IVIM-Morph: Motion-compensated quantitative Intra-voxel Incoherent Motion (IVIM) analysis for functional fetal lung maturity assessment from diffusion-weighted MRI data.

Quantitative analysis of pseudo-diffusion in diffusion-weighted magnetic resonance imaging (DWI) data shows potential for assessing fetal lung maturation and generating valuable imaging biomarkers. Yet, the clinical utility of DWI data is hindered by unavoidable fetal motion during acquisition. We present IVIM-morph, a self-supervised deep neural network model for motion-corrected quantitative analysis of DWI data using the Intra-voxel Incoherent Motion (IVIM) model. IVIM-morph combines two sub-networks, a registration sub-network, and an IVIM model fitting sub-network, enabling simultaneous estimation of IVIM model parameters and motion. To promote physically plausible image registration, we introduce a biophysically informed loss function that effectively balances registration and model-fitting quality. We validated the efficacy of IVIM-morph by establishing a correlation between the predicted IVIM model parameters of the lung and gestational age (GA) using fetal DWI data of 39 subjects. Our approach was compared against six baseline methods: 1) no motion compensation, 2) affine registration of all DWI images to the initial image, 3) deformable registration of all DWI images to the initial image, 4) deformable registration of each DWI image to its preceding image in the sequence, 5) iterative deformable motion compensation combined with IVIM model parameter estimation, and 6) self-supervised deep-learning-based deformable registration. IVIM-morph exhibited a notably improved correlation with gestational age (GA) when performing in-vivo quantitative analysis of fetal lung DWI data during the canalicular phase. Specifically, over 2 test groups of cases, it achieved an Rf2 of 0.44 and 0.52, outperforming the values of 0.27 and 0.25, 0.25 and 0.00, 0.00 and 0.00, 0.38 and 0.00, and 0.07 and 0.14 obtained by other methods. IVIM-morph shows potential in developing valuable biomarkers for non-invasive assessment of fetal lung maturity with DWI data. Moreover, its adaptability opens the door to potential applications in other clinical contexts where motion compensation is essential for quantitative DWI analysis. The IVIM-morph code is readily available at:https://github.com/TechnionComputationalMRILab/qDWI-Morph.

Modeling dynamic radial contrast enhanced MRI with linear time invariant systems for motion correction in quantitative assessment of kidney function.

Early identification of kidney function deterioration is essential to determine which newborn patients with congenital kidney disease should be considered for surgical intervention as opposed to observation. Kidney function can be measured by fitting a tracer kinetic (TK) model onto a series of Dynamic Contrast Enhanced (DCE) MR images and estimating the filtration rate parameter from the model. Unfortunately, breathing and large bulk motion events due to patient movement in the scanner create outliers and misalignments that introduce large errors in the TK model parameter estimates even when using a motion-robust dynamic radial VIBE sequence for DCE-MR imaging. The misalignments between the series of volumes are difficult to correct using standard registration due to 1) the large differences in geometry and contrast between volumes of the dynamic sequence and 2) the requirement of fast dynamic imaging to achieve high temporal resolution and motion deteriorates image quality. These difficulties reduce the accuracy and stability of registration over the dynamic sequence. An alternative registration approach is to generate noise and motion free templates of the original data from the TK model and use them to register each volume to its contrast-matched template. However, the TK models used to characterize DCE-MRI are tissue specific, non-linear and sensitive to the same motion and sampling artifacts that hinder registration in the first place. Hence, these can only be applied to register accurately pre-segmented regions of interest, such as kidneys, and might converge to local minima under the presence of large artifacts. Here we introduce a novel linear time invariant (LTI) model to characterize DCE-MR data for different tissue types within a volume. We approximate the LTI model as a sparse sum of first order LTI functions to introduce robustness to motion and sampling artifacts. Hence, this model is well suited for registration of the entire field of view of DCE-MR data with artifacts and outliers. We incorporate this LTI model into a registration framework and evaluate it on both synthetic data and data from 20 children. For each subject, we reconstructed the sequence of DCE-MR images, detected corrupted volumes acquired during motion, aligned the sequence of volumes and recovered the corrupted volumes using the LTI model. The results show that our approach correctly aligned the volumes, provided the most stable registration in time and improved the tracer kinetic model fit.

Quantitative In vivo MRI Assessment of Structural Asymmetries and Sexual Dimorphism of Transient Fetal Compartments in the Human Brain.

Structural asymmetries and sexual dimorphism of the human cerebral cortex have been identified in newborns, infants, children, adolescents, and adults. Some of these findings were linked with cognitive and neuropsychiatric disorders, which have roots in altered prenatal brain development. However, little is known about structural asymmetries or sexual dimorphism of transient fetal compartments that arise in utero. Thus, we aimed to identify structural asymmetries and sexual dimorphism in the volume of transient fetal compartments (cortical plate [CP] and subplate [SP]) across 22 regions. For this purpose, we used in vivo structural T2-weighted MRIs of 42 healthy fetuses (16.43-36.86 gestational weeks old, 15 females). We found significant leftward asymmetry in the volume of the CP and SP in the inferior frontal gyrus. The orbitofrontal cortex showed significant rightward asymmetry in the volume of CP merged with SP. Males had significantly larger volumes in regions belonging to limbic, occipital, and frontal lobes, which were driven by a significantly larger SP. Lastly, we did not observe sexual dimorphism in the growth trajectories of the CP or SP. In conclusion, these results support the hypothesis that structural asymmetries and sexual dimorphism in relative volumes of cortical regions are present during prenatal brain development.

Towards microstructure fingerprinting: Estimation of tissue properties from a dictionary of Monte Carlo diffusion MRI simulations.

Many closed-form analytical models have been proposed to relate the diffusion-weighted magnetic resonance imaging (DW-MRI) signal to microstructural features of white matter tissues. These models generally make assumptions about the tissue and the diffusion processes which often depart from the biophysical reality, limiting their reliability and interpretability in practice. Monte Carlo simulations of the random walk of water molecules are widely recognized to provide near groundtruth for DW-MRI signals. However, they have mostly been limited to the validation of simpler models rather than used for the estimation of microstructural properties. This work proposes a general framework which leverages Monte Carlo simulations for the estimation of physically interpretable microstructural parameters, both in single and in crossing fascicles of axons. Monte Carlo simulations of DW-MRI signals, or fingerprints, are pre-computed for a large collection of microstructural configurations. At every voxel, the microstructural parameters are estimated by optimizing a sparse combination of these fingerprints. Extensive synthetic experiments showed that our approach achieves accurate and robust estimates in the presence of noise and uncertainties over fixed or input parameters. In an in vivo rat model of spinal cord injury, our approach provided microstructural parameters that showed better correspondence with histology than five closed-form models of the diffusion signal: MMWMD, NODDI, DIAMOND, WMTI and MAPL. On whole-brain in vivo data from the human connectome project (HCP), our method exhibited spatial distributions of apparent axonal radius and axonal density indices in keeping with ex vivo studies. This work paves the way for microstructure fingerprinting with Monte Carlo simulations used directly at the modeling stage and not only as a validation tool.

Curved planar reformatting and convolutional neural network-based segmentation of the small bowel for visualization and quantitative assessment of pediatric Crohn's disease from MRI.

BACKGROUND: Contrast-enhanced MRI of the small bowel is an effective imaging sequence for the detection and characterization of disease burden in pediatric Crohn's disease (CD). However, visualization and quantification of disease burden requires scrolling back and forth through 3D images to follow the anatomy of the bowel, and it can be difficult to fully appreciate the extent of disease. PURPOSE: To develop and evaluate a method that offers better visualization and quantitative assessment of CD from MRI. STUDY TYPE: Retrospective. POPULATION: Twenty-three pediatric patients with CD. FIELD STRENGTH/SEQUENCE: 1.5T MRI system and T1 -weighted postcontrast VIBE sequence. ASSESSMENT: The convolutional neural network (CNN) segmentation of the bowel's lumen, wall, and background was compared with manual boundary delineation. We assessed the reproducibility and the capability of the extracted markers to differentiate between different levels of disease defined after a consensus review by two experienced radiologists. STATISTICAL TESTS: The segmentation algorithm was assessed using the Dice similarity coefficient (DSC) and boundary distances between the CNN and manual boundary delineations. The capability of the extracted markers to differentiate between different disease levels was determined using a t-test. The reproducibility of the extracted markers was assessed using the mean relative difference (MRD), Pearson correlation, and Bland-Altman analysis. RESULTS: Our CNN exhibited DSCs of 75 ± 18%, 81 ± 8%, and 97 ± 2% for the lumen, wall, and background, respectively. The extracted markers of wall thickness at the location of min radius (P = 0.0013) and the median value of relative contrast enhancement (P = 0.0033) could differentiate active and nonactive disease segments. Other extracted markers could differentiate between segments with strictures and segments without strictures (P < 0.05). The observers' agreement in measuring stricture length was >3 times superior when computed on curved planar reformatting images compared with the conventional scheme. DATA CONCLUSION: The results of this study show that the newly developed method is efficient for visualization and assessment of CD. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1565-1576.

Assessing the validity of the approximation of diffusion-weighted-MRI signals from crossing fascicles by sums of signals from single fascicles.

PURPOSE: To assess the validity of the superposition approximation for crossing fascicles, i.e., the assumption that the total diffusion-weighted MRI signal is the sum of the signals arising from each fascicle independently, even when the fascicles intermingle in a voxel. METHODS: Monte Carlo simulations were used to study the impact of the approximation on the diffusion-weighted MRI signal and to assess whether this approximate model allows microstructural features of interwoven fascicles to be accurately estimated, despite signal differences. RESULTS: Small normalized signal differences were observed, typically 10-3-10-2. The use of the approximation had little impact on the estimation of the crossing angle, the axonal density index, and the radius index in clinically realistic scenarios wherein the acquisition noise was the predominant source of errors. In the absence of noise, large systematic errors due to the superposition approximation only persisted for the radius index, mainly driven by a low sensitivity of diffusion-weighted MRI signals to small radii in general. CONCLUSION: The use of the superposition approximation rather than a model of interwoven fascicles does not adversely impact the estimation of microstructural features of interwoven fascicles in most current clinical settings. Magn Reson Med 79:2332-2345, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Spatially-constrained probability distribution model of incoherent motion (SPIM) for abdominal diffusion-weighted MRI.

Quantitative diffusion-weighted MR imaging (DW-MRI) of the body enables characterization of the tissue microenvironment by measuring variations in the mobility of water molecules. The diffusion signal decay model parameters are increasingly used to evaluate various diseases of abdominal organs such as the liver and spleen. However, previous signal decay models (i.e., mono-exponential, bi-exponential intra-voxel incoherent motion (IVIM) and stretched exponential models) only provide insight into the average of the distribution of the signal decay rather than explicitly describe the entire range of diffusion scales. In this work, we propose a probability distribution model of incoherent motion that uses a mixture of Gamma distributions to fully characterize the multi-scale nature of diffusion within a voxel. Further, we improve the robustness of the distribution parameter estimates by integrating spatial homogeneity prior into the probability distribution model of incoherent motion (SPIM) and by using the fusion bootstrap solver (FBM) to estimate the model parameters. We evaluated the improvement in quantitative DW-MRI analysis achieved with the SPIM model in terms of accuracy, precision and reproducibility of parameter estimation in both simulated data and in 68 abdominal in-vivo DW-MRIs. Our results show that the SPIM model not only substantially reduced parameter estimation errors by up to 26%; it also significantly improved the robustness of the parameter estimates (paired Student's t-test, p < 0.0001) by reducing the coefficient of variation (CV) of estimated parameters compared to those produced by previous models. In addition, the SPIM model improves the parameter estimates reproducibility for both intra- (up to 47%) and inter-session (up to 30%) estimates compared to those generated by previous models. Thus, the SPIM model has the potential to improve accuracy, precision and robustness of quantitative abdominal DW-MRI analysis for clinical applications.

Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (DIAMOND).

PURPOSE: To develop a statistical model for the tridimensional diffusion MRI signal at each voxel that describes the signal arising from each tissue compartment in each voxel. THEORY AND METHODS: In prior work, a statistical model of the apparent diffusion coefficient was shown to well-characterize the diffusivity and heterogeneity of the mono-directional diffusion MRI signal. However, this model was unable to characterize the three-dimensional anisotropic diffusion observed in the brain. We introduce a new model that extends the statistical distribution representation to be fully tridimensional, in which apparent diffusion coefficients are extended to be diffusion tensors. The set of compartments present at a voxel is modeled by a finite sum of unimodal continuous distributions of diffusion tensors. Each distribution provides measures of each compartment microstructural diffusivity and heterogeneity. RESULTS: The ability to estimate the tridimensional diffusivity and heterogeneity of multiple fascicles and of free diffusion is demonstrated. CONCLUSION: Our novel tissue model allows for the characterization of the intra-voxel orientational heterogeneity, a prerequisite for accurate tractography while also characterizing the overall tridimensional diffusivity and heterogeneity of each tissue compartment. The model parameters can be estimated from short duration acquisitions. The diffusivity and heterogeneity microstructural parameters may provide novel indicator of the presence of disease or injury. Magn Reson Med 76:963-977, 2016. © 2015 Wiley Periodicals, Inc.

Planar dGEMRIC Maps May Aid Imaging Assessment of Cartilage Damage in Femoroacetabular Impingement.

BACKGROUND: Three-dimensional (3-D) delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) helps quantify biochemical changes in articular cartilage that correlate with early-stage osteoarthritis. However, dGEMRIC analysis is performed slice by slice, limiting the potential of 3-D data to give an overall impression of cartilage biochemistry. We previously developed a computational algorithm to produce unfolded, or "planar," dGEMRIC maps of acetabular cartilage, but have neither assessed their application nor determined whether MRI-based grading of cartilage damage or dGEMRIC measurements predict intraoperative findings in hips with symptomatic femoroacetabular impingement (FAI). QUESTIONS/PURPOSES: (1) Does imaging-based assessment of acetabular cartilage damage correlate with intraoperative findings in hips with symptomatic FAI? (2) Does the planar dGEMRIC map improve this correlation? (3) Does the planar map improve the correlation between the dGEMRIC index and MRI-based grading of cartilage damage in hips with symptomatic FAI? (4) Does the planar map improve imaging-based evaluation time for hips with symptomatic FAI? METHODS: We retrospectively studied 47 hips of 45 patients with symptomatic FAI who underwent hip surgery between 2009 and 2013 and had a 1.5-T 3-D dGEMRIC scan within 6 months preoperatively. Our cohort included 25 males and 20 females with a mean ± SD age at surgery of 29 ± 11 years. Planar dGEMRIC maps were generated from isotropic, sagittal oblique TrueFISP and T1 sequences. A pediatric musculoskeletal radiologist with experience in hip MRI evaluated studies using radially reformatted sequences. For six acetabular subregions (anterior-peripheral [AP]; anterior-central [AC]; superior-peripheral [SP]; superior-central [SC]; posterior-peripheral [PP]; posterior-central [PC]), modified Outerbridge cartilage damage grades were recorded and region-of-interest T1 averages (the dGEMRIC index) were measured. Beck's intraoperative cartilage damage grades were compared with the Outerbridge grades and dGEMRIC indices. For a subset of 26 hips, 13 were reevaluated with the map and 13 without the map, and total evaluation times were recorded. RESULTS: There were no meaningful differences in the correlations obtained with versus without referencing the planar maps. Planar map-independent Outerbridge grades had a notable (p < 0.05) Spearman's rank correlation (ρ) with Beck's grades that was moderate in AP, SC, and PC (0.3 < ρ < 0.5) and strong in SP (ρ > 0.5). For map-dependent Outerbridge grades, ρ was moderate in AP, AC, and SC and strong in SP. Map-independent dGEMRIC indices had a ρ with Beck's grades that was moderate in AP and SC (-0.3 > ρ > -0.5) and strong in SP (ρ < -0.5). For map-dependent dGEMRIC indices, ρ was moderate in SC and strong in SP. Similarly, there were no meaningful, map-dependent differences in the correlations. When comparing Outerbridge grades and dGEMRIC indices, there were notable correlations across all subregions. Without the planar map, ρ was moderate in AC and PC and strong in AP, SP, SC, and PP. With the map, ρ was strong in all six subregions. In AC, there was a notable map-dependent improvement in this correlation (p < 0.001). Finally, referencing the planar dGEMRIC map during evaluation was associated with a decrease in mean evaluation time, from 207 ± 32 seconds to 152 ± 33 seconds (p = 0.001). CONCLUSIONS: Our work challenges the weak correlation between dGEMRIC and intraoperative findings of cartilage damage that was previously reported in hips with symptomatic FAI, suggesting that dGEMRIC has potential diagnostic use for this patient population. The planar dGEMRIC maps did not meaningfully alter the correlation of imaging-based evaluation of cartilage damage with intraoperative findings; however, they notably improved the correlation of dGEMRIC and MRI-based grading in AC, and their use incurred no additional time cost to imaging-based evaluation. Therefore, the planar maps may improve dGEMRIC's use as a continuous proxy for an otherwise discrete and simplified MRI-based grade of cartilage damage in hips with symptomatic FAI. LEVEL OF EVIDENCE: Level III, diagnostic study.

The anatomy and art of writing a successful grant application: a practical step-by-step approach.

Writing a compelling grant application is a skill that is crucial to conducting high-quality and high-impact scientific research. A successful grant proposal provides the resources necessary to foster activity in an important area of investigation. A concise and practical overview of the anatomy and art of grant writing is provided in this article, along with citations to resources that are particularly useful for junior investigators.

A fully Bayesian inference framework for population studies of the brain microstructure.

Models of the diffusion-weighted signal are of strong interest for population studies of the brain microstructure. These studies are typically conducted by extracting a scalar property from the model and subjecting it to null hypothesis significance testing. This process has two major limitations: the reported p-value is a weak predictor of the reproducibility of findings and evidence for the absence of microstructural alterations cannot be gained. To overcome these limitations, this paper proposes a Bayesian framework for population studies of the brain microstructure represented by multi-fascicle models. A hierarchical model is built over the biophysical parameters of the microstructure. Bayesian inference is performed by Hamiltonian Monte Carlo sampling and results in a joint posterior distribution over the latent microstructure parameters for each group. Inference from this posterior enables richer analyses of the brain microstructure beyond the dichotomy of significance testing. Using synthetic and in-vivo data, we show that our Bayesian approach increases reproducibility of findings from population studies and opens new opportunities in the analysis of the brain microstructure.

School-age effects of the newborn individualized developmental care and assessment program for preterm infants with intrauterine growth restriction: preliminary findings.

BACKGROUND: The experience in the newborn intensive care nursery results in premature infants' neurobehavioral and neurophysiological dysfunction and poorer brain structure. Preterms with severe intrauterine growth restriction are doubly jeopardized given their compromised brains. The Newborn Individualized Developmental Care and Assessment Program improved outcome at early school-age for preterms with appropriate intrauterine growth. It also showed effectiveness to nine months for preterms with intrauterine growth restriction. The current study tested effectiveness into school-age for preterms with intrauterine growth restriction regarding executive function (EF), electrophysiology (EEG) and neurostructure (MRI). METHODS: Twenty-three 9-year-old former growth-restricted preterms, randomized at birth to standard care (14 controls) or to the Newborn Individualized Developmental Care and Assessment Program (9 experimentals) were assessed with standardized measures of cognition, achievement, executive function, electroencephalography, and magnetic resonance imaging. The participating children were comparable to those lost to follow-up, and the controls to the experimentals, in terms of newborn background health and demographics. All outcome measures were corrected for mother's intelligence. Analysis techniques included two-group analysis of variance and stepwise discriminate analysis for the outcome measures, Wilks' lambda and jackknifed classification to ascertain two-group classification success per and across domains; canonical correlation analysis to explore relationships among neuropsychological, electrophysiological and neurostructural domains at school-age, and from the newborn period to school-age. RESULTS: Controls and experimentals were comparable in age at testing, anthropometric and health parameters, and in cognitive and achievement scores. Experimentals scored better in executive function, spectral coherence, and cerebellar volumes. Furthermore, executive function, spectral coherence and brain structural measures discriminated controls from experimentals. Executive function correlated with coherence and brain structure measures, and with newborn-period neurobehavioral assessment. CONCLUSION: The intervention in the intensive care nursery improved executive function as well as spectral coherence between occipital and frontal as well as parietal regions. The experimentals' cerebella were significantly larger than the controls'. These results, while preliminary, point to the possibility of long-term brain improvement even of intrauterine growth compromised preterms if individualized intervention begins with admission to the NICU and extends throughout transition home. Larger sample replications are required in order to confirm these results. CLINICAL TRIAL REGISTRATION: The study is registered as a clinical trial. The trial registration number is NCT00914108.

In vivo assessment of optimal b-value range for perfusion-insensitive apparent diffusion coefficient imaging.

PURPOSE: To assess the optimal b-values range for perfusion-insensitive apparent diffusion coefficient (ADC) imaging of abdominal organs using short-duration DW-MRI acquisitions with currently available ADC estimation methods. METHODS: DW-MRI data of 15 subjects were acquired with eight b-values in the range of 5-800 s∕mm(2). The reference-standard, a perfusion insensitive, ADC value (ADC(IVIM)), was computed using an intravoxel incoherent motion (IVIM) model with all acquired diffusion-weighted images. Simulated DW-MRI data was generated using an IVIM model with b-values in the range of 0-1200 s∕mm(2). Monoexponential ADC estimates were calculated using: (1) Two-point estimator (ADC(2)); (2) least squares three-point (ADC(3)) estimator and; (3) Rician noise model estimator (ADC(R)). The authors found the optimal b-values for perfusion-insensitive ADC calculations by minimizing the relative root mean square error (RRMS) between the ADC(IVIM) and the monoexponential ADC values for each estimation method and organ. RESULTS: Low b-value = 300 s∕mm(2) and high b-value = 1200 s∕mm(2) minimized the RRMS between the estimated ADC and the reference-standard ADC(IVIM) to less than 5% using the ADC(3) estimator. By considering only the in vivo DW-MRI data, the combination of low b-value = 270 s∕mm(2) and high b-value of 800 s∕mm(2) minimized the RRMS between the estimated ADC and the reference-standard ADC(IVIM) to <7% using the ADC(3) estimator. For all estimators, the RRMS between the estimated ADC and the reference standard ADC correlated strongly with the perfusion-fraction parameter of the IVIM model (r = [0.78-0.83], p ≤ 0.003). CONCLUSIONS: The perfusion compartment in DW-MRI signal decay correlates strongly with the RRMS in ADC estimates from short-duration DW-MRI. The impact of the perfusion compartment on ADC estimations depends, however, on the choice of b-values and estimation method utilized. Likewise, perfusion-related errors can be reduced to <7% by carefully selecting the b-values used for ADC calculations and method of estimation.

Reliable assessment of perfusivity and diffusivity from diffusion imaging of the body.

Diffusion-weighted MRI of the body has the potential to provide important new insights into physiological and microstructural properties. The intra-voxel incoherent motion (IVIM) model relates the observed DW-MRI signal decay to parameters that reflect perfusivity (D*) and its volume fraction (f), and diffusivity (D). However, the commonly used voxel-wise fitting of the IVIM model leads to parameter estimates with poor precision, which has hampered their practical usage. In this work, we increase the estimates' precision by introducing a model of spatial homogeneity, through which we obtain estimates of model parameters for all of the voxels at once, instead of solving for each voxel independently. Furthermore, we introduce an efficient iterative solver which utilizes a model-based bootstrap estimate of the distribution of residuals and a binary graph cut to generate optimal model parameter updates. Simulation experiments show that our approach reduces the relative root mean square error of the estimated parameters by 80% for the D* parameter and by 50% for the f and D parameters. We demonstrated the clinical impact of our model in distinguishing between enhancing and nonenhancing ileum segments in 24 Crohn's disease patients. Our model detected the enhanced segments with 91%/92% sensitivity/specificity which is better than the 81%/85% obtained by the voxel-independent approach.

School Age Effects of the Newborn Individualized Developmental Care and Assessment Program for Medically Low-Risk Preterm Infants: Preliminary Findings.

BACKGROUND: By school age, even low risk moderately preterm-born children show more neuro-cognitive deficits, underachievement, behavioral problems, and poor social adaptation than full-term peers. AIM: To evaluate the outcomes at school-age for moderately preterm-born children (29-33 weeks gestational age), appropriate in growth for gestational age (AGA) and medically at low-risk, randomized to Newborn Individualized Developmental Care and Assessment Program (NIDCAP) or standard care in the Newborn Intensive Care Unit. At school-age, the experimental (E) group will show better neuropsychological and neuro-electrophysiological function, as well as improved brain structure than the control (C) group. MATERIALS AND METHODS: The original sample consisted of 30 moderately preterm-born infants (29 to 33 weeks), 23 (8C and 15E) of them were evaluated at 8 years of age, corrected-for-prematurity with neuropsychological, EEG spectral coherence, and diffusion tensor magnetic resonance imaging (DT MRI) measures. RESULTS: E-performed significantly better than C-group children on the Kaufman Assessment Battery for Children-Second Edition (KABC-II) and trended towards better scores on the Rey-Osterrieth Complex Figure Test. They also showed more mature frontal and parietal brain connectivities, and more mature fiber tracts involving the internal capsule and the cingulum. Neurobehavioral results in the newborn period successfully predicted neuropsychological functioning at 8 years corrected age. CONCLUSION: Moderately preterm infants cared for with the NIDCAP intervention showed improved neuropsychological and neuro-electrophysiological function as well as improved brain structure at school-age.

Three-dimensional assessment of MR imaging-guided percutaneous cryotherapy using multi-performer repeated segmentations: the value of supervised learning.

RATIONALE AND OBJECTIVES: Accurate and reproducible segmentations of two-dimensional images are an important prerequisite for assessing tumor ablations three dimensionally (3D). We evaluated whether supervised learning methods would improve multiperformer repeated segmentations of magnetic resonance images (MRI) obtained before and after MRI-guided cryotherapy of renal cell carcinoma. MATERIALS AND METHODS: Three medical students independently performed five manual segmentations of a biopsy-proven renal cell carcinoma that was treated with percutaneous MRI-guided cryotherapy. Using pretreatment (T2-weighted fast recovery fast spin echo [FRFSE]) and posttreatment (T1-weighted, fat-suppressed, dynamically enhanced) MRIs, regions of tumor cryonecrosis were segmented. The same tasks were repeated after an experienced abdominal radiologist provided supervised learning. Segmentation sensitivity was compared with an estimated 3D-ground truth via voxel counts for regions of tumor, both before and after treatment, and for the regions of cryonecrosis. The sensitivity of each repeated segmentation was compared against the estimated ground truth using sensitivity, overlap index, and volume (mL). RESULTS: Supervised learning significantly improved posttreatment segmentation sensitivity (P = .03). With supervised learning, the ranges of the performance metrics over the segmentation performers were: pretreated tumor, sensitivity 0.902-0.999, overlap index 0.935-0.961, and volume 19.15-23.71 mL; posttreated tumor, sensitivity 0.923-0.991, overlap index 0.952-0.981, and volume 20.67-22.70 mL; in the ablation zone, sensitivity 0.938-0.969, overlap index 0.940-0.962, and volume 31.79-32.36 mL. CONCLUSIONS: Supervised learning improved multiperformer repeated segmentations of MRIs obtained before and after MRI-guided percutaneous cryotherapy of renal cell carcinoma. These methods may prove useful in aiding the 3D assessment of percutaneous tumor ablations.

Three-dimensional assessment of MRI-guided percutaneous cryotherapy of liver metastases.

OBJECTIVE: We report our initial investigation of the use of a 3D method for assessing percutaneous tumor ablations. We hypothesized that these 3D techniques could be used to assess the technical success of ablations and that 3D metrics would be predictive of treatment response. CONCLUSION: Three-dimensional assessment of percutaneous tumor ablations provides a quantitative evaluation of the technical success of the procedure. Three-dimensional computer-based techniques can both quantify coverage of a tumor and create a virtual ablation margin for percutaneous procedures, akin to a surgical margin. Although results are preliminary, 3D metrics were useful in predicting treatment response.

Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation.

Characterizing the performance of image segmentation approaches has been a persistent challenge. Performance analysis is important since segmentation algorithms often have limited accuracy and precision. Interactive drawing of the desired segmentation by human raters has often been the only acceptable approach, and yet suffers from intra-rater and inter-rater variability. Automated algorithms have been sought in order to remove the variability introduced by raters, but such algorithms must be assessed to ensure they are suitable for the task. The performance of raters (human or algorithmic) generating segmentations of medical images has been difficult to quantify because of the difficulty of obtaining or estimating a known true segmentation for clinical data. Although physical and digital phantoms can be constructed for which ground truth is known or readily estimated, such phantoms do not fully reflect clinical images due to the difficulty of constructing phantoms which reproduce the full range of imaging characteristics and normal and pathological anatomical variability observed in clinical data. Comparison to a collection of segmentations by raters is an attractive alternative since it can be carried out directly on the relevant clinical imaging data. However, the most appropriate measure or set of measures with which to compare such segmentations has not been clarified and several measures are used in practice. We present here an expectation-maximization algorithm for simultaneous truth and performance level estimation (STAPLE). The algorithm considers a collection of segmentations and computes a probabilistic estimate of the true segmentation and a measure of the performance level represented by each segmentation. The source of each segmentation in the collection may be an appropriately trained human rater or raters, or may be an automated segmentation algorithm. The probabilistic estimate of the true segmentation is formed by estimating an optimal combination of the segmentations, weighting each segmentation depending upon the estimated performance level, and incorporating a prior model for the spatial distribution of structures being segmented as well as spatial homogeneity constraints. STAPLE is straightforward to apply to clinical imaging data, it readily enables assessment of the performance of an automated image segmentation algorithm, and enables direct comparison of human rater and algorithm performance.

Three validation metrics for automated probabilistic image segmentation of brain tumours.

The validity of brain tumour segmentation is an important issue in image processing because it has a direct impact on surgical planning. We examined the segmentation accuracy based on three two-sample validation metrics against the estimated composite latent gold standard, which was derived from several experts' manual segmentations by an EM algorithm. The distribution functions of the tumour and control pixel data were parametrically assumed to be a mixture of two beta distributions with different shape parameters. We estimated the corresponding receiver operating characteristic curve, Dice similarity coefficient, and mutual information, over all possible decision thresholds. Based on each validation metric, an optimal threshold was then computed via maximization. We illustrated these methods on MR imaging data from nine brain tumour cases of three different tumour types, each consisting of a large number of pixels. The automated segmentation yielded satisfactory accuracy with varied optimal thresholds. The performances of these validation metrics were also investigated via Monte Carlo simulation. Extensions of incorporating spatial correlation structures using a Markov random field model were considered.

Quantitative MR imaging assessment of prostate gland deformation before and during MR imaging-guided brachytherapy.

RATIONALE AND OBJECTIVES: The authors performed this study to document the deformations that occur between pretreatment magnetic resonance (MR) imaging and intraoperative MR imaging during brachytherapy. MATERIALS AND METHODS: MR images obtained at 1.5 and 0.5 T in 10 patients with prostate cancer were analyzed for changes in the shape and substructure of the prostate. Three-dimensional models of the prostate were obtained. The authors measured anteroposterior dimension; total gland, peripheral zone, and central gland volumes; transverse dimension; and superoinferior height. RESULTS: Gland deformations were seen at visual inspection of the three-dimensional models. The anteroposterior dimension of the total gland, central gland, and peripheral zone increased from 1.5- to 0.5-T imaging (median dimension, 4.9, 1.5, and 1.8 mm, respectively), and the increase was greatest in the peripheral zone (P < .05, all comparisons). There was a decrease in the transverse dimension from 1.5- to 0.5-T imaging (median, 4.5 mm; P < .005). The total gland volume and the superoinferior height did not show a statistically significant change. CONCLUSION: There were significant deformations in the shape of the prostate, especially in the peripheral zone, between the two imaging studies. The likely causes of the shape change are differences in rectal filling (endorectal coil used in 1.5-T studies vs obturator in 0.5-T studies) and/or changes in patient position (supine vs lithotomy). These findings suggest that pretreatment images alone may not be reliable for accurate therapy planning. It may be useful to integrate pre-and intraoperative data.