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Objectives: Accurate registration of lung CT images is inevitable for numerous clinical applications. Usually, nonlinear intensity-based methods are used. Their accuracy is typically evaluated using corresponding anatomical points (landmarks; e.g. bifurcations of bronchial and vessel trees) annotated by medical experts in the images to register. As image registration can be interpreted as correspondence finding problem, these corresponding landmarks can also be used in feature- based registration techniques. Recently, approaches for automated identification of such landmark correspondences in lung CT images have been presented. In this work, a novel combination of variational nonlinear intensity- based registration with an approach for automated landmark correspondence detection in lung CT pairs is presented and evaluated. Methods: The main blocks of the proposed hybrid intensity- and feature-based registration scheme are a two-step landmark correspondence detection and the so-called CoLD (Combining Landmarks and Distance Measures) framework. The landmark correspondence identification starts with feature detection in one image followed by a blockmatching- based transfer of the features to the other image. The established correspondences are used to compute a thin-plate spline (TPS) transformation. Within CoLD, the TPS transformation is improved by minimization of an objective function consisting of a Normalized Gradient Field distance measure and a curvature regularizer; the landmark correspondences are guaranteed to be preserved by optimization on the kernel of the discretized landmark constraints. Results: Based on ten publicly available end-inspiration/expiration CT scan pairs with anatomical landmark sets annotated by medical experts from the DIR-Lab database, it is shown that the hybrid registration approach is superior in terms of accuracy: The mean distance of expert landmarks is decreased from 8.46 mm before to 1.15 mm after registration, outperforming both the TPS transformation (1.68 mm) and a nonlinear registration without usage of automatically detected landmarks (2.44 mm). The improvement is statistically significant in eight of ten datasets in comparison to TPS and in nine of ten datasets in comparison to the intensity-based registration. Furthermore, CoLD globally estimates the breathinginduced lung volume change well and results in smooth and physiologically plausible motion fields of the lungs. Conclusions: We demonstrated that our novel landmark-based registration pipeline outperforms both TPS and the underlying nonlinear intensity-based registration without landmark usage. This highlights the potential of automatic landmark correspondence detection for improvement of lung CT registration accuracy. [ABSTRACT FROM AUTHOR]

Objectives: A particular problem in image registration arises for multi-modal images taken from different imaging devices and/or modalities. Starting in 1995, mutual information has shown to be a very successful distance measure for multi-modal image registration. Therefore, mutual information is considered to be the state-of-the-art approach to multi-modal image registration. However, mutual information has also a number of well-known drawbacks. Its main disadvantage is that it is known to be highly non-convex and has typically many local maxima.Methods: This observation motivates us to seek a different image similarity measure which is better suited for optimization but as well capable to handle multi-modal images.Results: In this work, we investigate an alternative distance measure which is based on normalized gradients.Conclusions: As we show, the alternative approach is deterministic, much simpler, easier to interpret, fast and straightforward to implement, faster to compute, and also much more suitable to numerical optimization. [ABSTRACT FROM AUTHOR]

Studies employing functional magnetic resonance imaging have identified the human frontal eye field as being in the anterior and partly in the posterior wall, as well as at the base of the precentral sulcus. Moreover, it is known that the frontal eye field extends rostrally to the superior frontal sulcus. According to Brodmann’s cytoarchitectonic map, this region belongs to the dysgranular Brodmann area 6 of the premotor cortex. However, the frontal eye field in non-human primates has been located within the arcuate sulcus in Brodmann area 8, generating considerable debate about where to locate exactly the frontal eye field in humans. Functional studies of the primate frontal eye field have revealed a principal homology of voluntary saccadic control systems in human and old-world monkeys, especially the macaque. But these homologies seem to be contradicted by the reported topographic localization at the cytoarchitectonic level. Therefore, we studied the cytoarchitectonic structure of the posterior bank of the precentral sulcus of a human brain, employing newly developed spatial mapping techniques to provide data about whether or not this region should be considered cytoarchitecturally homogeneous or heterogeneous. We used functional magnetic resonance imaging results, as an initial guide in localizing a region which is activated by saccadic tasks. A maximum of activation was detected around the junction of the superior frontal sulcus and the precentral sulcus extending 1.5 cm along the precentral sulcus in direction of the lateral sulcus. Here, one human brain has been analyzed to obtain preliminary data about the cytoarchitectonical changes of a part of area 6. Statistical analysis of the three-dimensional architectonic data from this region allowed us to identify a zone at the posterior bank, which in other studies has been associated with a functional region that controls pursuit eye movements and performs sensory-to-motor transformations. We found two significant sectors along the ventral part of the posterior bank of the precentral sulcus. The caudal transition region coincides partly with a region that integrates retinal and eye position signals for target location, arm, and axial movements. The second more ventrally located region is attributed to process oral-facial movements. The caudal transition region coincides with our functional magnetic resonance imaging investigation. It was revealed that this region lies at the inferior frontal eye field, where a pronounced activation over a larger region can be stimulated. Currently, more studies are needed to combine functional magnetic resonance imaging data of maximal activation with data from whole histologic brain sections of more individuals and to quantify the variability of this region and its sub-regions by means of a standardized brain atlas. [ABSTRACT FROM AUTHOR]

Objectives: In this paper, we propose a novel registration technique, which combines the concepts of landmark and automatic, non-rigid intensity-based approaches. A general framework, which might be used for many different registration problems is presented. The novel approach enables the incorporation of different distance measures as well as different smoothers.Methods: The proposed scheme minimizes a regularized distance measure subject to some interpolation constraints. The desired deformation is computed iteratively using an Euler-scheme for the first variation of the chosen objective functional.Results: A fast and robust numerical scheme for the computation of the wanted minimizer is developed, implemented, and applied to various registration tasks. This includes the registration of pre- and post-intervention images of the human eye.Conclusions: A novel framework for a parameter-free, non-rigid registration scheme which allows for the additional incorporation of user-defined landmarks is proposed. It enhances the reliability of conventional approaches considerably and thereby their acceptability by practitioners in a clinical environment. [ABSTRACT FROM AUTHOR]

One-compartment models are widely used to quantify hemodynamic parameters such as perfusion, blood volume and mean transit time. These parameters are routinely used for clinical diagnosis and monitoring of disease development and are thus of high relevance. However, it is known that common estimation techniques are discretization dependent and values can be erroneous. In this paper we present a new model that enables systematic quantification of discretization errors. Specifically, we introduce a continuous flow model for tracer propagation within the capillary tissue, used to evaluate state-of-the-art one-compartment models. We demonstrate that one-compartment models are capable of recovering perfusion accurately when applied to only one compartment, i.e. the whole region of interest. However, substantial overestimation of perfusion occurs when applied to fractions of a compartment. We further provide values of the estimated overestimation for various discretization levels, and also show that overestimation can be observed in real-life applications. Common practice of using compartment models for fractions of tissue violates model assumptions and careful interpretation is needed when using the computed values for diagnosis and treatment planning., Competing Interests: The authors have declared that no competing interests exist.

We present a novel algorithm for the registration of pulmonary CT scans. Our method is designed for large respiratory motion by integrating sparse keypoint correspondences into a dense continuous optimization framework. The detection of keypoint correspondences enables robustness against large deformations by jointly optimizing over a large number of potential discrete displacements, whereas the dense continuous registration achieves subvoxel alignment with smooth transformations. Both steps are driven by the same normalized gradient fields data term. We employ curvature regularization and a volume change control mechanism to prevent foldings of the deformation grid and restrict the determinant of the Jacobian to physiologically meaningful values. Keypoint correspondences are integrated into the dense registration by a quadratic penalty with adaptively determined weight. Using a parallel matrix-free derivative calculation scheme, a runtime of about 5 min was realized on a standard PC. The proposed algorithm ranks first in the EMPIRE10 challenge on pulmonary image registration. Moreover, it achieves an average landmark distance of 0.82 mm on the DIR-Lab COPD database, thereby improving upon the state of the art in accuracy by 15%. Our algorithm is the first to reach the inter-observer variability in landmark annotation on this dataset.

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the kidneys requires proper motion correction and segmentation to enable an estimation of glomerular filtration rate through pharmacokinetic modeling. Traditionally, co-registration, segmentation, and pharmacokinetic modeling have been applied sequentially as separate processing steps. In this paper, a combined 4D model for simultaneous registration and segmentation of the whole kidney is presented. To demonstrate the model in numerical experiments, we used normalized gradients as data term in the registration and a Mahalanobis distance from the time courses of the segmented regions to a training set for supervised segmentation. By applying this framework to an input consisting of 4D image time series, we conduct simultaneous motion correction and two-region segmentation into kidney and background. The potential of the new approach is demonstrated on real DCE-MRI data from ten healthy volunteers.

Purpose: An important issue in computer-assisted surgery of the liver is a fast and reliable transfer of preoperative resection plans to the intraoperative situation. One problem is to match the planning data, derived from preoperative CT or MR images, with 3D ultrasound images of the liver, acquired during surgery. As the liver deforms significantly in the intraoperative situation non-rigid registration is necessary. This is a particularly challenging task because pre- and intraoperative image data stem from different modalities and ultrasound images are generally very noisy., Methods: One way to overcome these problems is to incorporate prior knowledge into the registration process. We propose a method of combining anatomical landmark information with a fast non-parametric intensity registration approach. Mathematically, this leads to a constrained optimization problem. As distance measure we use the normalized gradient field which allows for multimodal image registration., Results: A qualitative and quantitative validation on clinical liver data sets of three different patients has been performed. We used the distance of dense corresponding points on vessel center lines for quantitative validation. The combined landmark and intensity approach improves the mean and percentage of point distances above 3 mm compared to rigid and thin-plate spline registration based only on landmarks., Conclusion: The proposed algorithm offers the possibility to incorporate additional a priori knowledge-in terms of few landmarks-provided by a human expert into a non-rigid registration process.

A particular problem in image registration arises for multimodal images taken from different imaging devices and/or modalities. Starting in 1995, mutual information has shown to be a very successful distance measure for multi-modal image registration. However, mutual information has also a number of well-known drawbacks. Its main disadvantage is that it is known to be highly non-convex and has typically many local maxima. This observation motivate us to seek a different image similarity measure which is better suited for optimization but as well capable to handle multi-modal images. In this work we investigate an alternative distance measure which is based on normalized gradients and compare its performance to Mutual Information. We call the new distance measure Normalized Gradient Fields (NGF).

Serial histologic sections of a whole human brain may have extensions of up to 130 x 130 mm within the coronal plane around the temporal lobe. To date, however, technology has not provided a bright field microscope that is able to shift the object holder continuously in the x- and y-direction over such distances and still possess the same optical capabilities as comparable devices. We developed a new light microscope to continuously quantify such sections. We also developed the computing environment for controlling the device and for analyzing the data produced. In principle, we are now able to quantify each neuron of a human brain. The data ultimately will provide the most detailed structural information about the human brain ascertained thus far. Such detailed information of the spatial distribution of neurons is essential to develop realistic models for simulation of large-scale neuronal networks and to investigate the significance of neuronal arrangements with respect to neuronal signal processing in the CNS. After preprocessing of the data produced by the new microscope, we are able to detect lamination patterns in the spatial distribution of gravity centers of cells. Furthermore, morphological features like size of the projection area and mean staining intensity are visualized as a particle process. The particle process presents the sizes and staining intensity of perikaryons and allows a distinction of gray matter and white matter. These results provide evidence that the system works correctly and can be applied to a systematic analysis of a larger sequence of serial histologic sections. The objective of this study is to introduce the very large section analyzing microscope (VLSAM) and to present the initial data produced by the system. Moreover, we will discuss workload and future developments of the parallel image analysis system that are associated with the microscope.

The position, surface area and visual field representation of human visual areas V1, V2 and V3 were measured using fMRI in 7 subjects (14 hemispheres). Cortical visual field maps of the central 12 deg were measured using rotating wedge and expanding ring stimuli. The boundaries between areas were identified using an automated procedure to fit an atlas of the expected visual field map to the data. All position and surface area measurements were made along the boundary between white matter and gray matter. The representation of the central 2 deg of visual field in areas V1, V2, V3 and hV4 spans about 2100 mm2 and is centered on the lateral-ventral aspect of the occipital lobes at Talairach coordinates -29, -78, -11 and 25, -80, -9. The mean area between the 2-deg and 12-deg eccentricities for the primary visual areas was: V1: 1470 mm2; V2: 1115 mm2; and V3: 819 mm2. The sizes of areas V1, V2 and V3 varied by about a factor of 2.5 across individuals; the sizes of V1 and V2 are significantly correlated within individuals, but there is a very low correlation between V1 and V3. These in vivo measurements of normal human retinotopic visual areas can be used as a reference for comparison to unusual cases involving developmental plasticity, recovery from injury, identifying homology with animal models, or analyzing the computational resources available within the visual pathways.