PubMed · 15086289
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
Liron Pantanowitz, Kenneth B Miller, Jason C Ford, Bruce A Beckwith. 2004. The scratch test.. https://doi.org/10.5858/2004-128-598-tst
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
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.
Nine observers reviewed a previously assembled library of 320 chest computed radiography (CR) images. Observers participated in four sessions, reading a different 1/4 of the sample on each of four liquid crystal displays: a 2-megapixel (MP) consumer color display, a 2-MP business color display, a 2-MP medical-grade gray display, and a 3-MP gray display. Each display was calibrated according to the DICOM Part 14 standard. The viewing application required observer log-in, then randomized the order of the subsample seen on the display, and timed the responses of the observer to render a 1-5 judgment on the absence or presence of ILD on chest CRs. Selections of 1-2 were considered negative, 3 was indeterminate, and 4-5 were positive. The order of viewing sessions was also randomized for each observer. The experiment was conducted under controlled lighting, temperature, and sound conditions to mimic conditions typically found in a patient examination room. Lighting was indirect, and illuminance at the display face was 195 +/- 8% lux and was monitored over the course of the experiment. The average observer sensitivity for the 2 MP color consumer, 2 MP business color, 2 MP gray, and 3 MP gray displays were 83.7%, 84.1%, 85.5%, and 86.7%, respectively. The only pairwise significant difference was between the 2-MP consumer color and the 2-MP gray (P = 0.05). Effect of order within a session was not significant (P = 0.21): period 1 (84.3%), period 2 (86.2%), period 3 (85.4%), period 4 (84.1%). Observer specificity for the various displays was not statistically significant (P = 0.21). Finally, a timing analysis showed no significant difference between the displays for the user group (P = 0.13), ranging from 5.3 s (2 MP color business) to 5.9 s (3 MP Gray). There was, however, a reduction in time over the study that was significant (P < 0.001) for all users; the group average decreased from 6.5 to 4.7 s per image. Physical measurements of the resolution, contrast, and noise properties of the displays were acquired. Most notably, the noise of the displays varied by 3.5x between the lowest and highest noise displays. Differences in display noise were indicative of observer performance. However, the large difference in the magnitude of the noise was not predictive of the small difference (3%) in the observer sensitivity for various displays. This is likely because detection of interstitial lung disease is limited by "anatomical noise" rather than display or x-ray image noise.
Triggered event-related functional magnetic resonance imaging requires sparse intervals of temporally resolved functional data acquisitions, whose initiation corresponds to the occurrence of an event, typically an epileptic spike in the electroencephalographic trace. However, conventional fMRI time series are greatly affected by non-steady-state magnetization effects, which obscure initial blood oxygen level-dependent (BOLD) signals. Here, conventional echo-planar imaging and a post-processing solution based on principal component analysis were employed to remove the dominant eigenimages of the time series, to filter out the global signal changes induced by magnetization decay and to recover BOLD signals starting with the first functional volume. This approach was compared with a physical solution using radiofrequency preparation, which nullifies magnetization effects. As an application of the method, the detectability of the initial transient BOLD response in the auditory cortex, which is elicited by the onset of acoustic scanner noise, was used to demonstrate that post-processing-based removal of magnetization effects allows to detect brain activity patterns identical with those obtained using the radiofrequency preparation. Using the auditory responses as an ideal experimental model of triggered brain activity, our results suggest that reducing the initial magnetization effects by removing a few principal components from fMRI data may be potentially useful in the analysis of triggered event-related echo-planar time series. The implications of this study are discussed with special caution to remaining technical limitations and the additional neurophysiological issues of the triggered acquisition.