ResNCT: A Deep Learning Model for the Synthesis of Nephrographic Phase Images in CT Urography
arxiv(2024)
摘要
Purpose: To develop and evaluate a transformer-based deep learning model for
the synthesis of nephrographic phase images in CT urography (CTU) examinations
from the unenhanced and urographic phases.
Materials and Methods: This retrospective study was approved by the local
Institutional Review Board. A dataset of 119 patients (mean ± SD age, 65
± 12 years; 75/44 males/females) with three-phase CT urography studies was
curated for deep learning model development. The three phases for each patient
were aligned with an affine registration algorithm. A custom model, coined
Residual transformer model for Nephrographic phase CT image synthesis (ResNCT),
was developed and implemented with paired inputs of non-contrast and urographic
sets of images trained to produce the nephrographic phase images, that were
compared with the corresponding ground truth nephrographic phase images. The
synthesized images were evaluated with multiple performance metrics, including
peak signal to noise ratio (PSNR), structural similarity index (SSIM),
normalized cross correlation coefficient (NCC), mean absolute error (MAE), and
root mean squared error (RMSE).
Results: The ResNCT model successfully generated synthetic nephrographic
images from non-contrast and urographic image inputs. With respect to ground
truth nephrographic phase images, the images synthesized by the model achieved
high PSNR (27.8 ± 2.7 dB), SSIM (0.88 ± 0.05), and NCC (0.98 ±
0.02), and low MAE (0.02 ± 0.005) and RMSE (0.042 ± 0.016).
Conclusion: The ResNCT model synthesized nephrographic phase CT images with
high similarity to ground truth images. The ResNCT model provides a means of
eliminating the acquisition of the nephrographic phase with a resultant 33
reduction in radiation dose for CTU examinations.
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