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Zhao X, Wen Z. Super-resolution of diffusion-weighted images using space-customized learning model. Technol Health Care 2024; 32:423-435. [PMID: 38759065 PMCID: PMC11191469 DOI: 10.3233/thc-248037] [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: 05/19/2024]
Abstract
BACKGROUND Diffusion-weighted imaging (DWI) is a noninvasive method used for investigating the microstructural properties of the brain. However, a tradeoff exists between resolution and scanning time in clinical practice. Super-resolution has been employed to enhance spatial resolution in natural images, but its application on high-dimensional and non-Euclidean DWI remains challenging. OBJECTIVE This study aimed to develop an end-to-end deep learning network for enhancing the spatial resolution of DWI through post-processing. METHODS We proposed a space-customized deep learning approach that leveraged convolutional neural networks (CNNs) for the grid structural domain (x-space) and graph CNNs (GCNNs) for the diffusion gradient domain (q-space). Moreover, we represented the output of CNN as a graph using correlations defined by a Gaussian kernel in q-space to bridge the gap between CNN and GCNN feature formats. RESULTS Our model was evaluated on the Human Connectome Project, demonstrating the effective improvement of DWI quality using our proposed method. Extended experiments also highlighted its advantages in downstream tasks. CONCLUSION The hybrid convolutional neural network exhibited distinct advantages in enhancing the spatial resolution of DWI scans for the feature learning of heterogeneous spatial data.
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Affiliation(s)
- Xitong Zhao
- Department of Mathematics, College of Sciences, Shanghai University, Shanghai, China
| | - Zhijie Wen
- Department of Mathematics, College of Sciences, Shanghai University, Shanghai, China
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2
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Jha RR, Kumar BR, Pathak SK, Bhavsar A, Nigam A. TrGANet: Transforming 3T to 7T dMRI using Trapezoidal Rule and Graph based Attention Modules. Med Image Anal 2023; 87:102806. [PMID: 37030056 DOI: 10.1016/j.media.2023.102806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 01/12/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
Diffusion MRI (dMRI) is a non-invasive tool for assessing the white matter region of the brain by approximating the fiber streamlines, structural connectivity, and estimation of microstructure. This modality can yield useful information for diagnosing several mental diseases as well as for surgical planning. The higher angular resolution diffusion imaging (HARDI) technique is helpful in obtaining more robust fiber tracts by getting a good approximation of regions where fibers cross. Moreover, HARDI is more sensitive to tissue changes and can accurately represent anatomical details in the human brain at higher magnetic strengths. In other words, magnetic strengths affect the quality of the image, and hence high magnetic strength has good tissue contrast with better spatial resolution. However, a higher magnetic strength scanner (like 7T) is costly and unaffordable to most hospitals. Hence, in this work, we have proposed a novel CNN architecture for the transformation of 3T to 7T dMRI. Additionally, we have also reconstructed the multi-shell multi-tissue fiber orientation distribution function (MSMT fODF) at 7T from single-shell 3T. The proposed architecture consists of a CNN-based ODE solver utilizing the Trapezoidal rule and graph-based attention layer alongwith L1 and total variation loss. Finally, the model has been validated on the HCP data set quantitatively and qualitatively.
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Dalmaz O, Yurt M, Cukur T. ResViT: Residual Vision Transformers for Multimodal Medical Image Synthesis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2598-2614. [PMID: 35436184 DOI: 10.1109/tmi.2022.3167808] [Citation(s) in RCA: 67] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Generative adversarial models with convolutional neural network (CNN) backbones have recently been established as state-of-the-art in numerous medical image synthesis tasks. However, CNNs are designed to perform local processing with compact filters, and this inductive bias compromises learning of contextual features. Here, we propose a novel generative adversarial approach for medical image synthesis, ResViT, that leverages the contextual sensitivity of vision transformers along with the precision of convolution operators and realism of adversarial learning. ResViT's generator employs a central bottleneck comprising novel aggregated residual transformer (ART) blocks that synergistically combine residual convolutional and transformer modules. Residual connections in ART blocks promote diversity in captured representations, while a channel compression module distills task-relevant information. A weight sharing strategy is introduced among ART blocks to mitigate computational burden. A unified implementation is introduced to avoid the need to rebuild separate synthesis models for varying source-target modality configurations. Comprehensive demonstrations are performed for synthesizing missing sequences in multi-contrast MRI, and CT images from MRI. Our results indicate superiority of ResViT against competing CNN- and transformer-based methods in terms of qualitative observations and quantitative metrics.
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Elsaid NMH, Coupé P, Saykin AJ, Wu YC. Structural connectivity mapping in human hippocampal-subfields using super-resolution hybrid diffusion imaging: a feasibility study. Neuroradiology 2022; 64:1989-2000. [PMID: 35556149 PMCID: PMC9474597 DOI: 10.1007/s00234-022-02968-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: 01/02/2022] [Accepted: 04/27/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE The goal of the current study was to introduce a new methodology that holds a promise to be used in hippocampus-aging studies using sub-millimeter super-resolution hybrid diffusion imaging (HYDI) MRI. METHODS HYDI diffusion data were acquired in two groups of older and younger healthy participants recruited from the Indiana Alzheimer's Disease Research Center and community. These data were then transformed into super-resolution diffusion images before the hippocampal subfield analyses. We studied the correlation between the subjects' age and the structural connectivity involving the hippocampal subfields and the connectivity between the whole hippocampus and the cerebral cortex. RESULTS Structural integrity derived from the tractography streamlines between the hippocampal subfields was reduced in older than younger adults. CONCLUSION The findings offered a new promising framework, and they opened avenues for future studies to explore the relationship between the structural connectivity in the hippocampal area and different types of dementia.
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Affiliation(s)
- Nahla M H Elsaid
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Pierrick Coupé
- CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, PICTURA, Talence, F-33400, France
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
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Kebiri H, Canales-Rodríguez EJ, Lajous H, de Dumast P, Girard G, Alemán-Gómez Y, Koob M, Jakab A, Bach Cuadra M. Through-Plane Super-Resolution With Autoencoders in Diffusion Magnetic Resonance Imaging of the Developing Human Brain. Front Neurol 2022; 13:827816. [PMID: 35585848 PMCID: PMC9109939 DOI: 10.3389/fneur.2022.827816] [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: 12/02/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Fetal brain diffusion magnetic resonance images (MRI) are often acquired with a lower through-plane than in-plane resolution. This anisotropy is often overcome by classical upsampling methods such as linear or cubic interpolation. In this work, we employ an unsupervised learning algorithm using an autoencoder neural network for single-image through-plane super-resolution by leveraging a large amount of data. Our framework, which can also be used for slice outliers replacement, overperformed conventional interpolations quantitatively and qualitatively on pre-term newborns of the developing Human Connectome Project. The evaluation was performed on both the original diffusion-weighted signal and the estimated diffusion tensor maps. A byproduct of our autoencoder was its ability to act as a denoiser. The network was able to generalize fetal data with different levels of motions and we qualitatively showed its consistency, hence supporting the relevance of pre-term datasets to improve the processing of fetal brain images.
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Affiliation(s)
- Hamza Kebiri
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Erick J. Canales-Rodríguez
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Hélène Lajous
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Priscille de Dumast
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Gabriel Girard
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Yasser Alemán-Gómez
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Mériam Koob
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - András Jakab
- Center for MR Research University Children's Hospital Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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Kang L, Liu G, Huang J, Li J. Super-resolution method for MR images based on multi-resolution CNN. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103372] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Lipin M, Bennett J, Ying GS, Yu Y, Ashtari M. Improving the Quantification of the Lateral Geniculate Nucleus in Magnetic Resonance Imaging Using a Novel 3D-Edge Enhancement Technique. Front Comput Neurosci 2021; 15:708866. [PMID: 34924983 PMCID: PMC8677828 DOI: 10.3389/fncom.2021.708866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 11/02/2021] [Indexed: 11/13/2022] Open
Abstract
The lateral geniculate nucleus (LGN) is a small, inhomogeneous structure that relays major sensory inputs from the retina to the visual cortex. LGN morphology has been intensively studied due to various retinal diseases, as well as in the context of normal brain development. However, many of the methods used for LGN structural evaluations have not adequately addressed the challenges presented by the suboptimal routine MRI imaging of this structure. Here, we propose a novel method of edge enhancement that allows for high reliability and accuracy with regard to LGN morphometry, using routine 3D-MRI imaging protocols. This new algorithm is based on modeling a small brain structure as a polyhedron with its faces, edges, and vertices fitted with one plane, the intersection of two planes, and the intersection of three planes, respectively. This algorithm dramatically increases the contrast-to-noise ratio between the LGN and its surrounding structures as well as doubling the original spatial resolution. To show the algorithm efficacy, two raters (MA and ML) measured LGN volumes bilaterally in 19 subjects using the edge-enhanced LGN extracted areas from the 3D-T1 weighted images. The averages of the left and right LGN volumes from the two raters were 175 ± 8 and 174 ± 9 mm3, respectively. The intra-class correlations between raters were 0.74 for the left and 0.81 for the right LGN volumes. The high contrast edge-enhanced LGN images presented here, from a 7-min routine 3T-MRI acquisition, is qualitatively comparable to previously reported LGN images that were acquired using a proton density sequence with 30–40 averages and 1.5-h of acquisition time. The proposed edge-enhancement algorithm is not limited only to the LGN, but can significantly improve the contrast-to-noise ratio of any small deep-seated gray matter brain structure that is prone to high-levels of noise and partial volume effects, and can also increase their morphometric accuracy and reliability. An immensely useful feature of the proposed algorithm is that it can be used retrospectively on noisy and low contrast 3D brain images previously acquired as part of any routine clinical MRI visit.
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Affiliation(s)
- Mikhail Lipin
- Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Jean Bennett
- Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Gui-Shuang Ying
- Center for Preventative Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Yinxi Yu
- Center for Preventative Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Manzar Ashtari
- Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Sarasaen C, Chatterjee S, Breitkopf M, Rose G, Nürnberger A, Speck O. Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge. Artif Intell Med 2021; 121:102196. [PMID: 34763811 DOI: 10.1016/j.artmed.2021.102196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 10/07/2021] [Accepted: 10/12/2021] [Indexed: 10/20/2022]
Abstract
Dynamic imaging is a beneficial tool for interventions to assess physiological changes. Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial resolution is compromised. To overcome this spatio-temporal trade-off, this research presents a super-resolution (SR) MRI reconstruction with prior knowledge based fine-tuning to maximise spatial information while reducing the required scan-time for dynamic MRIs. A U-Net based network with perceptual loss is trained on a benchmark dataset and fine-tuned using one subject-specific static high resolution MRI as prior knowledge to obtain high resolution dynamic images during the inference stage. 3D dynamic data for three subjects were acquired with different parameters to test the generalisation capabilities of the network. The method was tested for different levels of in-plane undersampling for dynamic MRI. The reconstructed dynamic SR results after fine-tuning showed higher similarity with the high resolution ground-truth, while quantitatively achieving statistically significant improvement. The average SSIM of the lowest resolution experimented during this research (6.25% of the k-space) before and after fine-tuning were 0.939 ± 0.008 and 0.957 ± 0.006 respectively. This could theoretically result in an acceleration factor of 16, which can potentially be acquired in less than half a second. The proposed approach shows that the super-resolution MRI reconstruction with prior-information can alleviate the spatio-temporal trade-off in dynamic MRI, even for high acceleration factors.
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Affiliation(s)
- Chompunuch Sarasaen
- Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Germany; Institute for Medical Engineering, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany.
| | - Soumick Chatterjee
- Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany; Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg, Germany
| | - Mario Breitkopf
- Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany
| | - Georg Rose
- Institute for Medical Engineering, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany
| | - Andreas Nürnberger
- Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany; Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Oliver Speck
- Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; German Center for Neurodegenerative Disease, Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Germany; Leibniz Institute for Neurobiology, Magdeburg, Germany
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Dong M, Xie L, Das SR, Wang J, Wisse LEM, deFlores R, Wolk DA, Yushkevich PA. DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease. Neuroimage 2021; 243:118514. [PMID: 34450261 PMCID: PMC8604562 DOI: 10.1016/j.neuroimage.2021.118514] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 08/18/2021] [Accepted: 08/23/2021] [Indexed: 11/26/2022] Open
Abstract
Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures based on deformable registration can be confounded by MRI artifacts, resulting in over-estimation or underestimation of hippocampal atrophy. For example, the deformation-based-morphometry method ALOHA (Das et al., 2012) finds an increase in hippocampal volume in a substantial proportion of longitudinal scan pairs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, unexpected, given that the hippocampal gray matter is lost with age and disease progression. We propose an alternative approach to quantify disease progression in the hippocampal region: to train a deep learning network (called DeepAtrophy) to infer temporal information from longitudinal scan pairs. The underlying assumption is that by learning to derive time-related information from scan pairs, the network implicitly learns to detect progressive changes that are related to aging and disease progression. Our network is trained using two categorical loss functions: one that measures the network's ability to correctly order two scans from the same subject, input in arbitrary order; and another that measures the ability to correctly infer the ratio of inter-scan intervals between two pairs of same-subject input scans. When applied to longitudinal MRI scan pairs from subjects unseen during training, DeepAtrophy achieves greater accuracy in scan temporal ordering and interscan interval inference tasks than ALOHA (88.5% vs. 75.5% and 81.1% vs. 75.0%, respectively). A scalar measure of time-related change in a subject level derived from DeepAtrophy is then examined as a biomarker of disease progression in the context of AD clinical trials. We find that this measure performs on par with ALOHA in discriminating groups of individuals at different stages of the AD continuum. Overall, our results suggest that using deep learning to infer temporal information from longitudinal MRI of the hippocampal region has good potential as a biomarker of disease progression, and hints that combining this approach with conventional deformation-based morphometry algorithms may lead to improved biomarkers in the future.
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Affiliation(s)
- Mengjin Dong
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States.
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Jiancong Wang
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Robin deFlores
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Institut National de la Santé et de la Recherche Médicale (INSERM), Caen, France
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
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10
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Pin G, Coupé P, Nadal L, Manjon JV, Helmer C, Amieva H, Mazoyer B, Dartigues JF, Catheline G, Planche V. Distinct Hippocampal Subfields Atrophy in Older People With Vascular Brain Injuries. Stroke 2021; 52:1741-1750. [PMID: 33657856 DOI: 10.1161/strokeaha.120.031743] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE Many neurological or psychiatric diseases affect the hippocampus during aging. The study of hippocampal regional vulnerability may provide important insights into the pathophysiological mechanisms underlying these processes; however, little is known about the specific impact of vascular brain damage on hippocampal subfields atrophy. METHODS To analyze the effect of vascular injuries independently of other pathological conditions, we studied a population-based cohort of nondemented older adults, after the exclusion of people who were diagnosed with neurodegenerative diseases during the 14-year clinical follow-up period. Using an automated segmentation pipeline, 1.5T-magnetic resonance imaging at inclusion and 4 years later were assessed to measure both white matter hyperintensities and hippocampal subfields volume. Annualized rates of white matter hyperintensity progression and annualized rates of hippocampal subfields atrophy were then estimated in each participant. RESULTS We included 249 participants in our analyses (58% women, mean age 71.8, median Mini-Mental State Evaluation 29). The volume of the subiculum at baseline was the only hippocampal subfield volume associated with total, deep/subcortical, and periventricular white matter hyperintensity volumes, independently of demographic variables and vascular risk factors (β=-0.17, P=0.011; β=-0.25, P=0.020 and β=-0.14, P=0.029, respectively). In longitudinal measures, the annualized rate of subiculum atrophy was significantly higher in people with the highest rate of deep/subcortical white matter hyperintensity progression, independently of confounding factors (β=-0.32, P=0.014). CONCLUSIONS These cross-sectional and longitudinal findings highlight the links between vascular brain injuries and a differential vulnerability of the subiculum within the hippocampal loop, unbiased of the effect of neurodegenerative diseases, and particularly when vascular injuries affect deep/subcortical structures.
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Affiliation(s)
- Grégoire Pin
- University of Bordeaux, CNRS, UMR 5293, Institut des Maladies Neurodégénératives, France (G.P., L.N., B.M., V.P.).,Centre Mémoire de Ressources et de Recherches, Pôle de Neurosciences Cliniques, CHU de Bordeaux, France (G.P., L.N., J.-F.D., V.P.)
| | - Pierrick Coupé
- University of Bordeaux, CNRS, Bordeaux INP, Laboratoire Bordelais de Recherche en Informatique, UMR 5800, PICTURA, Talence, France (P.C.)
| | - Louis Nadal
- University of Bordeaux, CNRS, UMR 5293, Institut des Maladies Neurodégénératives, France (G.P., L.N., B.M., V.P.).,Centre Mémoire de Ressources et de Recherches, Pôle de Neurosciences Cliniques, CHU de Bordeaux, France (G.P., L.N., J.-F.D., V.P.)
| | - Jose V Manjon
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Spain (J.V.M.)
| | - Catherine Helmer
- University of Bordeaux, Inserm, UMR 1219, Bordeaux Population Health Research Center, France (C.H., H.A., J.-F.D.)
| | - Hélène Amieva
- University of Bordeaux, Inserm, UMR 1219, Bordeaux Population Health Research Center, France (C.H., H.A., J.-F.D.)
| | - Bernard Mazoyer
- University of Bordeaux, CNRS, UMR 5293, Institut des Maladies Neurodégénératives, France (G.P., L.N., B.M., V.P.)
| | - Jean-François Dartigues
- Centre Mémoire de Ressources et de Recherches, Pôle de Neurosciences Cliniques, CHU de Bordeaux, France (G.P., L.N., J.-F.D., V.P.).,University of Bordeaux, Inserm, UMR 1219, Bordeaux Population Health Research Center, France (C.H., H.A., J.-F.D.)
| | - Gwénaëlle Catheline
- EPHE, PSL, Bordeaux, France (G.C.).,University of Bordeaux, CNRS, UMR 5287, Institut de Neurosciences cognitives et intégratives d'Aquitaine, France (G.C.)
| | - Vincent Planche
- University of Bordeaux, CNRS, UMR 5293, Institut des Maladies Neurodégénératives, France (G.P., L.N., B.M., V.P.).,Centre Mémoire de Ressources et de Recherches, Pôle de Neurosciences Cliniques, CHU de Bordeaux, France (G.P., L.N., J.-F.D., V.P.)
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Zhao C, Dewey BE, Pham DL, Calabresi PA, Reich DS, Prince JL. SMORE: A Self-Supervised Anti-Aliasing and Super-Resolution Algorithm for MRI Using Deep Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:805-817. [PMID: 33170776 PMCID: PMC8053388 DOI: 10.1109/tmi.2020.3037187] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
High resolution magnetic resonance (MR) images are desired in many clinical and research applications. Acquiring such images with high signal-to-noise (SNR), however, can require a long scan duration, which is difficult for patient comfort, is more costly, and makes the images susceptible to motion artifacts. A very common practical compromise for both 2D and 3D MR imaging protocols is to acquire volumetric MR images with high in-plane resolution, but lower through-plane resolution. In addition to having poor resolution in one orientation, 2D MRI acquisitions will also have aliasing artifacts, which further degrade the appearance of these images. This paper presents an approach SMORE1 based on convolutional neural networks (CNNs) that restores image quality by improving resolution and reducing aliasing in MR images.2 This approach is self-supervised, which requires no external training data because the high-resolution and low-resolution data that are present in the image itself are used for training. For 3D MRI, the method consists of only one self-supervised super-resolution (SSR) deep CNN that is trained from the volumetric image data. For 2D MRI, there is a self-supervised anti-aliasing (SAA) deep CNN that precedes the SSR CNN, also trained from the volumetric image data. Both methods were evaluated on a broad collection of MR data, including filtered and downsampled images so that quantitative metrics could be computed and compared, and actual acquired low resolution images for which visual and sharpness measures could be computed and compared. The super-resolution method is shown to be visually and quantitatively superior to previously reported methods.
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12
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Princich JP, Donnelly-Kehoe PA, Deleglise A, Vallejo-Azar MN, Pascariello GO, Seoane P, Veron Do Santos JG, Collavini S, Nasimbera AH, Kochen S. Diagnostic Performance of MRI Volumetry in Epilepsy Patients With Hippocampal Sclerosis Supported Through a Random Forest Automatic Classification Algorithm. Front Neurol 2021; 12:613967. [PMID: 33692740 PMCID: PMC7937810 DOI: 10.3389/fneur.2021.613967] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/18/2021] [Indexed: 01/07/2023] Open
Abstract
Introduction: Several methods offer free volumetry services for MR data that adequately quantify volume differences in the hippocampus and its subregions. These methods are frequently used to assist in clinical diagnosis of suspected hippocampal sclerosis in temporal lobe epilepsy. A strong association between severity of histopathological anomalies and hippocampal volumes was reported using MR volumetry with a higher diagnostic yield than visual examination alone. Interpretation of volumetry results is challenging due to inherent methodological differences and to the reported variability of hippocampal volume. Furthermore, normal morphometric differences are recognized in diverse populations that may need consideration. To address this concern, we highlighted procedural discrepancies including atlas definition and computation of total intracranial volume that may impact volumetry results. We aimed to quantify diagnostic performance and to propose reference values for hippocampal volume from two well-established techniques: FreeSurfer v.06 and volBrain-HIPS. Methods: Volumetry measures were calculated using clinical T1 MRI from a local population of 61 healthy controls and 57 epilepsy patients with confirmed unilateral hippocampal sclerosis. We further validated the results by a state-of-the-art machine learning classification algorithm (Random Forest) computing accuracy and feature relevance to distinguish between patients and controls. This validation process was performed using the FreeSurfer dataset alone, considering morphometric values not only from the hippocampus but also from additional non-hippocampal brain regions that could be potentially relevant for group classification. Mean reference values and 95% confidence intervals were calculated for left and right hippocampi along with hippocampal asymmetry degree to test diagnostic accuracy. Results: Both methods showed excellent classification performance (AUC:> 0.914) with noticeable differences in absolute (cm3) and normalized volumes. Hippocampal asymmetry was the most accurate discriminator from all estimates (AUC:1~0.97). Similar results were achieved in the validation test with an automatic classifier (AUC:>0.960), disclosing hippocampal structures as the most relevant features for group differentiation among other brain regions. Conclusion: We calculated reference volumetry values from two commonly used methods to accurately identify patients with temporal epilepsy and hippocampal sclerosis. Validation with an automatic classifier confirmed the principal role of the hippocampus and its subregions for diagnosis.
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Affiliation(s)
- Juan Pablo Princich
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina.,Hospital de Pediatría J.P Garrahan, Departamento de Neuroimágenes, Buenos Aires, Argentina
| | - Patricio Andres Donnelly-Kehoe
- Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas (CIFASIS) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Grupo de Procesamiento de Señales Multimedia - División Neuroimágenes, Universidad Nacional de Rosario, Rosario, Argentina
| | - Alvaro Deleglise
- Instituto de Fisiología y Biofísica B. Houssay (IFIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas, Departamento de Fisiología y Biofísica, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Mariana Nahir Vallejo-Azar
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina
| | - Guido Orlando Pascariello
- Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas (CIFASIS) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Grupo de Procesamiento de Señales Multimedia - División Neuroimágenes, Universidad Nacional de Rosario, Rosario, Argentina
| | - Pablo Seoane
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina.,Hospital J.M Ramos Mejía, Centro de Epilepsia, Buenos Aires, Argentina
| | - Jose Gabriel Veron Do Santos
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina
| | - Santiago Collavini
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina.,Instituto de investigación en Electrónica, Control y Procesamiento de Señales (LEICI), Universidad Nacional de La Plata-Consejo Nacional de Investigaciones Científicas y Técnicas, La Plata, Argentina.,Instituto de Ingeniería y Agronomía, Universidad Nacional Arturo Jauretche, Florencio Varela, Argentina
| | - Alejandro Hugo Nasimbera
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina.,Hospital J.M Ramos Mejía, Centro de Epilepsia, Buenos Aires, Argentina
| | - Silvia Kochen
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina
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Park YW, Choi D, Park M, Ahn SJ, Ahn SS, Suh SH, Lee SK. Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging. J Alzheimers Dis 2021; 79:483-491. [PMID: 33337361 DOI: 10.3233/jad-200734] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Noninvasive identification of amyloid-β (Aβ) is important for better clinical management of mild cognitive impairment (MCI) patients. OBJECTIVE To investigate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ42 status when integrated with clinical profiles. METHODS A total of 407 MCI subjects from the Alzheimer's Disease Neuroimaging Initiative were allocated to training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampus were extracted from magnetic resonance imaging (MRI). A cut-off of <192 pg/mL was applied to define CSF Aβ42 status. After feature selection, random forest with subsampling methods were utilized to develop three models with which to predict CSF Aβ42: 1) a radiomics model; 2) a clinical model based on clinical profiles; and 3) a combined model based on radiomics and clinical profiles. The prediction performances thereof were validated in the test set. A prediction model using hippocampus volume was also developed and validated. RESULTS The best-performing radiomics model showed an area under the curve (AUC) of 0.674 in the test set. The best-performing clinical model showed an AUC of 0.758 in the test set. The best-performing combined model showed an AUC of 0.823 in the test set. The hippocampal volume model showed a lower performance, with an AUC of 0.543 in the test set. CONCLUSION Radiomics models from MRI can help predict CSF Aβ42 status in MCI patients and potentially triage the patients for invasive and costly Aβ tests.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Dongmin Choi
- Department of Computer Science, Yonsei University, Seoul, Korea
| | - Mina Park
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Jun Ahn
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Sang Hyun Suh
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
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Hett K, Ta VT, Oguz I, Manjón JV, Coupé P. Multi-scale graph-based grading for Alzheimer's disease prediction. Med Image Anal 2021; 67:101850. [PMID: 33075641 PMCID: PMC7725970 DOI: 10.1016/j.media.2020.101850] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 08/18/2020] [Accepted: 08/31/2020] [Indexed: 12/21/2022]
Abstract
The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer's disease (AD) is clinically relevant, and may above all have a significant impact on accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset.
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Affiliation(s)
- Kilian Hett
- CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, PICTURA, Talence F-33400, France; Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, TN, USA.
| | - Vinh-Thong Ta
- CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, PICTURA, Talence F-33400, France
| | - Ipek Oguz
- Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, TN, USA
| | - José V Manjón
- Universitat Politècnica de Valèncica, ITACA, Valencia 46022, Spain
| | - Pierrick Coupé
- CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, PICTURA, Talence F-33400, France
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Abstract
Spatial resolution of metabolic imaging with hyperpolarized 13C-labeled substrates is limited owing to the multidimensional nature of spectroscopic imaging and the transient characteristics of dissolution dynamic nuclear polarization. In this study, a patch-based algorithm (PA) is proposed to enhance spatial resolution of hyperpolarized 13C human brain images by exploiting compartmental information from the corresponding high-resolution 1H images. PA was validated in simulation and phantom studies. Effects of signal-to-noise ratio, upsampling factor, segmentation, and slice thickness on reconstructing 13C images were evaluated in simulation. PA was further applied to low-resolution human brain metabolite maps of hyperpolarized [1-13C] pyruvate and [1-13C] lactate with 3 compartment segmentations (gray matter, white matter, and cerebrospinal fluid). The performance of PA was compared with other conventional interpolation methods (sinc, nearest-neighbor, bilinear, and spline interpolations). The simulation and the phantom tests showed that PA improved spatial resolution by up to 8 times and enhanced the image contrast without compromising quantification accuracy or losing the intracompartment signal inhomogeneity, even in the case of low signal-to-noise ratio or inaccurate segmentation. PA also improved spatial resolution and image contrast of human 13C brain images. Dynamic analysis showed consistent performance of the proposed method even with the signal decay along time. In conclusion, PA can enhance low-resolution hyperpolarized 13C images in terms of spatial resolution and contrast by using a priori knowledge from high-resolution 1H magnetic resonance imaging while preserving quantification accuracy and intracompartment signal inhomogeneity.
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Affiliation(s)
| | - Jae Mo Park
- Advanced Imaging Research Center
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX; and
- Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX
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Higher b-values improve the correlation between diffusion MRI and the cortical microarchitecture. Neuroradiology 2020; 62:1411-1419. [PMID: 32483725 DOI: 10.1007/s00234-020-02462-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Accepted: 05/18/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE In diffusion MRI (dMRI), it remains unclear to know how much increase of b-value is conveying additional biological meaning. We tested the correlations between cortical microarchitecture and diffusion metrics computed from standard (1000 s/mm2), high (3000 s/mm2), to very high (5000 s/mm2) b-value dMRI. METHODS Healthy volunteers were scanned with a dMRI pulse sequence that was first optimized together with a T1-WI and T2-WI. Averaged cortical surface map of estimated myelin (T1-WI/T2-WI) was compared with surface maps of mean diffusivity (MD) computed from each b-value (MD1000, MD3000, and MD5000) and to surface map of mean kurtosis (MK computed from the 0-, 1000-, to 3000-s/mm2 shells) in 360 cortical parcels using Spearman correlations, multiple linear regressions, and Akaike information criteria (AIC). RESULTS Surface map from MD1000 showed variations not related to myelin but the MD3000 and MD5000 maps inversely mirrored estimated myelin map; lower MD values being observed in more myelinated cortical areas. MK mirrored myelinated cortical areas. Quantitatively, Spearman correlations between myelin and MD became more and more negative as long as b-values increased while the correlation was positive between myelin and MK. Multiple regression models confirmed negative associations between myelin and MD that were significantly better from MD1000 to MD3000 and MD5000 (R2 = 0.33, p < 0.001; R2 = 0.43, p < 0.001; and R2 = 0.50, p < 0.001) and positive association between myelin and MK (R2 = 0.53, p < 0.001). Comparisons of the 3 statistical models showed the best performances with MK and MD5000 (AICMK < AICMD5000 < AICMD3000 < AICMD1000). CONCLUSION Higher b-values are more closely related to subtle cellular variations of the cortical microarchitecture.
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17
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Differentiating patients with schizophrenia from healthy controls by hippocampal subfields using radiomics. Schizophr Res 2020; 223:337-344. [PMID: 32988740 DOI: 10.1016/j.schres.2020.09.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 08/11/2020] [Accepted: 09/14/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Accurately diagnosing schizophrenia is still challenging due to the lack of validated biomarkers. Here, we aimed to investigate whether radiomic features in bilateral hippocampal subfields from magnetic resonance images (MRIs) can differentiate patients with schizophrenia from healthy controls (HCs). METHODS A total of 152 participants with MRI (86 schizophrenia and 66 HCs) were allocated to training (n = 106) and test (n = 46) sets. Radiomic features (n = 642) from the bilateral hippocampal subfields processed with automatic segmentation techniques were extracted from T1-weighted MRIs. After feature selection, various combinations of classifiers (logistic regression, extra-trees, AdaBoost, XGBoost, or support vector machine) and subsampling were trained. The performance of the classifier was validated in the test set by determining the area under the curve (AUC). Furthermore, the association between selected radiomic features and clinical symptoms in schizophrenia was assessed. RESULTS Thirty radiomic features were identified to differentiate participants with schizophrenia from HCs. In the training set, the AUC exhibited poor to good performance (range: 0.683-0.861). The best performing radiomics model in the test set was achieved by the mutual information feature selection and logistic regression with an AUC, accuracy, sensitivity, and specificity of 0.821 (95% confidence interval 0.681-0.961), 82.1%, 76.9%, and 70%, respectively. Greater maximum values in the left cornu ammonis 1-3 subfield were associated with a higher severity of positive symptoms and general psychopathology in participants with schizophrenia. CONCLUSION Radiomic features from hippocampal subfields may be useful biomarkers for identifying schizophrenia.
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18
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Moghaddam HS, Aarabi MH, Mehvari-Habibabadi J, Sharifpour R, Mohajer B, Mohammadi-Mobarakeh N, Hashemi-Fesharaki SS, Elisevich K, Nazem-Zadeh MR. Distinct patterns of hippocampal subfield volume loss in left and right mesial temporal lobe epilepsy. Neurol Sci 2020; 42:1411-1421. [PMID: 32783160 DOI: 10.1007/s10072-020-04653-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 08/06/2020] [Indexed: 01/07/2023]
Abstract
OBJECTIVE To investigate the pattern and severity of hippocampal subfield volume loss in patients with left and right mesial temporal lobe epilepsy (mTLE) using quantitative MRI volumetric analysis. METHODS A total of 21 left and 14 right mTLE subjects, as well as 15 healthy controls, were enrolled in this cross-sectional study. A publically available magnetic resonance imaging (MRI) brain volumetry system (volBrain) was used for volumetric analysis of hippocampal subfields. The T1-weighted images were processed with a HIPS pipeline. RESULTS A distinct pattern of hippocampal subfield atrophy was found between left and right mTLE patients when compared with controls. Patients with left mTLE exhibited ipsilateral hippocampal atrophy and segmental volume depletion of the Cornu Ammonis (CA) 2/CA3, CA4/dentate gyrus (DG), and strata radiatum-lacunosum-moleculare (SR-SL-SM). Those with right mTLE exhibited similar ipsilateral hippocampal atrophy but with additional segmental CA1 volume depletion. More extensive bilateral subfield volume loss was apparent with right mTLE patients. CONCLUSION We demonstrate that left and right mTLE patients show a dissimilar pattern of hippocampal subfield atrophy, suggesting the pathophysiology of epileptogenesis in left and right mTLE to be different.
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Affiliation(s)
| | | | | | - Roya Sharifpour
- Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences (TUMS), Tehran, Iran.,Research Center for Molecular and Cellular Imaging, Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Bahram Mohajer
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Neda Mohammadi-Mobarakeh
- Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences (TUMS), Tehran, Iran.,Research Center for Molecular and Cellular Imaging, Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | | | - Kost Elisevich
- Department of Clinical Neurosciences, Spectrum Health, College of Human Medicine, Michigan State University, Grand Rapids, MI, 49503, USA
| | - Mohammad-Reza Nazem-Zadeh
- Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences (TUMS), Tehran, Iran. .,Research Center for Molecular and Cellular Imaging, Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
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Park S, Lee SM, Do KH, Lee JG, Bae W, Park H, Jung KH, Seo JB. Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer. Korean J Radiol 2020; 20:1431-1440. [PMID: 31544368 PMCID: PMC6757001 DOI: 10.3348/kjr.2019.0212] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 07/12/2019] [Indexed: 01/26/2023] Open
Affiliation(s)
- Sohee Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
| | - Kyung Hyun Do
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - June Goo Lee
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | | | | | | | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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20
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Differential annualized rates of hippocampal subfields atrophy in aging and future Alzheimer's clinical syndrome. Neurobiol Aging 2020; 90:75-83. [PMID: 32107063 DOI: 10.1016/j.neurobiolaging.2020.01.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 01/22/2020] [Accepted: 01/26/2020] [Indexed: 01/22/2023]
Abstract
Several studies have investigated the differential vulnerability of hippocampal subfields during aging and Alzheimer's disease (AD). Results were often contradictory, mainly because these works were based on concatenations of cross-sectional measures in cohorts with different ages or stages of AD, in the absence of a longitudinal design. Here, we investigated 327 participants from a population-based cohort of nondemented older adults with a 14-year clinical follow-up. MRI at baseline and 4 years later were assessed to measure the annualized rates of hippocampal subfields atrophy in each participant using an automatic segmentation pipeline with subsequent quality control. On the one hand, CA4 dentate gyrus was significantly more affected than the other subfields in the whole population (CA1-3: -0.68%/year; subiculum: -0.99%/year; and CA4-DG: -1.39%/year; p < 0.0001). On the other hand, the annualized rate of CA1-3 atrophy was associated with an increased risk of developing Alzheimer's clinical syndrome over time, independently of age, gender, educational level, and ApoE4 genotype (HR = 2.0; CI 95% 1.4-3.0). These results illustrate the natural history of hippocampal subfields atrophy during aging and AD by showing that the dentate gyrus is the most vulnerable subfield to the effects of aging while the cornu-ammonis is the primary target of AD pathophysiological processes, years before symptom onset.
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21
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Manjón JV, Bertó A, Romero JE, Lanuza E, Vivo-Hernando R, Aparici-Robles F, Coupe P. pBrain: A novel pipeline for Parkinson related brain structure segmentation. NEUROIMAGE-CLINICAL 2020; 25:102184. [PMID: 31982678 PMCID: PMC6992999 DOI: 10.1016/j.nicl.2020.102184] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 12/16/2019] [Accepted: 01/14/2020] [Indexed: 11/23/2022]
Abstract
A novel pipeline for Parkinson`s disease structure segmentation is presented. State-of-the-art fast multiatlas patch-based label fusion with systematic error correction is used to accurately and efficiently produce very competitive results in around 5 min. The proposed pipeline works at high resolution (0.5 mm) but it can work also with standard resolution (1 mm) T2 images allowing the analysis of large legacy databases. The proposed pipeline will be made publically available online through our volBrain platform.
Parkinson is a very prevalent neurodegenerative disease impacting the life of millions of people worldwide. Although its cause remains unknown, its functional and structural analysis is fundamental to advance in the search of a cure or symptomatic treatment. The automatic segmentation of deep brain structures related to Parkinson`s disease could be beneficial for the follow up and treatment planning. Unfortunately, there is not broadly available segmentation software to automatically measure Parkinson related structures. In this paper, we present a novel pipeline to segment three deep brain structures related to Parkinson's disease (substantia nigra, subthalamic nucleus and red nucleus). The proposed method is based on the multi-atlas label fusion technology that works on standard and high-resolution T2-weighted images. The proposed method also includes as post-processing a new neural network-based error correction step to minimize systematic segmentation errors. The proposed method has been compared to other state-of-the-art methods showing competitive results in terms of accuracy and execution time.
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Affiliation(s)
- José V Manjón
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, Valencia 46022, Spain.
| | - Alexa Bertó
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, Valencia 46022, Spain
| | - José E Romero
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, Valencia 46022, Spain
| | - Enrique Lanuza
- Department of Cell Biology, Universitat de Valencia, Burjassot, Valencia 46100, Spain
| | - Roberto Vivo-Hernando
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, Valencia 46022, Spain
| | | | - Pierrick Coupe
- CNRS, Univ. Bordeaux, LaBRI, UMR 5800, PICTURA, Talence F-33400, France
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Multimodal Hippocampal Subfield Grading For Alzheimer's Disease Classification. Sci Rep 2019; 9:13845. [PMID: 31554909 PMCID: PMC6761169 DOI: 10.1038/s41598-019-49970-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Accepted: 08/09/2019] [Indexed: 01/23/2023] Open
Abstract
Numerous studies have proposed biomarkers based on magnetic resonance imaging (MRI) to detect and predict the risk of evolution toward Alzheimer's disease (AD). Most of these methods have focused on the hippocampus, which is known to be one of the earliest structures impacted by the disease. To date, patch-based grading approaches provide among the best biomarkers based on the hippocampus. However, this structure is complex and is divided into different subfields, not equally impacted by AD. Former in-vivo imaging studies mainly investigated structural alterations of these subfields using volumetric measurements and microstructural modifications with mean diffusivity measurements. The aim of our work is to improve the current classification performances based on the hippocampus with a new multimodal patch-based framework combining structural and diffusivity MRI. The combination of these two MRI modalities enables the capture of subtle structural and microstructural alterations. Moreover, we propose to study the efficiency of this new framework applied to the hippocampal subfields. To this end, we compare the classification accuracy provided by the different hippocampal subfields using volume, mean diffusivity, and our novel multimodal patch-based grading framework combining structural and diffusion MRI. The experiments conducted in this work show that our new multimodal patch-based method applied to the whole hippocampus provides the most discriminating biomarker for advanced AD detection while our new framework applied into subiculum obtains the best results for AD prediction, improving by two percentage points the accuracy compared to the whole hippocampus.
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Patch-based super-resolution of arterial spin labeling magnetic resonance images. Neuroimage 2019; 189:85-94. [DOI: 10.1016/j.neuroimage.2019.01.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 12/21/2018] [Accepted: 01/03/2019] [Indexed: 11/22/2022] Open
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Sotiropoulos SN, Zalesky A. Building connectomes using diffusion MRI: why, how and but. NMR IN BIOMEDICINE 2019; 32:e3752. [PMID: 28654718 PMCID: PMC6491971 DOI: 10.1002/nbm.3752] [Citation(s) in RCA: 154] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 04/05/2017] [Accepted: 05/03/2017] [Indexed: 05/14/2023]
Abstract
Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments.
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Affiliation(s)
- Stamatios N. Sotiropoulos
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Melbourne School of EngineeringUniversity of MelbourneVictoriaAustralia
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Nie D, Trullo R, Lian J, Wang L, Petitjean C, Ruan S, Wang Q, Shen D. Medical Image Synthesis with Deep Convolutional Adversarial Networks. IEEE Trans Biomed Eng 2018; 65:2720-2730. [PMID: 29993445 PMCID: PMC6398343 DOI: 10.1109/tbme.2018.2814538] [Citation(s) in RCA: 276] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical image synthesis can be of great benefit by estimating a desired imaging modality without incurring an actual scan. In this paper, we propose a generative adversarial approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate a target image given a source image. To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN. Moreover, the FCN is designed to incorporate an image-gradient-difference-based loss function to avoid generating blurry target images. Long-term residual unit is also explored to help the training of the network. We further apply Auto-Context Model to implement a context-aware deep convolutional adversarial network. Experimental results show that our method is accurate and robust for synthesizing target images from the corresponding source images. In particular, we evaluate our method on three datasets, to address the tasks of generating CT from MRI and generating 7T MRI from 3T MRI images. Our method outperforms the state-of-the-art methods under comparison in all datasets and tasks.
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Affiliation(s)
- Dong Nie
- Department of Computer Science, Department of Radiology and BRIC, UNC-Chapel Hill, Chapel Hill, NC, 27510 USA ()
| | - Roger Trullo
- Department of Radiology and BRIC, UNC-Chapel Hill, and also with the Department of Computer Science, University of Normandy
| | - Jun Lian
- Department of Radiation Oncology, UNC-Chapel Hill
| | - Li Wang
- Department of Radiology and BRIC, UNC-Chapel Hill
| | | | - Su Ruan
- Department of Computer Science, University of Normandy
| | - Qian Wang
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China Radiology and Biomedical ()
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27510 USA, and also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea ()
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Quantitative evaluation of fiber tractography with a Delaunay triangulation-based interpolation approach. Med Biol Eng Comput 2018; 57:925-938. [PMID: 30483913 DOI: 10.1007/s11517-018-1932-y] [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: 02/20/2018] [Accepted: 11/17/2018] [Indexed: 10/27/2022]
Abstract
The recent challenge in high angular resolution diffusion imaging (HARDI) is to find a tractography process that provides information about the neural architecture within the white matter of the brain in a clinically feasible measurement time. The great success of the HARDI technique comes from its capability to overcome the problem of crossing fiber detection. However, it requires a large number of diffusion-weighted (DW) images which is problematic for clinical time and hardware. The main contribution of this paper is to develop a full tractography framework that gives an accurate estimate of the crossing fiber problem with the aim of reducing data acquisition time. We explore the interpolation in the gradient direction domain as a method to estimate the HARDI signal from a reduced set of DW images. The experimentation was performed in a first time on simulated data for a quantitative evaluation using the Tractometer system. We used, also, in vivo human brain data to demonstrate the potential of our pipeline. Results on both simulated and real data illustrate the effectiveness of our approach to perform the brain connectivity. Overall, we have shown that the proposed approach achieves competitive results to other tractography methods according to Tractometer connectivity metrics. Graphical Abstract.
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Hannoun S, Kocevar G, Durand-Dubief F, Stamile C, Naji A, Cotton F, Cavallari M, Guttmann CR, Sappey-Marinier D. Evidence of axonal damage in cerebellar peduncles without T2-lesions in multiple sclerosis. Eur J Radiol 2018; 108:114-119. [DOI: 10.1016/j.ejrad.2018.09.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 08/10/2018] [Accepted: 09/06/2018] [Indexed: 12/23/2022]
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Chen G, Dong B, Zhang Y, Lin W, Shen D, Yap PT. Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x- q Space. Front Neuroinform 2018; 12:57. [PMID: 30245622 PMCID: PMC6137306 DOI: 10.3389/fninf.2018.00057] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2018] [Accepted: 08/16/2018] [Indexed: 01/02/2023] Open
Abstract
Diffusion MRI requires sufficient coverage of the diffusion wavevector space, also known as the q-space, to adequately capture the pattern of water diffusion in various directions and scales. As a result, the acquisition time can be prohibitive for individuals who are unable to stay still in the scanner for an extensive period of time, such as infants. To address this problem, in this paper we harness non-local self-similar information in the x-q space of diffusion MRI data for q-space upsampling. Specifically, we first perform neighborhood matching to establish the relationships of signals in x-q space. The signal relationships are then used to regularize an ill-posed inverse problem related to the estimation of high angular resolution diffusion MRI data from its low-resolution counterpart. Our framework allows information from curved white matter structures to be used for effective regularization of the otherwise ill-posed problem. Extensive evaluations using synthetic and infant diffusion MRI data demonstrate the effectiveness of our method. Compared with the widely adopted interpolation methods using spherical radial basis functions and spherical harmonics, our method is able to produce high angular resolution diffusion MRI data with greater quality, both qualitatively and quantitatively.
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Affiliation(s)
- Geng Chen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Bin Dong
- Beijing International Center for Mathematical Research, Peking University, Beijing, China
| | - Yong Zhang
- Vancouver Research Center, Huawei Technologies Canada, Burnaby, BC, Canada
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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Bustin A, Voilliot D, Menini A, Felblinger J, de Chillou C, Burschka D, Bonnemains L, Odille F. Isotropic Reconstruction of MR Images Using 3D Patch-Based Self-Similarity Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:1932-1942. [PMID: 29994581 DOI: 10.1109/tmi.2018.2807451] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Isotropic three-dimensional (3D) acquisition is a challenging task in magnetic resonance imaging (MRI). Particularly in cardiac MRI, due to hardware and time limitations, current 3D acquisitions are limited by low-resolution, especially in the through-plane direction, leading to poor image quality in that dimension. To overcome this problem, super-resolution (SR) techniques have been proposed to reconstruct a single isotropic 3D volume from multiple anisotropic acquisitions. Previously, local regularization techniques such as total variation have been applied to limit noise amplification while preserving sharp edges and small features in the images. In this paper, inspired by the recent progress in patch-based reconstruction, we propose a novel isotropic 3D reconstruction scheme that integrates non-local and self-similarity information from 3D patch neighborhoods. By grouping 3D patches with similar structures, we enforce the natural sparsity of MR images, which can be expressed by a low-rank structure, leading to robust image reconstruction with high signal-to-noise ratio efficiency. An Augmented Lagrangian formulation of the problem is proposed to efficiently decompose the optimization into a low-rank volume denoising and a SR reconstruction. Experimental results in simulations, brain imaging and clinical cardiac MRI, demonstrate that the proposed joint SR and self-similarity learning framework outperforms current state-of-the-art methods. The proposed reconstruction of isotropic 3D volumes may be particularly useful for cardiac applications, such as myocardial infarction scar assessment by late gadolinium enhancement MRI.
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Yin S, You X, Yang X, Peng Q, Zhu Z, Jing XY. A joint space-angle regularization approach for single 4D diffusion image super-resolution. Magn Reson Med 2018; 80:2173-2187. [PMID: 29672917 DOI: 10.1002/mrm.27184] [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: 01/16/2018] [Revised: 02/28/2018] [Accepted: 02/28/2018] [Indexed: 11/08/2022]
Abstract
PURPOSE Low signal-to-noise-ratio and limited scan time of diffusion magnetic resonance imaging (dMRI) in current clinical settings impede obtaining images with high spatial and angular resolution (HSAR) for a reliable fiber reconstruction with fine anatomical details. To overcome this problem, we propose a joint space-angle regularization approach to reconstruct HSAR diffusion signals from a single 4D low resolution (LR) dMRI, which is down-sampled in both 3D-space and q-space. METHODS Different from the existing works which combine multiple 4D LR diffusion images acquired using specific acquisition protocols, the proposed method reconstructs HSAR dMRI from only a single 4D dMRI by exploring and integrating two key priors, that is, the nonlocal self-similarity in the spatial domain as a prior to increase spatial resolution and ridgelet approximations in the diffusion domain as another prior to increase the angular resolution of dMRI. To more effectively capture nonlocal self-similarity in the spatial domain, a novel 3D block-based nonlocal means filter is imposed as the 3D image space regularization term which is accurate in measuring the similarity and fast for 3D reconstruction. To reduce computational complexity, we use the L2 -norm instead of sparsity constraint on the representation coefficients. RESULTS Experimental results demonstrate that the proposed method can obtain the HSAR dMRI efficiently with approximately 2% per-voxel root-mean-square error between the actual and reconstructed HSAR dMRI. CONCLUSION The proposed approach can effectively increase the spatial and angular resolution of the dMRI which is independent of the acquisition protocol, thus overcomes the inherent resolution limitation of imaging systems.
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Affiliation(s)
- Shi Yin
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Xinge You
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Xin Yang
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Qinmu Peng
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ziqi Zhu
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
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Yang Z, He P, Zhou J, Wu X. Non-local diffusion-weighted image super-resolution using collaborative joint information. Exp Ther Med 2018; 15:217-225. [PMID: 29387188 PMCID: PMC5769290 DOI: 10.3892/etm.2017.5430] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Accepted: 08/10/2017] [Indexed: 12/13/2022] Open
Abstract
Due to the clinical durable scanning time and other physical constraints, the spatial resolution of diffusion-weighted magnetic resonance imaging (DWI) is highly limited. Using a post-processing method to improve the resolution of DWI holds the potential to improve the investigation of smaller white-matter structures and to reduce partial volume effects. In the present study, a novel non-local mean super-resolution method was proposed to increase the spatial resolution of DWI datasets. Based on a non-local strategy, joint information from the adjacent scanning directions was taken advantage of through the implementation of a novel weighting scheme. Besides this, an efficient rotationally invariant similarity measure was introduced for further improvement of high-resolution image reconstruction and computational efficiency. Quantitative and qualitative comparisons in synthetic and real DWI datasets demonstrated that the proposed method significantly enhanced the resolution of DWI, and is thus beneficial in improving the estimation accuracy for diffusion tensor imaging as well as high-angular resolution diffusion imaging.
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Affiliation(s)
- Zhipeng Yang
- School of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan 610065, P.R. China.,Department of Electronic Engineering, Chengdu University of Information Technology, Chengdu, Sichuan 610225, P.R. China
| | - Peiyu He
- School of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan 610065, P.R. China
| | - Jiliu Zhou
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, Sichuan 610225, P.R. China
| | - Xi Wu
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, Sichuan 610225, P.R. China
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Crombe A, Planche V, Raffard G, Bourel J, Dubourdieu N, Panatier A, Fukutomi H, Dousset V, Oliet S, Hiba B, Tourdias T. Deciphering the microstructure of hippocampal subfields with in vivo DTI and NODDI: Applications to experimental multiple sclerosis. Neuroimage 2018; 172:357-368. [PMID: 29409838 DOI: 10.1016/j.neuroimage.2018.01.061] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2017] [Revised: 01/16/2018] [Accepted: 01/24/2018] [Indexed: 12/23/2022] Open
Abstract
The hippocampus contains distinct populations of neurons organized into separate anatomical subfields and layers with differential vulnerability to pathological mechanisms. The ability of in vivo neuroimaging to pinpoint regional vulnerability is especially important for better understanding of hippocampal pathology at the early stage of neurodegenerative disorders and for monitoring future therapeutic strategies. This is the case for instance in multiple sclerosis whose neurodegenerative component can affect the hippocampus from the early stage. We challenged the capacity of two models, i.e. the classical diffusion tensor imaging (DTI) model and the neurite orientation dispersion and density imaging (NODDI) model, to compute quantitative diffusion MRI that could capture microstructural alterations in the individual hippocampal layers of experimental-autoimmune encephalomyelitis (EAE) mice, the animal model of multiple sclerosis. To achieve this, the hippocampal anatomy of a healthy mouse brain was first explored ex vivo with high resolution DTI and NODDI. Then, 18 EAE mice and 18 control mice were explored 20 days after immunization with in vivo diffusion MRI prior to sacrifice for the histological quantification of neurites and glial markers in each hippocampal layer. Fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) maps were computed from the DTI model while the orientation dispersion index (ODI), the neurite density index (NDI) and the volume fraction of isotropic diffusivity (isoVF) maps were computed from the NODDI model. We first showed in control mice that color-coded FA and ODI maps can delineate three main hippocampal layers. The quantification of FA, AD, RD, MD, ODI, NDI and isoVF presented differences within these 3 layers, especially within the molecular layer of the dentate gyrus which displayed a specific signature based on a combination of AD (or MD), ODI and NDI. Then, the comparison between EAE and control mice showed a decrease of AD (p = 0.036) and of MD (p = 0.033) selectively within the molecular layer of EAE mice while NODDI indices did not present any difference between EAE and control mice in any layer. Histological analyses confirmed the differential vulnerability of the molecular layer of EAE mice that exhibited decreased dendritic length and decreased dendritic complexity together with activated microglia. Dendritic length and intersections within the molecular layer were independent contributors to the observed decrease of AD (R2 = 0.37 and R2 = 0.40, p < 0.0001) and MD (R2 = 0.41 and R2 = 0.42, p < 0.0001). We therefore identified that NODDI maps can help to highlight the internal microanatomy of the hippocampus but NODDI still presents limitations in grey matter as it failed to capture selective dendritic alterations occurring at early stages of a neurodegenerative disease such as multiple sclerosis, whereas DTI maps were significantly altered.
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Affiliation(s)
- Amandine Crombe
- INSERM, U1215, Neurocentre Magendie, F-33000, Bordeaux, France; Univ. Bordeaux, F-33000, Bordeaux, France; CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, F-33000, Bordeaux, France; CHU de Bordeaux, F-33000, Bordeaux, France
| | - Vincent Planche
- INSERM, U1215, Neurocentre Magendie, F-33000, Bordeaux, France; Univ. Bordeaux, F-33000, Bordeaux, France
| | - Gerard Raffard
- Univ. Bordeaux, F-33000, Bordeaux, France; CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, F-33000, Bordeaux, France
| | - Julien Bourel
- INSERM, U1215, Neurocentre Magendie, F-33000, Bordeaux, France; Univ. Bordeaux, F-33000, Bordeaux, France
| | - Nadège Dubourdieu
- INSERM, U1215, Neurocentre Magendie, F-33000, Bordeaux, France; Univ. Bordeaux, F-33000, Bordeaux, France
| | - Aude Panatier
- INSERM, U1215, Neurocentre Magendie, F-33000, Bordeaux, France; Univ. Bordeaux, F-33000, Bordeaux, France
| | - Hikaru Fukutomi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Vincent Dousset
- INSERM, U1215, Neurocentre Magendie, F-33000, Bordeaux, France; Univ. Bordeaux, F-33000, Bordeaux, France; CHU de Bordeaux, F-33000, Bordeaux, France
| | - Stephane Oliet
- INSERM, U1215, Neurocentre Magendie, F-33000, Bordeaux, France; Univ. Bordeaux, F-33000, Bordeaux, France
| | - Bassem Hiba
- Univ. Bordeaux, F-33000, Bordeaux, France; CNRS UMR 5229, Centre de Neurosciences Cognitives, F-69675, Bron, France.
| | - Thomas Tourdias
- INSERM, U1215, Neurocentre Magendie, F-33000, Bordeaux, France; Univ. Bordeaux, F-33000, Bordeaux, France; CHU de Bordeaux, F-33000, Bordeaux, France.
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Planche V, Koubiyr I, Romero JE, Manjon JV, Coupé P, Deloire M, Dousset V, Brochet B, Ruet A, Tourdias T. Regional hippocampal vulnerability in early multiple sclerosis: Dynamic pathological spreading from dentate gyrus to CA1. Hum Brain Mapp 2018; 39:1814-1824. [PMID: 29331060 DOI: 10.1002/hbm.23970] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 01/03/2018] [Accepted: 01/04/2018] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Whether hippocampal subfields are differentially vulnerable at the earliest stages of multiple sclerosis (MS) and how this impacts memory performance is a current topic of debate. METHOD We prospectively included 56 persons with clinically isolated syndrome (CIS) suggestive of MS in a 1-year longitudinal study, together with 55 matched healthy controls at baseline. Participants were tested for memory performance and scanned with 3 T MRI to assess the volume of 5 distinct hippocampal subfields using automatic segmentation techniques. RESULTS At baseline, CA4/dentate gyrus was the only hippocampal subfield with a volume significantly smaller than controls (p < .01). After one year, CA4/dentate gyrus atrophy worsened (-6.4%, p < .0001) and significant CA1 atrophy appeared (both in the stratum-pyramidale and the stratum radiatum-lacunosum-moleculare, -5.6%, p < .001 and -6.2%, p < .01, respectively). CA4/dentate gyrus volume at baseline predicted CA1 volume one year after CIS (R2 = 0.44 to 0.47, p < .001, with age, T2 lesion-load, and global brain atrophy as covariates). The volume of CA4/dentate gyrus at baseline was associated with MS diagnosis during follow-up, independently of T2-lesion load and demographic variables (p < .05). Whereas CA4/dentate gyrus volume was not correlated with memory scores at baseline, CA1 atrophy was an independent correlate of episodic verbal memory performance one year after CIS (ß = 0.87, p < .05). CONCLUSION The hippocampal degenerative process spread from dentate gyrus to CA1 at the earliest stage of MS. This dynamic vulnerability is associated with MS diagnosis after CIS and will ultimately impact hippocampal-dependent memory performance.
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Affiliation(s)
- Vincent Planche
- Univ. Bordeaux, Bordeaux, F-33000, France.,Inserm U1215 - Neurocentre Magendie, Bordeaux, F-33000, France.,CHU de Bordeaux, Bordeaux, F-33000, France
| | - Ismail Koubiyr
- Univ. Bordeaux, Bordeaux, F-33000, France.,Inserm U1215 - Neurocentre Magendie, Bordeaux, F-33000, France
| | - José E Romero
- Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València, Camino de Vera s/n, Valencia, 46022, España
| | - José V Manjon
- Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València, Camino de Vera s/n, Valencia, 46022, España
| | - Pierrick Coupé
- Laboratoire Bordelais de Recherche en Informatique, UMR CNRS 5800, PICTURA, Talence, F-33405, France
| | | | - Vincent Dousset
- Univ. Bordeaux, Bordeaux, F-33000, France.,Inserm U1215 - Neurocentre Magendie, Bordeaux, F-33000, France.,CHU de Bordeaux, Bordeaux, F-33000, France
| | - Bruno Brochet
- Univ. Bordeaux, Bordeaux, F-33000, France.,Inserm U1215 - Neurocentre Magendie, Bordeaux, F-33000, France.,CHU de Bordeaux, Bordeaux, F-33000, France
| | - Aurélie Ruet
- Univ. Bordeaux, Bordeaux, F-33000, France.,Inserm U1215 - Neurocentre Magendie, Bordeaux, F-33000, France.,CHU de Bordeaux, Bordeaux, F-33000, France
| | - Thomas Tourdias
- Univ. Bordeaux, Bordeaux, F-33000, France.,Inserm U1215 - Neurocentre Magendie, Bordeaux, F-33000, France.,CHU de Bordeaux, Bordeaux, F-33000, France
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Hett K, Ta VT, Manjón JV, Coupé P. Graph of Hippocampal Subfields Grading for Alzheimer’s Disease Prediction. MACHINE LEARNING IN MEDICAL IMAGING 2018. [DOI: 10.1007/978-3-030-00919-9_30] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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35
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HIPS: A new hippocampus subfield segmentation method. Neuroimage 2017; 163:286-295. [DOI: 10.1016/j.neuroimage.2017.09.049] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Revised: 09/22/2017] [Accepted: 09/22/2017] [Indexed: 11/19/2022] Open
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Pisharady PK, Sotiropoulos SN, Duarte-Carvajalino JM, Sapiro G, Lenglet C. Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning. Neuroimage 2017; 167:488-503. [PMID: 28669918 DOI: 10.1016/j.neuroimage.2017.06.052] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Revised: 05/16/2017] [Accepted: 06/21/2017] [Indexed: 01/31/2023] Open
Abstract
We present a sparse Bayesian unmixing algorithm BusineX: Bayesian Unmixing for Sparse Inference-based Estimation of Fiber Crossings (X), for estimation of white matter fiber parameters from compressed (under-sampled) diffusion MRI (dMRI) data. BusineX combines compressive sensing with linear unmixing and introduces sparsity to the previously proposed multiresolution data fusion algorithm RubiX, resulting in a method for improved reconstruction, especially from data with lower number of diffusion gradients. We formulate the estimation of fiber parameters as a sparse signal recovery problem and propose a linear unmixing framework with sparse Bayesian learning for the recovery of sparse signals, the fiber orientations and volume fractions. The data is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible diffusion directions. Volume fractions of fibers along these directions define the dictionary weights. The proposed sparse inference, which is based on the dictionary representation, considers the sparsity of fiber populations and exploits the spatial redundancy in data representation, thereby facilitating inference from under-sampled q-space. The algorithm improves parameter estimation from dMRI through data-dependent local learning of hyperparameters, at each voxel and for each possible fiber orientation, that moderate the strength of priors governing the parameter variances. Experimental results on synthetic and in-vivo data show improved accuracy with a lower uncertainty in fiber parameter estimates. BusineX resolves a higher number of second and third fiber crossings. For under-sampled data, the algorithm is also shown to produce more reliable estimates.
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Affiliation(s)
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; Centre for Functional MRI of the Brain (FMRIB), University of Oxford, UK
| | | | - Guillermo Sapiro
- Electrical and Computer Engineering, Duke University, Durham, NC, USA; Biomedical Engineering and Computer Science, Duke University, Durham, NC, USA
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Vellmer S, Tonoyan AS, Suter D, Pronin IN, Maximov II. Validation of DWI pre-processing procedures for reliable differentiation between human brain gliomas. Z Med Phys 2017; 28:14-24. [PMID: 28532604 DOI: 10.1016/j.zemedi.2017.04.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Revised: 02/21/2017] [Accepted: 04/20/2017] [Indexed: 01/06/2023]
Abstract
Diffusion magnetic resonance imaging (dMRI) is a powerful tool in clinical applications, in particular, in oncology screening. dMRI demonstrated its benefit and efficiency in the localisation and detection of different types of human brain tumours. Clinical dMRI data suffer from multiple artefacts such as motion and eddy-current distortions, contamination by noise, outliers etc. In order to increase the image quality of the derived diffusion scalar metrics and the accuracy of the subsequent data analysis, various pre-processing approaches are actively developed and used. In the present work we assess the effect of different pre-processing procedures such as a noise correction, different smoothing algorithms and spatial interpolation of raw diffusion data, with respect to the accuracy of brain glioma differentiation. As a set of sensitive biomarkers of the glioma malignancy grades we chose the derived scalar metrics from diffusion and kurtosis tensor imaging as well as the neurite orientation dispersion and density imaging (NODDI) biophysical model. Our results show that the application of noise correction, anisotropic diffusion filtering, and cubic-order spline interpolation resulted in the highest sensitivity and specificity for glioma malignancy grading. Thus, these pre-processing steps are recommended for the statistical analysis in brain tumour studies.
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Affiliation(s)
- Sebastian Vellmer
- Experimental Physics III, TU Dortmund University, Dortmund, Germany.
| | | | - Dieter Suter
- Experimental Physics III, TU Dortmund University, Dortmund, Germany
| | | | - Ivan I Maximov
- Experimental Physics III, TU Dortmund University, Dortmund, Germany.
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Alexander DC, Zikic D, Ghosh A, Tanno R, Wottschel V, Zhang J, Kaden E, Dyrby TB, Sotiropoulos SN, Zhang H, Criminisi A. Image quality transfer and applications in diffusion MRI. Neuroimage 2017; 152:283-298. [DOI: 10.1016/j.neuroimage.2017.02.089] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 02/22/2017] [Accepted: 02/28/2017] [Indexed: 01/03/2023] Open
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Bahrami K, Shi F, Rekik I, Gao Y, Shen D. 7T-guided super-resolution of 3T MRI. Med Phys 2017; 44:1661-1677. [PMID: 28177548 DOI: 10.1002/mp.12132] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Revised: 12/22/2016] [Accepted: 01/13/2017] [Indexed: 11/11/2022] Open
Abstract
PURPOSE High-resolution MR images can depict rich details of brain anatomical structures and show subtle changes in longitudinal data. 7T MRI scanners can acquire MR images with higher resolution and better tissue contrast than the routine 3T MRI scanners. However, 7T MRI scanners are currently more expensive and less available in clinical and research centers. To this end, we propose a method to generate super-resolution 3T MRI that resembles 7T MRI, which is called as 7T-like MR image in this paper. METHODS First, we propose a mapping from 3T MRI to 7T MRI space, using regression random forest. The mapped 3T MR images serve as intermediate results with similar appearance as 7T MR images. Second, we predict the final higher resolution 7T-like MR images based on sparse representation, using paired local dictionaries for both the mapped 3T MR images and 7T MR images. RESULTS Based on 15 subjects with both 3T and 7T MR images, the predicted 7T-like MR images by our method can best match the ground-truth 7T MR images, compared to other methods. Meanwhile, the experiment on brain tissue segmentation shows that our 7T-like MR images lead to the highest accuracy in the segmentation of WM, GM, and CSF brain tissues, compared to segmentations of 3T MR images as well as the reconstructed 7T-like MR images by other methods. CONCLUSIONS We propose a novel method for prediction of high-resolution 7T-like MR images from low-resolution 3T MR images. Our predicted 7T-like MR images demonstrate better spatial resolution compared to 3T MR images, as well as prediction results by other comparison methods. Such high-quality 7T-like MR images could better facilitate disease diagnosis and intervention.
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Affiliation(s)
- Khosro Bahrami
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, 27510, USA
| | - Feng Shi
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, 27510, USA
| | - Islem Rekik
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, 27510, USA
| | - Yaozong Gao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, 27510, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, 27510, USA.,Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea
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Hu J, Wu X, Zhou J. Second-Order Regression-Based MR Image Upsampling. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:6462832. [PMID: 28465713 PMCID: PMC5390603 DOI: 10.1155/2017/6462832] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Revised: 03/09/2017] [Accepted: 03/15/2017] [Indexed: 11/22/2022]
Abstract
The spatial resolution of magnetic resonance imaging (MRI) is often limited due to several reasons, including a short data acquisition time. Several advanced interpolation-based image upsampling algorithms have been developed to increase the resolution of MR images. These methods estimate the voxel intensity in a high-resolution (HR) image by a weighted combination of voxels in the original low-resolution (LR) MR image. As these methods fall into the zero-order point estimation framework, they only include a local constant approximation of the image voxel and hence cannot fully represent the underlying image structure(s). To this end, we extend the existing zero-order point estimation to higher orders of regression, allowing us to approximate a mapping function between local LR-HR image patches by a polynomial function. Extensive experiments on open-access MR image datasets and actual clinical MR images demonstrate that our algorithm can maintain sharp edges and preserve fine details, while the current state-of-the-art algorithms remain prone to some visual artifacts such as blurring and staircasing artifacts.
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Affiliation(s)
- Jing Hu
- Department of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Xi Wu
- Department of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Jiliu Zhou
- Department of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
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Jain S, Sima DM, Sanaei Nezhad F, Hangel G, Bogner W, Williams S, Van Huffel S, Maes F, Smeets D. Patch-Based Super-Resolution of MR Spectroscopic Images: Application to Multiple Sclerosis. Front Neurosci 2017; 11:13. [PMID: 28197066 PMCID: PMC5281632 DOI: 10.3389/fnins.2017.00013] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Accepted: 01/09/2017] [Indexed: 01/16/2023] Open
Abstract
Purpose: Magnetic resonance spectroscopic imaging (MRSI) provides complementary information to conventional magnetic resonance imaging. Acquiring high resolution MRSI is time consuming and requires complex reconstruction techniques. Methods: In this paper, a patch-based super-resolution method is presented to increase the spatial resolution of metabolite maps computed from MRSI. The proposed method uses high resolution anatomical MR images (T1-weighted and Fluid-attenuated inversion recovery) to regularize the super-resolution process. The accuracy of the method is validated against conventional interpolation techniques using a phantom, as well as simulated and in vivo acquired human brain images of multiple sclerosis subjects. Results: The method preserves tissue contrast and structural information, and matches well with the trend of acquired high resolution MRSI. Conclusions: These results suggest that the method has potential for clinically relevant neuroimaging applications.
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Affiliation(s)
| | - Diana M Sima
- icometrix, R&DLeuven, Belgium; Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU LeuvenLeuven, Belgium
| | | | - Gilbert Hangel
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of ViennaVienna, Austria; Christian Doppler Laboratory for Clinical Molecular MR ImagingVienna, Austria
| | - Stephen Williams
- Centre for Imaging Sciences, University of Manchester Manchester, UK
| | - Sabine Van Huffel
- Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU LeuvenLeuven, Belgium; ImecLeuven, Belgium
| | - Frederik Maes
- Department of Electrical Engineering-ESAT, PSI Medical Image Computing, KU Leuven Leuven, Belgium
| | - Dirk Smeets
- icometrix, R&DLeuven, Belgium; BioImaging Lab, Universiteit AntwerpenAntwerp, Belgium
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Xie L, Pluta JB, Das SR, Wisse LEM, Wang H, Mancuso L, Kliot D, Avants BB, Ding SL, Manjón JV, Wolk DA, Yushkevich PA. Multi-template analysis of human perirhinal cortex in brain MRI: Explicitly accounting for anatomical variability. Neuroimage 2016; 144:183-202. [PMID: 27702610 DOI: 10.1016/j.neuroimage.2016.09.070] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2016] [Revised: 09/28/2016] [Accepted: 09/30/2016] [Indexed: 01/05/2023] Open
Abstract
RATIONAL The human perirhinal cortex (PRC) plays critical roles in episodic and semantic memory and visual perception. The PRC consists of Brodmann areas 35 and 36 (BA35, BA36). In Alzheimer's disease (AD), BA35 is the first cortical site affected by neurofibrillary tangle pathology, which is closely linked to neural injury in AD. Large anatomical variability, manifested in the form of different cortical folding and branching patterns, makes it difficult to segment the PRC in MRI scans. Pathology studies have found that in ~97% of specimens, the PRC falls into one of three discrete anatomical variants. However, current methods for PRC segmentation and morphometry in MRI are based on single-template approaches, which may not be able to accurately model these discrete variants METHODS: A multi-template analysis pipeline that explicitly accounts for anatomical variability is used to automatically label the PRC and measure its thickness in T2-weighted MRI scans. The pipeline uses multi-atlas segmentation to automatically label medial temporal lobe cortices including entorhinal cortex, PRC and the parahippocampal cortex. Pairwise registration between label maps and clustering based on residual dissimilarity after registration are used to construct separate templates for the anatomical variants of the PRC. An optimal path of deformations linking these templates is used to establish correspondences between all the subjects. Experimental evaluation focuses on the ability of single-template and multi-template analyses to detect differences in the thickness of medial temporal lobe cortices between patients with amnestic mild cognitive impairment (aMCI, n=41) and age-matched controls (n=44). RESULTS The proposed technique is able to generate templates that recover the three dominant discrete variants of PRC and establish more meaningful correspondences between subjects than a single-template approach. The largest reduction in thickness associated with aMCI, in absolute terms, was found in left BA35 using both regional and summary thickness measures. Further, statistical maps of regional thickness difference between aMCI and controls revealed different patterns for the three anatomical variants.
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Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
| | - John B Pluta
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, USA; Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | | | - Lauren Mancuso
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Dasha Kliot
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Brian B Avants
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, USA; School of Basic Sciences, Guangzhou Medical University, Guangzhou, China
| | - José V Manjón
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Camino de Vera s/n, Valencia, Spain
| | - David A Wolk
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, USA
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Diffusion-Weighted Images Superresolution Using High-Order SVD. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:3647202. [PMID: 27635150 PMCID: PMC5008020 DOI: 10.1155/2016/3647202] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 07/09/2016] [Accepted: 07/28/2016] [Indexed: 11/17/2022]
Abstract
The spatial resolution of diffusion-weighted imaging (DWI) is limited by several physical and clinical considerations, such as practical scanning times. Interpolation methods, which are widely used to enhance resolution, often result in blurred edges. Advanced superresolution scanning acquires images with specific protocols and long acquisition times. In this paper, we propose a novel single image superresolution (SR) method which introduces high-order SVD (HOSVD) to regularize the patch-based SR framework on DWI datasets. The proposed method was implemented on an adaptive basis which ensured a more accurate reconstruction of high-resolution DWI datasets. Meanwhile, the intrinsic dimensional decreasing property of HOSVD is also beneficial for reducing the computational burden. Experimental results from both synthetic and real DWI datasets demonstrate that the proposed method enhances the details in reconstructed high-resolution DWI datasets and outperforms conventional techniques such as interpolation methods and nonlocal upsampling.
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45
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Bahrami K, Shi F, Zong X, Shin HW, An H, Shen D. Reconstruction of 7T-Like Images From 3T MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:2085-97. [PMID: 27046894 PMCID: PMC5147737 DOI: 10.1109/tmi.2016.2549918] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
In the recent MRI scanning, ultra-high-field (7T) MR imaging provides higher resolution and better tissue contrast compared to routine 3T MRI, which may help in more accurate and early brain diseases diagnosis. However, currently, 7T MRI scanners are more expensive and less available at clinical and research centers. These motivate us to propose a method for the reconstruction of images close to the quality of 7T MRI, called 7T-like images, from 3T MRI, to improve the quality in terms of resolution and contrast. By doing so, the post-processing tasks, such as tissue segmentation, can be done more accurately and brain tissues details can be seen with higher resolution and contrast. To do this, we have acquired a unique dataset which includes paired 3T and 7T images scanned from same subjects, and then propose a hierarchical reconstruction based on group sparsity in a novel multi-level Canonical Correlation Analysis (CCA) space, to improve the quality of 3T MR image to be 7T-like MRI. First, overlapping patches are extracted from the input 3T MR image. Then, by extracting the most similar patches from all the aligned 3T and 7T images in the training set, the paired 3T and 7T dictionaries are constructed for each patch. It is worth noting that, for the training, we use pairs of 3T and 7T MR images from each training subject. Then, we propose multi-level CCA to map the paired 3T and 7T patch sets to a common space to increase their correlations. In such space, each input 3T MRI patch is sparsely represented by the 3T dictionary and then the obtained sparse coefficients are used together with the corresponding 7T dictionary to reconstruct the 7T-like patch. Also, to have the structural consistency between adjacent patches, the group sparsity is employed. This reconstruction is performed with changing patch sizes in a hierarchical framework. Experiments have been done using 13 subjects with both 3T and 7T MR images. The results show that our method outperforms previous methods and is able to recover better structural details. Also, to place our proposed method in a medical application context, we evaluated the influence of post-processing methods such as brain tissue segmentation on the reconstructed 7T-like MR images. Results show that our 7T-like images lead to higher accuracy in segmentation of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and skull, compared to segmentation of 3T MR images.
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Affiliation(s)
- Khosro Bahrami
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514 USA
| | - Feng Shi
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514 USA
| | - Xiaopeng Zong
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514 USA
| | - Hae Won Shin
- Departments of Neurology and Neurosurgery, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514 USA
| | - Hongyu An
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514 USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514 USA
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
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Non-local MRI denoising using random sampling. Magn Reson Imaging 2016; 34:990-9. [DOI: 10.1016/j.mri.2016.04.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Revised: 04/06/2016] [Accepted: 04/17/2016] [Indexed: 11/19/2022]
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Ning L, Setsompop K, Michailovich O, Makris N, Shenton ME, Westin CF, Rathi Y. A joint compressed-sensing and super-resolution approach for very high-resolution diffusion imaging. Neuroimage 2016; 125:386-400. [PMID: 26505296 PMCID: PMC4691422 DOI: 10.1016/j.neuroimage.2015.10.061] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Revised: 09/11/2015] [Accepted: 10/20/2015] [Indexed: 11/24/2022] Open
Abstract
Diffusion MRI (dMRI) can provide invaluable information about the structure of different tissue types in the brain. Standard dMRI acquisitions facilitate a proper analysis (e.g. tracing) of medium-to-large white matter bundles. However, smaller fiber bundles connecting very small cortical or sub-cortical regions cannot be traced accurately in images with large voxel sizes. Yet, the ability to trace such fiber bundles is critical for several applications such as deep brain stimulation and neurosurgery. In this work, we propose a novel acquisition and reconstruction scheme for obtaining high spatial resolution dMRI images using multiple low resolution (LR) images, which is effective in reducing acquisition time while improving the signal-to-noise ratio (SNR). The proposed method called compressed-sensing super resolution reconstruction (CS-SRR), uses multiple overlapping thick-slice dMRI volumes that are under-sampled in q-space to reconstruct diffusion signal with complex orientations. The proposed method combines the twin concepts of compressed sensing and super-resolution to model the diffusion signal (at a given b-value) in a basis of spherical ridgelets with total-variation (TV) regularization to account for signal correlation in neighboring voxels. A computationally efficient algorithm based on the alternating direction method of multipliers (ADMM) is introduced for solving the CS-SRR problem. The performance of the proposed method is quantitatively evaluated on several in-vivo human data sets including a true SRR scenario. Our experimental results demonstrate that the proposed method can be used for reconstructing sub-millimeter super resolution dMRI data with very good data fidelity in clinically feasible acquisition time.
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Affiliation(s)
- Lipeng Ning
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Kawin Setsompop
- Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | | | - Nikos Makris
- Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Martha E Shenton
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | | | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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Afzali M, Fatemizadeh E, Soltanian-Zadeh H. Interpolation of orientation distribution functions in diffusion weighted imaging using multi-tensor model. J Neurosci Methods 2015; 253:28-37. [DOI: 10.1016/j.jneumeth.2015.06.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2015] [Revised: 06/05/2015] [Accepted: 06/06/2015] [Indexed: 01/18/2023]
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49
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Chilla GS, Tan CH, Xu C, Poh CL. Diffusion weighted magnetic resonance imaging and its recent trend-a survey. Quant Imaging Med Surg 2015; 5:407-22. [PMID: 26029644 DOI: 10.3978/j.issn.2223-4292.2015.03.01] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2015] [Accepted: 01/15/2015] [Indexed: 12/14/2022]
Abstract
Since its inception in 1985, diffusion weighted magnetic resonance imaging has been evolving and is becoming instrumental in diagnosis and investigation of tissue functions in various organs including brain, cartilage, and liver. Even though brain related pathology and/or investigation remains as the main application, diffusion weighted magnetic resonance imaging (DWI) is becoming a standard in oncology and in several other applications. This review article provides a brief introduction of diffusion weighted magnetic resonance imaging, challenges involved and recent advancements.
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Affiliation(s)
- Geetha Soujanya Chilla
- 1 School of Chemical & Biomedical Engineering, Nanyang Technological University, Singapore 637459, Singapore ; 2 Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Cher Heng Tan
- 1 School of Chemical & Biomedical Engineering, Nanyang Technological University, Singapore 637459, Singapore ; 2 Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Chenjie Xu
- 1 School of Chemical & Biomedical Engineering, Nanyang Technological University, Singapore 637459, Singapore ; 2 Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Chueh Loo Poh
- 1 School of Chemical & Biomedical Engineering, Nanyang Technological University, Singapore 637459, Singapore ; 2 Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore
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50
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Guizard N, Coupé P, Fonov VS, Manjón JV, Arnold DL, Collins DL. Rotation-invariant multi-contrast non-local means for MS lesion segmentation. NEUROIMAGE-CLINICAL 2015; 8:376-89. [PMID: 26106563 PMCID: PMC4474283 DOI: 10.1016/j.nicl.2015.05.001] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2014] [Revised: 05/02/2015] [Accepted: 05/03/2015] [Indexed: 01/18/2023]
Abstract
Multiple sclerosis (MS) lesion segmentation is crucial for evaluating disease burden, determining disease progression and measuring the impact of new clinical treatments. MS lesions can vary in size, location and intensity, making automatic segmentation challenging. In this paper, we propose a new supervised method to segment MS lesions from 3D magnetic resonance (MR) images using non-local means (NLM). The method uses a multi-channel and rotation-invariant distance measure to account for the diversity of MS lesions. The proposed segmentation method, rotation-invariant multi-contrast non-local means segmentation (RMNMS), captures the MS lesion spatial distribution and can accurately and robustly identify lesions regardless of their orientation, shape or size. An internal validation on a large clinical magnetic resonance imaging (MRI) dataset of MS patients demonstrated a good similarity measure result (Dice similarity = 60.1% and sensitivity = 75.4%), a strong correlation between expert and automatic lesion load volumes (R2 = 0.91), and a strong ability to detect lesions of different sizes and in varying spatial locations (lesion detection rate = 79.8%). On the independent MS Grand Challenge (MSGC) dataset validation, our method provided competitive results with state-of-the-art supervised and unsupervised methods. Qualitative visual and quantitative voxel- and lesion-wise evaluations demonstrated the accuracy of RMNMS method. We propose a new multi-channel MS lesion segmentation technique. We adapt for lesion segmentation the non-local means operator to account for multi-contrast and rotation-invariant distance. The proposed method presents highly competitive results compared to state-of-the-art methods. The proposed method provides segmentation quality near inter-rater variability for MS lesion segmentation. Our non-local approach is able to detect structures that vary in size, shape and location such as MS lesions.
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Affiliation(s)
| | - Pierrick Coupé
- Laboratoire Bordelais de Recherche en Informatique, Unité Mixte de Recherche CNRS (UMR 5800), PICTURA Research Group, 351, Talence, France
| | | | - Jose V Manjón
- IBIME Research Group, ITACA Institute, Universidad Politécnica de Valencia, Medical Imaging Area, Valencia, Spain
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