Cardiac Magnetic Resonance (CMR) requires synchronization with the ECG to correct many types of noise. However, the complex heart motion frequently produces displaced slices that have to be either ignored or manually corrected since the ECG correction is useless in this case. This work presents a novel methodology that detects the motion artifacts in CMR using a saliency method that highlights the region where the heart chambers are located. Once the Region of Interest (RoI) is set, its center of gravity is determined for the set of slices composing the volume. The deviation of the gravity center is an estimation of the coherence between the slices and is used to find out slices with certain displacement. Validation was performed with distorted real images where a slice is artificially misaligned with respect to set of slices. The displaced slice is found with a Recall of 84% and F Score of 68%.
Autism Spectrum Disorder (ASD) is a complex neurological condition characterized by a triad of signs: stereotyped behaviors, verbal and non-verbal communication problems. The scientific community has been interested on quantifying anatomical brain alterations of this disorder. Several studies have focused on measuring brain cortical and sub-cortical volumes. This article presents a fully automatic method which finds out differences among patients diagnosed with autism and control patients. After the usual pre-processing, a template (MNI152) is registered to an evaluated brain which becomes then a set of regions. Each of these regions is the represented by the normalized histogram of intensities which is approximated by mixture of Gaussian (GMM). The gray and white matter are separated to calculate the mean and standard deviation of each Gaussian. These features are then used to train, region per region, a binary SVM classifier. The method was evaluated in an adult population aged from 18 to 35 years, from the public database Autism Brain Imaging Data Exchange (ABIDE). Highest discrimination values were found for the Right Middle Temporal Gyrus, with an Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) the curve of 0.72.
Magnetic resonance imaging (MRI) is widely used in medicine nowadays, yet a significant disadvantage is the amount of artifacts that affect the image during the acquisition process. This paper presents a strategy for automatic damage detection when the image is altered by movement or there is a loss of information due to magnetic susceptibility. This approach uses a conventional SV D to detect the variability between slices of the image and a region of damaged voxels within the volume. Using a simple derivative algorithm, the method was tested in several cases automatically revealing the distortion's location with a performance of 74% for slice damage and 55% for the volume's damaged region.
In this paper, a Bayesian super resolution (SR) method obtains high resolution (HR) brain Diffusion-Weighted Magnetic Resonance Imaging (DMRI) images from degraded low resolution (LR) images. Under a Bayesian formulation, the unknown HR image, the acquisition process and the unknown parameters are modeled as stochastic processes. The likelihood model is modeled using a Gaussian distribution to estimate the error between the a linear representation and the observations. The prior is introduced as a Multivariate Gaussian Distribution, for which the inverse of the covariance matrix is approximated by Laplacian-like functions that model the local relationships, capturing thereby non-homogeneous relationships between neighbor intensities. Experimental results show the method outperforms the base line by 2.56 dB when using PSNR as a metric of quality in a set of 35 cases.
Autism Spectrum Disorder (ASD) is a very complex neuro-developmental entity characterized by a wide range of signs. The high variability of reported anatomical changes has arisen the interest of the community to characterize the different patterns of the disorder. Studies so far have focused on measuring the volume of the cerebral cortex as well as the inner brain regions of the brain, and some studies have described consistent changes. This paper presents an automatic method that separates cases with autism from controls in a population between 18 to 35 years extracted from the open database Autism Brain Imaging Data Exchange (ABIDE). The method starts by segmenting a new case, using the delineations associated to the template MNI152. For doing so, the template is non rigidly registered to the input brain. Once these cortical and sub-cortical regions are available, each region is characterized by the histogram of intensities which is normalized. The Kullback-Leibler distance is used as a metric for training a binary SVM classifier, region per region. The highest discrimination values were found for the Right Superior Temporal Gyrus, region which the Area is Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve was 0.67.
Alzheimer's disease (AD) is a neurodegenerative disease that affects higher brain functions. Initial diagnosis of AD is based on the patient's clinical history and a battery of neuropsychological tests. The accuracy of the diagnosis is highly dependent on the examiner's skills and on the evolution of a variable clinical frame. This work presents an automatic strategy that learns probabilistic brain models for different stages of the disease, reducing the complexity, parameter adjustment and computational costs. The proposed method starts by setting a probabilistic class description using the information stored in the neuropsychological test, followed by constructing the different structural class models using membership values from the learned probabilistic functions. These models are then used as a reference frame for the classification problem: a new case is assigned to a particular class simply by projecting to the different models. The validation was performed using a leave-one-out cross-validation, two classes were used: Normal Control (NC) subjects and patients diagnosed with mild AD. In this experiment it is possible to achieve a sensibility and specificity of 80% and 79% respectively.
Fetal Magnetic Resonance (FMR) is an imaging technique that is becoming increasingly important as allows assessing brain development and thus make an early diagnostic of congenital abnormalities, spatial resolution is limited by the short acquisition time and the unpredictable fetus movements, in consequence the resulting images are characterized by non-parallel projection planes composed by anisotropic voxels. The sparse Bayesian representation is a flexible strategy which is able to model complex relationships. The Super-resolution is approached as a regression problem, the main advantage is the capability to learn data relations from observations. Quantitative performance evaluation was carried out using synthetic images, the proposed method demonstrates a better reconstruction quality compared with standard interpolation approach. The presented method is a promising approach to improve the information quality related with the 3-D fetal brain structure. It is important because allows assessing brain development and thus make an early diagnostic of congenital abnormalities.
Cardiac magnetic resonance imaging (cMRI) is an useful tool in diagnosis, prognosis and research since it functionally tracks the heart structure. Although useful, this imaging technique is limited in spatial resolution because heart is a constant moving organ, also there are other non controled conditions such as patient movements and volumetric changes during apnea periods when data is acquired, those conditions limit the time to capture high quality information. This paper presents a very fast and simple strategy to reconstruct high resolution 3D images from a set of low resolution series of 2D images. The strategy is based on an information reallocation algorithm which uses the DICOM header to relocate voxel intensities in a regular grid. An interpolation method is applied to fill empty places with estimated data, the interpolation resamples the low resolution information to estimate the missing information. As a final step a gaussian filter that denoises the final result. A reconstructed image evaluation is performed using as a reference a super-resolution reconstructed image. The evaluation reveals that the method maintains the general heart structure with a small loss in detailed information (edge sharpening and blurring), some artifacts related with input information quality are detected. The proposed method requires low time and computational resources.
Proc. SPIE. 8669, Medical Imaging 2013: Image Processing
KEYWORDS: Super resolution, Magnetic resonance imaging, Image segmentation, Image processing, Heart, 3D modeling, Image analysis, 3D image processing, Cardiovascular magnetic resonance imaging, Lawrencium
Acquisition of proper cardiac MR images is highly limited by continued heart motion and apnea periods. A typical
acquisition results in volumes with inter-slice separations of up to 8 mm. This paper presents a super-resolution
strategy that estimates a high-resolution image from a set of low-resolution image series acquired in different non-orthogonal orientations. The proposal is based on a Bayesian approach that implements a Maximum a Posteriori
(MAP) estimator combined with a Wiener filter. A pre-processing stage was also included, to correct or eliminate
differences in the image intensities and to transform the low-resolution images to a common spatial reference
system. The MAP estimation includes an observation image model that represents the different contributions to
the voxel intensities based on a 3D Gaussian function. A quantitative and qualitative assessment was performed
using synthetic and real images, showing that the proposed approach produces a high-resolution image with
significant improvements (about 3dB in PSNR) with respect to a simple trilinear interpolation. The Wiener
filter shows little contribution to the final result, demonstrating that the MAP uniformity prior is able to filter
out a large amount of the acquisition noise.