Publications

2016
Westin C-F, Knutsson H, Pasternak O, Szczepankiewicz F, Özarslan E, van Westen D, Mattisson C, Bogren M, O'Donnell LJ, Kubicki M, et al. Q-space Trajectory Imaging for Multidimensional Diffusion MRI of the Human Brain. Neuroimage. 2016;135 :345-62.Abstract

This work describes a new diffusion MR framework for imaging and modeling of microstructure that we call q-space trajectory imaging (QTI). The QTI framework consists of two parts: encoding and modeling. First we propose q-space trajectory encoding, which uses time-varying gradients to probe a trajectory in q-space, in contrast to traditional pulsed field gradient sequences that attempt to probe a point in q-space. Then we propose a microstructure model, the diffusion tensor distribution (DTD) model, which takes advantage of additional information provided by QTI to estimate a distributional model over diffusion tensors. We show that the QTI framework enables microstructure modeling that is not possible with the traditional pulsed gradient encoding as introduced by Stejskal and Tanner. In our analysis of QTI, we find that the well-known scalar b-value naturally extends to a tensor-valued entity, i.e., a diffusion measurement tensor, which we call the b-tensor. We show that b-tensors of rank 2 or 3 enable estimation of the mean and covariance of the DTD model in terms of a second order tensor (the diffusion tensor) and a fourth order tensor. The QTI framework has been designed to improve discrimination of the sizes, shapes, and orientations of diffusion microenvironments within tissue. We derive rotationally invariant scalar quantities describing intuitive microstructural features including size, shape, and orientation coherence measures. To demonstrate the feasibility of QTI on a clinical scanner, we performed a small pilot study comparing a group of five healthy controls with five patients with schizophrenia. The parameter maps derived from QTI were compared between the groups, and 9 out of the 14 parameters investigated showed differences between groups. The ability to measure and model the distribution of diffusion tensors, rather than a quantity that has already been averaged within a voxel, has the potential to provide a powerful paradigm for the study of complex tissue architecture.

Margulies DS, Ghosh SS, Goulas A, Falkiewicz M, Huntenburg JM, Langs G, Bezgin G, Eickhoff SB, Castellanos XF, Petrides M, et al. Situating the Default-mode Network along a Principal Gradient of Macroscale Cortical Organization. Proc Natl Acad Sci U S A. 2016;113 (44) :12574-9.Abstract

Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/motor functions and at the other end, transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface-and are precisely equidistant-from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input.

Zhang M, Golland P. Statistical Shape Analysis: From Landmarks to Diffeomorphisms. Med Image Anal. 2016;33 :155-8.Abstract

We offer a blazingly brief review of evolution of shape analysis methods in medical imaging. As the representations and the statistical models grew more sophisticated, the problem of shape analysis has been gradually redefined to accept images rather than binary segmentations as a starting point. This transformation enabled shape analysis to take its rightful place in the arsenal of tools for extracting and understanding patterns in large clinical image sets. We speculate on the future developments in shape analysis and potential applications that would bring this mathematically rich area to bear on clinical practice.

Wassermann D, Makris N, Rathi Y, Shenton M, Kikinis R, Kubicki M, Westin C-F. The White Matter Query Language: A Novel Approach for Describing Human White Matter Anatomy. Brain Struct Funct. 2016;221 (9) :4705-4721.Abstract

We have developed a novel method to describe human white matter anatomy using an approach that is both intuitive and simple to use, and which automatically extracts white matter tracts from diffusion MRI volumes. Further, our method simplifies the quantification and statistical analysis of white matter tracts on large diffusion MRI databases. This work reflects the careful syntactical definition of major white matter fiber tracts in the human brain based on a neuroanatomist's expert knowledge. The framework is based on a novel query language with a near-to-English textual syntax. This query language makes it possible to construct a dictionary of anatomical definitions that describe white matter tracts. The definitions include adjacent gray and white matter regions, and rules for spatial relations. This novel method makes it possible to automatically label white matter anatomy across subjects. After describing this method, we provide an example of its implementation where we encode anatomical knowledge in human white matter for ten association and 15 projection tracts per hemisphere, along with seven commissural tracts. Importantly, this novel method is comparable in accuracy to manual labeling. Finally, we present results applying this method to create a white matter atlas from 77 healthy subjects, and we use this atlas in a small proof-of-concept study to detect changes in association tracts that characterize schizophrenia.

Chen Z, Tie Y, Olubiyi O, O'Donnell L. Corticospinal Tract Modeling for Neurosurgical Planning by Tracking through Regions of Peritumoral Edema and Crossing Fibers using Two-Tensor Unscented Kalman Filter Tractography. Int J Comput Assist Radiol Surg. 2016;11 (8) :1475-86. PubMedAbstract

PURPOSE: The aim of this study was to present a tractography algorithm using a two-tensor unscented Kalman filter (UKF) to improve the modeling of the corticospinal tract (CST) by tracking through regions of peritumoral edema and crossing fibers.
METHODS: Ten patients with brain tumors in the vicinity of motor cortex and evidence of significant peritumoral edema were retrospectively selected for the study. All patients underwent 3-T magnetic resonance imaging (MRI) including functional MRI (fMRI) and a diffusion-weighted data set with 31 directions. Fiber tracking was performed using both single-tensor streamline and two-tensor UKF tractography methods. A two-region-of-interest approach was used to delineate the CST. Results from the two tractography methods were compared visually and quantitatively. fMRI was applied to identify the functional fiber tracts.
RESULTS: Single-tensor streamline tractography underestimated the extent of tracts running through the edematous areas and could only track the medial projections of the CST. In contrast, two-tensor UKF tractography tracked fanning projections of the CST despite peritumoral edema and crossing fibers. Based on visual inspection, the two-tensor UKF tractography delineated tracts that were closer to motor fMRI activations, and it was apparently more sensitive than single-tensor streamline tractography to define the tracts directed to the motor sites. The volume of the CST was significantly larger on two-tensor UKF than on single-tensor streamline tractography ([Formula: see text]).
CONCLUSION: Two-tensor UKF tractography tracks a larger volume CST than single-tensor streamline tractography in the setting of peritumoral edema and crossing fibers in brain tumor patients.

2015
Pace DF, Dalca AV, Geva T, Powell AJ, Moghari MH, Golland P. Interactive Whole-Heart Segmentation in Congenital Heart Disease. Med Image Comput Comput Assist Interv. 2015;9351 :80-8.Abstract
We present an interactive algorithm to segment the heart chambers and epicardial surfaces, including the great vessel walls, in pediatric cardiac MRI of congenital heart disease. Accurate whole-heart segmentation is necessary to create patient-specific 3D heart models for surgical planning in the presence of complex heart defects. Anatomical variability due to congenital defects precludes fully automatic atlas-based segmentation. Our interactive segmentation method exploits expert segmentations of a small set of short-axis slice regions to automatically delineate the remaining volume using patch-based segmentation. We also investigate the potential of active learning to automatically solicit user input in areas where segmentation error is likely to be high. Validation is performed on four subjects with double outlet right ventricle, a severe congenital heart defect. We show that strategies asking the user to manually segment regions of interest within short-axis slices yield higher accuracy with less user input than those querying entire short-axis slices.
Lou Y, Tannenbaum A. Inter-modality Deformable Registration. In: Jia, X., and Jiang, S.B. (Eds.). (2015). Graphics Processing Unit-Based High Performance Computing in Radiation Therapy. Vol. Ch 10. CRC Press ; 2015.Abstract
Deformable image registration (DIR) is one of the major problems in medical image processing, such as dose calculation [18], treatment planning [33] and scatter removal of cone beam CT (CBCT) [22]. It is of prime importance to establish a pixel-to-pixel correspondence between two images in many clinical scenarios. For instance, registration of a CT image to MRI of a patient taken at different time can provide complementary diagnostic information. For applications as such, since the deformation of the patient anatomy cannot be represented by a rigid transform, DIR is almost the sole means to establish this mapping. DIR can be generally categorized into intra-modality and inter-modality, or multi-modality. While intra-modality DIR can be easily handled by conventional intensity-based methods [11, 30], intermodality DIR problems are still far from being satisfactory. Yet, since different imaging modalities usually provide their unique angles to reveal patient anatomy and delineate microscopic disease, intermodality registration plays a key role to combine the information from multiple modalities to facilitate diagnostics and treatment of a certain disease.
Lou Y, Tannenbaum A. Inter-modality Deformable Registration. In: Jia, X., and Jiang, S.B. (Eds.). (2015). Graphics Processing Unit-Based High Performance Computing in Radiation Therapy. Vol. Ch 10. CRC Press ; 2015.Abstract
Deformable image registration (DIR) is one of the major problems in medical image processing, such as dose calculation [18], treatment planning [33] and scatter removal of cone beam CT (CBCT) [22]. It is of prime importance to establish a pixel-to-pixel correspondence between two images in many clinical scenarios. For instance, registration of a CT image to MRI of a patient taken at different time can provide complementary diagnostic information. For applications as such, since the deformation of the patient anatomy cannot be represented by a rigid transform, DIR is almost the sole means to establish this mapping. DIR can be generally categorized into intra-modality and inter-modality, or multi-modality. While intra-modality DIR can be easily handled by conventional intensity-based methods [11, 30], intermodality DIR problems are still far from being satisfactory. Yet, since different imaging modalities usually provide their unique angles to reveal patient anatomy and delineate microscopic disease, intermodality registration plays a key role to combine the information from multiple modalities to facilitate diagnostics and treatment of a certain disease.
Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng DD. Multi-Phase Feature Representation Learning for Neurodegenerative Disease Diagnosis. Artificial Life and Computational Intelligence. 2015;LNAI 8955 :350-9.Abstract
Feature learning with high dimensional neuroimaging features has been explored for the applications on neurodegenerative diseases. Low-dimensional biomarkers, such as mental status test scores and cerebrospinal fluid level, are essential in clinical diagnosis of neurological disorders, because they could be simple and effective for the clinicians to assess the disorder’s progression and severity. Rather than only using the low-dimensional biomarkers as inputs for decision making systems, we believe that such low-dimensional biomarkers can be used for enhancing the feature learning pipeline. In this study, we proposed a novel feature representation learning framework, Multi-Phase Feature Representation (MPFR), with low-dimensional biomarkers embedded. MPFR learns high-level neuroimaging features by extracting the associations between the low-dimensional biomarkers and the highdimensional neuroimaging features with a deep neural network. We validated the proposed framework using the Mini-Mental-State-Examination (MMSE) scores as a low-dimensional biomarker and multi-modal neuroimaging data as the high-dimensional neuroimaging features from the ADNI baseline cohort. The proposed approach outperformed the original neural network in both binary and ternary Alzheimer’s disease classification tasks.
Liu ACALCI 2015
Klein T, Wells III WM. RF Ultrasound Distribution-Based Confidence Maps. Int Conf Med Image Comput Comput Assist Interv. 2015;18 (Pt2) :595-602.Abstract
Ultrasound is becoming an ever increasingly important modality in medical care. However, underlying physical acquisition principles are prone to image artifacts and result in overall quality variation. Therefore processing medical ultrasound data remains a challenging task. We propose a novel distribution-based measure of assessing the confidence in the signal, which emphasizes uncertainty in attenuated as well as shadow regions. In contrast to the similar recently proposed method that relies on image intensities, the new approach makes use of the enveloped-detected radio-frequency data, facilitating the use of Nakagami speckle statistics. Employing J-divergence as distance measure for the random-walk based algorithm, provides a natural measure of similarity, yielding a more reliable estimate of confidence. For evaluation of the model’s performance, tests are conducted on the application of shadow detection. Additionally, computed maps are presented for different organs such as neck, liver and prostate, showcasing the properties of the model. The probabilistic approach is shown to have beneficial features for image processing tasks.
 
Klein MICCAI 2015
King F, Jayender J, Pieper S, Kapur T, Lasso A, Fichtinger G. An Immersive Virtual Reality Environment for Diagnostic Imaging. Int Conf Med Image Comput Comput Assist Interv. 2015;18(WS). King MICCAI WS 2015
Talos I-F, Jakab M, Kikinis R. CT-based Atlas of the Abdomen. Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.; 2015. Publisher's VersionAbstract
The Surgical Planning Laboratory at Brigham and Women's Hospital, Harvard Medical School, developed the SPL Abdominal Atlas. The atlas was derived from a computed tomography (CT) scan, using semi-automated image segmentation and three-dimensional reconstruction techniques. The current version consists of: 1. the original CT scan; 2. a set of detailed label maps; 3. a set of three-dimensional models of the labeled anatomical structures; 4. a mrml-file that allows loading all of the data into the 3D Slicer for visualization (see the tutorial associated with the atlas); 5. several pre-defined 3D-views (“anatomy teaching files”). The SPL Abdominal Atlas provides important reference information for surgical planning, anatomy teaching, and template driven segmentation. Visualization of the data requires Slicer 3. This software package can be downloaded from here. We are pleased to make this atlas available to our colleagues for free download. Please note that the data is being distributed under the Slicer license. By downloading these data, you agree to acknowledge our contribution in any of your publications that result form the use of this atlas. 
The Slicer4 version archived in a mrb (Medical Reality Bundle) file that contains the mrml scene file and all data for loading into Slicer 4 for displaying the volumes in 3D Slicer version 4.0 or greater, available for download.
This work is funded as part of the Neuroimaging Analysis Center, grant number P41 RR013218, by the NIH's National Center for Research Resources (NCRR) and grant number P41 EB015902, by the NIH's National Institute of Biomedical Imaging and Bioengineering (NIBIB) and the Google Faculty Research Award.
Contributors: Matthew D'Artista, Alex Kikinis, Tobias Schmidt, Svenja van der Gaag.
This atlas maybe viewed with our Open Anatomy Browser.
Richolt J, Jakab M, Kikinis R. MRI-based Atlas of the Knee. Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.; 2015. Publisher's VersionAbstract
The Surgical Planning Laboratory at Brigham and Women's Hospital, Harvard Medical School, developed the SPL Knee Atlas. The atlas was derived from a MRI scan, using semi-automated image segmentation and three-dimensional reconstruction techniques. The current version consists of: 1. the original MRI scan; 2. a set of detailed label maps; 3. a set of three-dimensional models of the labeled anatomical structures; 4. a mrml-file that allows loading all of the data into the 3D Slicer for visualization. 5. several pre-defined 3D views (“anatomy teaching files”). The SPL Knee Atlas provides important reference information for anatomy teaching, and template driven segmentation. Visualization of the data requires Slicer 3. This software package can be downloaded from here. We are pleased to make this atlas available to our colleagues for free download. Please note that the data is being distributed under the Slicer license. By downloading these data, you agree to acknowledge our contribution in any of your publications that result form the use of this atlas. 
The Slicer4 version archived in a mrb (Medical Reality Bundle) file that contains the mrml scene file and all data for loading into Slicer 4 for displaying the volumes in 3D Slicer version 4.0 or greater, available for download.
This work is funded as part of the Neuroimaging Analysis Center, grant number P41 RR013218, by the NIH's National Center for Research Resources (NCRR) and grant number P41 EB015902, by the NIH's National Institute of Biomedical Imaging and Bioengineering (NIBIB) and the Google Faculty Research Award.
Contributors: Matthew D'Artista, Alex Kikinis.
This atlas maybe viewed with our Open Anatomy Browser.
Jakab M, Kikinis R. CT-based Atlas of the Head and Neck. Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.; 2015.Abstract
This Head and Neck Atlas has been made available by the Surgical Planning Laboratory at Brigham and Women's Hospital. The data set consists of: 1. Reduced resolution (256x256) of the MANIX data set from the OSIRIX data sets. 2. A set of detailed label maps. 3. A set of three-dimensional models of the labeled anatomical structures. 4. Several pre-defined Scene Views (“anatomy teaching files”). 5. Annotation as supplementary information associated with a scene. 6. Anatomical model hierarchy. All in a mrb (Medical Reality Bundle) archive file that contains the mrml scene file and all data for loading into Slicer 4 for displaying the volumes in 3D Slicer version 4.0 or greater, available for download. The atlas data is made available under terms of the 3D Slicer License section B.
This work is funded as part of the Neuroimaging Analysis Center, grant number P41 EB015902, by the NIH's National Institute of Biomedical Imaging and Bioengineering (NIBIB) and the Google Faculty Research Award.
Contributors: Neha Agrawal, Matthew D'Artista, Susan Kikinis, Dashawn Richardson, Daniel Sachs.
This atlas maybe viewed with our Open Anatomy Browser.
O'Donnell LJ, Pasternak O. Does diffusion MRI tell us anything about the white matter? An overview of methods and pitfalls. Schizophr Res. 2015;161 (1) :133-41.Abstract
One key pitfall in diffusion magnetic resonance imaging (dMRI) clinical neuroimaging research is the challenge of understanding and interpreting the results of a complex analysis pipeline. The sophisticated algorithms employed by the analysis software, combined with the relatively non-specific nature of many diffusion measurements, lead to challenges in interpretation of the results. This paper is aimed at an intended audience of clinical researchers who are learning about dMRI or trying to interpret dMRI results, and who may be wondering "Does dMRI tell us anything about the white matter?" We present a critical review of dMRI methods and measures used in clinical neuroimaging research, focusing on the most commonly used analysis methods and the most commonly reported measures. We describe important pitfalls in every section, and provide extensive references for the reader interested in more detail.
Pasternak O, Westin C-F, Dahlben B, Bouix S, Kubicki M. The Extent of Diffusion MRI Markers of Neuroinflammation and White Matter Deterioration in Chronic Schizophrenia. Schizophr Res. 2015;161 (1) :113-8.Abstract
In a previous study we have demonstrated, using a novel diffusion MRI analysis called free-water imaging, that the early stages of schizophrenia are more likely associated with a neuroinflammatory response and less so with a white matter deterioration or a demyelination process. What is not known is how neuroinflammation and white matter deterioration change along the progression of the disorder. In this study we apply the free-water measures on a population of 29 chronic schizophrenia subjects and compare them with 25 matching controls. Our aim was to compare the extent of free-water imaging abnormalities in chronic subjects with the ones previously obtained for subjects at their first psychotic episode. We find that chronic subjects showed a limited extent of abnormal increase in the volume of the extracellular space, suggesting a less extensive neuroinflammatory response relative to patients at the onset of schizophrenia. At the same time, the chronic schizophrenia subjects had greater extent of reduced fractional anisotropy compared to the previous study, suggesting increased white matter deterioration along the progression of the disease. Our findings substantiate the role of neuroinflammation in the earlier stages of the disorder, and the effect of neurodegeneration that is worsening in the chronic phase.
Balasubramanian M, Mulkern RV, Wells III WM, Sundaram P, Orbach DB. Magnetic Resonance Imaging of Ionic Currents in Solution: The Effect of Magnetohydrodynamic Flow. Magn Reson Med. 2015;74 (4) :1145-55.Abstract

PURPOSE: Reliably detecting MRI signals in the brain that are more tightly coupled to neural activity than blood-oxygen-level-dependent fMRI signals could not only prove valuable for basic scientific research but could also enhance clinical applications such as epilepsy presurgical mapping. This endeavor will likely benefit from an improved understanding of the behavior of ionic currents, the mediators of neural activity, in the presence of the strong magnetic fields that are typical of modern-day MRI scanners. THEORY: Of the various mechanisms that have been proposed to explain the behavior of ionic volume currents in a magnetic field, only one-magnetohydrodynamic flow-predicts a slow evolution of signals, on the order of a minute for normal saline in a typical MRI scanner. METHODS: This prediction was tested by scanning a volume-current phantom containing normal saline with gradient-echo-planar imaging at 3 T. RESULTS: Greater signal changes were observed in the phase of the images than in the magnitude, with the changes evolving on the order of a minute. CONCLUSION: These results provide experimental support for the MHD flow hypothesis. Furthermore, MHD-driven cerebrospinal fluid flow could provide a novel fMRI contrast mechanism.

Szczepankiewicz F, Lasič S, van Westen D, Sundgren PC, Englund E, Westin C-F, Ståhlberg F, Lätt J, Topgaard D, Nilsson M. Quantification of Microscopic Diffusion Anisotropy Disentangles Effects of Orientation Dispersion from Microstructure: Applications in Healthy Volunteers and in Brain Tumors. Neuroimage. 2015;104 :241-52.Abstract
The anisotropy of water diffusion in brain tissue is affected by both disease and development. This change can be detected using diffusion MRI and is often quantified by the fractional anisotropy (FA) derived from diffusion tensor imaging (DTI). Although FA is sensitive to anisotropic cell structures, such as axons, it is also sensitive to their orientation dispersion. This is a major limitation to the use of FA as a biomarker for "tissue integrity", especially in regions of complex microarchitecture. In this work, we seek to circumvent this limitation by disentangling the effects of microscopic diffusion anisotropy from the orientation dispersion. The microscopic fractional anisotropy (μFA) and the order parameter (OP) were calculated from the contrast between signal prepared with directional and isotropic diffusion encoding, where the latter was achieved by magic angle spinning of the q-vector (qMAS). These parameters were quantified in healthy volunteers and in two patients; one patient with meningioma and one with glioblastoma. Finally, we used simulations to elucidate the relation between FA and μFA in various micro-architectures. Generally, μFA was high in the white matter and low in the gray matter. In the white matter, the largest differences between μFA and FA were found in crossing white matter and in interfaces between large white matter tracts, where μFA was high while FA was low. Both tumor types exhibited a low FA, in contrast to the μFA which was high in the meningioma and low in the glioblastoma, indicating that the meningioma contained disordered anisotropic structures, while the glioblastoma did not. This interpretation was confirmed by histological examination. We conclude that FA from DTI reflects both the amount of diffusion anisotropy and orientation dispersion. We suggest that the μFA and OP may complement FA by independently quantifying the microscopic anisotropy and the level of orientation coherence.
Poynton CB, Jenkinson M, Adalsteinsson E, Sullivan EV, Pfefferbaum A, Wells III WM. Quantitative Susceptibility Mapping by Inversion of a Perturbation Field Model: Correlation with Brain Iron in Normal Aging. IEEE Trans Med Imaging. 2015;34 (1) :339-53.Abstract

There is increasing evidence that iron deposition occurs in specific regions of the brain in normal aging and neurodegenerative disorders such as Parkinson's, Huntington's, and Alzheimer's disease. Iron deposition changes the magnetic susceptibility of tissue, which alters the MR signal phase, and allows estimation of susceptibility differences using quantitative susceptibility mapping (QSM). We present a method for quantifying susceptibility by inversion of a perturbation model, or "QSIP." The perturbation model relates phase to susceptibility using a kernel calculated in the spatial domain, in contrast to previous Fourier-based techniques. A tissue/air susceptibility atlas is used to estimate B0 inhomogeneity. QSIP estimates in young and elderly subjects are compared to postmortem iron estimates, maps of the Field-Dependent Relaxation Rate Increase, and the L1-QSM method. Results for both groups showed excellent agreement with published postmortem data and in vivo FDRI: statistically significant Spearman correlations ranging from Rho=0.905 to Rho=1.00 were obtained. QSIP also showed improvement over FDRI and L1-QSM: reduced variance in susceptibility estimates and statistically significant group differences were detected in striatal and brainstem nuclei, consistent with age-dependent iron accumulation in these regions.

Liu S, Liu S, Cai W, Che H, Pujol S, Kikinis R, Feng D, Fulham MJ. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. IEEE Trans Biomed Eng. 2015;62 (4) :1132-40.Abstract
The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease. Although studies have applied machine learning methods for the computer-aided diagnosis of AD, a bottleneck in the diagnostic performance was shown in previous methods, due to the lacking of efficient strategies for representing neuroimaging biomarkers. In this study, we designed a novel diagnostic framework with deep learning architecture to aid the diagnosis of AD. This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities. Compared to the previous state-of-the-art workflows, our method is capable of fusing multimodal neuroimaging features in one setting and has the potential to require less labeled data. A performance gain was achieved in both binary classification and multiclass classification of AD. The advantages and limitations of the proposed framework are discussed.

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