Learning the Regularization in DCE-MR Image Reconstruction for
Functional Imaging of Kidneys
- URL: http://arxiv.org/abs/2109.07548v1
- Date: Wed, 15 Sep 2021 19:27:53 GMT
- Title: Learning the Regularization in DCE-MR Image Reconstruction for
Functional Imaging of Kidneys
- Authors: Aziz Ko\c{c}anao\u{g}ullar{\i}, Cemre Ariyurek, Onur Afacan, Sila
Kurugol
- Abstract summary: We propose a single image trained deep neural network to reduce MRI under-sampling artifacts without reducing the accuracy of functional imaging markers.
Proposed approach results in kidney biomarkers that are highly correlated with the ground truth markers estimated using the CS reconstruction.
- Score: 1.2890174947191904
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Kidney DCE-MRI aims at both qualitative assessment of kidney anatomy and
quantitative assessment of kidney function by estimating the tracer kinetic
(TK) model parameters. Accurate estimation of TK model parameters requires an
accurate measurement of the arterial input function (AIF) with high temporal
resolution. Accelerated imaging is used to achieve high temporal resolution,
which yields under-sampling artifacts in the reconstructed images. Compressed
sensing (CS) methods offer a variety of reconstruction options. Most commonly,
sparsity of temporal differences is encouraged for regularization to reduce
artifacts. Increasing regularization in CS methods removes the ambient
artifacts but also over-smooths the signal temporally which reduces the
parameter estimation accuracy. In this work, we propose a single image trained
deep neural network to reduce MRI under-sampling artifacts without reducing the
accuracy of functional imaging markers. Instead of regularizing with a penalty
term in optimization, we promote regularization by generating images from a
lower dimensional representation. In this manuscript we motivate and explain
the lower dimensional input design. We compare our approach to CS
reconstructions with multiple regularization weights. Proposed approach results
in kidney biomarkers that are highly correlated with the ground truth markers
estimated using the CS reconstruction which was optimized for functional
analysis. At the same time, the proposed approach reduces the artifacts in the
reconstructed images.
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