KEYWORDS: Image registration, Picture Archiving and Communication System, Magnetic resonance imaging, Radiology, Tumors, Brain, 3D image processing, Neuroimaging
Spatial alignment of longitudinal images has been shown to improve the ability of radiologists to evaluate anatomical changes over time. However, although automated alignment using image registration algorithms is routinely performed in research applications, it is not as common within clinical workflows, partly due to limitations in the capabilities of many PACS viewing systems, as well as concerns regarding modification of the original image data. In this work, we propose an approach to integrate spatial registration into radiology workflows without altering the original pixel intensities of the acquired data. Baseline images and follow-up images are received from the PACS, co-registered within a standard reference space, and sent back to the PACS as a separate study. A key advantage of our approach is that the pixel data is not interpolated or modified in any way. The alignment of images is specified solely through modification of DICOM tags, allowing multiplanar reformation features of the PACS viewer to perform the spatial transformations. By leaving the imaging data intact, artifacts that may result from multiple transformations or oscillating interpolating functions are minimized. We demonstrate the use of this approach on magnetic resonance images of the brain, although the framework is applicable to any type of three-dimensional radiological imaging.
White matter lesion (WML) segmentation applied to magnetic resonance imaging (MRI) scans of people with multiple sclerosis has been an area of extensive research in recent years. As with most tasks in medical imaging, deep learning (DL) methods have proven very effective and have quickly replaced existing methods. Despite the improvement offered by these networks, there are still shortcomings with these DL approaches. In this work, we compare several DL algorithms, as well as methods for ensembling the results of those algorithms, for performing MS lesion segmentation. An ensemble approach is shown to best estimate total WML and has the highest agreement with manual delineations.
Fully automatic classification of magnetic resonance (MR) brain images into different contrasts is desirable for facilitating image processing pipelines, as well as for indexing and retrieving from medical image archives. In this paper, we present an approach based on a Siamese neural network to learn a discriminative feature representation for MR contrast classification. The proposed method is shown to outperform a traditional deep convolutional neural network method and a template matching method in identifying five different MR contrasts of input brain volumes with a variety of pathologies, achieving 98.59% accuracy. In addition, our approach permits one-shot learning, which allows generalization to new classes not seen in the training set with only one example of each new class. We demonstrate accurate one-shot learning performance on a sixth MR contrast that was not included in the original training.
This study examines the spatial distribution of microhemorrhages defined using susceptibility weighted images (SWI) in 46 patients with Traumatic Brain Injury (TBI) and applying region of interest (ROI) analysis using a brain atlas. SWI and 3D T1-weighted images were acquired on a 3T clinical Siemens scanner. A neuroradiologist reviewed all SWI images and manually labeled all identified microhemorrhages. To characterize the spatial distribution of microhemorrhages in standard Montreal Neurological Institute (MNI) space, the T1-weighted images were nonlinearly registered to the MNI template. This transformation was then applied to the co-registered SWI images and to the microhemorrhage coordinates. The frequencies of microhemorrhages were determined in major structures from ROIs defined in the digital Talairach brain atlas and in white matter tracts defined using a diffusion tensor imaging atlas. A total of 629 microhemorrhages were found with an average of 22±42 (range=1-179) in the 24 positive TBI patients. Microhemorrhages mostly congregated around the periphery of the brain and were fairly symmetrically distributed, although a number were found in the corpus callosum. From Talairach ROI analysis, microhemorrhages were most prevalent in the frontal lobes (65.1%). Restricting the analysis to WM tracts, microhemorrhages were primarily found in the corpus callosum (56.9%).
Robust and automatic non-rigid registration depends on many parameters that have not yet been systematically
explored. Here we determined how tissue classification influences non-linear fluid registration of brain MRI. Twin
data is ideal for studying this question, as volumetric correlations between corresponding brain regions that are
under genetic control should be higher in monozygotic twins (MZ) who share 100% of their genes when compared
to dizygotic twins (DZ) who share half their genes on average. When these substructure volumes are quantified
using tensor-based morphometry, improved registration can be defined based on which method gives higher MZ
twin correlations when compared to DZs, as registration errors tend to deplete these correlations. In a study of 92
subjects, higher effect sizes were found in cumulative distribution functions derived from statistical maps when
performing tissue classification before fluid registration, versus fluidly registering the raw images. This gives
empirical evidence in favor of pre-segmenting images for tensor-based morphometry.
Natasha Lepore, Anand Joshi, Richard Leahy, Caroline Brun, Yi-Yu Chou, Xavier Pennec, Agatha Lee, Marina Barysheva, Greig De Zubicaray, Margaret Wright, Katie McMahon, Arthur Toga, Paul Thompson
3D registration of brain MRI data is vital for many medical imaging applications. However, purely intensitybased
approaches for inter-subject matching of brain structure are generally inaccurate in cortical regions, due
to the highly complex network of sulci and gyri, which vary widely across subjects. Here we combine a surfacebased
cortical registration with a 3D fluid one for the first time, enabling precise matching of cortical folds,
but allowing large deformations in the enclosed brain volume, which guarantee diffeomorphisms. This greatly
improves the matching of anatomy in cortical areas. The cortices are segmented and registered with the software
Freesurfer. The deformation field is initially extended to the full 3D brain volume using a 3D harmonic mapping
that preserves the matching between cortical surfaces. Finally, these deformation fields are used to initialize a 3D
Riemannian fluid registration algorithm, that improves the alignment of subcortical brain regions. We validate
this method on an MRI dataset from 92 healthy adult twins. Results are compared to those based on volumetric
registration without surface constraints; the resulting mean templates resolve consistent anatomical features
both subcortically and at the cortex, suggesting that the approach is well-suited for cross-subject integration of
functional and anatomic data.
We developed an automated analysis pipeline to analyze 3D changes in ventricular morphology; it provides a highly
sensitive quantitative marker of Alzheimer's disease (AD) progression for MRI studies. In the ADNI image database, we
created expert delineations of the ventricles, as parametric surface meshes, in 6 brain MRI scans. These 6 images and
their embedded surfaces were fluidly registered to MRI scans of 80 AD patients, 80 individuals with mild cognitive
impairment (MCI), and 80 healthy controls. Surface averaging within subjects greatly reduced segmentation error.
Surface-based statistical maps revealed powerful correlations between surface morphology at baseline and (1) diagnosis,
(2) cognitive performance (MMSE scores), (3) depression, and (4) predicted future decline, over a 1 year interval, in 3
standard clinical scores (MMSE, global and sum-of-boxes CDR). We used a false discovery rate method (FDR) method
based on cumulative probability plots to find that 40 subjects were sufficient to discriminate AD from normal groups. 60
and 119 subjects, respectively, were required to correlate ventricular enlargement with MMSE and clinical depression.
Surface-based FDR, along with multi-atlas fluid registration to reduce segmentation error, will allow researchers to (1)
estimate sample sizes with adequate power to detect groups differences, and (2) compare the power of mapping methods
head-to-head, optimizing cost-effectiveness for future clinical trials.
KEYWORDS: Image registration, 3D image processing, Neuroimaging, Brain, Magnetic resonance imaging, 3D acquisition, Medical imaging, Detection and tracking algorithms, Magnetism, Binary data
Fluid registration is widely used in medical imaging to track anatomical changes, to correct image distortions,
and to integrate multi-modality data. Fluid mappings guarantee that the template image deforms smoothly into
the target, without tearing or folding, even when large deformations are required for accurate matching.
Here we implemented an intensity-based fluid registration algorithm, accelerated by using a filter designed
by Bro-Nielsen and Gramkow. We validated the algorithm on 2D and 3D geometric phantoms using the mean
square difference between the final registered image and target as a measure of the accuracy of the registration.
In tests on phantom images with different levels of overlap, varying amounts of Gaussian noise, and different
intensity gradients, the fluid method outperformed a more commonly used elastic registration method, both in
terms of accuracy and in avoiding topological errors during deformation. We also studied the effect of varying
the viscosity coefficients in the viscous fluid equation, to optimize registration accuracy. Finally, we applied the
fluid registration algorithm to a dataset of 2D binary corpus callosum images and 3D volumetric brain MRIs
from 14 healthy individuals to assess its accuracy and robustness.
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