Two-dimensional linear-combination model fitting of magnetic resonance spectra to define the macromolecule baseline using FiTAID, a Fitting Tool for Arrays of Interrelated Datasets

To propose the determination of the macromolecular baseline (MMBL) in clinical 1H MR spectra based on T(1) and T(2) differentiation using 2D fitting in FiTAID, a general Fitting Tool for Arrays of Interrelated Datasets.

Quantitative 1H-magnetic resonance spectroscopy of human brain: Influence of composition and parameterization of the basis set in linear combination model-fitting

Localized short-echo-time (1)H-MR spectra of human brain contain contributions of many low-molecular-weight metabolites and baseline contributions of macromolecules. Two approaches to model such spectra are compared and the data acquisition sequence, optimized for reproducibility, is presented. Modeling relies on prior knowledge constraints and linear combination of metabolite spectra. Investigated was what can be gained by basis parameterization, i.e., description of basis spectra as sums of parametric lineshapes. Effects of basis composition and addition of experimentally measured macromolecular baselines were investigated also. Both fitting methods yielded quantitatively similar values, model deviations, error estimates, and reproducibility in the evaluation of 64 spectra of human gray and white matter from 40 subjects. Major advantages of parameterized basis functions are the possibilities to evaluate fitting parameters separately, to treat subgroup spectra as independent moieties, and to incorporate deviations from straightforward metabolite models. It was found that most of the 22 basis metabolites used may provide meaningful data when comparing patient cohorts. In individual spectra, sums of closely related metabolites are often more meaningful. Inclusion of a macromolecular basis component leads to relatively small, but significantly different tissue content for most metabolites. It provides a means to quantitate baseline contributions that may contain crucial clinical information.

Uncovering the dynamics of cardiac systems using stochastic pacing and frequency domain analyses

Alternans of cardiac action potential duration (APD) is a well-known arrhythmogenic mechanism which results from dynamical instabilities. The propensity to alternans is classically investigated by examining APD restitution and by deriving APD restitution slopes as predictive markers. However, experiments have shown that such markers are not always accurate for the prediction of alternans. Using a mathematical ventricular cell model known to exhibit unstable dynamics of both membrane potential and Ca2+ cycling, we demonstrate that an accurate marker can be obtained by pacing at cycle lengths (CLs) varying randomly around a basic CL (BCL) and by evaluating the transfer function between the time series of CLs and APDs using an autoregressive-moving-average (ARMA) model. The first pole of this transfer function corresponds to the eigenvalue (λalt) of the dominant eigenmode of the cardiac system, which predicts that alternans occurs when λalt≤−1. For different BCLs, control values of λalt were obtained using eigenmode analysis and compared to the first pole of the transfer function estimated using ARMA model fitting in simulations of random pacing protocols. In all versions of the cell model, this pole provided an accurate estimation of λalt. Furthermore, during slow ramp decreases of BCL or simulated drug application, this approach predicted the onset of alternans by extrapolating the time course of the estimated λalt. In conclusion, stochastic pacing and ARMA model identification represents a novel approach to predict alternans without making any assumptions about its ionic mechanisms. It should therefore be applicable experimentally for any type of myocardial cell.

Methacholine responsiveness in mice from 2 to 8 wk of age

Many chronic human lung diseases have their origin in early childhood, yet most murine models used to study them utilize adult mice. An important component of the asthma phenotype is exaggerated airway responses, frequently modelled by methacholine (MCh) challenge. The present study was undertaken to characterize MCh responses in mice from 2 to 8 wk of age measuring absolute lung volume and volume-corrected respiratory mechanics as outcome variables. Female BALB/c mice aged 2, 3, 4, 6, and 8 wk were studied during cumulative intravenous MCh challenge. Following each MCh dose, absolute lung volume was measured plethysmographically at functional residual volume and during a slow inflation to 20-hPa transrespiratory pressure. Respiratory system impedance was measured continuously during the inflation maneuver and partitioned into airway and constant-phase parenchymal components by model fitting. Volume-corrected (specific) estimates of respiratory mechanics were calculated. Intravenous MCh challenge induced a predominantly airway response with no evidence of airway closure in any age group. No changes in functional residual volume were seen in mice of any age during the MCh challenge. The specific airway resistance MCh dose response curves did not show significant differences between the age groups. The results from the present study do not show systematic differences in MCh responsiveness in mice from 2 to 8 wk of age.

Brain metabolite composition during early human brain development as measured by quantitative in vivo 1H magnetic resonance spectroscopy

Biochemical maturation of the brain can be studied noninvasively by (1)H magnetic resonance spectroscopy (MRS) in human infants. Detailed time courses of cerebral tissue contents are known for the most abundant metabolites only, and whether or not premature birth affects biochemical maturation of the brain is disputed. Hence, the last trimester of gestation was observed in infants born prematurely, and their cerebral metabolite contents at birth and at expected term were compared with those of fullterm infants. Successful quantitative short-TE (1)H MRS was performed in three cerebral locations in 21 infants in 28 sessions (gestational age 32-43 weeks). The spectra were analyzed with linear combination model fitting, considerably extending the range of observable metabolites to include acetate, alanine, aspartate, cholines, creatines, gamma-aminobutyrate, glucose, glutamine, glutamate, glutathione, glycine, lactate, myo-inositol, macromolecular contributions, N-acetylaspartate, N-acetylaspartylglutamate, o-phosphoethanolamine, scyllo-inositol, taurine, and threonine. Significant effects of age and location were found for many metabolites, including the previously observed neuronal maturation reflected by an increase in N-acetylaspartate. Absolute brain metabolite content in premature infants at term was not considerably different from that in fullterm infants, indicating that prematurity did not affect biochemical brain maturation substantially in the studied population, which did not include infants of extremely low birthweight.

The trouble with quality filtering based on relative Cramér-Rao lower bounds.

Cramér Rao Lower Bounds (CRLB) have become the standard for expression of uncertainties in quantitative MR spectroscopy. If properly interpreted as a lower threshold of the error associated with model fitting, and if the limits of its estimation are respected, CRLB are certainly a very valuable tool to give an idea of minimal uncertainties in magnetic resonance spectroscopy (MRS), although other sources of error may be larger. Unfortunately, it has also become standard practice to use relative CRLB expressed as a percentage of the presently estimated area or concentration value as unsupervised exclusion criterion for bad quality spectra. It is shown that such quality filtering with widely used threshold levels of 20% to 50% CRLB readily causes bias in the estimated mean concentrations of cohort data, leading to wrong or missed statistical findings-and if applied rigorously-to the failure of using MRS as a clinical instrument to diagnose disease characterized by low levels of metabolites. Instead, absolute CRLB in comparison to those of the normal group or CRLB in relation to normal metabolite levels may be more useful as quality criteria. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.

Effect sizes and standardization in neighbourhood models of forest stands: potential biases and misinterpretations

Effects of conspecific neighbours on survival and growth of trees have been found to be related to species abundance. Both positive and negative relationships may explain observed abundance patterns. Surprisingly, it is rarely tested whether such relationships could be biased or even spurious due to transforming neighbourhood variables or influences of spatial aggregation, distance decay of neighbour effects and standardization of effect sizes. To investigate potential biases, communities of 20 identical species were simulated with log-series abundances but without species-specific interactions. No relationship of conspecific neighbour effects on survival or growth with species abundance was expected. Survival and growth of individuals was simulated in random and aggregated spatial patterns using no, linear, or squared distance decay of neighbour effects. Regression coefficients of statistical neighbourhood models were unbiased and unrelated to species abundance. However, variation in the number of conspecific neighbours was positively or negatively related to species abundance depending on transformations of neighbourhood variables, spatial pattern and distance decay. Consequently, effect sizes and standardized regression coefficients, often used in model fitting across large numbers of species, were also positively or negatively related to species abundance depending on transformation of neighbourhood variables, spatial pattern and distance decay. Tests using randomized tree positions and identities provide the best benchmarks by which to critically evaluate relationships of effect sizes or standardized regression coefficients with tree species abundance. This will better guard against potential misinterpretations.

Automated 3D Lumbar Intervertebral Disc Segmentation from MRI Data Sets

This paper proposed an automated three-dimensional (3D) lumbar intervertebral disc (IVD) segmentation strategy from Magnetic Resonance Imaging (MRI) data. Starting from two user supplied landmarks, the geometrical parameters of all lumbar vertebral bodies and intervertebral discs are automatically extracted from a mid-sagittal slice using a graphical model based template matching approach. Based on the estimated two-dimensional (2D) geometrical parameters, a 3D variable-radius soft tube model of the lumbar spine column is built by model fitting to the 3D data volume. Taking the geometrical information from the 3D lumbar spine column as constraints and segmentation initialization, the disc segmentation is achieved by a multi-kernel diffeomorphic registration between a 3D template of the disc and the observed MRI data. Experiments on 15 patient data sets showed the robustness and the accuracy of the proposed algorithm.

Postoperative splanchnic blood flow redistribution in response to fluid challenges in the presence and absence of endotoxemia in a porcine model

We hypothesized that fluid administration may increase regional splanchnic perfusion after abdominal surgery-even in the absence of a cardiac stroke volume (SV) increase and independent of accompanying endotoxemia. Sixteen anesthetized pigs underwent abdominal surgery with flow probe fitting around splanchnic vessels and carotid arteries. They were randomized to continuous placebo or endotoxin infusion, and when clinical signs of hypovolemia (mean arterial pressure, <60 mmHg; heart rate, >100 beats · min(-1); urine production, <0.5 mL · kg(-1) · h(-1); arterial lactate concentration, >2 mmol · L(-1)) and/or low pulmonary artery occlusion pressure (target 5-8 mmHg) were present, they received repeated boli of colloids (50 mL) as long as SV increased 10% or greater. Stroke volume and regional blood flows were monitored 2 min before and 30 min after fluid challenges. Of 132 fluid challenges, 45 (34%) resulted in an SV increase of 10% or greater, whereas 82 (62%) resulted in an increase of 10% or greater in one or more of the abdominal flows (P < 0.001). During blood flow redistribution, celiac trunk (19% of all measurements) and hepatic artery flow (15%) most often decreased, whereas portal vein (10%) and carotid artery (7%) flow decreased less frequently (P = 0.015, between regions). In control animals, celiac trunk (30% vs. 9%, P = 0.004) and hepatic artery (25% vs. 11%, P = 0.040) flow decreased more often than in endotoxin-infused pigs. Accordingly, blood flow redistribution is a common phenomenon in the postoperative period and is only marginally influenced by endotoxemia. Fluid management based on SV changes may not be useful for improving regional abdominal perfusion.

Pathology hinting as the combination of automatic segmentation with a statistical shape model

With improvements in acquisition speed and quality, the amount of medical image data to be screened by clinicians is starting to become challenging in the daily clinical practice. To quickly visualize and find abnormalities in medical images, we propose a new method combining segmentation algorithms with statistical shape models. A statistical shape model built from a healthy population will have a close fit in healthy regions. The model will however not fit to morphological abnormalities often present in the areas of pathologies. Using the residual fitting error of the statistical shape model, pathologies can be visualized very quickly. This idea is applied to finding drusen in the retinal pigment epithelium (RPE) of optical coherence tomography (OCT) volumes. A segmentation technique able to accurately segment drusen in patients with age-related macular degeneration (AMD) is applied. The segmentation is then analyzed with a statistical shape model to visualize potentially pathological areas. An extensive evaluation is performed to validate the segmentation algorithm, as well as the quality and sensitivity of the hinting system. Most of the drusen with a height of 85.5 microm were detected, and all drusen at least 93.6 microm high were detected.

Prosthetically driven, computer-guided implant planning for the edentulous maxilla: a model study

OBJECTIVES: To analyze computer-assisted diagnostics and virtual implant planning and to evaluate the indication for template-guided flapless surgery and immediate loading in the rehabilitation of the edentulous maxilla. MATERIALS AND METHODS: Forty patients with an edentulous maxilla were selected for this study. The three-dimensional analysis and virtual implant planning was performed with the NobelGuide software program (Nobel Biocare, Göteborg, Sweden). Prior to the computer tomography aesthetics and functional aspects were checked clinically. Either a well-fitting denture or an optimized prosthetic setup was used and then converted to a radiographic template. This allowed for a computer-guided analysis of the jaw together with the prosthesis. Accordingly, the best implant position was determined in relation to the bone structure and prospective tooth position. For all jaws, the hypothetical indication for (1) four implants with a bar overdenture and (2) six implants with a simple fixed prosthesis were planned. The planning of the optimized implant position was then analyzed as follows: the number of implants was calculated that could be placed in sufficient quantity of bone. Additional surgical procedures (guided bone regeneration, sinus floor elevation) that would be necessary due the reduced bone quality and quantity were identified. The indication of template-guided, flapless surgery or an immediate loaded protocol was evaluated. RESULTS: Model (a) - bar overdentures: for 28 patients (70%), all four implants could be placed in sufficient bone (total 112 implants). Thus, a full, flapless procedure could be suggested. For six patients (15%), sufficient bone was not available for any of their planned implants. The remaining six patients had exhibited a combination of sufficient or insufficient bone. Model (b) - simple fixed prosthesis: for 12 patients (30%), all six implants could be placed in sufficient bone (total 72 implants). Thus, a full, flapless procedure could be suggested. For seven patients (17%), sufficient bone was not available for any of their planned implants. The remaining 21 patients had exhibited a combination of sufficient or insufficient bone. DISCUSSION: In the maxilla, advanced atrophy is often observed, and implant placement becomes difficult or impossible. Thus, flapless surgery or an immediate loading protocol can be performed just in a selected number of patients. Nevertheless, the use of a computer program for prosthetically driven implant planning is highly efficient and safe. The three-dimensional view of the maxilla allows the determination of the best implant position, the optimization of the implant axis, and the definition of the best surgical and prosthetic solution for the patient. Thus, a protocol that combines a computer-guided technique with conventional surgical procedures becomes a promising option, which needs to be further evaluated and improved.

A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations

In the context of expensive numerical experiments, a promising solution for alleviating the computational costs consists of using partially converged simulations instead of exact solutions. The gain in computational time is at the price of precision in the response. This work addresses the issue of fitting a Gaussian process model to partially converged simulation data for further use in prediction. The main challenge consists of the adequate approximation of the error due to partial convergence, which is correlated in both design variables and time directions. Here, we propose fitting a Gaussian process in the joint space of design parameters and computational time. The model is constructed by building a nonstationary covariance kernel that reflects accurately the actual structure of the error. Practical solutions are proposed for solving parameter estimation issues associated with the proposed model. The method is applied to a computational fluid dynamics test case and shows significant improvement in prediction compared to a classical kriging model.

Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A novel Statistical Shape Model for treatment planning of Retinoblastoma

Purpose: Proper delineation of ocular anatomy in 3D imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic Resonance Imaging (MRI) is nowadays utilized in clinical practice for the diagnosis confirmation and treatment planning of retinoblastoma in infants, where it serves as a source of information, complementary to the Fundus or Ultrasound imaging. Here we present a framework to fully automatically segment the eye anatomy in the MRI based on 3D Active Shape Models (ASM), we validate the results and present a proof of concept to automatically segment pathological eyes. Material and Methods: Manual and automatic segmentation were performed on 24 images of healthy children eyes (3.29±2.15 years). Imaging was performed using a 3T MRI scanner. The ASM comprises the lens, the vitreous humor, the sclera and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens and the optic nerve, then aligning the model and fitting it to the patient. We validated our segmentation method using a leave-one-out cross validation. The segmentation results were evaluated by measuring the overlap using the Dice Similarity Coefficient (DSC) and the mean distance error. Results: We obtained a DSC of 94.90±2.12% for the sclera and the cornea, 94.72±1.89% for the vitreous humor and 85.16±4.91% for the lens. The mean distance error was 0.26±0.09mm. The entire process took 14s on average per eye. Conclusion: We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor and the lens using MRI. We additionally present a proof of concept for fully automatically segmenting pathological eyes. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor.