Estimating the arterial input function from dynamic contrast-enhanced MRI data with compensation for flow enhancement (I): Theory, method, and phantom experiments.

BACKGROUND: The arterial input function (AIF) represents the time-dependent arterial contrast agent (CA) concentration that is used in pharmacokinetic modeling. PURPOSE: To develop a novel method for estimating the AIF from dynamic contrast-enhanced (DCE-) MRI data, while compensating for flow enhancement. STUDY TYPE: Signal simulation and phantom measurements. PHANTOM MODEL: Time-intensity curves (TICs) were simulated for different numbers of excitation pulses modeling flow effects. A phantom experiment was performed in which a solution (without CA) was passed through a straight tube, at constant flow velocity. FIELD STRENGTH/SEQUENCE: Dynamic fast spoiled gradient echo (FSPGRs) at 3T MRI, both in the simulations and in the phantom experiment. TICs were generated for a duration of 373 seconds and sampled at intervals of 1.247 seconds (300 timepoints). ASSESSMENT: The proposed method first estimates the number of pulses that spins have received, and then uses this knowledge to accurately estimate the CA concentration. STATISTICAL TESTS: The difference between the median of the estimated number of pulses and the true value was determined, as well as the interquartile range (IQR) of the estimations. The estimated CA concentrations were evaluated in the same way. The estimated number of pulses was also used to calculate flow velocity. RESULTS: The difference between the median estimated and reference number of pulses varied from -0.005 to -1.371 (corresponding IQRs: 0.853 and 48.377) at true values of 10 and 180 pulses, respectively. The difference between the median estimated CA concentration and the reference value varied from -0.00015 to 0.00306 mmol/L (corresponding IQRs: 0.01989 and 1.51013 mmol/L) at true values of 0.5 and 8.0 mmol/l, respectively, at an intermediate value of 100 pulses. The estimated flow velocities in the phantom were within 10% of the reference value. DATA CONCLUSION: The proposed method accurately corrects the MRI signal affected by the inflow effect. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1190-1196.

Estimating the arterial input function from dynamic contrast-enhanced MRI data with compensation for flow enhancement (II): Applications in spine diagnostics and assessment of crohn's disease.

BACKGROUND: Pharmacokinetic (PK) models can describe microvascular density and integrity. An essential component of PK models is the arterial input function (AIF) representing the time-dependent concentration of contrast agent (CA) in the blood plasma supplied to a tissue. PURPOSE/HYPOTHESIS: To evaluate a novel method for subject-specific AIF estimation that takes inflow effects into account. STUDY TYPE: Retrospective study. SUBJECTS: Thirteen clinical patients referred for spine-related complaints; 21 patients from a study into luminal Crohn's disease with known Crohn's Disease Endoscopic Index of Severity (CDEIS). FIELD STRENGTH/SEQUENCE: Dynamic fast spoiled gradient echo (FSPGR) at 3T. ASSESSMENT: A population-averaged AIF, AIFs derived from distally placed regions of interest (ROIs), and the new AIF method were applied. Tofts' PK model parameters (including vp and Ktrans ) obtained with the three AIFs were compared. In the Crohn's patients Ktrans was correlated to CDEIS. STATISTICAL TESTS: The median values of the PK model parameters from the three methods were compared using a Mann-Whitney U-test. The associated variances were statistically assessed by the Brown-Forsythe test. Spearman's rank correlation coefficient was computed to test the correlation of Ktrans to CDEIS. RESULTS: The median vp was significantly larger when using the distal ROI approach, compared to the two other methods (P < 0.05 for both comparisons, in both applications). Also, the variances in vp were significantly larger with the ROI approach (P < 0.05 for all comparisons). In the Crohn's disease study, the estimated Ktrans parameter correlated better with the CDEIS (r = 0.733, P < 0.001) when the proposed AIF was used, compared to AIFs from the distal ROI method (r = 0.429, P = 0.067) or the population-averaged AIF (r = 0.567, P = 0.011). DATA CONCLUSION: The proposed method yielded realistic PK model parameters and improved the correlation of the Ktrans parameter with CDEIS, compared to existing approaches. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2018;47:1197-1204.

Comparison of image reconstruction techniques for optical projection tomography.

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.

Impact of partial coherence on the apparent optical transfer function derived from the response to amplitude edges.

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 Köhler 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.

Measuring murine chromosome orientation in interphase nuclei.

The nuclear architecture of a cell may change as a result of various diseases, including cancer. A variety of nuclear features are, therefore, of interest to cell biologists. Recently, several studies have investigated the orientation of chromosomes in the interphase nucleus either visually or semi-automatically. In this article an automated method to measure this orientation is presented. The theoretical difference between performing these measurements in two and three dimensions is discussed and experimentally verified. The results computed from measurements of murine nuclei correspond with results from visual inspection. We found significant differences in the orientation of chromosome 11 between nuclei from a PreB cell line of BALB/c origin and primary B nuclei from congenic [T38HxBALB/c]N wild-type mice. Since our new automatic method concurs with both the visual and semi-automatic methods, we conclude that the automatic method can replace these methods in assessing chromosome orientation.

Optical quality assessment of whole slide imaging systems for digital pathology.

Whole Slide Imaging (WSI) systems are high-throughput automated microscopes for digital pathology applications. We present a method for testing and monitoring the optical quality of WSI-systems using a measurement of the through-focus Optical Transfer Function (OTF) obtained from the edge response of a custom made resolution target, composed of sagittal and tangential edges. This enables quantitative analysis of a number of primary aberrations. The curvature of the best focus as a function of spatial frequency is indicative for spherical aberration, the argument of the OTF quantifies for coma, and the best focus as a function of field position for sagittal and tangential edges allows assessment of astigmatism and field curvature. The statistical error in the determined aberrations is typically below 20 mλ. We use the method to compare different tube lens designs and to study the effect of objective lens aging. The results are in good agreement with direct measurement of aberrations based on Shack-Hartmann wavefront sensing with a typical error ranging from 10 mλ to 40 mλ.

Quantifying resolution limiting factors in subtomogram averaged cryo-electron tomography using simulations.

Cryo-electron tomography (CET) is the only available technique capable of characterizing the structure of biological macromolecules in conditions close to the native state. With the advent of subtomogram averaging, as a post-processing step to CET, resolutions in the (sub-) nanometer range have become within reach. In addition to advances in instrumentation and experiments, the reconstruction scheme has improved by inclusion of more accurate contrast transfer function (CTF) correction methods, better defocus estimation, and better alignments of the tilt-series and subtomograms. To quantify the importance of each contribution, we have split the full process from data collection to reconstruction into different steps. For the purpose of evaluation we have acquired tilt-series of ribosomes in such a way that we could precisely determine the defocus of each macromolecule. Then, we simulated tilt-series using the InSilicoTEM package and applied tomogram reconstruction and subtomogram averaging. Through large scale simulations under different conditions and parameter settings we find that tilt-series alignment is the resolution limiting factor for our experimental data. Using simulations, we find that when this alignment inaccuracy is alleviated, tilted CTF correction improves the final resolution, or equivalently, the same resolution can be achieved using less particles. Furthermore, we predict from which resolution onwards better CTF correction and defocus estimation methods are required. We obtain a final average using 3198 ribosomes with a resolution of 2.2nm on the experimental data. Our simulations suggest that with the same number of particles a resolution of 1.2nm could be achieved by improving the tilt-series alignment.

Differences in nuclear DNA organization between lymphocytes, Hodgkin and Reed-Sternberg cells revealed by structured illumination microscopy.

Advances in light microscopy have enabled the visualization of DNA in the interphase nucleus with more detail than is visible with conventional light microscopy. The nuclear architecture is assumed to be different in cancer cells compared to normal cells. In this paper we have studied, for the first time, the organization of nuclear DNA and that of DNA-free space in control lymphocytes, Hodgkin cells and Reed-Sternberg cells using 3D structured illumination microscopy (SIM). We have observed detail in these SIM images that was not observed in conventional widefield images. We have measured the size distribution of the DNA structure using granulometry and noted a significant, progressive increase in the amount of sub-micron structures from control lymphocytes to Hodgkin cells to Reed-Sternberg cells. The DNA-free space changes as well; "holes" in the DNA distribution start to appear in the malignant cells. We have studied whether these "holes" are nucleoli by staining for upstream binding factor (UBF), a protein associated with the nucleolus. We have found that the relative UBF content progressively and significantly decreases-or is absent-in the DNA-free space when measured as either the Pearson correlation coefficient with the DNA-free space or as the number of "holes" that contain UBF. Similar differences exist within the population of Reed-Sternberg cells between binucleated and multinucleated cells with four or more subnuclei. To our knowledge, this is the first study that investigates the changes of the nuclear DNA structure in any disease with superresolution light microscopy.

Three-dimensional structured illumination microscopy using Lukosz bound apodization reduces pixel negativity at no resolution cost.

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.

Image formation modeling in cryo-electron microscopy.

Accurate modeling of image formation in cryo-electron microscopy is an important requirement for quantitative image interpretation and optimization of the data acquisition strategy. Here we present a forward model that accounts for the specimen's scattering properties, microscope optics, and detector response. The specimen interaction potential is calculated with the isolated atom superposition approximation (IASA) and extended with the influences of solvent's dielectric and ionic properties as well as the molecular electrostatic distribution. We account for an effective charge redistribution via the Poisson-Boltzmann approach and find that the IASA-based potential forms the dominant part of the interaction potential, as the contribution of the redistribution is less than 10%. The electron wave is propagated through the specimen by a multislice approach and the influence of the optics is included via the contrast transfer function. We incorporate the detective quantum efficiency of the camera due to the difference between signal and noise transfer characteristics, instead of using only the modulation transfer function. The full model was validated against experimental images of 20S proteasome, hemoglobin, and GroEL. The simulations adequately predict the effects of phase contrast, changes due to the integrated electron flux, thickness, inelastic scattering, detective quantum efficiency and acceleration voltage. We suggest that beam-induced specimen movements are relevant in the experiments whereas the influence of the solvent amorphousness can be neglected. All simulation parameters are based on physical principles and, when necessary, experimentally determined.

Phase retrieval in in-line x-ray phase contrast imaging based on total variation minimization.

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.

Total variation minimization approach in in-line x-ray phase-contrast tomography.

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.

Image filtering in structured illumination microscopy using the Lukosz bound.

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.

DenseUNets with feedback non-local attention for the segmentation of specular microscopy images of the corneal endothelium with guttae.

Corneal guttae, which are the abnormal growth of extracellular matrix in the corneal endothelium, are observed in specular images as black droplets that occlude the endothelial cells. To estimate the corneal parameters (endothelial cell density [ECD], coefficient of variation [CV], and hexagonality [HEX]), we propose a new deep learning method that includes a novel attention mechanism (named fNLA), which helps to infer the cell edges in the occluded areas. The approach first derives the cell edges, then infers the well-detected cells, and finally employs a postprocessing method to fix mistakes. This results in a binary segmentation from which the corneal parameters are estimated. We analyzed 1203 images (500 contained guttae) obtained with a Topcon SP-1P microscope. To generate the ground truth, we performed manual segmentation in all images. Several networks were evaluated (UNet, ResUNeXt, DenseUNets, UNet++, etc.) and we found that DenseUNets with fNLA provided the lowest error: a mean absolute error of 23.16 [cells/mm[Formula: see text]] in ECD, 1.28 [%] in CV, and 3.13 [%] in HEX. Compared with Topcon's built-in software, our error was 3-6 times smaller. Overall, our approach handled notably well the cells affected by guttae, detecting cell edges partially occluded by small guttae and discarding large areas covered by extensive guttae.

Characterization of an x-ray phase contrast imaging system based on the miniature synchrotron MIRRORCLE-6X.

PURPOSE: The implementation of in-line x-ray phase contrast imaging (PCI) for soft-tissue patient imaging is hampered by the lack of a bright and spatially coherent x-ray source that fits into the hospital environment. This article provides a quantitative characterization of the phase-contrast enhancement of a PCI system based on the miniature synchrotron technology MIRRORCLE-6X. METHODS: The phase-contrast effect was measured using an edge response of a plexiglass plate as a function of the incident angle of radiation. We have developed a comprehensive x-ray propagation model based on the system's components, properties, and geometry in order to interpret the measurement data. Monte-Carlo simulations are used to estimate the system's spectral properties and resolution. RESULTS: The measured ratio of the detected phase-contrast to the absorption contrast is currently in the range 100% to 200%. Experiments show that with the current implementation of the MIRRORCLE-6X, a target smaller than 30-40 µm does not lead to a larger phase-contrast. The reason for this is that the fraction of x-rays produced by the material (carbon filament and glue) that is used for mounting the target in the electron beam is more than 25% of the total amount of x-rays produced. This increases the apparent source size. The measured phase-contrast is at maximum two times larger than the absorption contrast with the current set-up. CONCLUSIONS: Calculations based on our model of the present imaging system predict that the phase-contrast can be up to an order of magnitude larger than the absorption contrast in case the materials used for mounting the target in the electron beam do not (or hardly) produce x-rays. The methods described in this paper provide vital feedback for guiding future modifications to the design of the x-ray target of MIRRORCLE-type system and configuration of the in-line PCI systems in general.

Attenuation coefficient estimation in Fourier-domain OCT of multi-layered phantoms.

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.

Analysis of attenuation coefficient estimation in Fourier-domain OCT of semi-infinite media.

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.

Deep Learning for Assessing the Corneal Endothelium from Specular Microscopy Images up to 1 Year after Ultrathin-DSAEK Surgery.

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.

An Efficient Method for Multi-Parameter Mapping in Quantitative MRI Using B-Spline Interpolation.

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 (i.e. lookup table) 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 (T1,T2) , proton density (PD ) , receiver phase ( φ0 ), transmit field inhomogeneity ( B1+ ), 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 ( T1,T2,B1+ ) by - on average - an order of magnitude. In effect, the applied dictionary was reduced from 1.47GB to 464KB . Furthermore, the proposed method was equally robust against undersampling artifacts as dictionary matching. 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.

Convolutional neural network-based regression for biomarker estimation in corneal endothelium microscopy images.

The morphometric parameters of the corneal endothelium - cell density (ECD), cell size variation (CV), and hexagonality (HEX) - provide clinically relevant information about the cornea. To estimate these parameters, the endothelium is commonly imaged with a non-contact specular microscope and cell segmentation is performed to these images. In previous work, we have developed several methods that, combined, can perform an automated estimation of the parameters: the inference of the cell edges, the detection of the region of interest (ROI), a post-processing method that combines both images (edges and ROI), and a refinement method that removes false edges. In this work, we first explore the possibility of using a CNN-based regressor to directly infer the parameters from the edge images, simplifying the framework. We use a dataset of 738 images coming from a study related to the implantation of a Baerveldt glaucoma device and a standard clinical care regarding DSAEK corneal transplantation, both from the Rotterdam Eye Hospital and both containing images of unhealthy endotheliums. This large dataset allows us to build a large training set that makes this approach feasible. We achieved a mean absolute percentage error (MAPE) of 4.32% for ECD, 7.07% for CV, and 11.74% for HEX. These results, while promising, do not outperform our previous work. In a second experiment, we explore the use of the CNN-based regressor to improve the post-processing method of our previous approach in order to adapt it to the specifics of each image. Our results showed no clear benefit and proved that our previous post-processing is already highly reliable and robust.