Neurosurgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Lea Kahanov
- Department of Neurosurgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Leo Joskowicz
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Edmond. J. Safra Campus, Givat Ram, 9190401, Jerusalem, Israel.
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Yigal Shoshan
- Department of Neurosurgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel
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Kashyap S, Oliveira ÍAF, Uludağ K. Feasibility of high-resolution perfusion imaging using arterial spin labeling MRI at 3 Tesla. Front Physiol 2024; 14:1271254. [PMID: 38235379 PMCID: PMC10791866 DOI: 10.3389/fphys.2023.1271254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 11/27/2023] [Indexed: 01/19/2024] Open
Abstract
Cerebral blood flow (CBF) is a critical physiological parameter of brain health, and it can be non-invasively measured with arterial spin labeling (ASL) MRI. In this study, we evaluated and optimized whole-brain, high-resolution ASL as an alternative to the low-resolution ASL employed in the routine assessment of CBF in both healthy participants and patients. Two high-resolution protocols (i.e., pCASL and FAIR-Q2TIPS (PASL) with 2 mm isotropic voxels) were compared to a default clinical pCASL protocol (3.4 × 3.4 × 4 mm 3), all of whom had an acquisition time of ≈ 5 min. We assessed the impact of high-resolution acquisition on reducing partial voluming and improving sensitivity to the perfusion signal, and evaluated the effectiveness of z-deblurring on the ASL data. We compared the quality of whole-brain ASL acquired using three available head coils with differing number of receive channels (i.e., 20, 32, and 64ch). We found that using higher coil counts (32 and 64ch coils as compared to 20ch) offers improved signal-to-noise ratio (SNR) and acceleration capabilities that are beneficial for ASL imaging at 3 Tesla (3 T). The inherent reduction in partial voluming effects with higher resolution acquisitions improves the resolving power of perfusion without impacting the sensitivity. In conclusion, our results suggest that high-resolution ASL (2 to 2.5 mm isotropic voxels) has the potential to become a new standard for perfusion imaging at 3 T and increase its adoption into clinical research and cognitive neuroscience applications.
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Affiliation(s)
- Sriranga Kashyap
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
| | | | - Kâmil Uludağ
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
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Wren-Jarvis J, Powers R, Lazerwitz MC, Xiao J, Cai LT, Choi HL, Brandes-Aitken A, Chu R, Trimarchi KJ, Garcia RD, Rowe MA, Steele MC, Marco EJ, Mukherjee P. White matter microstructure of children with sensory over-responsivity is associated with affective behavior. J Neurodev Disord 2024; 16:1. [PMID: 38166648 PMCID: PMC10759342 DOI: 10.1186/s11689-023-09513-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Sensory processing dysfunction (SPD) is linked to altered white matter (WM) microstructure in school-age children. Sensory over-responsivity (SOR), a form of SPD, affects at least 2.5% of all children and has substantial deleterious impact on learning and mental health. However, SOR has not been well studied using microstructural imaging such as diffusion MRI (dMRI). Since SOR involves hypersensitivity to external stimuli, we test the hypothesis that children with SOR require compensatory neuroplasticity in the form of superior WM microstructural integrity to protect against internalizing behavior, leaving those with impaired WM microstructure vulnerable to somatization and depression. METHODS Children ages 8-12 years old with neurodevelopmental concerns were assessed for SOR using a comprehensive structured clinical evaluation, the Sensory Processing 3 Dimensions Assessment, and underwent 3 Tesla MRI with multishell multiband dMRI. Tract-based spatial statistics was used to measure diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) metrics from global WM and nineteen selected WM tracts. Correlations of DTI and NODDI measures with measures of somatization and emotional disturbance from the Behavioral Assessment System for Children, 3rd edition (BASC-3), were computed in the SOR group and in matched children with neurodevelopmental concerns but not SOR. RESULTS Global WM fractional anisotropy (FA) is negatively correlated with somatization and with emotional disturbance in the SOR group but not the non-SOR group. Also observed in children with SOR are positive correlations of radial diffusivity (RD) and free water fraction (FISO) with somatization and, in most cases, emotional disturbance. These effects are significant in boys with SOR, whereas the study is underpowered for girls. The most affected white matter are medial lemniscus and internal capsule sensory tracts, although effects of SOR are observed in many cerebral, cerebellar, and brainstem tracts. CONCLUSION White matter microstructure is related to affective behavior in children with SOR.
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Affiliation(s)
- Jamie Wren-Jarvis
- Department of Radiology & Biomedical Imaging, University of California, UCSF, 185 Berry St, Suite 350, Box 0946, San Francisco, CA, 94143-0946, USA
| | - Rachel Powers
- Department of Radiology & Biomedical Imaging, University of California, UCSF, 185 Berry St, Suite 350, Box 0946, San Francisco, CA, 94143-0946, USA
- Cortica Healthcare, 4000 Civic Center Dr., Suite 100, San Rafael, CA, 94903, USA
| | - Maia C Lazerwitz
- Cortica Healthcare, 4000 Civic Center Dr., Suite 100, San Rafael, CA, 94903, USA
| | - Jaclyn Xiao
- Department of Radiology & Biomedical Imaging, University of California, UCSF, 185 Berry St, Suite 350, Box 0946, San Francisco, CA, 94143-0946, USA
| | - Lanya T Cai
- Department of Radiology & Biomedical Imaging, University of California, UCSF, 185 Berry St, Suite 350, Box 0946, San Francisco, CA, 94143-0946, USA
| | - Hannah L Choi
- Department of Radiology & Biomedical Imaging, University of California, UCSF, 185 Berry St, Suite 350, Box 0946, San Francisco, CA, 94143-0946, USA
| | - Annie Brandes-Aitken
- Cortica Healthcare, 4000 Civic Center Dr., Suite 100, San Rafael, CA, 94903, USA
| | - Robyn Chu
- Cortica Healthcare, 4000 Civic Center Dr., Suite 100, San Rafael, CA, 94903, USA
| | - Kaitlyn J Trimarchi
- Cortica Healthcare, 4000 Civic Center Dr., Suite 100, San Rafael, CA, 94903, USA
| | - Rafael D Garcia
- Cortica Healthcare, 4000 Civic Center Dr., Suite 100, San Rafael, CA, 94903, USA
| | - Mikaela A Rowe
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Mary C Steele
- Lifetime Neurodevelopmental Care Center, San Rafael, CA, USA
| | - Elysa J Marco
- Cortica Healthcare, 4000 Civic Center Dr., Suite 100, San Rafael, CA, 94903, USA.
- Lifetime Neurodevelopmental Care Center, San Rafael, CA, USA.
| | - Pratik Mukherjee
- Department of Radiology & Biomedical Imaging, University of California, UCSF, 185 Berry St, Suite 350, Box 0946, San Francisco, CA, 94143-0946, USA.
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Van Dyken PC, MacKinley M, Khan AR, Palaniyappan L. Cortical Network Disruption Is Minimal in Early Stages of Psychosis. SCHIZOPHRENIA BULLETIN OPEN 2024; 5:sgae010. [PMID: 39144115 PMCID: PMC11207789 DOI: 10.1093/schizbullopen/sgae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Background and Hypothesis Schizophrenia is associated with white matter disruption and topological reorganization of cortical connectivity but the trajectory of these changes, from the first psychotic episode to established illness, is poorly understood. Current studies in first-episode psychosis (FEP) patients using diffusion magnetic resonance imaging (dMRI) suggest such disruption may be detectable at the onset of psychosis, but specific results vary widely, and few reports have contextualized their findings with direct comparison to young adults with established illness. Study Design Diffusion and T1-weighted 7T MR scans were obtained from N = 112 individuals (58 with untreated FEP, 17 with established schizophrenia, 37 healthy controls) recruited from London, Ontario. Voxel- and network-based analyses were used to detect changes in diffusion microstructural parameters. Graph theory metrics were used to probe changes in the cortical network hierarchy and to assess the vulnerability of hub regions to disruption. The analysis was replicated with N = 111 (57 patients, 54 controls) from the Human Connectome Project-Early Psychosis (HCP-EP) dataset. Study Results Widespread microstructural changes were found in people with established illness, but changes in FEP patients were minimal. Unlike the established illness group, no appreciable topological changes in the cortical network were observed in FEP patients. These results were replicated in the early psychosis patients of the HCP-EP datasets, which were indistinguishable from controls in most metrics. Conclusions The white matter structural changes observed in established schizophrenia are not a prominent feature in the early stages of this illness.
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Affiliation(s)
- Peter C Van Dyken
- Neuroscience Graduate Program, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Michael MacKinley
- Lawson Health Research Institute, London Health Sciences Centre, London, ON, Canada
| | - Ali R Khan
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Lena Palaniyappan
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, London, ON, Canada
- Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
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Kossmann MRP, Ehret F, Roohani S, Winter SF, Ghadjar P, Acker G, Senger C, Schmid S, Zips D, Kaul D. Histopathologically confirmed radiation-induced damage of the brain - an in-depth analysis of radiation parameters and spatio-temporal occurrence. Radiat Oncol 2023; 18:198. [PMID: 38087368 PMCID: PMC10717523 DOI: 10.1186/s13014-023-02385-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Radiation-induced damage (RID) after radiotherapy (RT) of primary brain tumors and metastases can be challenging to clinico-radiographically distinguish from tumor progression. RID includes pseudoprogression and radiation necrosis; the latter being irreversible and often associated with severe symptoms. While histopathology constitutes the diagnostic gold standard, biopsy-controlled clinical studies investigating RID remain limited. Whether certain brain areas are potentially more vulnerable to RID remains an area of active investigation. Here, we analyze histopathologically confirmed cases of RID in relation to the temporal and spatial dose distribution. METHODS Histopathologically confirmed cases of RID after photon-based RT for primary or secondary central nervous system malignancies were included. Demographic, clinical, and dosimetric data were collected from patient records and treatment planning systems. We calculated the equivalent dose in 2 Gy fractions (EQD22) and the biologically effective dose (BED2) for normal brain tissue (α/β ratio of 2 Gy) and analyzed the spatial and temporal distribution using frequency maps. RESULTS Thirty-three patients were identified. High-grade glioma patients (n = 18) mostly received one normofractionated RT series (median cumulative EQD22 60 Gy) to a large planning target volume (PTV) (median 203.9 ccm) before diagnosis of RID. Despite the low EQD22 and BED2, three patients with an accelerated hyperfractionated RT developed RID. In contrast, brain metastases patients (n = 15; 16 RID lesions) were often treated with two or more RT courses and with radiosurgery or fractionated stereotactic RT, resulting in a higher cumulative EQD22 (median 162.4 Gy), to a small PTV (median 6.7 ccm). All (n = 34) RID lesions occurred within the PTV of at least one of the preceding RT courses. RID in the high-grade glioma group showed a frontotemporal distribution pattern, whereas, in metastatic patients, RID was observed throughout the brain with highest density in the parietal lobe. The cumulative EQD22 was significantly lower in RID lesions that involved the subventricular zone (SVZ) than in lesions without SVZ involvement (median 60 Gy vs. 141 Gy, p = 0.01). CONCLUSIONS Accelerated hyperfractionated RT can lead to RID despite computationally low EQD22 and BED2 in high-grade glioma patients. The anatomical location of RID corresponded to the general tumor distribution of gliomas and metastases. The SVZ might be a particularly vulnerable area.
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Affiliation(s)
- Mario R P Kossmann
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
- Department of Radiotherapy and Radiation Oncology, Pius-Hospital Oldenburg, Georgstr. 12, 26121, Oldenburg, Germany
| | - Felix Ehret
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Siyer Roohani
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Sebastian F Winter
- Division of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Pirus Ghadjar
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Güliz Acker
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurosurgery, Charitéplatz 1, 10117, Berlin, Germany
| | - Carolin Senger
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Simone Schmid
- Charité - Universitätsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neuropathology, Charitéplatz 1, 10117, Berlin, Germany
| | - Daniel Zips
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David Kaul
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany.
- Charité - Universitätsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany.
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Edwards NC, Lao PJ, Alshikho MJ, Ericsson OM, Rizvi B, Petersen ME, O’Bryant S, Flores-Aguilar L, Simoes S, Mapstone M, Tudorascu DL, Janelidze S, Hansson O, Handen BL, Christian BT, Lee JH, Lai F, Rosas HD, Zaman S, Lott IT, Yassa MA, Gutierrez J, Wilcock DM, Head E, Brickman AM. Cerebrovascular disease drives Alzheimer plasma biomarker concentrations in adults with Down syndrome. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.28.23298693. [PMID: 38076904 PMCID: PMC10705616 DOI: 10.1101/2023.11.28.23298693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Importance By age 40 years over 90% of adults with Down syndrome (DS) have Alzheimer's disease (AD) pathology and most progress to dementia. Despite having few systemic vascular risk factors, individuals with DS have elevated cerebrovascular disease (CVD) markers that track with the clinical progression of AD, suggesting a role for CVD that is hypothesized to be mediated by inflammatory factors. Objective To examine the pathways through which small vessel CVD contributes to AD-related pathophysiology and neurodegeneration in adults with DS. Design Cross sectional analysis of neuroimaging, plasma, and clinical data. Setting Participants were enrolled in Alzheimer's Biomarker Consortium - Down Syndrome (ABC-DS), a multisite study of AD in adults with DS. Participants One hundred eighty-five participants (mean [SD] age=45.2 [9.3] years) with available MRI and plasma biomarker data were included. White matter hyperintensity (WMH) volumes were derived from T2-weighted FLAIR MRI scans and plasma biomarker concentrations of amyloid beta (Aβ42/Aβ40), phosphorylated tau (p-tau217), astrocytosis (glial fibrillary acidic protein, GFAP), and neurodegeneration (neurofilament light chain, NfL) were measured with ultrasensitive immunoassays. Main Outcomes and Measures We examined the bivariate relationships of WMH, Aβ42/Aβ40, p-tau217, and GFAP with age-residualized NfL across AD diagnostic groups. A series of mediation and path analyses examined causal pathways linking WMH and AD pathophysiology to promote neurodegeneration in the total sample and groups stratified by clinical diagnosis. Results There was a direct and indirect bidirectional effect through GFAP of WMH on p-tau217 concentration, which was associated with NfL concentration in the entire sample. Among cognitively stable participants, WMH was directly and indirectly, through GFAP, associated with p-tau217 concentration, and in those with MCI, there was a direct effect of WMH on p-tau217 and NfL concentrations. There were no associations of WMH with biomarker concentrations among those diagnosed with dementia. Conclusions and Relevance The findings suggest that among individuals with DS, CVD promotes neurodegeneration by increasing astrocytosis and tau pathophysiology in the presymptomatic phases of AD. This work joins an emerging literature that implicates CVD and its interface with neuroinflammation as a core pathological feature of AD in adults with DS.
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Affiliation(s)
- Natalie C. Edwards
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York City, NY, USA
- Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
- Department of Neuroscience, Columbia University, New York City, NY, USA
| | - Patrick J. Lao
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York City, NY, USA
- Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Mohamad J. Alshikho
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York City, NY, USA
- Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Olivia M. Ericsson
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York City, NY, USA
- Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Batool Rizvi
- Department of Neurobiology & Behavior, University of California, Irvine, CA, USA
| | | | - Sid O’Bryant
- University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Lisi Flores-Aguilar
- Department of Pathology and Laboratory Medicine, University of California Irvine School of Medicine, University of California, Irvine, CA, USA
| | - Sabrina Simoes
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York City, NY, USA
- Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Mark Mapstone
- Department of Neurology, University of California, Irvine, CA, USA
| | - Dana L. Tudorascu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | | | | | - Joseph H. Lee
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York City, NY, USA
- Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Florence Lai
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - H Diana Rosas
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
- Department of Radiology, Center for Neuroimaging of Aging and neurodegenerative Diseases, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Shahid Zaman
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Ira T. Lott
- Department of Pediatrics and Neurology, School of Medicine, University of California, Irvine, CA, USA
| | - Michael A. Yassa
- Department of Neurobiology & Behavior, University of California, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
| | - José Gutierrez
- Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Donna M. Wilcock
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
- Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Elizabeth Head
- Department of Pathology and Laboratory Medicine, University of California Irvine School of Medicine, University of California, Irvine, CA, USA
| | - Adam M. Brickman
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York City, NY, USA
- Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
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Bianconi A, Rossi LF, Bonada M, Zeppa P, Nico E, De Marco R, Lacroce P, Cofano F, Bruno F, Morana G, Melcarne A, Ruda R, Mainardi L, Fiaschi P, Garbossa D, Morra L. Deep learning-based algorithm for postoperative glioblastoma MRI segmentation: a promising new tool for tumor burden assessment. Brain Inform 2023; 10:26. [PMID: 37801128 PMCID: PMC10558414 DOI: 10.1186/s40708-023-00207-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 09/16/2023] [Indexed: 10/07/2023] Open
Abstract
OBJECTIVE Clinical and surgical decisions for glioblastoma patients depend on a tumor imaging-based evaluation. Artificial Intelligence (AI) can be applied to magnetic resonance imaging (MRI) assessment to support clinical practice, surgery planning and prognostic predictions. In a real-world context, the current obstacles for AI are low-quality imaging and postoperative reliability. The aim of this study is to train an automatic algorithm for glioblastoma segmentation on a clinical MRI dataset and to obtain reliable results both pre- and post-operatively. METHODS The dataset used for this study comprises 237 (71 preoperative and 166 postoperative) MRIs from 71 patients affected by a histologically confirmed Grade IV Glioma. The implemented U-Net architecture was trained by transfer learning to perform the segmentation task on postoperative MRIs. The training was carried out first on BraTS2021 dataset for preoperative segmentation. Performance is evaluated using DICE score (DS) and Hausdorff 95% (H95). RESULTS In preoperative scenario, overall DS is 91.09 (± 0.60) and H95 is 8.35 (± 1.12), considering tumor core, enhancing tumor and whole tumor (ET and edema). In postoperative context, overall DS is 72.31 (± 2.88) and H95 is 23.43 (± 7.24), considering resection cavity (RC), gross tumor volume (GTV) and whole tumor (WT). Remarkably, the RC segmentation obtained a mean DS of 63.52 (± 8.90) in postoperative MRIs. CONCLUSIONS The performances achieved by the algorithm are consistent with previous literature for both pre-operative and post-operative glioblastoma's MRI evaluation. Through the proposed algorithm, it is possible to reduce the impact of low-quality images and missing sequences.
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Affiliation(s)
- Andrea Bianconi
- Neurosurgery, Department of Neuroscience, University of Turin, via Cherasco 15, 10126, Turin, Italy.
| | | | - Marta Bonada
- Neurosurgery, Department of Neuroscience, University of Turin, via Cherasco 15, 10126, Turin, Italy
| | - Pietro Zeppa
- Neurosurgery, Department of Neuroscience, University of Turin, via Cherasco 15, 10126, Turin, Italy
| | - Elsa Nico
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA
| | - Raffaele De Marco
- Neurosurgery, Department of Neuroscience, University of Turin, via Cherasco 15, 10126, Turin, Italy
| | | | - Fabio Cofano
- Neurosurgery, Department of Neuroscience, University of Turin, via Cherasco 15, 10126, Turin, Italy
| | - Francesco Bruno
- Neurooncology, Department of Neuroscience, University of Turin, Turin, Italy
| | - Giovanni Morana
- Neuroradiology, Department of Neuroscience, University of Turin, Turin, Italy
| | - Antonio Melcarne
- Neurosurgery, Department of Neuroscience, University of Turin, via Cherasco 15, 10126, Turin, Italy
| | - Roberta Ruda
- Neurooncology, Department of Neuroscience, University of Turin, Turin, Italy
| | - Luca Mainardi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Pietro Fiaschi
- IRCCS Ospedale Policlinico S. Martino, Genoa, Italy
- Dipartimento di Neuroscienze, Riabilitazione, Oftalmologia, Genetica e Scienze Materno-Infantili, Univeristy of Genoa, Genoa, Italy
| | - Diego Garbossa
- Neurosurgery, Department of Neuroscience, University of Turin, via Cherasco 15, 10126, Turin, Italy
| | - Lia Morra
- Dipartimento di Automatica e Informatica, Politecnico di Torino, Turin, Italy
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35
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Kushol R, Parnianpour P, Wilman AH, Kalra S, Yang YH. Effects of MRI scanner manufacturers in classification tasks with deep learning models. Sci Rep 2023; 13:16791. [PMID: 37798392 PMCID: PMC10556074 DOI: 10.1038/s41598-023-43715-5] [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: 11/15/2022] [Accepted: 09/27/2023] [Indexed: 10/07/2023] Open
Abstract
Deep learning has become a leading subset of machine learning and has been successfully employed in diverse areas, ranging from natural language processing to medical image analysis. In medical imaging, researchers have progressively turned towards multi-center neuroimaging studies to address complex questions in neuroscience, leveraging larger sample sizes and aiming to enhance the accuracy of deep learning models. However, variations in image pixel/voxel characteristics can arise between centers due to factors including differences in magnetic resonance imaging scanners. Such variations create challenges, particularly inconsistent performance in machine learning-based approaches, often referred to as domain shift, where the trained models fail to achieve satisfactory or improved results when confronted with dissimilar test data. This study analyzes the performance of multiple disease classification tasks using multi-center MRI data obtained from three widely used scanner manufacturers (GE, Philips, and Siemens) across several deep learning-based networks. Furthermore, we investigate the efficacy of mitigating scanner vendor effects using ComBat-based harmonization techniques when applied to multi-center datasets of 3D structural MR images. Our experimental results reveal a substantial decline in classification performance when models trained on one type of scanner manufacturer are tested with data from different manufacturers. Moreover, despite applying ComBat-based harmonization, the harmonized images do not demonstrate any noticeable performance enhancement for disease classification tasks.
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Affiliation(s)
- Rafsanjany Kushol
- Department of Computing Science, University of Alberta, Edmonton, Canada.
| | - Pedram Parnianpour
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
| | - Alan H Wilman
- Departments of Radiology and Diagnostic Imaging and Biomedical Engineering, University of Alberta, Edmonton, Canada
| | - Sanjay Kalra
- Department of Computing Science, University of Alberta, Edmonton, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Canada
| | - Yee-Hong Yang
- Department of Computing Science, University of Alberta, Edmonton, Canada
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Cepeda S, García-García S, Arrese I, Herrero F, Escudero T, Zamora T, Sarabia R. The Río Hortega University Hospital Glioblastoma dataset: A comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM). Data Brief 2023; 50:109617. [PMID: 37808543 PMCID: PMC10551826 DOI: 10.1016/j.dib.2023.109617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 10/10/2023] Open
Abstract
Glioblastoma, a highly aggressive primary brain tumor, is associated with poor patient outcomes. Although magnetic resonance imaging (MRI) plays a critical role in diagnosing, characterizing, and forecasting glioblastoma progression, public MRI repositories present significant drawbacks, including insufficient postoperative and follow-up studies as well as expert tumor segmentations. To address these issues, we present the "Río Hortega University Hospital Glioblastoma Dataset (RHUH-GBM)," a collection of multiparametric MRI images, volumetric assessments, molecular data, and survival details for glioblastoma patients who underwent total or near-total enhancing tumor resection. The dataset features expert-corrected segmentations of tumor subregions, offering valuable ground truth data for developing algorithms for postoperative and follow-up MRI scans.
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Affiliation(s)
- Santiago Cepeda
- Department of Neurosurgery, Río Hortega University Hospital, Dulzaina 2, 47012 Valladolid, Spain
| | - Sergio García-García
- Department of Neurosurgery, Río Hortega University Hospital, Dulzaina 2, 47012 Valladolid, Spain
| | - Ignacio Arrese
- Department of Neurosurgery, Río Hortega University Hospital, Dulzaina 2, 47012 Valladolid, Spain
| | - Francisco Herrero
- Department of Radiology, Río Hortega University Hospital, Dulzaina 2, 47012 Valladolid, Spain
| | - Trinidad Escudero
- Department of Radiology, Río Hortega University Hospital, Dulzaina 2, 47012 Valladolid, Spain
| | - Tomás Zamora
- Department of Pathology, Río Hortega University Hospital, Dulzaina 2, 47012 Valladolid, Spain
| | - Rosario Sarabia
- Department of Neurosurgery, Río Hortega University Hospital, Dulzaina 2, 47012 Valladolid, Spain
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Alushaj E, Hemachandra D, Kuurstra A, Menon RS, Ganjavi H, Sharma M, Kashgari A, Barr J, Reisman W, Khan AR, MacDonald PA. Subregional analysis of striatum iron in Parkinson's disease and rapid eye movement sleep behaviour disorder. Neuroimage Clin 2023; 40:103519. [PMID: 37797434 PMCID: PMC10568416 DOI: 10.1016/j.nicl.2023.103519] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/24/2023] [Accepted: 09/26/2023] [Indexed: 10/07/2023]
Abstract
The loss of dopamine in the striatum underlies motor symptoms of Parkinson's disease (PD). Rapid eye movement sleep behaviour disorder (RBD) is considered prodromal PD and has shown similar neural changes in the striatum. Alterations in brain iron suggest neurodegeneration; however, the literature on striatal iron has been inconsistent in PD and scant in RBD. Toward clarifying pathophysiological changes in PD and RBD, and uncovering possible biomarkers, we imaged 26 early-stage PD patients, 16 RBD patients, and 39 age-matched healthy controls with 3 T MRI. We compared mean susceptibility using quantitative susceptibility mapping (QSM) in the standard striatum (caudate, putamen, and nucleus accumbens) and tractography-parcellated striatum. Diffusion MRI permitted parcellation of the striatum into seven subregions based on the cortical areas of maximal connectivity from the Tziortzi atlas. No significant differences in mean susceptibility were found in the standard striatum anatomy. For the parcellated striatum, the caudal motor subregion, the most affected region in PD, showed lower iron levels compared to healthy controls. Receiver operating characteristic curves using mean susceptibility in the caudal motor striatum showed a good diagnostic accuracy of 0.80 when classifying early-stage PD from healthy controls. This study highlights that tractography-based parcellation of the striatum could enhance sensitivity to changes in iron levels, which have not been consistent in the PD literature. The decreased caudal motor striatum iron was sufficiently sensitive to PD, but not RBD. QSM in the striatum could contribute to development of a multivariate or multimodal biomarker of early-stage PD, but further work in larger datasets is needed to confirm its utility in prodromal groups.
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Affiliation(s)
- Erind Alushaj
- Department of Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada; Western Institute for Neuroscience, Western University, London, Ontario, Canada
| | - Dimuthu Hemachandra
- Robarts Research Institute, Western University, London, Ontario, Canada; School of Biomedical Engineering, Western University, London, Ontario, Canada
| | - Alan Kuurstra
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Ravi S Menon
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Hooman Ganjavi
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - Manas Sharma
- Department of Radiology, Western University, London, Ontario, Canada; Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada
| | - Alia Kashgari
- Department of Medicine, Respirology Division, Western University, London, Ontario, Canada
| | - Jennifer Barr
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - William Reisman
- Department of Medicine, Respirology Division, Western University, London, Ontario, Canada
| | - Ali R Khan
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Penny A MacDonald
- Western Institute for Neuroscience, Western University, London, Ontario, Canada; Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada.
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Ciceri T, Squarcina L, Giubergia A, Bertoldo A, Brambilla P, Peruzzo D. Review on deep learning fetal brain segmentation from Magnetic Resonance images. Artif Intell Med 2023; 143:102608. [PMID: 37673558 DOI: 10.1016/j.artmed.2023.102608] [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: 12/21/2022] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 09/08/2023]
Abstract
Brain segmentation is often the first and most critical step in quantitative analysis of the brain for many clinical applications, including fetal imaging. Different aspects challenge the segmentation of the fetal brain in magnetic resonance imaging (MRI), such as the non-standard position of the fetus owing to his/her movements during the examination, rapid brain development, and the limited availability of imaging data. In recent years, several segmentation methods have been proposed for automatically partitioning the fetal brain from MR images. These algorithms aim to define regions of interest with different shapes and intensities, encompassing the entire brain, or isolating specific structures. Deep learning techniques, particularly convolutional neural networks (CNNs), have become a state-of-the-art approach in the field because they can provide reliable segmentation results over heterogeneous datasets. Here, we review the deep learning algorithms developed in the field of fetal brain segmentation and categorize them according to their target structures. Finally, we discuss the perceived research gaps in the literature of the fetal domain, suggesting possible future research directions that could impact the management of fetal MR images.
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Affiliation(s)
- Tommaso Ciceri
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; Department of Information Engineering, University of Padua, Padua, Italy
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alice Giubergia
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; Department of Information Engineering, University of Padua, Padua, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padua, Padua, Italy; University of Padua, Padova Neuroscience Center, Padua, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Denis Peruzzo
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
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Akinci D’Antonoli T, Todea RA, Leu N, Datta AN, Stieltjes B, Pruefer F, Wasserthal J. Development and Evaluation of Deep Learning Models for Automated Estimation of Myelin Maturation Using Pediatric Brain MRI Scans. Radiol Artif Intell 2023; 5:e220292. [PMID: 37795138 PMCID: PMC10546368 DOI: 10.1148/ryai.220292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 06/20/2023] [Accepted: 07/07/2023] [Indexed: 10/06/2023]
Abstract
Purpose To predict the corresponding age of myelin maturation from brain MRI scans in infants and young children by using a deep learning algorithm and to build upon previously published models. Materials and Methods Brain MRI scans acquired between January 1, 2011, and March 17, 2021, in our institution in patients aged 0-3 years were retrospectively retrieved from the archive. An ensemble of two-dimensional (2D) and three-dimensional (3D) convolutional neural network models was trained and internally validated in 710 patients to predict myelin maturation age on the basis of radiologist-generated labels. The model ensemble was tested on an internal dataset of 123 patients and two external datasets of 226 (0-25 months of age) and 383 (0-2 months of age) healthy children and infants, respectively. Mean absolute error (MAE) and Pearson correlation coefficients were used to assess model performance. Results The 2D, 3D, and 2D-plus-3D ensemble models showed MAE values of 1.43, 2.55, and 1.77 months, respectively, on the internal test set, values of 2.26, 2.27, and 1.22 months on the first external test set, and values of 0.44, 0.27, and 0.31 months on the second external test set. The ensemble model outperformed the previous state-of-the-art model on the same external test set (MAE = 1.22 vs 2.09 months). Conclusion The proposed deep learning model accurately predicted myelin maturation age using pediatric brain MRI scans and may help reduce the time needed to complete this task, as well as interobserver variability in radiologist predictions.Keywords: Pediatrics, MR Imaging, CNS, Brain/Brain Stem, Convolutional Neural Network (CNN), Artificial Intelligence, Pediatric Imaging, Myelin Maturation, Brain MRI, Neuroradiology Supplemental material is available for this article. © RSNA, 2023.
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Affiliation(s)
- Tugba Akinci D’Antonoli
- From the Department of Pediatric Radiology (T.A.D., N.L., F.P.) and
Department of Pediatric Neurology and Developmental Medicine (A.N.D.),
University Children’s Hospital Basel, Spitalstrasse 33, 4056 Basel,
Switzerland; Institute of Radiology and Nuclear Medicine, Cantonal Hospital
Basel, Basel, Switzerland (T.A.D.); and Department of Neuroradiology, Clinic of
Radiology and Nuclear Medicine (R.A.T.) and Department of Research and Analysis,
Clinic of Radiology and Nuclear Medicine (B.S., J.W.), University Hospital
Basel, Basel, Switzerland
| | - Ramona-Alexandra Todea
- From the Department of Pediatric Radiology (T.A.D., N.L., F.P.) and
Department of Pediatric Neurology and Developmental Medicine (A.N.D.),
University Children’s Hospital Basel, Spitalstrasse 33, 4056 Basel,
Switzerland; Institute of Radiology and Nuclear Medicine, Cantonal Hospital
Basel, Basel, Switzerland (T.A.D.); and Department of Neuroradiology, Clinic of
Radiology and Nuclear Medicine (R.A.T.) and Department of Research and Analysis,
Clinic of Radiology and Nuclear Medicine (B.S., J.W.), University Hospital
Basel, Basel, Switzerland
| | - Nora Leu
- From the Department of Pediatric Radiology (T.A.D., N.L., F.P.) and
Department of Pediatric Neurology and Developmental Medicine (A.N.D.),
University Children’s Hospital Basel, Spitalstrasse 33, 4056 Basel,
Switzerland; Institute of Radiology and Nuclear Medicine, Cantonal Hospital
Basel, Basel, Switzerland (T.A.D.); and Department of Neuroradiology, Clinic of
Radiology and Nuclear Medicine (R.A.T.) and Department of Research and Analysis,
Clinic of Radiology and Nuclear Medicine (B.S., J.W.), University Hospital
Basel, Basel, Switzerland
| | - Alexandre N. Datta
- From the Department of Pediatric Radiology (T.A.D., N.L., F.P.) and
Department of Pediatric Neurology and Developmental Medicine (A.N.D.),
University Children’s Hospital Basel, Spitalstrasse 33, 4056 Basel,
Switzerland; Institute of Radiology and Nuclear Medicine, Cantonal Hospital
Basel, Basel, Switzerland (T.A.D.); and Department of Neuroradiology, Clinic of
Radiology and Nuclear Medicine (R.A.T.) and Department of Research and Analysis,
Clinic of Radiology and Nuclear Medicine (B.S., J.W.), University Hospital
Basel, Basel, Switzerland
| | - Bram Stieltjes
- From the Department of Pediatric Radiology (T.A.D., N.L., F.P.) and
Department of Pediatric Neurology and Developmental Medicine (A.N.D.),
University Children’s Hospital Basel, Spitalstrasse 33, 4056 Basel,
Switzerland; Institute of Radiology and Nuclear Medicine, Cantonal Hospital
Basel, Basel, Switzerland (T.A.D.); and Department of Neuroradiology, Clinic of
Radiology and Nuclear Medicine (R.A.T.) and Department of Research and Analysis,
Clinic of Radiology and Nuclear Medicine (B.S., J.W.), University Hospital
Basel, Basel, Switzerland
| | - Friederike Pruefer
- From the Department of Pediatric Radiology (T.A.D., N.L., F.P.) and
Department of Pediatric Neurology and Developmental Medicine (A.N.D.),
University Children’s Hospital Basel, Spitalstrasse 33, 4056 Basel,
Switzerland; Institute of Radiology and Nuclear Medicine, Cantonal Hospital
Basel, Basel, Switzerland (T.A.D.); and Department of Neuroradiology, Clinic of
Radiology and Nuclear Medicine (R.A.T.) and Department of Research and Analysis,
Clinic of Radiology and Nuclear Medicine (B.S., J.W.), University Hospital
Basel, Basel, Switzerland
| | - Jakob Wasserthal
- From the Department of Pediatric Radiology (T.A.D., N.L., F.P.) and
Department of Pediatric Neurology and Developmental Medicine (A.N.D.),
University Children’s Hospital Basel, Spitalstrasse 33, 4056 Basel,
Switzerland; Institute of Radiology and Nuclear Medicine, Cantonal Hospital
Basel, Basel, Switzerland (T.A.D.); and Department of Neuroradiology, Clinic of
Radiology and Nuclear Medicine (R.A.T.) and Department of Research and Analysis,
Clinic of Radiology and Nuclear Medicine (B.S., J.W.), University Hospital
Basel, Basel, Switzerland
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Azam H, Tariq H, Shehzad D, Akbar S, Shah H, Khan ZA. Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks. Brain Sci 2023; 13:1255. [PMID: 37759856 PMCID: PMC10526767 DOI: 10.3390/brainsci13091255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 08/09/2023] [Accepted: 08/21/2023] [Indexed: 09/29/2023] Open
Abstract
This research comprises experiments with a deep learning framework for fully automating the skull stripping from brain magnetic resonance (MR) images. Conventional techniques for segmentation have progressed to the extent of Convolutional Neural Networks (CNN). We proposed and experimented with a contemporary variant of the deep learning framework based on mask region convolutional neural network (Mask-RCNN) for all anatomical orientations of brain MR images. We trained the system from scratch to build a model for classification, detection, and segmentation. It is validated by images taken from three different datasets: BrainWeb; NAMIC, and a local hospital. We opted for purposive sampling to select 2000 images of T1 modality from data volumes followed by a multi-stage random sampling technique to segregate the dataset into three batches for training (75%), validation (15%), and testing (10%) respectively. We utilized a robust backbone architecture, namely ResNet-101 and Functional Pyramid Network (FPN), to achieve optimal performance with higher accuracy. We subjected the same data to two traditional methods, namely Brain Extraction Tools (BET) and Brain Surface Extraction (BSE), to compare their performance results. Our proposed method had higher mean average precision (mAP) = 93% and content validity index (CVI) = 0.95%, which were better than comparable methods. We contributed by training Mask-RCNN from scratch for generating reusable learning weights known as transfer learning. We contributed to methodological novelty by applying a pragmatic research lens, and used a mixed method triangulation technique to validate results on all anatomical modalities of brain MR images. Our proposed method improved the accuracy and precision of skull stripping by fully automating it and reducing its processing time and operational cost and reliance on technicians. This research study has also provided grounds for extending the work to the scale of explainable artificial intelligence (XAI).
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Affiliation(s)
- Humera Azam
- Department of Computer Science, University of Karachi, Karachi 75270, Pakistan
| | - Humera Tariq
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Danish Shehzad
- Department of Computer Science, The Superior University, Lahore 54590, Pakistan
| | - Saad Akbar
- College of Computing and Information Sciences, Karachi Institute of Economics and Technology, Karachi 75190, Pakistan;
| | - Habib Shah
- Department of Computer Science, College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia;
| | - Zamin Ali Khan
- Department of Computer Science, IQRA University, Karachi 71500, Pakistan;
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Mark IT, Wren-Jarvis J, Xiao J, Cai LT, Parekh S, Bourla I, Lazerwitz MC, Rowe MA, Marco EJ, Mukherjee P. Neurite orientation dispersion and density imaging of white matter microstructure in sensory processing dysfunction with versus without comorbid ADHD. Front Neurosci 2023; 17:1136424. [PMID: 37492404 PMCID: PMC10363610 DOI: 10.3389/fnins.2023.1136424] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 06/19/2023] [Indexed: 07/27/2023] Open
Abstract
Introduction Sensory Processing Dysfunction (SPD) is common yet understudied, affecting up to one in six children with 40% experiencing co-occurring challenges with attention. The neural architecture of SPD with Attention Deficit and Hyperactivity Disorder (ADHD) (SPD+ADHD) versus SPD without ADHD (SPD-ADHD) has yet to be explored in diffusion tensor imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI) has yet to be examined. Methods The present study computed DTI and NODDI biophysical model parameter maps of one hundred children with SPD. Global, regional and voxel-level white matter tract measures were analyzed and compared between SPD+ADHD and SPD-ADHD groups. Results SPD+ADHD children had global WM Fractional Anisotropy (FA) and Neurite Density Index (NDI) that trended lower than SPD-ADHD children, primarily in boys only. Data-driven voxelwise and WM tract-based analysis revealed statistically significant decreases of NDI in boys with SPD+ADHD compared to those with SPD-ADHD, primarily in projection tracts of the internal capsule and commissural fibers of the splenium of the corpus callosum. Conclusion We conclude that WM microstructure is more delayed/disrupted in boys with SPD+ADHD compared to SPD-ADHD, with NODDI showing a larger effect than DTI. This may represent the combined WM pathology of SPD and ADHD, or it may result from a greater degree of SPD WM pathology causing the development of ADHD.
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Affiliation(s)
- Ian T. Mark
- Department of Radiology and Biomedical Imaging, University of California – San Francisco, San Francisco, CA, United States
| | - Jamie Wren-Jarvis
- Department of Radiology and Biomedical Imaging, University of California – San Francisco, San Francisco, CA, United States
| | - Jaclyn Xiao
- Department of Radiology and Biomedical Imaging, University of California – San Francisco, San Francisco, CA, United States
| | - Lanya T. Cai
- Department of Radiology and Biomedical Imaging, University of California – San Francisco, San Francisco, CA, United States
| | - Shalin Parekh
- Department of Radiology and Biomedical Imaging, University of California – San Francisco, San Francisco, CA, United States
| | - Ioanna Bourla
- Department of Radiology and Biomedical Imaging, University of California – San Francisco, San Francisco, CA, United States
| | - Maia C. Lazerwitz
- Department of Radiology and Biomedical Imaging, University of California – San Francisco, San Francisco, CA, United States
- Cortica Healthcare, San Rafael, CA, United States
| | - Mikaela A. Rowe
- Department of Radiology and Biomedical Imaging, University of California – San Francisco, San Francisco, CA, United States
- Cortica Healthcare, San Rafael, CA, United States
| | | | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California – San Francisco, San Francisco, CA, United States
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Taylor PA, Glen DR, Reynolds RC, Basavaraj A, Moraczewski D, Etzel JA. Editorial: Demonstrating quality control (QC) procedures in fMRI. Front Neurosci 2023; 17:1205928. [PMID: 37325035 PMCID: PMC10264898 DOI: 10.3389/fnins.2023.1205928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 05/12/2023] [Indexed: 06/17/2023] Open
Affiliation(s)
- Paul A. Taylor
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Daniel R. Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Richard C. Reynolds
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Arshitha Basavaraj
- Data Science and Sharing Team, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Dustin Moraczewski
- Data Science and Sharing Team, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Joset A. Etzel
- Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, United States
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Peter Binding L, Neal Taylor P, O'Keeffe AG, Giampiccolo D, Fleury M, Xiao F, Caciagli L, de Tisi J, Winston GP, Miserocchi A, McEvoy A, Duncan JS, Vos SB. The impact of temporal lobe epilepsy surgery on picture naming and its relationship to network metric change. Neuroimage Clin 2023; 38:103444. [PMID: 37300974 PMCID: PMC10300575 DOI: 10.1016/j.nicl.2023.103444] [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: 03/08/2023] [Revised: 05/04/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Anterior temporal lobe resection (ATLR) is a successful treatment for medically-refractory temporal lobe epilepsy (TLE). In the language-dominant hemisphere, 30%- 50% of individuals experience a naming decline which can impact upon daily life. Measures of structural networks are associated with language performance pre-operatively. It is unclear if analysis of network measures may predict post-operative decline. METHODS White matter fibre tractography was performed on preoperative diffusion MRI of 44 left lateralised and left resection individuals with TLE to reconstruct the preoperative structural network. Resection masks, drawn on co-registered pre- and post-operative T1-weighted MRI scans, were used as exclusion regions on pre-operative tractography to estimate the post-operative network. Changes in graph theory metrics, cortical strength, betweenness centrality, and clustering coefficient were generated by comparing the estimated pre- and post-operative networks. These were thresholded based on the presence of the connection in each patient, ranging from 75% to 100% in steps of 5%. The average graph theory metric across thresholds was taken. We incorporated leave-one-out cross-validation with smoothly clipped absolute deviation (SCAD) least absolute shrinkage and selection operator (LASSO) feature selection and a support vector classifier to assess graph theory metrics on picture naming decline. Picture naming was assessed via the Graded Naming Test preoperatively and at 3 and 12 months post-operatively and the outcome was classified using the reliable change index (RCI) to identify clinically significant decline. The best feature combination and model was selected using the area under the curve (AUC). The sensitivity, specificity and F1-score were also reported. Permutation testing was performed to assess the machine learning model and selected regions difference significance. RESULTS A combination of clinical and graph theory metrics were able to classify outcome of picture naming at 3 months with an AUC of 0.84. At 12 months, change in strength to cortical regions was best able to correctly classify outcome with an AUC of 0.86. Longitudinal analysis revealed that betweenness centrality was the best metric to identify patients who declined at 3 months, who will then continue to experience decline from 3 to 12 months. Both models were significantly higher AUC values than a random classifier. CONCLUSION Our results suggest that inferred changes of network integrity were able to correctly classify picture naming decline after ATLR. These measures may be used to prospectively to identify patients who are at risk of picture naming decline after surgery and could potentially be utilised to assist tailoring the resection in order to prevent this decline.
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Affiliation(s)
- Lawrence Peter Binding
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom.
| | - Peter Neal Taylor
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; CNNP lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, United Kingdom
| | - Aidan G O'Keeffe
- School of Mathematical Sciences, University of Nottingham, United Kingdom; Institute of Epidemiology and Healthcare, UCL, London WC1E 6BT, United Kingdom
| | - Davide Giampiccolo
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom; Department of Neurosurgery, Institute of Neurosciences, Cleveland Clinic London, United Kingdom
| | - Marine Fleury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Fenglai Xiao
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom
| | - Lorenzo Caciagli
- MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Jane de Tisi
- MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine, Division of Neurology, Queens University, Kingston, Canada
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
| | - Andrew McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom
| | - Sjoerd B Vos
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, United Kingdom; Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
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Mohamad Mostafa A, El-Meligy MA, Abdullah Alkhayyal M, Alnuaim A, Sharaf M. A framework for brain tumor detection based on segmentation and features fusion using MRI images. Brain Res 2023; 1806:148300. [PMID: 36842569 DOI: 10.1016/j.brainres.2023.148300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/17/2023] [Accepted: 02/21/2023] [Indexed: 02/26/2023]
Abstract
Irregular growth of cells in the skull is recognized as a brain tumor that can have two types such as benign and malignant. There exist various methods which are used by oncologists to assess the existence of brain tumors such as blood tests or visual assessments. Moreover, the noninvasive magnetic resonance imaging (MRI) technique without ionizing radiation has been commonly utilized for diagnosis. However, the segmentation in 3-dimensional MRI is time-consuming and the outcomes mainly depend on the operator's experience. Therefore, a novel and robust automated brain tumor detector has been suggested based on segmentation and fusion of features. To improve the localization results, we pre-processed the images using Gaussian Filter (GF), and SynthStrip: a tool for brain skull stripping. We utilized two known benchmarks for training and testing i.e., Figshare and Harvard. The proposed methodology attained 99.8% accuracy, 99.3% recall, 99.4% precision, 99.5% F1 score, and 0.989 AUC. We performed the comparative analysis of our approach with prevailing DL, classical, and segmentation-based approaches. Additionally, we also performed the cross-validation using Harvard dataset attaining 99.3% identification accuracy. The outcomes exhibit that our approach offers significant outcomes than existing methods and outperforms them.
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Affiliation(s)
- Almetwally Mohamad Mostafa
- Department of Information Systems, College of Computer and Information Sciences, King Saud University, P.O. BOX 51178, Riyadh 11543, Saudi Arabia.
| | - Mohammed A El-Meligy
- Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia.
| | - Maram Abdullah Alkhayyal
- Department of Information Systems, College of Computer and Information Sciences, King Saud University, P.O. BOX 51178, Riyadh 11543, Saudi Arabia.
| | - Abeer Alnuaim
- Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, P.O. BOX 22459, Riyadh 11495, Saudi Arabia.
| | - Mohamed Sharaf
- Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia.
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Zhang H, Zhuang Y, Xia S, Jiang H. Deep Learning Network with Spatial Attention Module for Detecting Acute Bilirubin Encephalopathy in Newborns Based on Multimodal MRI. Diagnostics (Basel) 2023; 13:diagnostics13091577. [PMID: 37174968 PMCID: PMC10178403 DOI: 10.3390/diagnostics13091577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 04/25/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
BACKGROUND Acute bilirubin encephalopathy (ABE) is a significant cause of neonatal mortality and disability. Early detection and treatment of ABE can prevent the further development of ABE and its long-term complications. Due to the limited classification ability of single-modal magnetic resonance imaging (MRI), this study aimed to validate the classification performance of a new deep learning model based on multimodal MRI images. Additionally, the study evaluated the effect of a spatial attention module (SAM) on improving the model's diagnostic performance in distinguishing ABE. METHODS This study enrolled a total of 97 neonates diagnosed with ABE and 80 neonates diagnosed with hyperbilirubinemia (HB, non-ABE). Each patient underwent three types of multimodal imaging, which included T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and an apparent diffusion coefficient (ADC) map. A multimodal MRI classification model based on the ResNet18 network with spatial attention modules was built to distinguish ABE from non-ABE. All combinations of the three types of images were used as inputs to test the model's classification performance, and we also analyzed the prediction performance of models with SAMs through comparative experiments. RESULTS The results indicated that the diagnostic performance of the multimodal image combination was better than any single-modal image, and the combination of T1WI and T2WI achieved the best classification performance (accuracy = 0.808 ± 0.069, area under the curve = 0.808 ± 0.057). The ADC images performed the worst among the three modalities' images. Adding spatial attention modules significantly improved the model's classification performance. CONCLUSION Our experiment showed that a multimodal image classification network with spatial attention modules significantly improved the accuracy of ABE classification.
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Affiliation(s)
- Huan Zhang
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China
| | - Yi Zhuang
- Department of Radiology, Affiliated Children's Hospital of Jiangnan University, Wuxi 214036, China
| | - Shunren Xia
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China
| | - Haoxiang Jiang
- Department of Radiology, Affiliated Children's Hospital of Jiangnan University, Wuxi 214036, China
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Iglesias JE. A ready-to-use machine learning tool for symmetric multi-modality registration of brain MRI. Sci Rep 2023; 13:6657. [PMID: 37095168 PMCID: PMC10126156 DOI: 10.1038/s41598-023-33781-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/19/2023] [Indexed: 04/26/2023] Open
Abstract
Volumetric registration of brain MRI is routinely used in human neuroimaging, e.g., to align different MRI modalities, to measure change in longitudinal analysis, to map an individual to a template, or in registration-based segmentation. Classical registration techniques based on numerical optimization have been very successful in this domain, and are implemented in widespread software suites like ANTs, Elastix, NiftyReg, or DARTEL. Over the last 7-8 years, learning-based techniques have emerged, which have a number of advantages like high computational efficiency, potential for higher accuracy, easy integration of supervision, and the ability to be part of a meta-architectures. However, their adoption in neuroimaging pipelines has so far been almost inexistent. Reasons include: lack of robustness to changes in MRI modality and resolution; lack of robust affine registration modules; lack of (guaranteed) symmetry; and, at a more practical level, the requirement of deep learning expertise that may be lacking at neuroimaging research sites. Here, we present EasyReg, an open-source, learning-based registration tool that can be easily used from the command line without any deep learning expertise or specific hardware. EasyReg combines the features of classical registration tools, the capabilities of modern deep learning methods, and the robustness to changes in MRI modality and resolution provided by our recent work in domain randomization. As a result, EasyReg is: fast; symmetric; diffeomorphic (and thus invertible); agnostic to MRI modality and resolution; compatible with affine and nonlinear registration; and does not require any preprocessing or parameter tuning. We present results on challenging registration tasks, showing that EasyReg is as accurate as classical methods when registering 1 mm isotropic scans within MRI modality, but much more accurate across modalities and resolutions. EasyReg is publicly available as part of FreeSurfer; see https://surfer.nmr.mgh.harvard.edu/fswiki/EasyReg .
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Affiliation(s)
- Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, 02129, USA.
- Department of Medical Physics and Biomedical Engineering, University College London, London, WC1V 6LJ, UK.
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, 02139, USA.
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Clunie DA, Flanders A, Taylor A, Erickson B, Bialecki B, Brundage D, Gutman D, Prior F, Seibert JA, Perry J, Gichoya JW, Kirby J, Andriole K, Geneslaw L, Moore S, Fitzgerald TJ, Tellis W, Xiao Y, Farahani K, Luo J, Rosenthal A, Kandarpa K, Rosen R, Goetz K, Babcock D, Xu B, Hsiao J. Report of the Medical Image De-Identification (MIDI) Task Group - Best Practices and Recommendations. ARXIV 2023:arXiv:2303.10473v2. [PMID: 37033463 PMCID: PMC10081345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Affiliation(s)
| | | | | | | | | | | | | | - Fred Prior
- University of Arkansas for Medical Sciences
| | | | | | | | - Justin Kirby
- Frederick National Laboratory for Cancer Research
| | | | | | | | | | | | - Ying Xiao
- University of Pennsylvania Health System
| | | | - James Luo
- National Heart, Lung, and Blood Institute (NHLBI)
| | - Alex Rosenthal
- National Institute of Allergy and Infectious Diseases (NIAID)
| | - Kris Kandarpa
- National Institute of Biomedical Imaging and Bioengineering (NIBIB)
| | - Rebecca Rosen
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
| | | | - Debra Babcock
- National Institute of Neurological Disorders and Stroke (NINDS)
| | - Ben Xu
- National Institute on Alcohol Abuse and Alcoholism (NIAAA)
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Zhang J, Treyer V, Sun J, Zhang C, Gietl A, Hock C, Razansky D, Nitsch RM, Ni R. Automatic analysis of skull thickness, scalp-to-cortex distance and association with age and sex in cognitively normal elderly. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.19.524484. [PMID: 36711717 PMCID: PMC9882276 DOI: 10.1101/2023.01.19.524484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Personalized neurostimulation has been a potential treatment for many brain diseases, which requires insights into brain/skull geometry. Here, we developed an open source efficient pipeline BrainCalculator for automatically computing the skull thickness map, scalp-to-cortex distance (SCD), and brain volume based on T 1 -weighted magnetic resonance imaging (MRI) data. We examined the influence of age and sex cross-sectionally in 407 cognitively normal older adults (71.9±8.0 years, 60.2% female) from the ADNI. We demonstrated the compatibility of our pipeline with commonly used preprocessing packages and found that BrainSuite Skullfinder was better suited for such automatic analysis compared to FSL Brain Extraction Tool 2 and SPM12- based unified segmentation using ground truth. We found that the sphenoid bone and temporal bone were thinnest among the skull regions in both females and males. There was no increase in regional minimum skull thickness with age except in the female sphenoid bone. No sex difference in minimum skull thickness or SCD was observed. Positive correlations between age and SCD were observed, faster in females (0.307%/y) than males (0.216%/y) in temporal SCD. A negative correlation was observed between age and whole brain volume computed based on brain surface (females -1.031%/y, males -0.998%/y). In conclusion, we developed an automatic pipeline for MR-based skull thickness map, SCD, and brain volume analysis and demonstrated the sex-dependent association between minimum regional skull thickness, SCD and brain volume with age. This pipeline might be useful for personalized neurostimulation planning.
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Affiliation(s)
- Junhao Zhang
- Institute for Regenerative Medicine, University of Zurich, 8952 Zurich, Switzerland
- Institute for Biomedical Engineering, ETH Zurich & University of Zurich, 8093 Zurich, Switzerland
| | - Valerie Treyer
- Institute for Regenerative Medicine, University of Zurich, 8952 Zurich, Switzerland
- Department of Nuclear Medicine, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Junfeng Sun
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chencheng Zhang
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Anton Gietl
- Institute for Regenerative Medicine, University of Zurich, 8952 Zurich, Switzerland
| | - Christoph Hock
- Institute for Regenerative Medicine, University of Zurich, 8952 Zurich, Switzerland
- Neurimmune, Schlieren, Switzerland
| | - Daniel Razansky
- Institute for Biomedical Engineering, ETH Zurich & University of Zurich, 8093 Zurich, Switzerland
| | - Roger M Nitsch
- Institute for Regenerative Medicine, University of Zurich, 8952 Zurich, Switzerland
- Neurimmune, Schlieren, Switzerland
| | - Ruiqing Ni
- Institute for Regenerative Medicine, University of Zurich, 8952 Zurich, Switzerland
- Institute for Biomedical Engineering, ETH Zurich & University of Zurich, 8093 Zurich, Switzerland
- Zentrum für Neurowissenschaften Zurich, Zurich, Switzerland
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Provins C, MacNicol E, Seeley SH, Hagmann P, Esteban O. Quality control in functional MRI studies with MRIQC and fMRIPrep. FRONTIERS IN NEUROIMAGING 2023; 1:1073734. [PMID: 37555175 PMCID: PMC10406249 DOI: 10.3389/fnimg.2022.1073734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 12/19/2022] [Indexed: 08/10/2023]
Abstract
The implementation of adequate quality assessment (QA) and quality control (QC) protocols within the magnetic resonance imaging (MRI) research workflow is resource- and time-consuming and even more so is their execution. As a result, QA/QC practices highly vary across laboratories and "MRI schools", ranging from highly specialized knowledge spots to environments where QA/QC is considered overly onerous and costly despite evidence showing that below-standard data increase the false positive and false negative rates of the final results. Here, we demonstrate a protocol based on the visual assessment of images one-by-one with reports generated by MRIQC and fMRIPrep, for the QC of data in functional (blood-oxygen dependent-level; BOLD) MRI analyses. We particularize the proposed, open-ended scope of application to whole-brain voxel-wise analyses of BOLD to correspondingly enumerate and define the exclusion criteria applied at the QC checkpoints. We apply our protocol on a composite dataset (n = 181 subjects) drawn from open fMRI studies, resulting in the exclusion of 97% of the data (176 subjects). This high exclusion rate was expected because subjects were selected to showcase artifacts. We describe the artifacts and defects more commonly found in the dataset that justified exclusion. We moreover release all the materials we generated in this assessment and document all the QC decisions with the expectation of contributing to the standardization of these procedures and engaging in the discussion of QA/QC by the community.
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Affiliation(s)
- Céline Provins
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Eilidh MacNicol
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Saren H. Seeley
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Oscar Esteban
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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50
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Praveenkumar S, Kalaiselvi T, Somasundaram K. Methods of Brain Extraction from Magnetic Resonance Images of Human Head: A Review. Crit Rev Biomed Eng 2023; 51:1-40. [PMID: 37581349 DOI: 10.1615/critrevbiomedeng.2023047606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Abstract
Medical images are providing vital information to aid physicians in diagnosing a disease afflicting the organ of a human body. Magnetic resonance imaging is an important imaging modality in capturing the soft tissues of the brain. Segmenting and extracting the brain is essential in studying the structure and pathological condition of brain. There are several methods that are developed for this purpose. Researchers in brain extraction or segmentation need to know the current status of the work that have been done. Such an information is also important for improving the existing method to get more accurate results or to reduce the complexity of the algorithm. In this paper we review the classical methods and convolutional neural network-based deep learning brain extraction methods.
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Affiliation(s)
| | - T Kalaiselvi
- Department of Computer Science and Applications, Gandhigram Rural Institute, Gandhigram 624302, Tamil Nadu, India
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