KEYWORDS: Motion estimation, Magnetic resonance imaging, 3D image processing, 3D image reconstruction, 3D acquisition, Head, 3D modeling, Reconstruction algorithms, Medical imaging, Fetus
We describe a free software tool which combines a set of algorithms that provide a framework for building 3D
volumetric images of regions of moving anatomy using multiple fast multi-slice MRI studies. It is specifically
motivated by the clinical application of unsedated fetal brain imaging, which has emerged as an important area
for image analysis. The tool reads multiple DICOM image stacks acquired in any angulation into a consistent
patient coordinate frame and allows the user to select regions to be locally motion corrected. It combines
algorithms for slice motion estimation, bias field inconsistency correction and 3D volume reconstruction from
multiple scattered slice stacks. The tool is built onto the RView (http://rview.colin-studholme.net) medical
image display software and allows the user to inspect slice stacks, and apply both stack and slice level motion
estimation that incorporates temporal constraints based on slice timing and interleave information read from
the DICOM data. Following motion estimation an algorithm for bias field inconsistency correction provides the
user with the ability to remove artifacts arising from the motion of the local anatomy relative to the imaging
coils. Full 3D visualization of the slice stacks and individual slice orientations is provided to assist in evaluating
the quality of the motion correction and final image reconstruction. The tool has been evaluated on a range of
clinical data acquired on GE, Siemens and Philips MRI scanners.
Understanding human brain development in utero and detecting cortical abnormalities related to specific clinical
conditions is an important area of research. In this paper, we describe and evaluate methodology for detection and
mapping of delays in early cortical folding from population-based studies of fetal brain anatomies imaged in utero.
We use a general linear modeling framework to describe spatiotemporal changes in curvature of the developing
brain and explore the ability to detect and localize delays in cortical folding in the presence of uncertainty
in estimation of the fetal age. We apply permutation testing to examine which regions of the brain surface
provide the most statistical power to detect a given folding delay at a given developmental stage. The presented
methodology is evaluated using MR scans of fetuses with normal brain development and gestational ages ranging
from 20.57 to 27.86 weeks. This period is critical in early cortical folding and the formation of the primary and
secondary sulci. Finally, we demonstrate a clinical application of the framework for detection and localization of
folding delays in fetuses with isolated mild ventriculomegaly.
Recent studies reported the development of methods for rigid registration of 2D fetal brain imaging data to
correct for unconstrained fetal and maternal motion, and allow the formation of a true 3D image of conventional
fetal brain anatomy from conventional MRI. Diffusion tensor imaging provides additional valuable insight into
the developing brain anatomy, however the correction of motion artifacts in clinical fetal diffusion imaging is
still a challenging problem. This is due to the challenging problem of matching lower signal-to-noise ratio
diffusion weighted EPI slice data to recover between-slice motion, compounded by the presence of possible
geometric distortions in the EPI data. In addition, the problem of estimating a diffusion model (such as a
tensor) on a regular grid that takes into account the inconsistent spatial and orientation sampling of the diffusion
measurements needs to be solved in a robust way. Previous methods have used slice to volume registration within
the diffusion dataset. In this work, we describe an alternative approach that makes use of an alignment of diffusion
weighted EPI slices to a conventional structural MRI scan which provides a geometrically correct reference image.
After spatial realignment of each diffusion slice, a tensor field representing the diffusion profile is estimated by
weighted least squared fitting. By qualitative and quantitative evaluation of the results, we confirm the proposed
algorithm successfully corrects the motion and reconstructs the diffusion tensor field.
Recent advances in MR and image analysis allow for reconstruction of high-resolution 3D images from clinical
in utero scans of the human fetal brain. Automated segmentation of tissue types from MR images (MRI) is
a key step in the quantitative analysis of brain development. Conventional atlas-based methods for adult brain
segmentation are limited in their ability to accurately delineate complex structures of developing tissues from
fetal MRI. In this paper, we formulate a novel geometric representation of the fetal brain aimed at capturing the
laminar structure of developing anatomy. The proposed model uses a depth-based encoding of tissue occurrence
within the fetal brain and provides an additional anatomical constraint in a form of a laminar prior that can
be incorporated into conventional atlas-based EM segmentation. Validation experiments are performed using
clinical in utero scans of 5 fetal subjects at gestational ages ranging from 20.5 to 22.5 weeks. Experimental
results are evaluated against reference manual segmentations and quantified in terms of Dice similarity coefficient
(DSC). The study demonstrates that the use of laminar depth-encoded tissue priors improves both the overall
accuracy and precision of fetal brain segmentation. Particular refinement is observed in regions of the parietal
and occipital lobes where the DSC index is improved from 0.81 to 0.82 for cortical grey matter, from 0.71 to
0.73 for the germinal matrix, and from 0.81 to 0.87 for white matter.
Neural networks (NN) are typically developed to minimize the squared difference between the network's output and the target value for a set of training patterns; namely the mean squared error (MSE). However, lower MSE does not necessarily translate into a clinically more useful decision model. The purpose of this study was to investigate the particle swarm optimization (PSO) algorithm as an alternative way of NN optimization with clinically relevant objective functions (e.g., ROC and partial ROC area indices). The PSO algorithm was evaluated with respect to a NN-based CAD system developed to discriminate mammographic regions of interest (ROIs) that contained masses from normal regions based on 8 computer-extracted morphology-oriented features. Neural networks were represented as points (particle locations) in a D-dimensional search/optimization space where each dimension corresponded to one adaptable NN parameter. The study database of 1,337 ROIs (681 with masses, 656 normal) was split into two subsets to implement two-fold cross-validation sampling scheme. Neural networks were optimized with the PSO algorithm and the following objective functions (1) MSE, (2) ROC area index AUC, and (3) partial ROC area indices TPFAUC with TPF=0.90 and TPF=0.98. For comparison, performance of neural networks of the same architecture trained with the traditional backpropagation algorithm was also evaluated. Overall, the study showed that when the PSO algorithm optimized network parameters using a particular training objective, the NN test performance was superior with respect to the corresponding performance index. This was particularly true for the partial ROC area indices where statistically significant improvements were observed.
Mutual information is a popular intensity-based image similarity measure mainly used in image registration. This measure has been also very successful as the similarity metric in our knowledge-based computer-assisted detection (CADe) system for the detection of masses in screening mammograms. Our CADe system is designed to assess a new, query case based on its similarity with known cases stored in the knowledge database. However, intensity-based mutual information captures only relationships between the gray level values of corresponding pixels. This study presents a novel advancement of our CADe system by incorporating neighborhood textural information when estimating the mutual information of two images. Specifically, an entropy filter is applied to the images, effectively replacing each image pixel value with its neighborhood entropy. This pixel-based entropy is a localized measure of image texture. Then, the information-theoretic CAD system is asked to make a decision regarding the query case using the texture-based mutual information similarity metric. The entropy-based image enhancement and MI-based decision making processes are repeated at different neighborhood scales. Finally, an artificial network merges intensity-based and texture-based decisions to investigate possible improvements in mass detection performance. Given a database of 1,820 regions of interest (ROIs) extracted from screening mammograms (901 depicting a biopsy-proven mass and 919 depicting normal parenchyma) and a leave-one out sampling scheme, the study showed that our CADe system achieves an ROC area of 0.87±0.01 using the intensity-based ROC. The ROC performance for the texture-based CADe system ranges from 0.69±0.01 to 0.83±0.01 depending on the scale of analysis. The synergistic approach of the ANN using both intensity-based and texture-based information resulted in statistically significantly better performance with an ROC area index of 0.93±0.01.
We present a risk stratification methodology for predictions made by computer-assisted detection (CAD) systems.
For each positive CAD prediction, the proposed technique assigns an individualized confidence measure
as a function of the actual CAD output, the case-specific uncertainty of the prediction estimated from the
system's performance for similar cases and the value of the operating decision threshold. The study was performed using a mammographic database containing 1,337 regions of interest (ROIs) with known ground truth
(681 with masses, 656 with normal parenchyma). Two types of decision models (1) a support vector machine
(SVM) with a radial basis function kernel and (2) a back-propagation neural network (BPNN) were developed
to detect masses based on 8 morphological features automatically extracted from each ROI. The study shows
that as requirements on the minimum confidence value are being restricted, the positive predictive value (PPV)
for qualifying cases steadily improves (from PPV = 0.73 to PPV = 0.97 for the SVM, from PPV = 0.67 to
PPV = 0.95 for the BPNN). The proposed confidence metric was successfully applied for stratification of CAD
recommendations into 3 categories of different expected reliability: HIGH (PPV = 0.90), LOW (PPV = 0.30)
and MEDIUM (all remaining cases). Since radiologists often disregard accurate CAD cues, an individualized
confidence measure should improve their ability to correctly process visual cues and thus reduce the interpretation
error associated with the detection task. While keeping the clinically determined operating point satisfied,
the proposed methodology draws the CAD users' attention to cases/regions of highest risk while helping them
confidently eliminate cases with low risk.
We introduce a computer-assisted detection (CAD) system for the automated detection of breast masses in screening mammograms. The system targets the directional behavior of the neighborhood pixels surrounding a reference image pixel. The underlying hypothesis is that in the presence of a mass the directional properties of the breast tissue surrounding the mass should be altered. The hypothesis was tested using a database of 1,337 mammographic regions of interest (ROIs) extracted from DDSM mammograms. There were 681 ROIs containing a biopsy-proven mass centered in the ROI (340 malignant, 341 benign) and 656 ROIs depicting normal breast parenchyma. Initially, eight main directional propagations were identified and modeled given the center of the ROI as the reference pixel. Subsequently, eight novel morphological features were extracted for each direction. The features were designed to characterize the disturbance occurring in normal breast parenchyma due to the presence of a mass. Finally, the extracted features were merged using a back propagation neural network (BPANN). The network served as a non linear classifier trained to determine the presence of a mass centered at the reference image pixel. The BPANN was trained and tested using a leave-one-out sampling scheme. Its performance was evaluated with Receiver Operating Characteristics (ROC) analysis. Our CAD system showed an ROC area index of Az=0.88±0.01 for discriminating mass vs. normal ROIs. Detection performance was robust for both malignant (Az=0.88±0.01) and benign masses (Az=0.87±0.01). Thus, the proposed directional neighborhood analysis (DNA) can be applied effectively to identify suspicious masses in screening mammograms.
We present a novel technique that provides a case-specific confidence measure for artificial neural network (ANN) based computer-assisted diagnostic (CAD) decisions. The technique relies on the analysis of the feature space neighborhood for each query case and dynamically creates a validation set that allows estimation of a local accuracy of the decisions made by the network. Then a case-specific reliability measure is assigned to each system's response, which can be used to stratify network's predictions according to the acceptable validation error value. The study was performed using a database containing 1,337 mammographic regions of interest (ROIs) with biopsy-proven diagnosis (681 with masses, 656 with normal parenchyma). Two types of neural networks (1) a feed forward network with error back propagation (BPNN) and (2) a generalized regression neural network with RBF nodes (GRNN) were developed to detect masses based on 8 morphological features automatically extracted from each ROI. The performance of the networks was evaluated with Receiver Operating Characteristics (ROC) analysis. The study shows that as the threshold on the acceptable validation error declines, the technique rejects more CAD decisions as not reliable enough. However, the ROC performance for the reliable results steadily improves (from Az = 0.88 to Az = 0.98 for BPNN, from Az = 0.86 to Az = 0.97 for GRNN). The proposed technique provides a stratification strategy for predictions made by CAD tools and can be applied to any type of decision algorithms.
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