Inhomogeneous Bias Research Articles

Relative Homogenization of Climatic Time Series

Homogenization of the time series of observed climatic data aims to remove non-climatic biases caused by technical changes during the history of the climate observations. The spatial redundancy of climate information helps to recognize station-specific inhomogeneities with statistical methods, but the correct detection and removal of inhomogeneity biases is generally not easy for the combined effects of individual inhomogeneities. In a homogenization procedure, several time series of a given climatic variable observed in one climatic region are usually homogenized together via a large number of spatial comparisons between them. Such procedures are called relative homogenization. A relative homogenization procedure may include one or more homogenization cycles where a cycle includes the steps of time series comparison, inhomogeneity detection and corrections for inhomogeneities, and they may include other steps like the filtering of outlier values or spatial interpolations for infilling data gaps. Relative homogenization methods differ according to the number and content of the individual homogenization cycles, the procedure for the time series comparisons, the statistical inhomogeneity detection method, the way of the inhomogeneity bias removal, among other specifics. Efficient homogenization needs the use of tested statistical methods to be included in partly or fully automated homogenization procedures. Due to the large number and high variety of homogenization experiments fulfilled in the Spanish MULTITEST project (2015–2017), its method comparison test results are still the most informative about the efficiencies of homogenization methods in use. This study presents a brief review of the advances in relative homogenization, recalls some key results of the MULTITEST project, and analyzes some theoretical aspects of successful homogenization.

Reproducibility Analysis of Radiomic Features on T2-weighted MR Images after Processing and Segmentation Alterations in Neuroblastoma Tumors.

Purpose To evaluate the reproducibility of radiomics features extracted from T2-weighted MR images in patients with neuroblastoma. Materials and Methods A retrospective study included 419 patients (mean age, 29 months ± 34 [SD]; 220 male, 199 female) with neuroblastic tumors diagnosed between 2002 and 2023, within the scope of the PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers (ie, PRIMAGE) project, involving 746 T2/T2*-weighted MRI sequences at diagnosis and/or after initial chemotherapy. Images underwent processing steps (denoising, inhomogeneity bias field correction, normalization, and resampling). Tumors were automatically segmented, and 107 shape, first-order, and second-order radiomics features were extracted, considered as the reference standard. Subsequently, the previous image processing settings were modified, and volumetric masks were applied. New radiomics features were extracted and compared with the reference standard. Reproducibility was assessed using the concordance correlation coefficient (CCC); intrasubject repeatability was measured using the coefficient of variation (CoV). Results When normalization was omitted, only 5% of the radiomics features demonstrated high reproducibility. Statistical analysis revealed significant changes in the normalization and resampling processes (P < .001). Inhomogeneities removal had the least impact on radiomics (83% of parameters remained stable). Shape features remained stable after mask modifications, with a CCC greater than 0.90. Mask modifications were the most favorable changes for achieving high CCC values, with a radiomics features stability of 70%. Only 7% of second-order radiomics features showed an excellent CoV of less than 0.10. Conclusion Modifications in the T2-weighted MRI preparation process in patients with neuroblastoma resulted in changes in radiomics features, with normalization identified as the most influential factor for reproducibility. Inhomogeneities removal had the least impact on radiomics features. Keywords: Pediatrics, MR Imaging, Oncology, Radiomics, Reproducibility, Repeatability, Neuroblastic Tumors Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Safdar and Galaria in this issue.

The Verification of Sea Level Anomaly (SLA) Versus Tide Gauge for Geoid Modelling

The increasing long-term sea level rise is of particular interest and needs a significant investment in coastal protection procedures. The coastal sea level has been verified with tide gauges and well maintained with reliable accuracy of data since a few centuries ago. Satellite altimeter has been used for sea level study and increasingly for operational purposes. However, the precision of altimetry data in coastal areas is frequently eliminated by the geographically correlated orbit errors, which induce a regional inhomogeneous mission bias. This study presents an effort to verify sea level anomaly based on altimetry data with tide gauge stations as an early procedure to provide a good sea level anomaly data for mean sea surface, gravity anomaly, and geoid determination over Malaysian seas. The altimetry data is validated by comparing the sea level anomaly with ground truth data (tidal data) near the coastal area. A total of nine selected areas were chosen to represent the ground truth data. The findings found that the altimetry data over the South China Sea (Pulau Tioman, Geting, Cendering, Bintulu, and Kota Kinabalu) provided a good pattern, high correlation, and a minimum root mean square error (RMSE) value after the verification with tidal sea level anomaly data. However, it clearly shows that Tawau station over the Celebes Sea provides a poor pattern, poor correlation, and a high RMSE value with tidal sea level anomaly data. In conclusion, further enhancements are expected from the refined processing and filtering of altimetry data for the sea level study in the coastal zone.

Bayesian reconstruction of dark matter distribution from peculiar velocities: accounting for inhomogeneous Malmquist bias

ABSTRACT We present a Bayesian velocity field reconstruction algorithm that performs the reconstruction of the mass density field using only peculiar velocity data. Our method consistently accounts for the inhomogeneous Malmquist (IHM) bias using analytical integration along the line of sight. By testing our method on a simulation, we show that our method gives an unbiased reconstruction of the velocity field. We show that not accounting for the IHM bias can lead to significant biases in the Bayesian reconstructions. We applied our method to a peculiar velocity data set consisting of the SFI++ and 2MTF Tully–Fisher catalogues and the A2 supernovae compilation, thus obtaining a novel velocity reconstruction in the local Universe. Our velocity reconstructions have a cosmological power spectrum consistent with the theoretical expectation. Furthermore, we obtain a full description of the uncertainties on reconstruction through samples of the posterior distribution. We validate our velocity reconstruction of the local Universe by comparing it to an independent reconstruction using the 2M++ galaxy catalogue, obtaining good agreement between the two reconstructions. Using Bayesian model comparison, we find that our velocity model performs better than the adaptive kernel smoothed velocity with the same peculiar velocity data. However, our velocity model does not perform as well as the velocity reconstruction from the 2M++ galaxy catalogue, due to the sparse and noisy nature of the peculiar velocity tracer samples. The method presented here provides a way to include peculiar velocity data in initial condition reconstruction frameworks.

Distinguishing between benign and malignant breast lesions using diffusion weighted imaging and intravoxel incoherent motion: A systematic review and meta-analysis

PurposeWe sought to evaluate the diagnostic performance of diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) for distinguishing between benign and malignant breast tumors by performing a meta-analysis. MethodsWe comprehensively searched the electronic databases PubMed and Embase from January 2000 to April 2020 for studies in English. Studies were included if they reported the sensitivity and specificity for identifying benign and malignant breast lesions using DWI or IVIM. Studies were reviewed according to QUADAS-2. The data inhomogeneity and publication bias were also assessed. In order to explore the influence of different field strengths and different b values on diagnostic efficiency, we conducted subgroup analysis. ResultsWe analyzed 79 studies, which included a total of 6294 patients with 4091 malignant lesions and 2793 benign lesions. Overall, the pooled sensitivity and specificity of ADC for detecting malignant breast tumors were 0.87 (0.86–0.88) and 0.80 (0.78–0.81), respectively. The PLR was 5.09 (4.16–6.24); the NLR was 0.15 (0.13–0.18); and the DOR was 38.95 (28.87–52.54). The AUC value was 0.9297. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity and specificity was 0.85 (0.82–0.88) and 0.87(0.83–0.90), respectively; the PLR was 5.65 (3.91–8.18); the NLR was 0.17 (0.12–0.26); and the DOR was 38.44 (23.57–62.69). The AUC value was 0.9265. Most of parameters demonstrated considerable statistically significant heterogeneity (P < 0.05, I2>50 %) except the pooled DOR, PLR of D and the pooled DOR and NLR of D*. ConclusionsOur meta-analysis indicated that DWI and IVIM had high sensitivity and specificity in the differential diagnosis of breast lesions; and compared with DWI, IVIM could not further increase the diagnostic performance. There was no significant difference in diagnostic accuracy.

Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test-retest and image registration analyses.

To assess the repeatability of radiomic features in magnetic resonance (MR) imaging of glioblastoma (GBM) tumors with respect to test-retest, different image registration approaches and inhomogeneity bias field correction. We analyzed MR images of 17 GBM patients including T1- and T2-weighted images (performed within the same imaging unit on two consecutive days). For image segmentation, we used a comprehensive segmentation approach including entire tumor, active area of tumor, necrotic regions in T1-weighted images, and edema regions in T2-weighted images (test studies only; registration to retest studies is discussed next). Analysis included N3, N4 as well as no bias correction performed on raw MR images. We evaluated 20 image registration approaches, generated by cross-combination of four transformation and five cost function methods. In total, 714 images (17 patients×2 images×((4 transformations×5 cost functions)+1 test image) and 2856 segmentations (714 images×4 segmentations) were prepared for feature extraction. Various radiomic features were extracted, including the use of preprocessing filters, specifically wavelet (WAV) and Laplacian of Gaussian (LOG), as well as discretization into fixed bin width and fixed bin count (16, 32, 64, 128, and 256), Exponential, Gradient, Logarithm, Square and Square Root scales. Intraclass correlation coefficients (ICC) were calculated to assess the repeatability of MRI radiomic features (high repeatability defined as ICC≥95%). In our ICC results, we observed high repeatability (ICC≥95%) with respect to image preprocessing, different image registration algorithms, and test-retest analysis, for example: RLNU and GLNU from GLRLM, GLNU and DNU from GLDM, Coarseness and Busyness from NGTDM, GLNU and ZP from GLSZM, and Energy and RMS from first order. Highest fraction (percent) of repeatable features was observed, among registration techniques, for the method Full Affine transformation with 12 degrees of freedom using Mutual Information cost function (mean 32.4%), and among image processing methods, for the method Laplacian of Gaussian (LOG) with Sigma (2.5-4.5mm) (mean 78.9%). The trends were relatively consistent for N4, N3, or no bias correction. Our results showed varying performances in repeatability of MR radiomic features for GBM tumors due to test-retest and image registration. The findings have implications for appropriate usage in diagnostic and predictive models.

Harnessing the Power of Global Health Studies for Sickle Cell Disease: Validation of a Rapid, Open-Source, Paper-Based Screening Assay in a Cohort of 1103 Tanzanian Children

Introduction: Global health research is hampered by the lack of inexpensive and reliable assays to annotate clinical study cohorts for geographically localized endemic genetic disorders. The most prevalent modifier in Africa is sickle cell disease (SCD, HbSS). In SCD, sickling of erythrocytes by polymerization of hemoglobin S (HbS) causes vaso-occlusion and hemolysis, leading to significant morbidity/mortality and constituting a major healthcare and economic burden. The inhomogeneity bias introduced into the clinical data of patient cohorts by the changes associated to undiagnosed SCD is a significant challenge. Thus, our aims were to: a) optimize a paper-based screening test for SCD for its application in suboptimal and resource-limited conditions, b) demonstrate the feasibility of retrospective SCD annotation of a large cohort of children enrolled in a global health research study, and c) extract novel evidence on potential differences in clinical presentation between febrile children with and without SCD enrolled in this cohort.Materials & Methods: First, we adapted and simplified a paper-based SCD assay (Piety et al. 2017) for suboptimal sample conditions (frozen EDTA-whole blood) and limited-resource settings. Five microlitres of thawed EDTA-whole blood was solubilized (high phosphate buffer with saponin/sodium hydrosulfite), and blotted on standard blotting paper. Dots formed by capillarity, and their aspect depended on HbS%. A high-resolution scanner and two smartphone brands were compared for image capture. An algorithm for visual calling of the presence an HbS allele was developed, and the freeware ImageJ determined peripheral/central pixel intensities. An HbS dosimetric curve was determined after identification of optimal parameter correlations by scatter plot matrix. Second, blood samples from 1103 children aged 9 to 35 months enrolled in a randomized open prospective non-inferiority pediatric cohort study on algorithm-based management of febrile illnesses in Tanzania (ePOCT Trial, 2014-6, NCT02225769, Swiss/Tanzanian IRB approval, Keitel et al.2017) were assayed with the optimized paper test. In parallel, confirmation by gold-standard alkaline Hb electrophoresis (Helena Biosciences, UK) was performed. Third, clinical data from the newly SCD-annotated ePOCT database was analyzed (STATA software suite) to identify clinical features associated with febrile illnesses in the HbAA/AS/SS groups.Results: All EDTA-blood samples frozen onsite in Tanzania (shipped and then stored for >1 year) could be assayed. The test was rapid (5'), inexpensive (0.05 USD/test) and accessible (widely available reagents, no requirement for costly equipment, open-source freeware). Determination of HbS status (homozygous HbSS/AA or heterozygous HbSA) was effective both visually and by informatic means. Smartphone cameras yielded identical results (R2=0.96 & 0.97) when compared to a high-definition scanner. Local prevalence was at 1.6% and 13.6% for HbSS and HbAS respectively, in line with previously published data. By visual sorting of blood dots, the assay was 100% sensitive and 75.8% specific for the determination of HbSS-SCD and 97.6% sensitive and 87.5% specific for the identification of an HbS allele (in an hetero- or homozygous state). After analysis of the annotated ePOCT multiparametric clinical dataset, we observed that cough as the main complaint was more prevalent in children with HbSS (OR 3.1, 95% CI: 1.1-8.3). HbSS children were more severely malnourished (MUAC/age average z-scores -0.7 vs 0.0, p=0.01) and slightly more anemic (Hb 9.1 vs 9.8 g/dL, p=0.06) when compared to HbAA counterparts. Interestingly, children with HbAS had a better nutrition status than those with HbAA (MUAC/age average z-score 0.14, p<0.01). Also, education of mothers of HbSS children was significantly lower (primary education OR 0.3 (95% CI: 0.1-0.8).Conclusions: We demonstrated that the retrospective annotation of clinical cohorts for SCD with an optimized paper-based assay is feasible, straightforward and inexpensive. Our approach has the potential to eliminate the interpretation bias associated with SCD, and thus facilitate the downstream analysis of valuable datasets in other studies. As a proof of principle, we examined a properly annotated dataset highlighting novel features of febrile illness in children with SCD and HbAS carriers in Africa. DisclosuresNo relevant conflicts of interest to declare.

An adaptive-scale active contour model for inhomogeneous image segmentation and bias field estimation

The active contour model is a widely used method for image segmentation. Most existing active contour models yield poor performance when applied to images with severe intensity inhomogeneity. To address this issue, we propose an adaptive-scale active contour model (ASACM) based on image entropy and semi-naive Bayesian classifier, which achieves simultaneous segmentation and bias field estimation for images with severe intensity inhomogeneity. Firstly, an adaptive scale operator is constructed to adaptively adjust the scale of the ASACM according to the degree of the intensity inhomogeneity. Secondly, we define an improved bias field estimation term via distributing a dependent-membership function for each pixel to estimate the bias field in severe inhomogeneous images. Thirdly, a new penalty term is proposed using piecewise polynomial, which helps to avoid time-consuming re-initialization process and instability in conventional penalty term. The experimental results demonstrate that the proposed ASACM consistently outperforms many state-of-the-art models in segmentation accuracy, segmentation efficiency and robustness w.r.t initialization and noise.

Bias induced ferromagnetism and half-metallicity in graphene nano-ribbons

Towards spin selective electronics made of three coordinated carbon atoms, here we computationally propose robust and reversibly bias driven evolution of pristine undoped graphene nano-ribbons(GNR) into ferromagnetic-semiconductor, metal or a half metal, irrespective of their edge configurations. The evolution is a result of a rare ferromagnetic(FM) order emerging among nearest neighbouring(n-n) sites, in positively biased regions in their in-homogeneous bias unit-cells, in attempt to cooperatively minimise on-site Coulomb repulsion and kinetic energy, while maximising localization of electrons at the positively biased sites. The phenomenon appears to be a general property of in-homogeneously biased Coulomb correlated bipartite systems. Consequences are particularly rich in zigzag edged graphene nano-ribbons(ZGNR) due to the contest of bias driven n-n FM order and the inter-edge antiferromagnetic order inherent to ZGNRs, leading to systematic closing of gap for one of the spins, amounting to bias controlled unmissable opening of window for FM-semiconducting and half-metallic transport.

Breast Lesions: Diagnosis Using Diffusion Weighted Imaging at 1.5T and 3.0T—Systematic Review and Meta-analysis

IntroductionWe compared the diagnostic performance of diffusion weighted imaging (DWI) acquired with 1.5T and 3.0T magnetic resonance (MR) units in differentiating malignant breast lesions from benign ones. Materials and MethodsA comprehensive search of the PubMed and Embase databases was performed for studies reported from January 1, 2000 to February 19, 2016. The quality of the included studies was assessed. Statistical analysis included pooling of diagnostic sensitivity and specificity and assessing data inhomogeneity and publication bias. ResultsA total of 61 studies were included after a full-text review. These included 4778 patients and 5205 breast lesions. The overall sensitivity and specificity were 90% (95% confidence interval [CI], 88%-92%) and 86% (95% CI, 82%-89%), respectively. The pooled diagnostic odds ratio was 53 (95% CI, 37-74). For breast cancer versus benign lesions, the area under the curve was 0.94 (95% CI, 0.92-0.96). For the 44 studies that used a 1.5T MR unit, the pooled sensitivity and specificity were 91% (95% CI, 89%-92%) and 86% (95% CI, 81%-90%), respectively. For the 17 studies that used a 3.0T MR unit, the pooled sensitivity and specificity were 88% (95% CI, 83%-91%) and 84% (95% CI, 0.78-0.89), respectively. Publication bias and significant heterogeneity were observed; however, no threshold was found among the 61 studies. No significant difference was found in the sensitivity or specificity between the subgroups. ConclusionThe results of the comparison between the subgroups that had used either a 1.5T or 3.0T MR unit suggest that the diagnostic accuracy for breast cancer compared with benign lesions is not significantly different.

A Modified Brain MR Image Segmentation and Bias Field Estimation Model Based on Local and Global Information.

Because of the poor radio frequency coil uniformity and gradient-driven eddy currents, there is much noise and intensity inhomogeneity (bias) in brain magnetic resonance (MR) image, and it severely affects the segmentation accuracy. Better segmentation results are difficult to achieve by traditional methods; therefore, in this paper, a modified brain MR image segmentation and bias field estimation model based on local and global information is proposed. We first construct local constraints including image neighborhood information in Gaussian kernel mapping space, and then the complete regularization is established by introducing nonlocal spatial information of MR image. The weighting between local and global information is automatically adjusted according to image local information. At the same time, bias field information is coupled with the model, and it makes the model reduce noise interference but also can effectively estimate the bias field information. Experimental results demonstrate that the proposed algorithm has strong robustness to noise and bias field is well corrected.

An efficient level set method for simultaneous intensity inhomogeneity correction and segmentation of MR images

Intensity inhomogeneity (bias field) is a common artefact in magnetic resonance (MR) images, which hinders successful automatic segmentation. In this work, a novel algorithm for simultaneous segmentation and bias field correction is presented. The proposed energy functional allows for explicit regularization of the bias field term, making the model more flexible, which is crucial in presence of strong inhomogeneities. An efficient minimization procedure, attempting to find the global minimum, is applied to the energy functional. The algorithm is evaluated qualitatively and quantitatively using a synthetic example and real MR images of different organs. Comparisons with several state-of-the-art methods demonstrate the superior performance of the proposed technique. Desirable results are obtained even for images with strong and complicated inhomogeneity fields and sparse tissue structures.

Confinement by biased velocity jumps: Aggregation of &lt;em&gt; escherichia coli&lt;/em&gt;

We investigate a one-dimensional linear kinetic equation derived from a velocity jump process modelling bacterial chemotaxis in presence of an external chemical signal centered at the origin. We prove the existence of a positive equilibrium distribution with an exponential decay at infinity. We deduce a hypocoercivity result, namely: the solution of the Cauchy problem converges exponentially fast towards the stationary state. The strategy follows [J. Dolbeault, C. Mouhot, and C. Schmeiser, Hypocoercivity for linear kinetic equations conserving mass, Trans. AMS 2014]. The novelty here is that the equilibrium does not belong to the null spaces of the collision operator and of the transport operator. From a modelling viewpoint, it is related to the observation that exponential confinement is generated by a spatially inhomogeneous bias in the velocity jump process.

Fine feature retention of magnetic resonance images using intensity inhomogeneity weighted non-local filtering

Electronic noise in the presence of intensity inhomogeneity severely inhibits the robustness of many traditional filtering algorithms. We propose an adaptive filter that utilises the intensity inhomogeneity bias field often observed in magnetic resonance imaging as a map for directing the magnitude of smoothing. This adaptive filter allows for controlled filtering of images where high signal-to-noise (SNR) regions are smoothed less, while low SNR regions are smoothed more intensely. We validated our method with a phantom and MRIs of the lumbar spine. Results showed that the proposed algorithm improved filtering of noise in the presence of intensity inhomogeneity.

Adaptive Active Contour Model: a Localized Mutual Information Approach for Medical Image Segmentation

Troubles are often met when traditional active contours extract boundaries of medical images with inhomogeneous bias and various noises. Focusing on such a circumstance, a localized mutual information active contour model is discussed in the paper. By defining neighborhood of each point on the level set, mutual information is introduced to describe the relationship between the zero level set and image field. A driving energy term is then generated by integrating all the information. In addition, an expanding energy and internal energy are designed to regularize the driving energy. Contrary to piecewise constant model, new model has a better command of driving the contours without initialization.

Large-scale bulk flows from the Cosmicflows-2 catalogue

The Cosmicflows-2 catalogue is a compendium of peculiar velocity measurements. While it has many objects in common with the COMPOSITE catalogue, a previously analysed collection of peculiar velocity data found to give an unexpectedly large bulk flow on large scales, the data in Cosmicflows-2 have been reanalysed to ensure consistency between distances measured using different methods. In particular, a focus on accurate distances led the authors of the Cosmicflows-2 to not correct for homogeneous or inhomogeneous Malmquist bias, both or which are corrected for in the COMPOSITE compilation. We find remarkable agreement between the COMPOSITE and the Cosmicflows-2 if the small EFAR sample of clusters located in two dense superclusters is removed from both surveys, giving results that are inconsistent with the $\Lambda$ cold dark matter standard model with Planck central parameters at the 98\% level. On smaller scales we find overall agreement between data sets and consistency with the standard model.

Exemplar-Based Human Action Pose Correction

The launch of Xbox Kinect has built a very successful computer vision product and made a big impact on the gaming industry. This sheds lights onto a wide variety of potential applications related to action recognition. The accurate estimation of human poses from the depth image is universally a critical step. However, existing pose estimation systems exhibit failures when facing severe occlusion. In this paper, we propose an exemplar-based method to learn to correct the initially estimated poses. We learn an inhomogeneous systematic bias by leveraging the exemplar information within a specific human action domain. Furthermore, as an extension, we learn a conditional model by incorporation of pose tags to further increase the accuracy of pose correction. In the experiments, significant improvements on both joint-based skeleton correction and tag prediction are observed over the contemporary approaches, including what is delivered by the current Kinect system. Our experiments for the facial landmark correction also illustrate that our algorithm can improve the accuracy of other detection/estimation systems.

Effects of pump pulse propagation and spatial distribution of bias fields on terahertz generation from photoconductive antennas

Through theoretical analysis and numerical simulation, we show that bipolar terahertz pulses generated from photo-excited small-gap or mid-gap photoconductive (PC) antennas actually stem from a unique combination of spatially inhomogeneous bias field and propagation effects of pump pulses through the PC if the influence of fast carrier recombination on terahertz radiation is excluded. This finding provides new insights on the formation of bipolar terahertz pulses, instead of the traditional view based on the Drude-Lorentz model and the space-charge screening effect. We find large-aperture PC antennas always emit unipolar terahertz pulses rather than bipolar ones because the bias fields in this case can be considered homogeneous. We also show that the saturation of the peak amplitude of terahertz pulses at high pump fluence can be correctly predicted using our model without invoking space-charge screening.

Effects of image contrast on functional MRI image registration

Lack of tissue contrast and existing inhomogeneous bias fields from multi-channel coils have the potential to degrade the output of registration algorithms; and consequently degrade group analysis and any attempt to accurately localize brain function. Non-invasive ways to improve tissue contrast in fMRI images include the use of low flip angles (FAs) well below the Ernst angle and longer repetition times (TR). Techniques to correct intensity inhomogeneity are also available in most mainstream fMRI data analysis packages; but are not used as part of the pre-processing pipeline in many studies. In this work, we use a combination of real data and simulations to show that simple-to-implement acquisition/pre-processing techniques can significantly improve the outcome of both functional-to-functional and anatomical-to-functional image registrations. We also emphasize the need of tissue contrast on EPI images to be able to appropriately evaluate the quality of the alignment. In particular, we show that the use of low FAs (e.g., θ≤40°), when physiological noise considerations permit such an approach, significantly improves accuracy, consistency and stability of registration for data acquired at relatively short TRs (TR≤2s). Moreover, we also show that the application of bias correction techniques significantly improves alignment both for array-coil data (known to contain high intensity inhomogeneity) as well as birdcage-coil data. Finally, improvements in alignment derived from the use of the first infinite-TR volumes (ITVs) as targets for registration are also demonstrated. For the purpose of quantitatively evaluating the different scenarios, two novel metrics were developed: Mean Voxel Distance (MVD) to evaluate registration consistency, and Deviation of Mean Voxel Distance (dMVD) to evaluate registration stability across successive alignment attempts.

Ventilation‐based segmentation of the lungs using hyperpolarized 3He MRI

To develop an automated segmentation method to differentiate the ventilated lung volume on (3) He magnetic resonance imaging (MRI). Computational processing (CP) for each subject consisted of the following three essential steps: 1) inhomogeneity bias correction, 2) whole lung segmentation, and 3) subdivision of the lung segmentation into regions of similar ventilation. Evaluation consisted of two comparative analyses: i) comparison of the number of defects scored by two human readers in 43 subjects, and ii) simultaneous truth and performance level estimation (STAPLE) in 18 subjects in which the ventilation defects were manually segmented by four human readers. There was excellent correlation between the number of ventilation defects tabulated by CP and reader #1 (intraclass correlation coefficient [ICC] = 0.86), CP and reader #2 (ICC = 0.85), and between the two readers (ICC = 0.97). The STAPLE results from the second analysis yielded the following sensitivity/specificity numbers: CP (0.898/0.905), radiologist #1 (0.743/0.897), radiologist #2 (0.501/0.985), radiologist #3 (0.898/0.848), and the first author (0.600/0.984). We developed and evaluated an automated method for quantifying the ventilated lung volume on (3) He MRI. The findings strongly indicate that our proposed algorithmic processing may be a reliable, automatic method for quantitating ventilation defects.