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Optical properties, such as the attenuation coefficients of multi-layer tissue samples, could be used as a biomarker for diagnosis and disease progression in clinical practice. In this paper, we present a method to estimate the attenuation coefficients in a multi-layer sample by fitting a single scattering model for the OCT signal to the recorded OCT signal. In addition, we employ numerical simulations to obtain the theoretically achievable precision and accuracy of the estimated parameters under various experimental conditions. Finally, the method is applied to two sets of measurements obtained from a multi-layer phantom by two experimental OCT systems: one with a large and one with a small Rayleigh length. Numerical and experimental results show an accurate estimation of the attenuation coefficients when using multiple B-scans.

Quantitative MRI methods that estimate multiple physical parameters simultaneously often require the fitting of a computational complex signal model defined through the Bloch equations. Repeated Bloch simulations can be avoided by matching the measured signal with a precomputed signal dictionary on a discrete parameter grid, as used in MR Fingerprinting. However, accurate estimation requires discretizing each parameter with a high resolution and consequently high computational and memory costs for dictionary generation, storage, and matching. Here, we reduce the required parameter resolution by approximating the signal between grid points through B-spline interpolation. The interpolant and its gradient are evaluated efficiently which enables a least-squares fitting method for parameter mapping. The resolution of each parameter was minimized while obtaining a user-specified interpolation accuracy. The method was evaluated by phantom and in-vivo experiments using fully-sampled and undersampled unbalanced (FISP) MR fingerprinting acquisitions. Bloch simulations incorporated relaxation effects ($T_1,T_2$), proton density ($PD$), receiver phase ($\phi_0$), transmit field inhomogeneity ($B_1^+$), and slice profile. Parameter maps were compared with those obtained from dictionary matching, where the parameter resolution was chosen to obtain similar signal (interpolation) accuracy. For both the phantom and the in-vivo acquisition, the proposed method approximated the parameter maps obtained through dictionary matching while reducing the parameter resolution in each dimension ($T_1,T_2,B_1^+$) by, on average, an order of magnitude. In effect, the applied dictionary was reduced from 1.47 GB to 464 KB. Dictionary fitting with B-spline interpolation reduces the computational and memory costs of dictionary-based methods and is therefore a promising method for multi-parametric mapping., Comment: 14 pages, 9 figures, accepted for publication in IEEE Transaction on Medical Imaging (TMI)

Rationale and Objectives The objective of this study was to develop and validate a predictive magnetic resonance imaging (MRI) activity score for ileocolonic Crohn disease activity based on both subjective and semiautomatic MRI features. Materials and Methods An MRI activity score (the “virtual gastrointestinal tract [VIGOR]” score) was developed from 27 validated magnetic resonance enterography datasets, including subjective radiologist observation of mural T2 signal and semiautomatic measurements of bowel wall thickness, excess volume, and dynamic contrast enhancement (initial slope of increase). A second subjective score was developed based on only radiologist observations. For validation, two observers applied both scores and three existing scores to a prospective dataset of 106 patients (59 women, median age 33) with known Crohn disease, using the endoscopic Crohn's Disease Endoscopic Index of Severity (CDEIS) as a reference standard. Results The VIGOR score (17.1 × initial slope of increase + 0.2 × excess volume + 2.3 × mural T2) and other activity scores all had comparable correlation to the CDEIS scores (observer 1: r = 0.58 and 0.59, and observer 2: r = 0.34–0.40 and 0.43–0.51, respectively). The VIGOR score, however, improved interobserver agreement compared to the other activity scores (intraclass correlation coefficient = 0.81 vs 0.44–0.59). A diagnostic accuracy of 80%–81% was seen for the VIGOR score, similar to the other scores. Conclusions The VIGOR score achieves comparable accuracy to conventional MRI activity scores, but with significantly improved reproducibility, favoring its use for disease monitoring and therapy evaluation.

Extraction of image-based biomarkers, such as the presence, visibility, or thickness of a certain layer, from 3-D optical coherence tomography data provides relevant clinical information. We present a method to simultaneously determine the number of visible layers in the outer retina and segment them. The method is based on a model selection approach with special attention given to the balance between the quality of a fit and model complexity. This will ensure that a more complex model is selected only if this is sufficiently supported by the data. The performance of the method was evaluated on healthy and retinitis pigmentosa (RP) affected eyes. In addition, the reproducibility of automatic method and manual annotations was evaluated on healthy eyes. Good agreement between the segmentation performed manually by amedical doctor and results obtained fromthe automatic segmentation was found. The mean unsigned deviation for all outer retinal layers in healthy and RP affected eyes varied between 2.6 and $4.9~\mu \text{m}$ . The reproducibility of the automatic method was similar to the reproducibility of the manual segmentation. Overall, the method provides a flexible and accurate solution for determining the visibility and location of outer retinal layers and could be used as an aid for the disease diagnosis and monitoring.

Purpose: Simulating low-­dose Computed Tomography (CT) facilitates in-­silico studies into the required dose for a diagnostic task. Conventionally, low-­‐dose CT images are created by adding noise to the projection data. However, in practice the raw data is often simply not available. This paper presents a new method for simulating patient-­‐specific, low-dose CT images without the needof the original projection data. Methods: The low-­dose CT simulation method included the following: (1) computation of a virtual sinogram from a high dose CT image through aradon transform; (2) simulation of a 'reduced'­‐dose sinogram with appropriateamounts of noise; (3) subtraction of the high-­‐dose virtual sinogram from thereduced-­‐dose sinogram; (4) reconstruction of a noise volume via filtered back-projection; (5) addition of the noise image to the original high-dose image. Therequired scanner-­Specific parameters, such as the apodization window, bowtiefilter, the X-ray tube output parameter (reflecting the photon flux) and the detector read-­out noise, were retrieved from calibration images of a watercylinder. The low-­‐dose simulation method was evaluated by comparing thenoise characteristics in simulated images with experimentally acquireddata.Results:The models used to recover the scanner-­specific parameters fitted accurately tothe calibration data, and the values of the parameters were comparable to valuesreported in literature. Finally, the simulated low-dose images accurately reproduced the noise characteristics in experimentally acquired low-dose­‐volumes.Conclusion:The developed methods truthfully simulate low-­dose CT imaging for a specificscanner and reconstruction using filtered backprojection. The scanner-­‐specificparameters can be estimated from calibration data.

The attenuation coefficient (AC) is an optical property of tissue that can be estimated from optical coherence tomography (OCT) data. In this paper, we aim to estimate the AC accurately by compensating for the shape of the focused beam. For this, we propose a method to estimate the axial PSF model parameters and AC by fitting a model for an OCT signal in a homogenous sample to the recorded OCT signal. In addition, we employ numerical analysis to obtain the theoretical optimal precision of the estimated parameters for different experimental setups. Finally, the method is applied to OCT B-scans obtained from homogeneous samples. The numerical and experimental results show accurate estimations of the AC and the focus location when the focus is located inside the sample.

Purpose: To present a fully automatic method to estimate the corneal endothelium parameters from specular microscopy images and to use it to study a one-year follow-up after ultrathin Descemet stripping automated endothelial keratoplasty. Methods: We analyzed 383 post ultrathin Descemet stripping automated endothelial keratoplasty images from 41 eyes acquired with a Topcon SP-1P specular microscope at 1, 3, 6, and 12 months after surgery. The estimated parameters were endothelial cell density (ECD), coefficient of variation (CV), and hexagonality (HEX). Manual segmentation was performed in all images. Results: Our method provided an estimate for ECD, CV, and HEX in 98.4% of the images, whereas Topcon’s software had a success rate of 71.5% for ECD/CV and 30.5% for HEX. For the images with estimates, the percentage error in our method was 2.5% for ECD, 5.7% for CV, and 5.7% for HEX, whereas Topcon’s software provided an error of 7.5% for ECD, 17.5% for CV, and 18.3% for HEX. Our method was significantly better than Topcon’s (P < 0.0001) and was not statistically significantly different from the manual assessments (P > 0.05). At month 12, the subjects presented an average ECD = 1377 ± 483 [cells/mm2 ], CV = 26.1 ± 5.7 [%], and HEX = 58.1 ± 7.1 [%]. Conclusions: The proposed method obtains reliable and accurate estimations even in challenging specular images of pathologic corneas. Translational Relevance: CV and HEX, not currently used in the clinic owing to a lack of reliability in automatic methods, are useful biomarkers to analyze the postoperative healing process. Our accurate estimations allow now for their clinical use.

BackgroundCorneal endothelium (CE) images provide valuable clinical information regarding the health state of the cornea. Computation of the clinical morphometric parameters requires the segmentation of endothelial cell images. Current techniques to image the endothelium in vivo deliver low quality images, which makes automatic segmentation a complicated task. Here, we present two convolutional neural networks (CNN) to segment CE images: a global fully convolutional approach based on U-net, and a local sliding-window network (SW-net). We propose to use probabilistic labels instead of binary, we evaluate a preprocessing method to enhance the contrast of images, and we introduce a postprocessing method based on Fourier analysis and watershed to convert the CNN output images into the final cell segmentation. Both methods are applied to 50 images acquired with an SP-1P Topcon specular microscope. Estimates are compared against a manual delineation made by a trained observer.ResultsU-net (AUC=0.9938) yields slightly sharper, clearer images than SW-net (AUC=0.9921). After postprocessing, U-net obtains a DICE=0.981 and a MHD=0.22 (modified Hausdorff distance), whereas SW-net yields a DICE=0.978 and a MHD=0.30. U-net generates a wrong cell segmentation in only 0.48% of the cells, versus 0.92% for the SW-net. U-net achieves statistically significant better precision and accuracy than both, Topcon and SW-net, for the estimates of three clinical parameters: cell density (ECD), polymegethism (CV), and pleomorphism (HEX). The mean relative error in U-net for the parameters is 0.4% in ECD, 2.8% in CV, and 1.3% in HEX. The computation time to segment an image and estimate the parameters is barely a few seconds.ConclusionsBoth methods presented here provide a statistically significant improvement over the state of the art. U-net has reached the smallest error rate. We suggest a segmentation refinement based on our previous work to further improve the performance.

PurposePharmacokinetic models facilitate assessment of properties of the micro-vascularization based on DCE-MRI data. However, accurate pharmacokinetic modeling in the liver is challenging since it has two vascular inputs and it is subject to large deformation and displacement due to respiration.MethodsWe propose an improved pharmacokinetic model for the liver that (1) analytically models the arrival-time of the contrast agent for both inputs separately; (2) implicitly compensates for signal fluctuations that can be modeled by varying applied flip-angle e.g. due to B1-inhomogeneity. Orton's AIF model is used to analytically represent the vascular input functions. The inputs are independently embedded into the Sourbron model. B1-inhomogeneity-driven variations of flip-angles are accounted for to justify the voxel's displacement with respect to a pre-contrast image.ResultsThe new model was shown to yield lower root mean square error (RMSE) after fitting the model to all but a minority of voxels compared to Sourbron's approach. Furthermore, it outperformed this existing model in the majority of voxels according to three model-selection criteria.ConclusionOur work primarily targeted to improve pharmacokinetic modeling for DCE-MRI of the liver. However, other types of pharmacokinetic models may also benefit from our approaches, since the techniques are generally applicable.

Registration of images in the presence of intra-image signal fluctuations is a challenging task. The definition of an appropriate objective function measuring the similarity between the images is crucial for accurate registration. This paper introduces an objective function that embeds local phase features derived from the monogenic signal in the modality independent neighborhood descriptor (MIND). The image similarity relies on the autocorrelation of local structure (ALOST) which has two important properties: 1) low sensitivity to space-variant intensity distortions (e.g., differences in contrast enhancement in MRI); 2) high distinctiveness for ‘salient’ image features such as edges. The ALOST method is quantitatively compared to the MIND approach based on three different datasets: thoracic CT images, synthetic and real abdominal MR images. The proposed method outperformed the NMI and MIND similarity measures on these three datasets. The registration of dynamic contrast enhanced and post-contrast MR images of patients with Crohn's disease led to relative contrast enhancement measures with the highest correlation $({\rm r}=0.56)$ to the Crohn's disease endoscopic index of severity.

Aim To investigate relationships between epicardial fat volume and atherosclerosis in multiple major vessel beds. Methods and results From the population-based Rotterdam Study, 2298 participants underwent computed tomography examinations to quantify epicardial fat volume and atherosclerotic calcification volume in the coronary arteries, aortic arch, and extracranial and intracranial internal carotid arteries. Using linear regression modelling, we investigated relationships of epicardial fat volume with atherosclerotic calcification volume in each vessel bed, adjusting for conventional cardiovascular risk factors (waist circumference, systolic and diastolic blood pressure, total and high-density lipoprotein cholesterol, smoking, diabetes, and usage of blood pressure-lowering and lipid-lowering medication). To test whether associations of epicardial fat with calcification per vessel bed were independent of calcification elsewhere, we created a model in which all vessel beds were entered together. We found that a larger epicardial fat volume was associated with larger calcification volumes in the coronary arteries, aortic arch, and extracranial carotid arteries in both sexes. After adjustment for cardiovascular risk factors, larger epicardial fat volume was related to coronary and extracranial carotid artery calcification volume in males only [difference in calcification volume per SD increase in epicardial fat volume: 0.12 (95% confidence interval, CI: 0.04; 0.19) and 0.14 (95% CI: 0.06; 0.22)]. These associations remained unchanged after entering all vessel beds into one model. Conclusion Larger volumes of epicardial fat are associated with larger amounts of coronary and extracranial carotid artery atherosclerosis in males, independent of cardiovascular risk factors. This could imply that epicardial fat also exerts a systemic effect on atherosclerosis development. Future longitudinal research is warranted to further disentangle these relationships with a specific focus on sex differences.

This paper studies a novel method to compensate for respiratory and peristaltic motions in abdominal dynamic contrast enhanced magnetic resonance imaging. The method consists of two steps: 1) expiration-phase “template” construction and retrospective gating of the data to the template; and 2) nonrigid registration of the gated volumes. Landmarks annotated by three experts were used to directly assess the registration performance. A tri-exponential function fit to time intensity curves from regions of interest was used to indirectly assess the performance. One of the parameters of the tri-exponential fit was used to quantify the contrast enhancement. Our method achieved a mean target registration error (MTRE) of 2.12, 2.27, and 2.33 mm with respect to annotations by expert, which was close to the average interobserver variability (2.07 mm). A state-of-the-art registration method achieved an MTRE of 2.83–3.10 mm. The correlation coefficient of the contrast enhancement parameter to the Crohn's disease endoscopic index of Severity ( ${\rm r} = 0.60, {\rm p} = 0.004$ ) was higher than the correlation coefficient for the relative contrast enhancement measurements values of two observers ( ${\rm r(Observer 1)} = 0.29, {\rm p} = 0.2; {\rm r(Observer 2)} = 0.45, {\rm p} = 0.04$ ). Direct and indirect assessments show that the expiration-based gating and a nonrigid registration approach effectively corrects for respiratory motion and peristalsis. The method facilitates improved enhancement measurement in the bowel wall in patients with Crohn's disease.

Corneal endothelium images obtained by in vivo specular microscopy provide important information to assess the health status of the cornea. Estimation of clinical parameters, such as cell density, polymegethism, and pleomorphism, requires accurate cell segmentation. State-of-the-art techniques to automatically segment the endothelium are error-prone when applied to images with low contrast and/or large variation in cell size. Here, we propose an automatic method to segment the endothelium. Starting with an oversegmented image comprised of superpixels obtained from a stochastic watershed segmentation, the proposed method uses intensity and shape information of the superpixels to identify and merge those that constitute a cell, using support vector machines. We evaluated the automatic segmentation on a data set of in vivo specular microscopy images (Topcon SP-1P), obtaining 95.8% correctly merged cells and 2.0% undersegmented cells. We also evaluated the parameter estimation against the results of the vendor’s built-in software, obtaining a statistically significant better precision in all parameters and a similar or better accuracy. The parameter estimation was also evaluated on three other data sets from different imaging modalities (confocal microscopy, phase-contrast microscopy, and fluorescence confocal microscopy) and tissue types ( ex vivo corneal endothelium and retinal pigment epithelium). In comparison with the estimates of the data sets’ authors, we achieved statistically significant better accuracy and precision in all parameters except pleomorphism, where a similar accuracy and precision were obtained.

Clinical parameters related to the corneal endothelium can only be estimated by segmenting endothelial cell images. Specular microscopy is the current standard technique to image the endothelium, but its low SNR make the segmentation a complicated task. Recently, we proposed a method to segment such images by starting with an oversegmented image and merging the superpixels that constitute a cell. Here, we show how our merging method provides better results than optimizing the segmentation itself. Furthermore, our method can provide accurate results despite the degree of the initial oversegmentation, resulting into a precision and recall of 0.91 for the optimal oversegmentation.

Purpose: To develop a method for intra-patient registration of pre- and post-contrast abdominal MR images with large local deformations and large intensity variations. Method: A hybrid method is proposed to deal with this problem. It consists of two coupled techniques: (1) descriptor matching (DM) at the original resolution using a discrete optimization strategy to avoid getting trapped in a local minimum; (2) continuous optimization to refine the registration outcome based on autocorrelation of local image structure (ALOST). Our method—called DM-ALOST—has become insensitive to the local uptake of contrast agent by exploiting the mean phase and the phase congruency extracted from the multi-scale monogenic signal. The method was extensively tested on abdominal MR data of 30 patients with Crohn’s disease. Results: DM-ALOST produced significantly larger mean Dice coefficients than two state-of-the-art methods (Formula presented.). Conclusion: Both qualitative and quantitative tests demonstrated improved registration using the proposed method compared to the state-of-the-art. The DM-ALOST method facilitates measurement of corresponding features from different abdominal MR images, which can aid to assess certain diseases, particularly Crohn’s disease.

We present a comparison of image reconstruction techniques for optical projection tomography. We compare conventional filtered back projection, sinogram filtering using the frequency–distance relationship (FDR), image deconvolution, and 2D point-spread-function-based iterative reconstruction. The latter three methods aim to remove the spatial blurring in the reconstructed image originating from the limited depth of field caused by the point spread function of the imaging system. The methods are compared based on simulated data, experimental optical projection tomography data of single fluorescent beads, and high-resolution optical projection tomography imaging of an entire zebrafish larva. We demonstrate that the FDR method performs poorly on data acquired with high numerical aperture optical imaging systems. We show that the deconvolution technique performs best on highly sparse data with low signal-to-noise ratio. The point-spread-function-based reconstruction method is superior for nonsparse objects and data of high signal-to-noise ratio.

People with diabetes mellitus need annual screening to check for the development of diabetic retinopathy (DR). Tracking small retinal changes due to early diabetic retinopathy lesions in longitudinal fundus image sets is challenging due to intra- and intervisit variability in illumination and image quality, the required high registration accuracy, and the subtle appearance of retinal lesions compared to other retinal features. This paper presents a robust and flexible approach for automated detection of longitudinal retinal changes due to small red lesions by exploiting normalized fundus images that significantly reduce illumination variations and improve the contrast of small retinal features. To detect spatio-temporal retinal changes, the absolute difference between the extremes of the multiscale blobness responses of fundus images from two time points is proposed as a simple and effective blobness measure. DR related changes are then identified based on several intensity and shape features by a support vector machine classifier. The proposed approach was evaluated in the context of a regular diabetic retinopathy screening program involving subjects ranging from healthy (no retinal lesion) to moderate (with clinically relevant retinal lesions) DR levels. Evaluation shows that the system is able to detect retinal changes due to small red lesions with a sensitivity of ${\text{80}}\%$ at an average false positive rate of 1 and 2.5 lesions per eye on small and large fields-of-view of the retina, respectively.

We present an investigation of the impact of partial coherence on optical imaging systems with the focus on whole slide imaging (WSI) systems for digital pathology. The investigation is based on the analysis of the edge response of the optical system, which gives rise to an apparent optical transfer function (OTF) that can be linked to two elementary complex functions Q and U. The function Q is directly related to the transmission cross coefficient (TCC) and can be identified with the performance function first introduced by Kintner and Sillitto. The function U depends on the TCC in a more involved way. When there are no aberrations the Q-function corresponds to the real part of the apparent OTF and the U function to the imaginary part of the apparent OTF. Close to the incoherent limit the effect of the U function is a mere shift of the edge compared to the fully incoherent case. We propose a new expression for the dependence of the depth of focus (DOF) on spatial frequency and on the partial coherence factor σ, and validate it by simulation. Partial coherence effects are investigated experimentally on a WSI system with a compact LED-based Kohler illumination unit with variable condenser NA. This unit incorporates a top hat diffuser for providing a reasonably uniform illumination field, with variations below 10% across the imaged field of view. The measurements of the apparent through-focus OTF derived from edges on a custom resolution chart for different σ were substantially in agreement with the simulations. Finding an optimal value for σ is not straightforward as lateral resolution and the level of edge ringing improve with increasing σ, whereas edge contrast and DOF improve with decreasing σ. We assess that the trade-off for the particular application of WSI systems for digital pathology is optimized for a σ value in the range of 0.55-0.75.

Accurate quantification of retinal structures in 3-D optical coherence tomography data of eyes with pathologies provides clinically relevant information. We present an approach to jointly segment retinal layers and lesions in eyes with topology-disrupting retinal diseases by a loosely coupled level set framework. In the new approach, lesions are modeled as an additional space-variant layer delineated by auxiliary interfaces. Furthermore, the segmentation of interfaces is steered by local differences in the signal between adjacent retinal layers, thereby allowing the approach to handle local intensity variations. The accuracy of the proposed method of both layer and lesion segmentation has been evaluated on eyes affected by central serous retinopathy and age-related macular degeneration. In addition, layer segmentation of the proposed approach was evaluated on eyes without topology-disrupting retinal diseases. Good agreement between the segmentation performed manually by a medical doctor and results obtained from the automatic segmentation was found for all data types. The mean unsigned error for all interfaces varied between 2.3 and 11.9 $\mu \text{m}$ (0.6–3.1 pixels). Furthermore, lesion segmentation showed a Dice coefficient of 0.68 for drusen and 0.89 for fluid pockets. Overall, the method provides a flexible and accurate solution to jointly segment lesions and retinal layers.

CT angiography (CTA) and SPECT myocardial perfusion imaging (MPI) are complementary imaging techniques to assess coronary artery disease (CAD). Spatial integration and combined visualization of SPECT MPI and CTA data may facilitate correlation of myocardial perfusion defects and subtending coronary arteries and thus offer additional diagnostic value over either stand-alone or side-by-side interpretation of the respective datasets from the 2 modalities. In this study, we investigated the additional diagnostic value of a software-based CTA/SPECT MPI image fusion system over conventional side-by-side analysis in patients with suspected CAD. Methods: Seventeen symptomatic patients who underwent both CTA and SPECT MPI within a 90-d period were included in our study; 7 of them also underwent invasive coronary angiography (ICA). The potential benefits of the synchronized multimodal heart visualization (SMARTVis) system in assessing CAD were investigated through a case study involving 4 experts from 2 medical centers, in which we performed, first, a side-by-side analysis using structured CTA and SPECT reports and, second, an integrated analysis using the SMARTVis system in addition to the reports. Results: The fused interpretation led to a more accurate diagnosis, reflected in an increase in the individual observers' sensitivity and specificity to correctly refer for invasive angiography eventually followed by revascularization. For the first, second, third, and fourth observers, the respective sensitivities improved from 50%, 60%, 80%, and 80% to 70%, 80%, 100%, and 90% and the respective specificities from 100%, 94%, 83%, and 83% to 100%, 100%, 94%, and 83%. Additionally, the interobserver diagnosis agreement increased from 74% to 84%. The improvement was primarily found in patients presenting with CAD in more vessels than the number of reported perfusion defects. Conclusion: Integrated analysis of cardiac CTA and SPECT MPI using the SMARTVis system results in an improved diagnostic performance.

CT colonography (CTC) is one of the recommended methods for colorectal cancer screening. The subject's preparation is one of the most burdensome aspects of CTC with a cathartic bowel preparation. Tagging of the bowel content with an oral contrast medium facilitates CTC with limited bowel preparation. Unfortunately, such preparations adversely affect the 3-D image quality. Thus far, data acquired after very limited bowel preparation were evaluated with a 2-D reading strategy only. Existing cleansing algorithms do not work sufficiently well to allow a primary 3-D reading strategy. We developed an electronic cleansing algorithm, aimed to realize optimal 3-D image quality for low-dose CTC with 24-h limited bowel preparation. The method employs a principal curvature flow algorithm to remove heterogeneities within poorly tagged fecal residue. In addition, a pattern recognition-based approach is used to prevent polyp-like protrusions on the colon surface from being removed by the method. Two experts independently evaluated 40 CTC cases by means of a primary 2-D approach without involvement of electronic cleansing as well as by a primary 3-D method after electronic cleansing. The data contained four variations of 24-h limited bowel preparation and was based on a low radiation dose scanning protocol. The sensitivity for lesions ≥ 6 mm was significantly higher for the primary 3-D reading strategy (84%) than for the primary 2-D reading strategy (68%) (p = 0.031). The reading time was increased from 5:39 min (2-D) to 7:09 min (3-D) (p = 0.005); the readers' confidence was reduced from 2.3 (2-D) to 2.1 (3-D) ( p = 0.013) on a three-point Likert scale. Polyp conspicuity for cleansed submerged lesions was similar to not submerged lesions (p = 0.06). To our knowledge, this study is the first to describe and clinically validate an electronic cleansing algorithm that facilitates low-dose CTC with 24-h limited bowel preparation.

Rationale and Objectives The aim of this study was to automatically detect and quantify calcium lesions for the whole heart as well as per coronary artery on non-contrast-enhanced cardiac computed tomographic images. Materials and Methods Imaging data from 366 patients were randomly selected from patients who underwent computed tomographic calcium scoring assessments between July 2004 and May 2009 at Erasmum MC, Rotterdam. These data included data sets with 1.5-mm and 3.0-mm slice spacing reconstructions and were acquired using four different scanners. The scores of manual observers, who annotated the data using commercially available software, served as ground truth. An automatic method for detecting and quantifying calcifications for each of the four main coronary arteries and the whole heart was trained on 209 data sets and tested on 157 data sets. Statistical testing included determining Pearson's correlation coefficients and Bland-Altman analysis to compare performance between the system and ground truth. Wilcoxon's signed-rank test was used to compare the interobserver variability to the system's performance. Results Automatic detection of calcified objects was achieved with sensitivity of 81.2% per calcified object in the 1.5-mm data set and sensitivity of 86.6% per calcified object in the 3.0-mm data set. The system made an average of 2.5 errors per patient in the 1.5-mm data set and 2.2 errors in the 3.0-mm data set. Pearson's correlation coefficients of 0.97 ( P R values over Agatston, mass, and volume scores for each of the arteries (left circumflex coronary artery, right coronary artery, and left main and left anterior descending coronary arteries) were 0.93, 0.96, and 0.99, respectively, for the 1.5-mm scans. Similarly, for 3.0-mm scans, R values were 0.94, 0.94, and 0.99, respectively. Risk category assignment was correct in 95% and 89% of the data sets in the 1.5-mm and 3-mm scans. Conclusions An automatic vessel-specific coronary artery calcium scoring system was developed, and its feasibility for calcium scoring in individual vessels and risk category classification has been demonstrated.

A well-known reading pitfall in computed tomography (CT) colonography is posed by artifacts at T-junctions, i.e., locations where air-fluid levels interface with the colon wall. This paper presents a scale-invariant method to determine material fractions in voxels near such T-junctions. The proposed electronic cleansing method particularly improves the segmentation at those locations. The algorithm takes a vector of Gaussian derivatives as input features. The measured features are made invariant to the orientation-dependent apparent scale of the data and normalized in a way to obtain equal noise variance. A so-called parachute model is introduced that maps Gaussian derivatives onto material fractions near T-junctions. Projection of the noisy derivatives onto the model yields improved estimates of the true, underlying feature values. The method is shown to render an accurate representation of the object boundary without artifacts near junctions. Therefore, it enhances the reading of CT colonography in a 3-D display mode.

PurposeThis paper presents and studies a framework for reliable modeling of diffusion MRI using a data-acquisition adaptive prior.MethodsAutomated relevance determination estimates the mean of the posterior distribution of a rank-2 dual tensor model exploiting Jeffreys prior (JARD). This data-acquisition prior is based on the Fisher information matrix and enables the assessment whether two tensors are mandatory to describe the data. The method is compared to Maximum Likelihood Estimation (MLE) of the dual tensor model and to FSL’s ball-and-stick approach.ResultsMonte Carlo experiments demonstrated that JARD’s volume fractions correlated well with the ground truth for single and crossing fiber configurations. In single fiber configurations JARD automatically reduced the volume fraction of one compartment to (almost) zero. The variance in fractional anisotropy (FA) of the main tensor component was thereby reduced compared to MLE. JARD and MLE gave a comparable outcome in data simulating crossing fibers. On brain data, JARD yielded a smaller spread in FA along the corpus callosum compared to MLE. Tract-based spatial statistics demonstrated a higher sensitivity in detecting age-related white matter atrophy using JARD compared to both MLE and the ball-and-stick approach.ConclusionsThe proposed framework offers accurate and precise estimation of diffusion properties in single and dual fiber regions.

Image registration of medical images in the presence of large intra-image signal fluctuations is a challenging task. Our paper addresses this problem by introducing a new concept based on the structure tensor of the local phase. The local phase is calculated from the monogenic signal representation of the images. The local phase image is hardly affected by unwanted signal fluctuations due to a space-variant background and a space-variant contrast. The boundary structure tensor combines the responses of edges and corners/junctions in one tensor, which has several advantages, compared to other structure tensors. We reorient the structure tensor during the registration by means of the finite-strain technique. The structure tensor is only calculated once during a preprocessing step. The results demonstrate that the proposed method effectively deals with large signal fluctuations. It performs significantly better than competing techniques.

PURPOSE We evaluated the accuracy of a recently developed fundus image registration method (Weighted Vasculature Registration, or WeVaR) and compared it to two top-ranked state-of-the-art commercial fundus mosaicking programs (i2k Retina, DualAlign LLC, and Merge Eye Care PACS, formerly named OIS AutoMontage) in the context of diabetic retinopathy (DR) screening. METHODS Fundus images of 70 diabetic patients who visited the Rotterdam Eye Hospital in 2012 and 2013 for a DR screening program were registered by all three programs. The registration results were used to produce mosaics from fundus photos that were normalized for luminance and contrast to improve the visibility of small details. These mosaics subsequently were evaluated and ranked by two expert graders to assess the registration accuracy. RESULTS Merge Eye Care PACS had high registration failure rates compared to WeVaR and i2k Retina (P = 8 × 10(-6) and P = 0.002, respectively). WeVaR showed significantly higher registration accuracy than i2k Retina in intravisit (P ≤ 0.0036) and intervisit (P ≤ 0.0002) mosaics. Therefore, fundus mosaics processed by WeVaR were more likely to have a higher score (odds ratio [OR] = 2.5, P = 10(-5) for intravisit and OR = 2.2, P = 0.006 for intervisit mosaics). WeVaR was preferred more often by the graders than i2k Retina (OR = 6.1, P = 7 × 10(-6)). CONCLUSIONS WeVaR produced intra- and intervisit fundus mosaics with higher registration accuracy than Merge Eye Care PACS and i2k Retina. Merge Eye Care PACS had higher registration failures than the other two programs. Highly accurate registration methods, such as WeVaR, may potentially be used for more efficient human grading and in computer-aided screening systems for detecting DR progression.

One of the major challenges in computer vision is to create automated systems that perform tasks with at least the same competences as human experts. In particular for automated inspection of natural objects this is not easy to achieve. The task is hampered by large in-class variations and complex 3D-morphology of the objects and subtle argumentations of experts. For example, in our horticultural case we deal with quality assessment of young tomato plants, which requires experienced specialists. We submit that automation of such a task employing an explicit model of the objects and their assessment is preferred over a black-box model obtained from modelling input-output relations only. We propose to employ ontologies for representing the geometrical shapes, object parts and quality classes associated with the explicit models. Our main contribution is the description of a method to develop a white-box computer vision application in which the needed expert knowledge is defined by: (i) decomposing the task of the inspection system into subtasks and (ii) identifying the algorithms that execute the subtasks. This method describes the interaction between the task decomposition and the needed task-specific knowledge, and studies the delicate balance between general domain knowledge and task-specific details. As a proof of principle of this methodology, we work through a horticultural case study and argue that the method leads to a robust, well-performing, and extendable computer vision system.

The generalized Radon (or Hough) transform is a well-known tool for detecting parameterized shapes in an image. The Radon transform is a mapping between the image space and a parameter space. The coordinates of a point in the latter correspond to the parameters of a shape in the image. The amplitude at that point corresponds to the amount of evidence for that shape. In this paper we discuss three important aspects of the Radon transform. The first aspect is discretization. Using concepts from sampling theory we derive a set of sampling criteria for the generalized Radon transform. The second aspect is accuracy. For the specific case of the Radon transform for spheres, we examine how well the location of the maxima matches the true parameters. We derive a correction term to reduce the bias in the estimated radii. The third aspect concerns a projection-based algorithm to reduce memory requirements.

Biotechnology today is a well-established paradigm in many areas of human endeavor, such as the pharmaceutical industry, agriculture, management of the environment and many others. Meanwhile, biology is undergoing a spectacular transition: whereas systematic biology was replaced gradually by molecular biology, the latter is rapidly being transformed into a new systematic era in which entire genomes are being charted by ever more sophisticated analytical techniques. In the wake of this onslaught of data, new fields are germinating, such as bioinformatics in an attempt to find answers to fundamental questions, answers that may be hidden in the massive amounts of data already available today.

To evaluate a semi-automatic method for delineation of the bowel wall and measurement of the wall thickness in patients with Crohn's disease. 53 patients with suspected or proven Crohn's disease were selected. Two radiologists independently supervised the delineation of regions with active Crohn's disease on MRI, yielding manual annotations (Ano1, Ano2). Three observers manually measured the maximal bowel wall thickness of each annotated segment. An active contour segmentation approach semi-automatically delineated the bowel wall. For each active region, two segmentations (Seg1, Seg2) were obtained by independent observers, in which the maximum wall thickness was automatically determined. The overlap between (Seg1, Seg2) was compared with the overlap of (Ano1, Ano2) using Wilcoxon's signed rank test. The corresponding variances were compared using the Brown-Forsythe test. The variance of the semi-automatic thickness measurements was compared with the overall variance of manual measurements through an F-test. Furthermore, the intraclass correlation coefficient (ICC) of semi-automatic thickness measurements was compared with the ICC of manual measurements through a likelihood-ratio test. Patient demographics: median age, 30 years; interquartile range, 25-38 years; 33 females. The median overlap of the semi-automatic segmentations (Seg1 vs Seg2: 0.89) was significantly larger than the median overlap of the manual annotations (Ano1 vs Ano2: 0.72); p = 1.4 × 10(-5). The variance in overlap of the semi-automatic segmentations was significantly smaller than the variance in overlap of the manual annotations (p = 1.1 × 10(-9)). The variance of the semi-automated measurements (0.46 mm(2)) was significantly smaller than the variance of the manual measurements (2.90 mm(2), p = 1.1 × 10(-7)). The ICC of semi-automatic measurement (0.88) was significantly higher than the ICC of manual measurement (0.45); p = 0.005. The semi-automatic technique facilitates reproducible delineation of regions with active Crohn's disease. The semi-automatic thickness measurement sustains significantly improved interobserver agreement. Advances in knowledge: Automation of bowel wall thickness measurements strongly increases reproducibility of these measurements, which are commonly used in MRI scoring systems of Crohn's disease activity

The quality of the reconstructed image in structured illumination microscopy (SIM) depends on various aspects of the image filtering process. To optimize the trade-off between resolution and ringing artifacts, which lead to negative intensities, we extend Lukosz-bound filtering to 3D SIM and derive the parametrization of the 3D SIM cut-off. We compare the use of the Lukosz-bound as apodization filter to triangular apodization and find a tenfold reduction in the most negative pixel value with a minimal resolution loss. We test this algorithm on experimental SIM images of tubulin filaments and DAPI stained DNA structure in cancer cells and find a substantial reduction in the most negative pixel value and the percentage of pixels with a negative value. This means that there is no longer a need to clip the final image to avoid these negative pixel values.

Fluorescence in situ hybridization allows the enumeration of chromosomal abnormalities in interphase cell nuclei. This process is called dot counting. To estimate the distribution of chromosomes per cell, a large number of cells have to be analyzed, especially when the frequency of aberrant cells is low. Automation of dot counting is required because manual counting is tedious, fatiguing, and time-consuming. We developed a completely automated fluorescence microscope system that can examine 500 cells in approximately 15 min to determine the number of labeled chromosomes (seen as dots) in each cell nucleus. This system works with two fluorescent dyes, one for the DNA hybridization dots and one for the cell nucleus. After the stage has moved to a new field, the image is automatically focused, acquired by a Photometrics KAF 1400 camera (Photometrics Ltd., Tuscon, AZ, USA), and then analyzed on a Macintosh Quadra 840AV (Apple Computer, Inc., Cupertino, CA, USA) computer. After the required number of cells has been analyzed, the user may interact to correct the computer by working with a gallery of the cell images. The automated dot counter has been tested on a number of normal specimens where 4,'6-diamidino-2-phenylindole (DAPI) was used for the nucleus counterstain and a centromeric 8 probe was used to mark the desired chromosome. The slides contained lymphocytes from cultured blood. We compared the results of the dot counter with manual counting. Manually obtained results, published in the literature, were used as the "ground truth." For a normal specimen, 97.5% of cells will have two dots. Fully automated scanning of 13 slides showed that an average of 89% of all nuclei were counted correctly. In other words, an average of 11% has to be interactively corrected, using a monitor display. The machine accuracies, after interactive correction, are comparable to panels of human experts (manual). The fully automatically obtained results are biased with respect to manual counting. An error analysis is given, and different causes are discussed.

Various aspects of image filtering affect the final image quality in Structured Illumination Microscopy, in particular the regularization parameter and type of regularization function, the relative height of the side bands, and the shape of the apodization function. We propose an apodization filter without adjustable parameters based on the application of the Lukosz bound in order to guarantee a non-negative point spread function. Simulations of digital resolution charts and experimental data of chromatin structures and of actin filaments show artefact free reconstructions for a wide range of filter parameters. In general, a trade-off is observed between sharpness and noise suppression.

Fluorescence in situ hybridization allows the enumeration of chromosomal abnormalities in interphase cell nuclei. This process is called dot counting. To estimate the distribution of chromosomes per cell, a large number of cells have to be analysed, particularly when the frequency of aberrant cells is low. Automation of dot counting is desirable because manual counting is tedious, fatiguing, and time consuming. We have developed a completely automated fluorescence microscope system that counts fluorescent hybridization dots for one probe in interphase cell nuclei. This system works with two fluorescent dyes—one for the DNA hybridization dots and one for the cell nucleus. A fully automated scanning procedure has been used for the image acquisition. After an image is acquired it has to be analysed in order to find the nuclei and to detect the dots. This article focuses upon the dot detection procedure. Three different algorithms are presented. The problems of 'overlapping' dots and split dots are discussed. The automated dot counter has been tested on a number of normal specimens where DAPI was used for the nucleus counter stain and a centromeric probe was used to mark the chromosome 12. The slides contained lymphocytes from cultured blood. The performance of the different algorithms has been evaluated and compared with manually obtained results. The automated counting results approximate the results of manual counting.

International audience; State-of-the-art techniques for phase retrieval in propagation based X-ray phase-contrast imaging are aiming to solve an underdetermined linear system of equations. They commonly employ Tikhonov regularization - an L2-norm regularized deconvolution scheme - despite some of its limitations. We present a novel approach to phase retrieval based on Total Variation (TV) minimization. We incorporated TV minimization for deconvolution in phase retrieval using a variety of the most common linear phase-contrast models. The results of our TV minimization was compared with Tikhonov regularized deconvolution on simulated as well as experimental data. The presented method was shown to deliver improved accuracy in reconstructions based on a single distance as well as multiple distance phase-contrast images corrupted by noise and hampered by errors due to nonlinear imaging effects. (C) 2013 Optical Society of America

International audience; The reconstruction problem in in-line X-ray Phase-Contrast Tomography is usually approached by solving two independent linearized sub-problems: phase retrieval and tomographic reconstruction. Both problems are often ill-posed and require the use of regularization techniques that lead to artifacts in the reconstructed image. We present a novel reconstruction approach that solves two coupled linear problems algebraically. Our approach is based on the assumption that the frequency space of the tomogram can be divided into bands that are accurately recovered and bands that are undefined by the observations. This results in an underdetermined linear system of equations. We investigate how this system can be solved using three different algebraic reconstruction algorithms based on Total Variation minimization. These algorithms are compared using both simulated and experimental data. Our results demonstrate that in many cases the proposed algebraic algorithms yield a significantly improved accuracy over the conventional L2-regularized closed-form solution. This work demonstrates that algebraic algorithms may become an important tool in applications where the acquisition time and the delivered radiation dose must be minimized. (C) 2013 Optical Society of America

Accurate detection and quantification of coronary artery stenoses is an essential requirement for treatment planning of patients with suspected coronary artery disease. We present a method to automatically detect and quantify coronary artery stenoses in computed tomography coronary angiography. First, centerlines are extracted using a two-point minimum cost path approach and a subsequent refinement step. The resulting centerlines are used as an initialization for lumen segmentation, performed using graph cuts. Then, the expected diameter of the healthy lumen is estimated by applying robust kernel regression to the coronary artery lumen diameter profile. Finally, stenoses are detected and quantified by computing the difference between estimated and expected diameter profiles. We evaluated our method using the data provided in the Coronary Artery Stenoses Detection and Quantification Evaluation Framework. Using 30 testing datasets, the method achieved a detection sensitivity of 29 % and a positive predictive value (PPV) of 24 % as compared to quantitative coronary angiography (QCA), and a sensitivity of 21 % and a PPV of 23 % as compared manual assessment based on consensus reading of CTA by 3 observers. The stenoses degree was estimated with an absolute average difference of 31 %, a root mean square difference of 39.3 % when compared to QCA, and a weighted kappa value of 0.29 when compared to CTA. A Dice of 68 and 65 % was reported for lumen segmentation of healthy and diseased vessel segments respectively. According to the ranking of the evaluation framework, our method finished fourth for stenosis detection, second for stenosis quantification and second for lumen segmentation.

Zero-crossing of a derivative of Gaussian filter is a well-known edge location criterion. Examples are the Laplacian, the second derivative in the gradient direction (SDGD) and the sum of the Laplacian and SDGD (PLUS). Derivative operators can easily be implemented by convoluting the primitive image with a derivative of a Gaussian. Gaussian filter displaces the equipotential of half height inwards for convex edge and outwards for concave edges. A Difference-of-Gaussian (DoG) filter is similar to the Laplacian-of-Gaussian but with opposite sign and causes a convex edge shift inwards. This paper introduces the Multiple-of-Gaussian filters to reduce curvature-based location error. Using a linear combination ofN Gaussians (N>2) with proper weigths, the edge shifts can be reduced to 1/(2N−3) of the ones produced by a similar Laplacian-of-Gaussian filter.

In this paper we propose a recursive implementation of the Gaussian filter. This implementation yields an infinite impulse response filter that has six MADDs per dimension independent of the value of σ in the Gaussian kernel. In contrast to the Deriche implementation (1987), the coefficients of our recursive filter have a simple, closed-form solution for a desired value of the Gaussian σ. Our implementation is, in general, faster than (1) an implementation based upon direct convolution with samples of a Gaussian, (2) repeated convolutions with a kernel such as the uniform filter, and (3) an FFT implementation of a Gaussian filter.

The aim of this study was to automatically detect and quantify calcium lesions for the whole heart as well as per coronary artery on non-contrast-enhanced cardiac computed tomographic images.Imaging data from 366 patients were randomly selected from patients who underwent computed tomographic calcium scoring assessments between July 2004 and May 2009 at Erasmum MC, Rotterdam. These data included data sets with 1.5-mm and 3.0-mm slice spacing reconstructions and were acquired using four different scanners. The scores of manual observers, who annotated the data using commercially available software, served as ground truth. An automatic method for detecting and quantifying calcifications for each of the four main coronary arteries and the whole heart was trained on 209 data sets and tested on 157 data sets. Statistical testing included determining Pearson's correlation coefficients and Bland-Altman analysis to compare performance between the system and ground truth. Wilcoxon's signed-rank test was used to compare the interobserver variability to the system's performance.Automatic detection of calcified objects was achieved with sensitivity of 81.2% per calcified object in the 1.5-mm data set and sensitivity of 86.6% per calcified object in the 3.0-mm data set. The system made an average of 2.5 errors per patient in the 1.5-mm data set and 2.2 errors in the 3.0-mm data set. Pearson's correlation coefficients of 0.97 (P.001) for both 1.5-mm and 3.0-mm scans with respect to the calcium volume score of the whole heart were found. The average R values over Agatston, mass, and volume scores for each of the arteries (left circumflex coronary artery, right coronary artery, and left main and left anterior descending coronary arteries) were 0.93, 0.96, and 0.99, respectively, for the 1.5-mm scans. Similarly, for 3.0-mm scans, R values were 0.94, 0.94, and 0.99, respectively. Risk category assignment was correct in 95% and 89% of the data sets in the 1.5-mm and 3-mm scans.An automatic vessel-specific coronary artery calcium scoring system was developed, and its feasibility for calcium scoring in individual vessels and risk category classification has been demonstrated.

In this paper a novel groupwise registration algorithm is proposed for the unbiased registration of a large number of densely sampled point clouds. The method fits an evolving mean shape to each of the example point clouds thereby minimizing the total deformation. The registration algorithm alternates between a computationally expensive, but parallelizable, deformation step of the mean shape to each example shape and a very inexpensive step updating the mean shape.

This work presents the development of a non-destructive imaging technique for the investigation of the microstructure of cementite grains embedded in a ferrite matrix of medium-carbon steel. The measurements were carried out at the material science beamline of the European Synchrotron Radiation Facility (ESRF) ID11. It was shown that in-line X-ray phase-contrast tomography (PCT) can be used for the detection of cementite grains of several microns in size. X-ray PCT of the cementite structure can be achieved by either a ‘single distance’ or a ‘multiple distance’ acquisition protocol. The latter permits quantitative phase retrieval. A second imaging technique, X-ray diffraction-contrast tomography (DCT), was employed to obtain information about the shapes and crystallographic orientations of the distinct ferrite grains surrounding the cementite structures. The initial results demonstrate the feasibility of determining the geometry of the cementite grains after the austenite-ferrite phase-transformation in a non-destructive manner. The results obtained with PCT and DCT are verified with ex-situ optical microscopy studies of the same specimen.

We present an extension of the symmetric ICP algorithm that is unbiased for an arbitrary number (N≥2) of shapes, using rigid transformations and scaling. The method does not require the selection of a reference shape or registration order and hence it is unbiased towards any of the registered shapes. The functional to be minimized is non-linear in the transformation parameters and thus computationally complex. We therefore propose a first order approximation that estimates the transformation parameters in a closed form, with computational complexity $\mathcal{O}(N^{2})$. Using a set of wrist bones, we show that the least-squares minimization and the proposed approximation converge to the same solution. Experiments also show that the proposed algorithms lead to smaller registration errors than algorithms that select a reference shape or register to an evolving mean shape. The low computational cost and trivial parallelization enable the alignment of large numbers of bones.

We have developed an imaging system for high speed image acquisition in fluorescence microscopy. The use of a two- dimensional CCD array in a special operation mode called TDI (Time Delayed Integration) permits a significant increase in photon integration time compared to 1D scanners (higher signal-to-noise ratio) without compromising the total data throughput rate. Instead of a start–stop system we use continuous stage motion in the CCDs parallel shift direction. Synchronizing the parallel clock and the stage velocity guarantees a one-to-one relationship between a moving cell and its image onto the CCD. Compared to start-stop system, TDI scanning offers a speed improvement, negligible blurring in the scanning direction and a complete suppression of pixel variability boosting the SNR more than 10 dB.

Frank R. Boddeke, Lucas J. van Vliet, Hans Netten and Ian T. YoungPattern Recognition Group of the Faculty of Applied PhysicsDelft University of TechnologyLorentzweg 1, 2628 CJ Delft, The NetherlandsAbstractIn the literature many autofocus algorithms have been proposed and compared (Groen et al. 1985;Firestone at al. 1991; Yeo et al. 1993; Price and Gough 1994) for use in optical microscopy (brightfield and fluorescence microscopy). Most of the focus criteria measure the high frequency contents ofa recorded image as a measure of focus. In this paper we show that a focus criteria should measure thesignal power of the middle frequency, since defocusing mainly reduces the frequencies around halfthe cut-off frequency of the optical system. The filter that provides the required band–pass filteringdepends strongly on the sampling density of the camera. There are two practical combinations ofsampling density and one-dimensional digital band-pass filter:• Sampling at the Nyquist frequency and the {1,0,–1} filter;• Sampling at half the Nyquist frequency and the {1,–1} filter.The latter is to be preferred due to noise considerations and the fact that it uses four times fewersample points. Calculation speed can also be increased by further reducing the sampling densityperpendicular to the filter (on chip or in software) down to 1/8 of the Nyquist frequency. We havedesigned a three-phase autofocus algorithm that works well in fluorescence and bright fieldmicroscopy. The phases are:• Coarse, find the region near focus (step size of typically a few microns);• Fine, find a quadratic region around focus (step size around one micron);• Refine, use a quadratic fit on samples around the peak to find the in-focus position.We found that the final focus error is smaller than the mechanical reproducibility of our z-axis (50nm) for light levels down to 400 photo-electrons per pixel (sampling at the Nyquist frequency using acooled CCD camera with pixels of 6.8

We propose a minimum cost path approach to track the centerlines of the internal and external carotid arteries in multispectral MR data. User interaction is limited to the annotation of three seed points. The cost image is based on both a measure of vessel medialness and lumen intensity similarity in two MRA image sequences: Black Blood MRA and Phase Contrast MRA. After intensity inhomogeneity correction and noise reduction, the two images are aligned using affine registration. The two parameters that control the contrast of the cost image were determined in an optimization experiment on 40 training datasets. Experiments on the training datasets also showed that a cost image composed of a combination of gradient-based medialness and lumen intensity similarity increases the tracking accuracy compared to using only one of the constituents. Furthermore, centerline tracking using both MRA sequences outperformed tracking using only one of these MRA images. An independent test set of 152 images from 38 patients served to validate the technique. The centerlines of 148 images were successfully extracted using the parameters optimized on the training sets. The average mean distance to the reference standard, manually annotated centerlines, was 0.98 mm, which is comparable to the in-plane resolution. This indicates that the proposed method has a high potential to replace the manual centerline annotation.

The trapeziometacarpal joint enables the prehensile function of the thumb. Unfortunately, this joint is vulnerable to osteoarthritis (OA) that typically affects the local shape of the trapezium. A novel, local statistical shape model is defined that employs a differentiable locality measure based on the weighted variance of point coordinates per mode. The simplicity of the function and the smooth derivative enable to quickly determine localized components for densely sampled surfaces. The method is employed to assess a set of 60 trapezia (38 healthy, 22 with OA). The localized components predominantly model regions affected by OA, contrary to shape variations found with PCA. Furthermore, identification of pathological trapezia based on the localized modes of variation is improved compared to PCA.