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Circular holography is a novel reconstruction technique for Breast Microwave Radar (BMR) imaging. Compared to current state of the art BMR image formation methods, this reconstruction approach yields spatially accurate images with higher signal to noise ratios and no artifacts. Nevertheless, a preclinical study is required to assess the feasibility of this technique in realistic breast imaging scenarios...
Segmenting regions of high angiogenic activity corresponding to malignant tumors from DCE-MRI is a time-consuming task requiring processing of data in 4 dimensions. Quantitative analyses developed thus far are highly sensitive to external factors and are valid only under certain operating assumptions, which need not be valid for breast carcinomas. In this paper, we have developed a novel Statistical...
We present a novel method for automated learning of features from unlabeled image patches for classification of tumor architecture. In contrast to previous manually-designed feature detectors (e.g., Gabor basis function), the proposed method utilizes inexpensive un-labeled data to construct features. The algorithm, also known as reconstruction independent subspace analysis, can be described as a two-layer...
We present an automatic method to track individual nodule progression in a lung cancer mouse model. Fourteen A/J mice received an intraperitoneal injection of urethane. Respiratory-gated micro-CT images of the lungs were taken 8, 22, and 37 weeks after injection, at which 195, 585 and 636 nodules were manually detected. The three images from every animal were registered and their nodules matched with...
Enhancing brain tumor segmentation for accurate tumor volume measurement is a challenging task due to the large variation of tumor appearance and shape, which makes it difficult to incorporate prior knowledge commonly used by other medical image segmentation tasks. In this paper, a novel idea of confidence surface is proposed to guide the segmentation of enhancing brain tumor using information across...
PET-CT is now accepted as the best imaging technique for non-invasive staging of lung cancers, and a computer-based abnormality detection is potentially useful to assist the reading physicians in diagnosis. In this paper, we present a new fully-automatic approach to detect abnormalities in the thorax based on global context inference. A max-margin learning-based method is designed to infer the global...
Dynamic contrast enhanced magnetic resonance (DCE-MR) imaging is an exciting tool to study the pharmacokinetics of a suspected tumor tissue. Nonetheless, the inevitable partial volume effect in DCE-MR images may seriously hinder the quantitative analysis of the kinetic parameters. In this work, based on the conventional three-tissue compartment model, we propose an unsupervised nonnegative blind source...
Automatic detection of brain tumor is a difficult task due to variations in type, size, location and shape of tumors. In this paper, a multi-modality framework for automatic tumor detection is presented, fusing different Magnetic Resonance Imaging modalities including T1-weighted, T2-weighted, and T1 with gadolinium contrast agent. The intensity, shape deformation, symmetry, and texture features were...
Traditional intensity-based registration methods are often insufficient for tumour tracking in time-series ultrasound, where the low signal-to-noise ratio significantly degrades the quality of the output images, and topological changes may occur as the anatomical structures slide in and out of the focus plane. To overcome these issues, we propose a hybrid feature-based Log-Demons registration method...
In the present work we propose a novel, efficient strategy for modelling tumour induced brain deformation as a prior for non-rigid image registration in non-diffeomorphic registration problems seen in serial or cross-population brain tumour imaging studies. Here, the presence of pathology dramatically alters the morphological and textural appearance of the anatomical structures under consideration...
Positron emission tomography (PET) is a tomographic imaging technique that allows for accurate non-invasive in vivo measurements of regional tissue function in man. It is the most selective and sensitive imaging modality able to measure molecular pathways and interactions in vivo at a picomolar level. PET is an important tool in drug development and can be used in different ways. Firstly, it is possible...
The development of anti-cancer therapies demands new biomarkers in order to assess efficacy. Established measurements that rely on changes in tumor size from a structural acquisition may not be the most appropriate. There is also the desire to move away from manual techniques in favor or more automated analysis methods. The variety of MRI sequences and availability of PET radiotracers provides scientists...
The estimation and analysis of kinetic parameters in dynamic PET is frequently confounded by noise and partial volume effects. We propose a new constrained model of dynamic PET to address these limitations. The proposed formulation incorporates an explicit partial volume model in which each image voxel is represented as a mixture of different pure tissue types with distinct temporal dynamics. A two...
Three different DCE-MRI quantification methods: model-free-based, compartment-model-based and principal component analysis, are compared by evaluating parameter maps for histological defined volumes of vital and non-vital tumor tissue. To obtain an accurate spatial correspondence between histology and DCE-MRI, a two-step registration process was used involving dense histological sampling, a reference...
Pharmacokinetic (PK) modeling of tumors provides information about perfusion and vascular permeability of tumors. Identifying arterial input function (AIF) is crucial in PK modeling using dynamic contrast enhanced (DCE)-MRI. An adaptive complex independent component analysis method is developed to identify and separate AIF from complex DCE-MRI data. The results are compared with a previously introduced...
Malignant gliomas represent an aggressive class of central nervous system neoplasms which are often treated by maximal surgical resection. Herein, we seek to improve the methods available to quantify the extent of tumors as seen on magnetic resonance imaging using Internet-based, collaborative labeling. In a study of clinically acquired images, we demonstrate that teams of minimally trained human...
Arterial Spin Labeling (ASL) is a recent MRI perfusion technique which enables quantification of cerebral blood flow (CBF). The presence of regions with atypical CBF can characterize a pathology. In brain tumors for instance, perfusion increase can be directly related to the grading of the malignant tissues. It is therefore of great interest to identify these regions in order to provide the patients...
For deformable registration of computed tomography (CT) scans in image guided radiation therapy (IGRT) we apply Riemannian elasticity regularization. We explore the use of spatially varying elasticity parameters to encourage bone rigidity and local tissue volume change only in the gross tumor volume (GTV) and the lungs. We evaluate the method on the point-validated 4DCT breathing thorax POPI-model...
Conventional automated segmentation techniques for magnetic resonance imaging (MRI) fail to perform in a robust and consistent manner when brain anatomy differs wildly from expectations — as is often the case in brain cancers. We propose a novel out-of-atlas technique to estimate the spatial extent of abnormal brain regions by combining multi-atlas based segmentation with semi-local non-parametric...
Micro-tomography produces high resolution images of biological structures such as vascular networks. In this paper, we present a new approach for segmenting vascular network into pathological and normal regions from considering their micro-vessel 3D structure only. We consider a partition of the volume obtained by a watershed algorithm based on the distance from the nearest vessel. Each territory...
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