Facial analysis technology for the detection of Down syndrome in the Democratic Republic of the Congo.

Down syndrome is one of the most common chromosomal anomalies affecting the world's population, with an estimated frequency of 1 in 700 live births. Despite its relatively high prevalence, diagnostic rates based on clinical features have remained under 70% for most of the developed world and even lower in countries with limited resources. While genetic and cytogenetic confirmation greatly increases the diagnostic rate, such resources are often non-existent in many low- and middle-income countries, particularly in Sub-Saharan Africa. To address the needs of countries with limited resources, the implementation of mobile, user-friendly and affordable technologies that aid in diagnosis would greatly increase the odds of success for a child born with a genetic condition. Given that the Democratic Republic of the Congo is estimated to have one of the highest rates of birth defects in the world, our team sought to determine if smartphone-based facial analysis technology could accurately detect Down syndrome in individuals of Congolese descent. Prior to technology training, we confirmed the presence of trisomy 21 using low-cost genomic applications that do not need advanced expertise to utilize and are available in many low-resourced countries. Our software technology trained on 132 Congolese subjects had a significantly improved performance (91.67% accuracy, 95.45% sensitivity, 87.88% specificity) when compared to previous technology trained on individuals who are not of Congolese origin (p < 5%). In addition, we provide the list of most discriminative facial features of Down syndrome and their ranges in the Congolese population. Collectively, our technology provides low-cost and accurate diagnosis of Down syndrome in the local population.

Assessment of Renal Cell Carcinoma by Texture Analysis in Clinical Practice: A Six-Site, Six-Platform Analysis of Reliability.

BACKGROUND. Multiple commercial and open-source software applications are available for texture analysis. Nonstandard techniques can cause undesirable variability that impedes result reproducibility and limits clinical utility. OBJECTIVE. The purpose of this study is to measure agreement of texture metrics extracted by six software packages. METHODS. This retrospective study included 40 renal cell carcinomas with contrast-enhanced CT from The Cancer Genome Atlas and Imaging Archive. Images were analyzed by seven readers at six sites. Each reader used one of six software packages to extract commonly studied texture features. Inter- and intrareader agreement for segmentation was assessed with intraclass correlation coefficients (ICCs). First-order (available in six packages) and second-order (available in three packages) texture features were compared between software pairs using Pearson correlation. RESULTS. Inter- and intrareader agreement was excellent (ICC, 0.93-1). First-order feature correlations were strong (r ≥ 0.8, p < .001) between 75% (21/28) of software pairs for mean intensity and SD, 48% (10/21) for entropy, 29% (8/28) for skewness, and 25% (7/28) for kurtosis. Of 15 second-order features, only cooccurrence matrix correlation, gray-level nonuniformity, and run-length nonuniformity showed strong correlation between software packages (r = 0.90-1, p < .001). CONCLUSION. Variability in first- and second-order texture features was common across software configurations and produced inconsistent results. Standardized algorithms and reporting methods are needed before texture data can be reliably used for clinical applications. CLINICAL IMPACT. It is important to be aware of variability related to texture software processing and configuration when reporting and comparing outputs.

Harmonization of radiomic feature variability resulting from differences in CT image acquisition and reconstruction: assessment in a cadaveric liver.

Studies investigating the effects of computed tomography (CT) image acquisition and reconstruction parameters have mostly been limited to non-human phantoms to limit exposure to patients. This study investigates these variations using a cadaveric liver and determines harmonization methods to mitigate these variations. A reference CT scan of a cadaveric liver was acquired along with 16 modified scans. Modified scans were obtained with altered image acquisition and reconstruction parameters. In each slice, the liver was segmented and used to calculate 142 features. Student's t-tests assessed differences between reference and modified scans for each feature after correcting for multiple comparisons. Features were harmonized between reference and modified scans using histogram normalization, pixel resampling, Butterworth filtering, resampling and filtering combined, and ComBat harmonization. The number of features reflecting significant differences before and after harmonization were compared across imaging parameters. Reducing the field-of-view (FOV) and using coronal instead of axial scans resulted in the greatest number of features reflecting significant differences (67.6%, and 35.9%, respectively) and resulted in the greatest median relative change in feature values (25.4% and 18.2%, respectively). Changes in tube voltage, pitch, and slice interval resulted in the smallest number of features reflecting significance (0.7%) with median relative changes in feature <2%. Histogram normalization reduced or maintained the number of significantly different features for all scans, while ComBat reduced the number of significantly different features to zero for all scans. The remaining harmonization methods had mixed effects: resampling reduced the number of features reflecting significant differences for half of the imaging parameters, while filtering alone and filtering combined with resampling both reduced the number of features reflecting significance for 10 of the 16 parameters. The dependence of radiomic features on image acquisition and reconstruction parameters varies in a cadaveric liver; however, various harmonization methods have shown promise in mitigating these dependencies, particularly ComBat.

Increased sensitivity and signal-to-noise ratio in diffusion-weighted MRI using multi-echo acquisitions.

Post-mortem diffusion MRI (dMRI) enables acquisitions of structural imaging data with otherwise unreachable resolutions - at the expense of longer scanning times. These data are typically acquired using highly segmented image acquisition strategies, thereby resulting in an incomplete signal decay before the MRI encoding continues. Especially in dMRI, with low signal intensities and lengthy contrast encoding, such temporal inefficiency translates into reduced image quality and longer scanning times. This study introduces Multi Echo (ME) acquisitions to dMRI on a human MRI system - a time-efficient approach, which increases SNR (Signal-to-Noise Ratio) and reduces noise bias for dMRI images. The benefit of the introduced ME-dMRI method was validated using numerical Monte Carlo simulations and showcased on a post-mortem brain of a wild chimpanzee. The proposed Maximum Likelihood Estimation echo combination results in an optimal SNR without detectable signal bias. The combined strategy comes at a small price in scanning time (here 30% additional) and leads to a substantial SNR increase (here white matter: ~ 1.6x, equivalent to 2.6 averages, grey matter: ~ 1.9x, equivalent to 3.6 averages) and a general reduction of the noise bias.

Immunohistochemical Study of Ezrin Expression in Colorectal Carcinoma: A Comparative Study between Objective Method and Digital Quantitative Assessment.

BACKGROUND: Colorectal cancer is one of the leading causes of cancer death in both developed and developing nations. It is the third most common type of cancer and the fourth leading cause of cancer-related deaths worldwide. Ezrin is involved in maintaining cell structure and cell motility. Expression levels of the ezrin gene correlate with numerous human malignancies. MATERIAL AND METHODS: Ezrin expression was evaluated in fifty one cases of colorectal carcinoma by using two methods; objective and quantitative method to determine the statistical relation between ezrin objective analysis score and clinicopathological parameters and to do a comparative study between both methods of analysis. RESULTS: Ezrin was expressed in 92.2% of cases, and it showed a statistical significant relation with tumor grade. A statistically significant relation was found between ezrin objective analysis score and ezrin quantitative analysis score (P-value <0.05). A strong positive Pearson correlation exists between both methods of analysis (R=0.868). CONCLUSION: Ezrin has a role in colorectal cancer progression and it might provide clinically valuable information in predicting the behavior of colorectal cancer. Digital pathology offers the potential for improvements in quality, efficacy and safety. It will be necessary to carry out similar studies on a larger sample size in order to elucidate the possible prognostic significance of ezrin in colorectal carcinoma and ensure the ability of digital pathology to transform the practice of diagnostic pathology. 
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Radiation dose reduction and static image quality assessment using a channelized hotelling observer on an angiography system upgraded with clarity IQ.

The goal of this paper was the comparison of radiation dose and imaging quality before and after the Clarity IQ technology installation in a Philips AlluraXper FD20/20 angiography system using a Channelized Hotelling Observer model (CHO). The core characteristics of the Allura Clarity IQ technology are its real-time noise reduction algorithms (NRT) combined with state-of-the-art hardware; this technology allows to implement acquisition protocols able to significantly reduce patient entrance dose. To measure the system performances in terms of image quality we used a contrast detail phantom in a clinical scatter condition. A Leeds TO10 phantom has been imaged between two 10 cm thick homogeneous solid water slabs. Fluoroscopy images were acquired using a cerebral protocol at 3 dose levels (low, medium and high) with a field- of view (FOV) of 31 cm. Cineangiography images were acquired using a cerebral protocol at 2 fps. Thus, 4 acquisitions were obtained for the conventional technology and 4 acquisitions were taken after the Clarity IQ upgrade, for a total of 8 different image sets. A validated 40 Gabor channels CHO with an internal noise model compared the image sets. Human observers' studies were carried out to tune the internal noise parameter. We showed that the CHO did not detect any significant difference between any of the image sets acquired using the two technologies. Consequently, this x-ray imaging technology provides a non-inferior image quality with an average patient dose reduction of 57% and 28% respectively in cineangiography and fluoroscopy. The Clarity IQ installation has certainly allowed a considerable improvement in patient and staff safety, while maintaining the same image quality.

Inter-laboratory comparison of Ki-67 proliferating index detected by visual assessment and automated digital image analysis.

BACKGROUND: Proliferation rate is a major determinant of the biologic behavior of the tumor and provides information that can be used to guide treatment decisions. METHODS: This ring study included 27 pathologists from 14 Institutions, in order to assess inter-observer concordance between pathologists in Croatia. We analyzed Ki-67 proliferative index on ten randomly selected breast cancer samples comparing consistency between visual assessment using light microscopy compared to digital image analyses results from one central laboratory as a referral value. RESULTS: When we analyzed Ki-67 as numeric value high concordance rate was found between Ki-67 score visually assessed in all participating Institutions compared to referral value assessed by digital image analysis (ICC 0.76, 95% CI 0.58-0.91), and Krippendorff's alpha was 0.79 (95% CI 0.58-1.00). Concordance was better in slides with higher Ki-67 values. When we categorized Ki-67 values according to generally accepted 20% cut-off value we noticed the lower concordance rate among participants in our study. CONCLUSION: Proliferation remains one of the most important parameters for tumor characterization helpful in making clinical decisions, but it should be used with great caution. Standardization of the Ki-67 assessment is essential and proliferating index should be expressed as exact numeric value. For patients with proliferative index near the cut-off value, other factors must be considered in making clinical decisions.

Designing a rigorous microscopy experiment: Validating methods and avoiding bias.

Images generated by a microscope are never a perfect representation of the biological specimen. Microscopes and specimen preparation methods are prone to error and can impart images with unintended attributes that might be misconstrued as belonging to the biological specimen. In addition, our brains are wired to quickly interpret what we see, and with an unconscious bias toward that which makes the most sense to us based on our current understanding. Unaddressed errors in microscopy images combined with the bias we bring to visual interpretation of images can lead to false conclusions and irreproducible imaging data. Here we review important aspects of designing a rigorous light microscopy experiment: validation of methods used to prepare samples and of imaging system performance, identification and correction of errors, and strategies for avoiding bias in the acquisition and analysis of images.

Practical approaches to automated digital image analysis of Ki-67 labeling index in 997 breast carcinomas and causes of discordance with visual assessment.

The Ki-67 labeling index (LI) is an important prognostic factor in breast carcinoma. The Ki-67 LI is traditionally calculated via unaided microscopic estimation; however, inter-observer and intra-observer variability and low reproducibility are problems with this visual assessment (VA) method. For more accurate assessment and better reproducibility with Ki-67 LI, digital image analysis was introduced recently. We used both VA and automated digital image analysis (ADIA) (Ventana Virtuoso image management software) to estimate Ki-67 LI for 997 cases of breast carcinoma, and compared VA and ADIA results. VA and ADIA were highly correlated (intraclass correlation coefficient 0.982, and Spearman's correlation coefficient 0.966, p<0.05). We retrospectively analyzed cases with a greater than 5% difference between VA and ADIA results. The cause of these differences was: (1) tumor heterogeneity (98 cases, 56.0%), (2) VA interpretation error (32 cases, 18.3%), (3) misidentification of tumor cells (26 cases, 14.9%), (4) poor immunostaining or slide quality (16 cases, 9.1%), and (5) Estimation of non-tumor cells (3 cases, 1.7%). There were more discrepancies between VA and ADIA results in the group with a VA value of 10-20% compared to groups with <10% and ≥20%. Although ADIA is more accurate than VA, there are some limitations. Therefore, ADIA findings require confirmation by a pathologist.

A registration method for improving quantitative assessment in probabilistic diffusion tractography.

Diffusion MRI-based probabilistic tractography is a powerful tool for non-invasively investigating normal brain architecture and alterations in structural connectivity associated with disease states. Both voxelwise and region-of-interest methods of analysis are capable of integrating population differences in tract amplitude (streamline count or density), given proper alignment of the tracts of interest. However, quantification of tract differences (between groups, or longitudinally within individuals) has been hampered by two related features of white matter. First, it is unknown to what extent healthy individuals differ in the precise location of white matter tracts, and to what extent experimental factors influence perceived tract location. Second, white matter lacks the gross neuroanatomical features (e.g., gyri, histological subtyping) that make parcellation of grey matter plausible - determining where tracts "should" lie within larger white matter structures is difficult. Accurately quantifying tractographic connectivity between individuals is thus inherently linked to the difficulty of identifying and aligning precise tract location. Tractography is often utilized to study neurological diseases in which the precise structural and connectivity abnormalities are unknown, underscoring the importance of accounting for individual differences in tract location when evaluating the strength of structural connectivity. We set out to quantify spatial variance in tracts aligned through a standard, whole-brain registration method, and to assess the impact of location mismatch on groupwise assessments of tract amplitude. We then developed a method for tract alignment that enhances the existing standard whole brain registration, and then tested whether this method improved the reliability of groupwise contrasts. Specifically, we conducted seed-based probabilistic diffusion tractography from primary motor, supplementary motor, and visual cortices, projecting through the corpus callosum. Streamline counts decreased rapidly with movement from the tract center (-35% per millimeter); tract misalignment of a few millimeters caused substantial compromise of amplitude comparisons. Alignment of tracts "peak-to-peak" is essential for accurate amplitude comparisons. However, for all transcallosal tracts registered through the whole-brain method, the mean separation distance between an individual subject's tract and the average tract (3.2 mm) precluded accurate comparison: at this separation, tract amplitudes were reduced by 74% from peak value. In contrast, alignment of subcortical tracts (thalamo-putaminal, pallido-rubral) was substantially better than alignment for cortical tracts; whole-brain registration was sufficient for these subcortical tracts. We demonstrated that location mismatches in cortical tractography were sufficient to produce false positive and false negative amplitude estimates in both groupwise and longitudinal comparisons. We then showed that our new tract alignment method substantially reduced location mismatch and improved both reliability and statistical power of subsequent quantitative comparisons.

An international multicenter study to evaluate reproducibility of automated scoring for assessment of Ki67 in breast cancer.

The nuclear proliferation biomarker Ki67 has potential prognostic, predictive, and monitoring roles in breast cancer. Unacceptable between-laboratory variability has limited its clinical value. The International Ki67 in Breast Cancer Working Group investigated whether Ki67 immunohistochemistry can be analytically validated and standardized across laboratories using automated machine-based scoring. Sets of pre-stained core-cut biopsy sections of 30 breast tumors were circulated to 14 laboratories for scanning and automated assessment of the average and maximum percentage of tumor cells positive for Ki67. Seven unique scanners and 10 software platforms were involved in this study. Pre-specified analyses included evaluation of reproducibility between all laboratories (primary) as well as among those using scanners from a single vendor (secondary). The primary reproducibility metric was intraclass correlation coefficient between laboratories, with success considered to be intraclass correlation coefficient >0.80. Intraclass correlation coefficient for automated average scores across 16 operators was 0.83 (95% credible interval: 0.73-0.91) and intraclass correlation coefficient for maximum scores across 10 operators was 0.63 (95% credible interval: 0.44-0.80). For the laboratories using scanners from a single vendor (8 score sets), intraclass correlation coefficient for average automated scores was 0.89 (95% credible interval: 0.81-0.96), which was similar to the intraclass correlation coefficient of 0.87 (95% credible interval: 0.81-0.93) achieved using these same slides in a prior visual-reading reproducibility study. Automated machine assessment of average Ki67 has the potential to achieve between-laboratory reproducibility similar to that for a rigorously standardized pathologist-based visual assessment of Ki67. The observed intraclass correlation coefficient was worse for maximum compared to average scoring methods, suggesting that maximum score methods may be suboptimal for consistent measurement of proliferation. Automated average scoring methods show promise for assessment of Ki67 scoring, but requires further standardization and subsequent clinical validation.

Quality assessment of Ki67 staining using cell line proliferation index and stain intensity features.

Breast cancer is the most frequent cancer among women worldwide. Ki67 can be used as an immunohistochemical pseudo marker for cell proliferation to determine how aggressive the cancer is and thereby the treatment of the patient. No standard Ki67 staining protocol exists, resulting in inter-laboratory stain variability. Therefore, it is important to determine the quality control of a staining protocol to ensure correct diagnosis and treatment of patients. Currently, quality control is performed by the organization NordiQC that use an expert panel-based qualitative assessment system. However, no objective method exists to determine the quality of a staining protocol. In this study, we propose an algorithm, to objectively assess staining quality from segmented cell nuclei structures extracted from cell lines. The cell nuclei were classified into either Ki67 positive or negative to determine the Ki67 proliferation index within the cell lines. A Ki67 stain quality model based on ordinal logistic regression was developed to determine the quality of a staining protocol from features extracted from the segmented cell nuclei in the cell lines. The algorithm was able to segment and classify Ki67 positive cell nuclei with a sensitivity and positive predictive value (PPV) of 0.90 and 0.94 and Ki67 negative cell nuclei with a sensitivity and PPV of 0.78 and 0.78. The mean difference between a manual and automatic Ki67 proliferation index was -0.003 with a standard deviation of 0.056. The ordinal logistic regression model found that the stain intensity for both the Ki67 positive and Ki67 negative cell nuclei were statistically significant as parameters determining the stain quality from the cell line cores. The framework shows great promise for using cell nuclei information from cell lines to predict the staining quality of staining protocols. © 2018 International Society for Advancement of Cytometry.

Why rankings of biomedical image analysis competitions should be interpreted with care.

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future.

Performance assessment of a software-based coincidence processor for the EXPLORER total-body PET scanner.

Coincidence processing in positron emission tomography (PET) is typically done during acquisition of the data. However, on the EXPLORER total-body PET scanner we plan, in addition, to store unpaired single events (i.e. singles) for post-acquisition coincidence processing. A software-based coincidence processor was developed for EXPLORER and its performance was assessed. Our results showed that the performance of the coincidence processor could be significantly impacted by the type of data storage (Peripheral Component Interconnect Express (PCIe)-attached solid state drive (SSD) versus RAID 6 hard disk drives (HDDs)) especially when multiple data files were processed in parallel. We showed that a 48-thread computer node with dual Intel Xeon E5-2650 v4 central processing units (CPUs) and a PCIe SSD was sufficient to process approximately 120 M singles s-1 at an incoming singles rate of approximately 150 Mcps. With two computer nodes, near real-time coincidence processing became possible at this incoming singles rate.

A comprehensive theoretical comparison of proton imaging set-ups in terms of spatial resolution.

We present a comprehensive analytical comparison of four types of proton imaging set-ups and, to this end, develop a mathematical framework to calculate the width of the uncertainty envelope around the most likely proton path depending on set-up geometry, detector properties, and proton beam parameters. As a figure of merit for the spatial resolution achievable with each set-up, we use the frequency [Formula: see text] at which the modular transfer function of a density step decreases below 10%. We verify the analytical results with Monte Carlo simulations. We find that set-ups which track the angle and position of individual protons in front of and behind the phantom would yield an average spatial resolution of 0.3-0.35 lp mm-1 assuming realistic geometric parameters (i.e. 30-40 cm distance between detector and phantom, 15-20 cm phantom thickness). For set-ups combining pencil beam scanning with either a position sensitive detector, e.g. an x-ray flat panel, or with a position insensitive detector, e.g. a range telescope, we find an average spatial resolution of about 0.1 lp mm-1 for an 8 mm FWHM beam spot size. The pixel information improves the spatial resolution by less than 10%. In both set-up types, performance can be significantly improved by reducing the pencil beam size down to 2 mm FWHM. In this case, the achievable spatial resolution reaches about 0.25 lp mm-1. Our results show that imaging set-ups combining double scattering with a pixel detector can provide sufficient spatial resolution only under very stringent conditions and are not ideally suited for computed tomography applications. We further propose a region-of-interest method for set-ups with a pixel detector to filter out protons which have undergone nuclear reactions and discuss the impact of tracker detector uncertainties on the most likely path.

Quaternion wavelet transform based full reference image quality assessment for multiply distorted images.

Most of real-world image distortions are multiply distortion rather than single distortion. To address this issue, in this paper we propose a quaternion wavelet transform (QWT) based full reference image quality assessment (FR IQA) metric for multiply distorted images, which jointly considers the local similarity of phase and magnitude of each subband via QWT. Firstly, the reference images and distorted images are decomposed by QWT, and then the similarity of amplitude and phase are calculated on each subband, thirdly the IQA metric is constructed by the weighting method considering human visual system (HVS) characteristics, and lastly the scores of each subband are averaged to get the quality score of test image. Experimental results show that the proposed method outperforms the state of art in multiply distorted IQA.

Non-compact myocardium assessment by cardiac magnetic resonance: dependence on image analysis method.

To compare image analysis methods for the assessment of left ventricle non-compaction from cardiac magnetic resonance (CMR) imaging. CMR images were analyzed in 20 patients and 10 normal subjects. A reference model of the MR signal was introduced and validated based on image data. Non-compact (NC) myocardium size and distribution were assessed by tracing a single, continuous contour delimiting trabeculated region (Jacquier) or by one-by-one selection of trabeculae (Grothoff). The global non-compact/compact (NC/C) ratio, the NC mass, and the segmental NC/C ratio were assessed. Results were compared with the reference model. A significant difference between Grothoff and Jacquier approaches in the estimation of NC/C ratio (32.08 ± 6.63 vs. 19.81 ± 5.72, p < 0.0001) and NC mass (26.59 ± 8.36 vs. 14.15 ± 5.73 g/m2, p < 0.0001) was found. The Grothoff approach better matches the expected signal distribution. Inter-observer reproducibility of both Grothoff and Jacquier methods was adequate (9.71 and 8.22%, respectively) with no significant difference between observers. Jacquier and Grothoff approaches are not interchangeable so that specific diagnostic thresholds should be used for different image analysis methods. Grothoff method seems to better capture the true extension of trabeculated tissue.

Automatic calculation of patient size metrics in computed tomography: What level of computational accuracy do we need?

OBJECTIVES: To compare the effectiveness of two different patient size metrics based on water equivalent diameter (Dw ), the mid-scan water equivalent diameter Dw_c , and the mean (average) water equivalent diameter in the imaged region, Dw_ave , for automatic detection of accidental changes in computed tomography (CT) acquisition protocols. METHODS: Patient biometric data (height and weight) were available from a previous survey for 80 adult chest examinations, and 119 adult single-acquisition chest-abdomen-pelvis (CAP) examinations for two 16 slice scanners (GE LightSpeed and Toshiba Aquilion RXL) equipped with automatic tube current modulation (ATCM). Dw_c and Dw_ave were calculated from the archived CT images. Size-specific dose estimates (SSDE) were obtained from volume CT dose index (CTDIvol ), using the conversion factors for a patient diameter of Dw_c . RESULTS: CTDIvol and SSDE correlate better with Dw_ave than with Dw_c . R-squared values of linear fits to CTDIvol of CAP examinations were 0.81-0.89 for Dw_c and 0.93-0.94 for Dw_ave (SSDE: 0.69-080 for Dw_c , 0.87-0.92 for Dw_ave ). Percentage differences between Dw_c and Dw_ave were -4 ± 4% for chest and +5 ± 4% for CAP examinations (in % of Dw_ave ). However, small Dw variations translated as larger variations in CTDIvol for these ATCM systems (e.g., a 24% increase in Dw doubled CTDIvol ). The dependence of CTDIvol on Dw_ave was similar for chest and CAP examinations performed with similar ATCM parameters, while use of Dw_c resulted in a clear separation of the same data according to examination type. Maximum Dw variation in the imaged region was 5.6 ± 1.6 cm for chest and 6.5 ± 1.4 cm for CAP examinations. CONCLUSIONS: Dw_ave is a better metric than Dw_c for binning similar-sized patients in dose comparison studies, despite the additional computational effort required for its calculation Therefore, when implementing automatic determination of Dw for SSDE calculations, automatic calculation of Dw_ave should be considered.

Real-time motion analytics during brain MRI improve data quality and reduce costs.

Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional 'buffer data', an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more.