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Contrast- and noise-dependent spatial resolution measurement for deep convolutional neural network-based noise reduction in CT using patient data. | LitMetric

AI Article Synopsis

  • Deep convolutional neural networks (DCNN) are increasingly used for noise reduction in clinical CT scans, highlighting the need to accurately measure their spatial resolution in actual patient data rather than just physical phantoms.
  • This study proposes a new framework that utilizes patient data to evaluate the spatial resolution characteristics of DCNN methods by incorporating techniques like lesion insertion and modulation transfer function measurement.
  • Results indicate that the spatial resolution of DCNN reconstructions worsens significantly with lower lesion contrast, decreased radiation dose, or increased denoising strength, contrasting with the more stable performance of traditional filtered back-projection (FBP) methods.

Article Abstract

Deep convolutional neural network (DCNN)-based noise reduction methods have been increasingly deployed in clinical CT. Accurate assessment of their spatial resolution properties is required. Spatial resolution is typically measured on physical phantoms, which may not represent the true performance of DCNN in patients as it is typically trained and tested with patient images and the generalizability of DNN to physical phantoms is questionable. In this work, we proposed a patient-data-based framework to measure the spatial resolution of DCNN methods, which involves lesion- and noise-insertion in projection domain, lesion ensemble averaging, and modulation transfer function measurement using an oversampled edge spread function from the cylindrical lesion signal. The impact of varying lesion contrast, dose levels, and CNN denoising strengths were investigated for a ResNet-based DCNN model trained using patient images. The spatial resolution degradation of DCNN reconstructions becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. The measured 50%/10% MTF spatial frequencies of DCNN with highest denoising strength were (-500 HU:0.36/0.72 mm; -100 HU:0.32/0.65 mm; -50 HU:0.27/0.53 mm; -20 HU:0.18/0.36 mm; -10 HU:0.15/0.30 mm), while the 50%/10% MTF values of FBP were almost kept constant of 0.38/0.76 mm.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187613PMC
http://dx.doi.org/10.1117/12.2654972DOI Listing

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