PubMed · 15732891
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Lynn Legg. Panoramic radiography.. https://pubmed.ncbi.nlm.nih.gov/15732891/
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MOTIVATION: 10x Genomics VisiumHD enables spatial transcriptomics at 2 µm × 2 µm resolution but exhibits slide-specific, non-periodic striping artifacts due to lane-width variability. These multiplicative row/column effects distort bin total counts and can bias downstream analyses. The state-of-the-art destriping approach is the normalization procedure used as a preprocessing step in bin2cell; it applies sequential high-quantile row- then column-wise normalization, which is asymmetric and can introduce edge effects/macro-stripes and distortions of large-scale total-count structure. RESULTS: We propose a statistical destriping approach that leverages nuclei segmentation from the co-registered H&E image. Assuming transcript abundance is constant within each nucleus, we model bin counts with a negative binomial distribution whose mean is a product of a nucleus-specific concentration and row- and column-specific stripe-factors reflecting lane-width variation. We fit all parameters in a generalized linear modeling framework with cross-validated regularization on stripe-factors and iterative dispersion estimation, and use the fitted parameters to correct the observed counts into a destriped image. On synthetic data with known ground truth, our method improves stripe-factor estimation accuracy and reduces error in corrected counts relative to bin2cell and bin2cell-derived baselines. Across four public VisiumHD slides, it consistently lowers striping intensity while substantially better preserving biological signal present in the large-scale global count structure and avoiding the artifacts introduced by other methods. AVAILABILITY AND IMPLEMENTATION: All source code and links to publicly available data used for this study are available at https://github.com/paolamalsot/destriping-GLM.
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BACKGROUND AND PURPOSES: To quantify the cold or hot spot induced in IMRT treatment plans due to the presence of metal artifact in CT image data sets stemming from dental work. PATIENTS AND METHODS: Metal artifact corrected image data sets of five patients have been analyzed. IMRT plans were generated using five different planning image data sets: (a) uncorrected (UC) (b) homogeneous uncorrected (HUC), (c) sinogram completion corrected (SCC), (d) minimum value corrected (MVC), and (e) image set (d) subsequently corrected with a streak artifacts reduction algorithm (SAR-MVC). The SAR-MVC data set is assumed to be the closest approximation to the absence of metal artifacts and has therefore been taken as the reference image data set. An IMRT plan was generated for each of the image datasets (a)-(e). The resulting IMRT treatment plans for data sets (a)-(d) were then projected onto the reference data set (e) and recalculated. The reference dose distribution (e) was then subtracted from these recalculated dose distributions. Using dose difference analysis, the cold and hot spots in organs at risk (OARs) and the target volumes (TVs) were quantified. RESULTS: When compared to the reference dose distribution, the UC, HUC, and SCC plans exhibited hot spots showing on average more than 1.0 Gy hot dose in the left and right parotids. For the UC, HUC, and SCC recalculated plans, subvolumes of the clinical target volumes (CTV) were under dosed on average by more than 0.9 Gy. On the other hand, the MVC plan showed less than 0.3 Gy hot dose in both parotids, and the cold dose in the CTVs were reduced by up to 0.8 Gy. CONCLUSIONS: The presence of dental metal artifacts in head and neck planning CT data sets can lead to relative hot spots in OARs and relative cold spots in regions of the TVs when compared to the reference data set that more closely approximates the patient anatomy. This effect can be reduced if a simple minimum value correction (MVC) method for the dental metal artifacts is employed.