A conduction velocity adapted eikonal model for electrophysiology problems with re-excitability evaluation
The propagation of an electrical stimulus in the cardiac tissue is mathematically described by the bidomain model (Tung, 1978; Clements et al., 2004) that is a system of a parabolic reaction-diffusion and an elliptic PDEs describing the electrical state of an intracellular and an extracellular continuum media, separated by the cell membrane. The electrical state of the cell membrane characterising the reaction term is described by a non-linear system of ODEs that either represents the biophysical fluxes of the ion species across the cell membrane ((Luo and Rudy, 1991; 1994; Ten Tusscher et al., 2004)), or that tries to...
Source: Medical Image Analysis - November 3, 2017 Category: Radiology Authors: Cesare Corrado, Nejib Zemzemi Source Type: research
Myocardial Strain Computed at Multiple Spatial Scales from Tagged Magnetic Resonance Imaging: Estimating Cardiac Biomarkers for CRT Patients
Cardiac deformation is driven by the electromechanics and perfusion of the myocardium, and is intrinsically tied to the disease state of the heart. As such, cardiac deformation has been widely analysed for the characterisation and detection of different diseases, both in more clinical (Hsu et al., 2012; Jackson et al., 2014; Sohal et al., 2014) and methodological (Duchateau and Sermesant, 2016; Peressutti et al., 2017) work. (Source: Medical Image Analysis)
Source: Medical Image Analysis - October 30, 2017 Category: Radiology Authors: Matthew Sinclair, Devis Peressutti, Esther Puyol-Ant ón, Wenjia Bai, Simone Rivolo, Jessica Webb, Simon Claridge, Thomas Jackson, David Nordsletten, Myrianthi Hadjicharalambous, Eric Kerfoot, Christopher A. Rinaldi, Daniel Rueckert, Andrew P. King Source Type: research
Landmark-based Deep Multi-Instance Learning for Brain Disease Diagnosis
Brain morphometric pattern analysis using structural magnetic resonance imaging (MRI) data are proven to be effective in identifying anatomical differences between populations of Alzheimer ’s disease (AD) patients and normal controls (NC), and in helping evaluate the progression of mild cognitive impairment (MCI), a prodromal stage of AD. In the literature, extensive MRI-based approaches have been developed to assist clinicians in interpreting and assessing structural changes of the brain (Jack et al., 1999; Ashburner and Friston, 2000; Cuingnet et al., 2011; Chu et al., 2012). (Source: Medical Image Analysis)
Source: Medical Image Analysis - October 27, 2017 Category: Radiology Authors: Mingxia Liu, Jun Zhang, Ehsan Adeli, Dinggang Shen Source Type: research
A Scale-Space Curvature Matching Algorithm for the Reconstruction of Complex Proximal Humeral Fractures
The treatment of comminuted fractures of the proximal humerus is challenging and the optimal procedure remains controversial (Cvetanovich et al., 2016; Gerber et al., 2004). Open reduction and internal fixation using conventional or locking plates is the mainstay of therapy for the young and active patient (Gerber et al., 2004; Grubhofer et al., 2016), while best results are obtained if anatomical or near anatomical reduction can be achieved (Gerber et al., 2004). Anatomical reduction is a pre-requisite for a joint-preserving surgical treatment of a fractured proximal humerus. (Source: Medical Image Analysis)
Source: Medical Image Analysis - October 27, 2017 Category: Radiology Authors: Lazaros Vlachopoulos, G ábor Székely, Christian Gerber, Philipp Fürnstahl Source Type: research
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Source: Medical Image Analysis - October 21, 2017 Category: Radiology Source Type: research
Automatic Initialization and Quality Control of Large-Scale Cardiac MRI Segmentations
Continuous advances in imaging technology, which enable ever more comprehensive phenotyping of human anatomy and physiology and concomitant reduction of imaging costs, have resulted in widespread use of imaging in large clinical trials and population imaging studies (Murdoch and Detsky, 2013). There has been an emergence of large-scale population imaging databases (Rueckert et al., 2016), opening up challenges and opportunities for the understanding of disease phenotypes, and for the delivery of precision imaging (Frangi et al., 2016). (Source: Medical Image Analysis)
Source: Medical Image Analysis - October 14, 2017 Category: Radiology Authors: X énia Albá, Karim Lekadir, Marco Pereañez, Pau Medrano-Gracia, Alistair A. Young, Alejandro F. Frangi Source Type: research
Intensity Inhomogeneity Correction of SD-OCT Data Using Macular Flatspace
Optical coherence tomography (OCT) is a widely used modality for imaging the retina as it is non-invasive, non-ionizing, provides three-dimensional data, and can be rapidly acquired (Hee et al., 1995). It uses near-infrared light to measure the reflectivity of the retina, producing a clear image of the retinal structure and the cellular layers comprising it. OCT improves upon traditional 2D en-face plane photography by providing depth information, which enables measurements of layer thicknesses that are known to change with certain diseases (Medeiros et al., 2009; Saidha et al., 2011). (Source: Medical Image Analysis)
Source: Medical Image Analysis - October 11, 2017 Category: Radiology Authors: Andrew Lang, Aaron Carass, Bruno M. Jedynak, Sharon D. Solomon, Peter A. Calabresi, Jerry L. Prince Source Type: research
Director Field Analysis (DFA): Exploring Local White Matter Geometric Structure in Diffusion MRI
Diffusion MRI is a powerful non-invasive imaging technique widely used to explore white matter in the human brain. Diffusion Tensor Imaging (DTI) (Basser et al., 1994) is used to reconstruct a tensor field from diffusion weighted images (DWIs). High Angular Resolution Diffusion Imaging (Tuch et al., 2002; Frank, 2002; Descoteaux et al., 2007; Tournier et al., 2007; Cheng et al., 2010; 2015; 2014; Özarslan et al., 2013), which makes no assumption of a 3D Gaussian distribution of the diffusion propagator, is used to reconstruct a general function field from DWIs, (e.g., an Orientation Distribution Function (ODF) or...
Source: Medical Image Analysis - October 11, 2017 Category: Radiology Authors: Jian Cheng, Peter J. Basser Source Type: research
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
Accurate brain tumor segmentation is of great importance in cancer diagnosis, treatment planning, and treatment outcome evaluation. Since manual segmentation of brain tumors is laborious (Bauer et al., 2013), an enormous effort has devoted to the development of semi-automatic or automatic brain tumor segmentation methods. Most of the existing brain tumor segmentation studies are focusing on gliomas that are the most common brain tumors in adults and can be measured by Magnetic Resonance Imaging (MRI) with multiple sequences, such as T2-weighted fluid attenuated inversion recovery (Flair), T1-weighted (T1), T1-weighted c...
Source: Medical Image Analysis - October 5, 2017 Category: Radiology Authors: Xiaomei Zhao, Yihong Wu, Guidong Song, Zhenye Li, Yazhuo Zhang, Yong Fan Source Type: research
Large-scale Retrieval for Medical Image Analytics: A Comprehensive Review
Medical image analytics plays a central role in clinical diagnosis, image-guided surgery and pattern discovery. Many protocols and modalities of digital imaging techniques have been adopted to generate medical images, including magnetic resonance imaging (MRI) (Slichter, 2013), computed tomography (CT) (Hsieh, 2009), photon emission tomography (PET) (Bailey et al., 2005), ultrasound (Szabo, 2004), fluorescence microscopy (Lichtman and Conchello, 2005), X-ray (Lewis, 2004) and others. Generally, these medical images reflect specific aspects (anatomy, function) of tissue types/organs that require an accurate interpretation...
Source: Medical Image Analysis - October 2, 2017 Category: Radiology Authors: Zhongyu Li, Xiaofan Zhang, Henning M üller, Shaoting Zhang Source Type: research
Full Left Ventricle Quantification via Deep Multitask Relationships Learning
Accurate quantification of left ventricle (LV) from cardiac imaging is among the most clinically important and most frequently demanded tasks for identification and diagnosis of cardiac diseases (Karamitsos et al., 2009). To provide a comprehensive global and regional cardiac function assessment, full quantification of cardiac LV is required, which simultaneously quantifies, for every frame in the whole cardiac cycle, multiple types of cardiac indices, such as cavity and myocardium areas , regional wall thicknesses, LV dimension and cardiac phase, as shown in Fig. 1. (Source: Medical Image Analysis)
Source: Medical Image Analysis - September 26, 2017 Category: Radiology Authors: Wufeng Xue, Gary Brahm, Sachin Pandey, Stephanie Leung, Shuo Li Source Type: research
Segmenting Hippocampal Subfields from 3T MRI with Multi-modality Images
The hippocampus plays an important role in both episodic and long-term memory encoding and retrieval (Zeineh et al., 2003; Squire et al., 2004; Moscovitch et al., 2006). Clinically, hippocampal volumetric change is an important biomarker for early diagnosis of many neurological diseases, including Alzheimer’s Disease (AD) and schizophrenia (Bobinski et al., 1996; Csernansky et al., 1998; Jack et al., 2000; De Leon et al., 2006; Schuff et al., 2009). Previously, most automatic hippocampus segmentation techniques treated the hippocampus as a whole structure (Fischl et al., 2002; Smith et al., 2004; Patenaude et...
Source: Medical Image Analysis - September 21, 2017 Category: Radiology Authors: Zhengwang Wu, Yaozong Gao, Feng Shi, Guangkai Ma, Valerie Jewells, Dinggang Shen Source Type: research
Integrating Geometric Configuration and Appearance Information into a Unified Framework for Anatomical Landmark Localization
A large number of medical image analysis applications rely on automatic anatomical landmark localization algorithms as a preliminary step for, e.g. segmentation based on deformable and statistical shape models (Heimann and Meinzer, 2009; Zhang et al., 2012; Lay et al., 2013), registration of images using rigid (Hajnal et al., 2001) and deformable transformations (Johnson and Christensen, 2002; Urschler et al., 2006), construction of anatomical atlases for population studies (Toews et al., 2010), or to focus on anatomical structures of interest for computer-aided diagnosis (Doi, 2007) and regression tasks like skeleta...
Source: Medical Image Analysis - September 20, 2017 Category: Radiology Authors: Martin Urschler, Thomas Ebner, Darko Štern Source Type: research
Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time
Probing brain tissue structure with time-dependent properties of the diffusion MRI (dMRI) signal is gaining momentum in the dMRI community (see e.g. Moonen et al., 1991; Le Bihan, 1995; Assaf et al., 1998; Novikov et al., 2014; De Santis et al., 2016; Ning et al., 2016; Fieremans et al., 2016). Yet, effectively representing the four-dimensional dMRI signal – varying over three-dimensional q-space and diffusion time – is still a sought-after a nd unsolved challenge. To specifically represent the dMRI signal in this qτ-space, which has been coined by Novikov et al. (Source: Medical Image Analysis)
Source: Medical Image Analysis - September 13, 2017 Category: Radiology Authors: Rutger H.J. Fick, Alexandra Petiet, Mathieu Santin, Anne-Charlotte Philippe, Stephane Lehericy, Rachid Deriche, Demian Wassermann Source Type: research
An Efficient Riemannian Statistical Shape Model using Differential Coordinates
Statistical models of shape have been established as one of the most successful methods for understanding the geometric variability of anatomical structures. Shape modeling is of particular interest in image guided diagnosis where morphological changes of anatomies have been hypothesized to be linked to various disorders. Based on a set of training shapes, statistical shape models efficiently parametrize the geometric variability of the biological objects under study. This in turn is not only useful in imposing shape constraints in synthesis and analysis problems but also in understanding the processes behind growth and di...
Source: Medical Image Analysis - September 13, 2017 Category: Radiology Authors: Christoph von Tycowicz, Felix Ambellan, Anirban Mukhopadhyay, Stefan Zachow Source Type: research
