English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recent research has explored using neural networks to reconstructundersampled magnetic resonance imaging (MRI) data. Because of the complexityof the artifacts in the reconstructed images, there is a need to developtask-based approaches of image quality. Common metrics for evaluating imagequality like the normalized root mean squared error (NRMSE) and structuralsimilarity (SSIM) are global metrics which average out impact of subtlefeatures in the images. Using measures of image quality which incorporate asubtle signal for a specific task allow for image quality assessment whichlocally evaluates the effect of undersampling on a signal. We used a U-Net toreconstruct under-sampled images with 2x, 3x, 4x and 5x fold 1-D undersamplingrates. Cross validation was performed for a 500 and a 4000 image training setwith both structural similarity (SSIM) and mean squared error (MSE) losses. Atwo alternative forced choice (2-AFC) observer study was carried out fordetecting a subtle signal (small blurred disk) from images with the 4000 imagetraining set. We found that for both loss functions and training set sizes, thehuman observer performance on the 2-AFC studies led to a choice of a 2xundersampling but the SSIM and NRMSE led to a choice of a 3x undersampling. Forthis task, SSIM and NRMSE led to an overestimate of the achievableundersampling using a U-Net before a steep loss of image quality when comparedto the performance of human observers in the detection of a subtle lesion.

Task-Based Assessment for Neural Networks: Evaluating Undersampled MRI Reconstructions based on Human Observer Signal Detection