Browsing by Subject "Harmonization"

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Open Access
Challenges and Opportunities in Supply Chain Environmental Sustainability Disclosure: Navigating the Request-Response Process between Stakeholders and Suppliers
(2013-04-26) Jiang, Lin; Lab, Jessica; Lai, Phillip; Qian, Yifei; Rau, Peter
Environmental sustainability is growing in importance to organizations in many different sectors. The need to account for suppliers’ environmental performance through sustainability surveys is taking up a greater portion of the daily job responsibilities of sustainability professionals. This report incorporates insights from interviews with 15 organizations across multiple industries that address the current challenges and opportunities confronting those in the sustainability supply chain disclosure process. In addition, we analyze 31 collected sustainability surveys based on four survey-level characteristics (survey level, type, purpose and industry) and on four question-level characteristics (question format, nature, topic and subtopic). The resulting data show that, while it would be difficult to establish a single common survey or set of questions, opportunities exist for the standardization of question wording and format, which would constitute a step towards reducing the amount of time that organizations spend on responding to surveys. This report provides a roadmap for taking this project forward based on these results, centering on the creation of a web-based platform containing a repository of standard-worded and formatted questions covering a broad range of environmental topics. Using this platform, organizations could select questions to send to their suppliers based on their own preferences, while suppliers could reduce the amount of time spent on responding to survey requests. This establishes a path forward in supply chain sustainability disclosure, with the potential to reduce systemic inefficiencies and redundancies in this process.
Open Access
Inter-site and inter-scanner diffusion MRI data harmonization.
(Neuroimage, 2016-07-15) Mirzaalian, H; Ning, L; Savadjiev, P; Pasternak, O; Bouix, S; Michailovich, O; Grant, G; Marx, CE; Morey, RA; Flashman, LA; George, MS; McAllister, TW; Andaluz, N; Shutter, L; Coimbra, R; Zafonte, RD; Coleman, MJ; Kubicki, M; Westin, CF; Stein, MB; Shenton, ME; Rathi, Y
We propose a novel method to harmonize diffusion MRI data acquired from multiple sites and scanners, which is imperative for joint analysis of the data to significantly increase sample size and statistical power of neuroimaging studies. Our method incorporates the following main novelties: i) we take into account the scanner-dependent spatial variability of the diffusion signal in different parts of the brain; ii) our method is independent of compartmental modeling of diffusion (e.g., tensor, and intra/extra cellular compartments) and the acquired signal itself is corrected for scanner related differences; and iii) inter-subject variability as measured by the coefficient of variation is maintained at each site. We represent the signal in a basis of spherical harmonics and compute several rotation invariant spherical harmonic features to estimate a region and tissue specific linear mapping between the signal from different sites (and scanners). We validate our method on diffusion data acquired from seven different sites (including two GE, three Philips, and two Siemens scanners) on a group of age-matched healthy subjects. Since the extracted rotation invariant spherical harmonic features depend on the accuracy of the brain parcellation provided by Freesurfer, we propose a feature based refinement of the original parcellation such that it better characterizes the anatomy and provides robust linear mappings to harmonize the dMRI data. We demonstrate the efficacy of our method by statistically comparing diffusion measures such as fractional anisotropy, mean diffusivity and generalized fractional anisotropy across multiple sites before and after data harmonization. We also show results using tract-based spatial statistics before and after harmonization for independent validation of the proposed methodology. Our experimental results demonstrate that, for nearly identical acquisition protocol across sites, scanner-specific differences can be accurately removed using the proposed method.
Open Access
Radiomic feature variability on cone-beam CT images for lung SBRT
(2018) Geng, Ruiqi
This study aims to (1) investigate methodology for harmonization of radiomics features between planning CT and on-board CBCT and establish a workflow to harmonize images taken from different scanning protocols and over the course of radiotherapy treatments using normalization, and (2) examine feature variability of longitudinal cone-beam CT radiomics for 3 different fractionation schemes and a time-point during treatment indicative of early treatment response.
All CBCT images acquired over the course of lung SBRT for each patient were registered with corresponding planning CT. A volume-of-interest (VOI) in a homogeneous soft-tissue region that would not change over the course of radiotherapy was selected on the planning CT. The VOI was applied to all CBCT images of the same patient taken at different days. The first CBCT was normalized to the planning CT using the ratio of their respective mean VOI pixel values. Subsequent CBCT images were normalized using the ratio of that CBCT’s mean VOI pixel value to the first CBCT’s mean VOI pixel value. Forty-three features characterizing image intensity and morphology in fine and coarse textures were extracted from the planning CT, all original CBCT images, and all normalized CBCT images. T-test on extracted features from CBCT images with and without normalization indicates the effect of normalization on the distribution of various features. Mutual information between the planning CT and the first CBCT with and without normalization was calculated to assess the effectiveness of normalization on harmonizing radiomics features.
Of 72 NSCLC patients treated with lung SBRT, 18 received 15-18 Gy / fraction for 3 fractions; 36 received 12-12.5 Gy / fraction for 4 fractions; 18 received 8-10 Gy / fraction for 5 fractions. We studied 7 sets of CBCT images from the same treatment fraction as a ‘test-retest’ baseline to study the additional daily CBCT images. Fifty-five gray level intensity and textural features were extracted from the CBCT images. Test-retest images were used to determine the smallest detectable change (C=1.96*SD) indicating significant variation with a 95% confidence level. Here, the significance of feature variation depended on the choice of a minimum number of patients for which a feature changed more than ’C’. Analysis of which features change at which moment during treatment with different fractionation schemes was used to investigate a time-point indicative of early tumor response.
T-test on planning CT and CBCT images of the 72 patients indicated that normalization with a soft tissue VOI reduced the number of features with significant variation (p<0.05) by 55%. Following lung SBRT, 30 features changed significantly for at least 10% of all patients. For patients treated with 3 fractions, 49 features changed at Fraction 2, and 49 at Fraction 3; there was 100% overlap between features at both fractions. For patients treated with 4 fractions, 45, 45, and 48 features changed at Fraction 2-4 respectively; there was 92% overlap between features at Fraction 2 and the remaining fractions. For patients treated with 5 fractions, 12, 18, 14, and 25 features changed at Fraction 2-5; there was 36%, 48%, and 48% overlap between features at Fraction 2-4 and the remaining fractions respectively.
Normalization can potentially harmonize radiomics features on both planning CT and on-board CBCT. Feature variability depends on the selection of normalization VOI and extraction VOI. Significant changes in gray level radiomic features were observed over the course of lung SBRT. Different fractionation schemes corresponded to different patterns of feature variation. Higher fractional dose corresponded to a larger number of variable features and high overlap of variable features at an earlier time-point.