135 research outputs found
Nonparametric joint shape learning for customized shape modeling
We present a shape optimization approach to compute patient-specific models in customized prototyping applications. We design a coupled shape prior to model the transformation between a related pair of surfaces, using a nonparametric joint probability density estimation. The coupled shape prior forces with the help of application-specific data forces and smoothness forces drive a surface deformation
towards a desired output surface. We demonstrate the usefulness of the method for generating customized shape models in applications of hearing aid design and pre-operative to intra-operative anatomic surface estimation
A New 3-D automated computational method to evaluate in-stent neointimal hyperplasia in in-vivo intravascular optical coherence tomography pullbacks
Abstract. Detection of stent struts imaged in vivo by optical coherence
tomography (OCT) after percutaneous coronary interventions (PCI) and
quantification of in-stent neointimal hyperplasia (NIH) are important.
In this paper, we present a new computational method to facilitate the
physician in this endeavor to assess and compare new (drug-eluting)
stents. We developed a new algorithm for stent strut detection and utilized
splines to reconstruct the lumen and stent boundaries which provide
automatic measurements of NIH thickness, lumen and stent area. Our
original approach is based on the detection of stent struts unique characteristics:
bright reflection and shadow behind. Furthermore, we present
for the first time to our knowledge a rotation correction method applied
across OCT cross-section images for 3D reconstruction and visualization
of reconstructed lumen and stent boundaries for further analysis in
the longitudinal dimension of the coronary artery. Our experiments over
OCT cross-sections taken from 7 patients presenting varying degrees of
NIH after PCI illustrate a good agreement between the computer method
and expert evaluations: Bland-Altmann analysis revealed a mean difference
for lumen cross-section area of 0.11 ± 0.70mm2 and for the stent
cross-section area of 0.10 ± 1.28mm2
Anatomical landmark based registration of contrast enhanced T1-weighted MR images
In many problems involving multiple image analysis, an im- age registration step is required. One such problem appears in brain tumor imaging, where baseline and follow-up image volumes from a tu- mor patient are often to-be compared. Nature of the registration for a change detection problem in brain tumor growth analysis is usually rigid or affine. Contrast enhanced T1-weighted MR images are widely used in clinical practice for monitoring brain tumors. Over this modality, con- tours of the active tumor cells and whole tumor borders and margins are visually enhanced. In this study, a new technique to register serial contrast enhanced T1 weighted MR images is presented. The proposed fully-automated method is based on five anatomical landmarks: eye balls, nose, confluence of sagittal sinus, and apex of superior sagittal sinus. Af- ter extraction of anatomical landmarks from fixed and moving volumes, an affine transformation is estimated by minimizing the sum of squared distances between the landmark coordinates. Final result is refined with a surface registration, which is based on head masks confined to the sur- face of the scalp, as well as to a plane constructed from three of the extracted features. The overall registration is not intensity based, and it depends only on the invariant structures. Validation studies using both synthetically transformed MRI data, and real MRI scans, which included several markers over the head of the patient were performed. In addition, comparison studies against manual landmarks marked by a radiologist, as well as against the results obtained from a typical mutual information based method were carried out to demonstrate the effectiveness of the proposed method
Registration of brain tumor images using hyper-elastic regularization
In this paper, we present a method to estimate a deformation
field between two instances of a brain volume having tumor. The novelties
include the assessment of the disease progress by observing the healthy tissue
deformation and usage of the Neo-Hookean strain energy density model as
a regularizer in deformable registration framework. Implementations on synthetic
and patient data provide promising results, which might have relevant
use in clinical problems
DeshuffleGAN: A Self-Supervised GAN to Improve Structure Learning
Generative Adversarial Networks (GANs) triggered an increased interest in
problem of image generation due to their improved output image quality and
versatility for expansion towards new methods. Numerous GAN-based works attempt
to improve generation by architectural and loss-based extensions. We argue that
one of the crucial points to improve the GAN performance in terms of realism
and similarity to the original data distribution is to be able to provide the
model with a capability to learn the spatial structure in data. To that end, we
propose the DeshuffleGAN to enhance the learning of the discriminator and the
generator, via a self-supervision approach. Specifically, we introduce a
deshuffling task that solves a puzzle of randomly shuffled image tiles, which
in turn helps the DeshuffleGAN learn to increase its expressive capacity for
spatial structure and realistic appearance. We provide experimental evidence
for the performance improvement in generated images, compared to the baseline
methods, which is consistently observed over two different datasets.Comment: Accepted at ICIP 202
3D ball skinning using PDEs for generation of smooth tubular surfaces
We present an approach to compute a smooth, interpolating skin of an ordered set of
3D balls. By construction, the skin is constrained to be C1 continuous, and for each
ball, it is tangent to the ball along a circle of contact. Using an energy formulation,
we derive differential equations that are designed to minimize the skin’s surface area,
mean curvature, or convex combination of both. Given an initial skin, we update the
skin’s parametric representation using the differential equations until convergence
occurs. We demonstrate the method’s usefulness in generating interpolating skins
of balls of different sizes and in various configurations
Computational Model of Electroconvulsive Therapy Considering Electric Field Dependent Skin Conductivity
Improvements in electroconvulsive therapy (ECT) outcomes have followed refinement in device electrical output and electrode montage. The physical properties of the ECT stimulus, together with those of the patient’s head, determine the impedances measured by the device and govern current delivery to the brain and ECT outcomes. However, the precise relations among physical properties of the stimulus, patient head anatomy, and patient-specific impedance to the passage of current are long-standing questions in ECT research and practice. In this thesis, we develop a computational framework based on diverse clinical data sets. We developed anatomical MRI-derived models of transcranial electrical stimulation (tES) that included changes in tissue conductivity due to local electrical current flow. These “adaptive” models simulate ECT both during therapeutic stimulation using high current and when dynamic impedance is measured, as well as prior to stimulation when low current is used to measure static impedance. We modeled two scalp layers: a superficial scalp layer with adaptive conductivity that increases with electric field up to a subject-specific maximum, and a deep scalp layer with a subject-specific fixed conductivity. We demonstrated that variation in these scalp parameters may explain clinical data on subject-specific static impedance and dynamic impedance, their imperfect correlation across subjects, their relationships to seizure threshold, and the role of head anatomy. Adaptive tES models demonstrated that current flow changes local tissue conductivity which in turn shapes current delivery to the brain in a manner not accounted for in fixed tissue conductivity models. Our predictions that variation in individual skin properties, rather than other aspects of anatomy, largely govern the relationship between static impedance, dynamic impedance, and ECT current delivery to the brain, themselves depend on assumptions about tissue properties. Broadly, our novel modeling pipeline opens the door to explore how adaptive-scalp conductivity may impact transcutaneous electrical stimulation (tES). Lastly, we incorporate the (device specific) role of frequency with a single overall assumption allowing quasi-static stimulations of ECT: appropriately parametrizing effective resistivity at single representative frequency (e.g., at 1 kHz), including subject-specific and adaptive skin resistivities. We only stipulate that our functions for (adaptive) resistivity at 1 kHz explain local tissue resistivity as they impact the static and dynamic impedance measures by specific ECT devices (e.g., Thymatron)
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