# DL-based CT image formation with characterization and control of resolution and noise

> **NIH NIH R21** · JOHNS HOPKINS UNIVERSITY · 2024 · $200,788

## Abstract

Deep learning (DL) based CT image formation methods have proliferated over the past few years. The
existing approaches mostly follow the paradigm established in computer vision, and build a deep neural
network (DNN) with standard modules that capture salient image features useful for computer vision tasks.
These standard modules also work very well for CT images, placing DL-based CT image formation methods at
the forefront of research and innovation. However, current DNNs are oblivious to the fact that CT images,
unlike natural images, must be interpretable by a radiologist to make a diagnosis. CT image interpretation is
affected by image features such as image resolution and noise variance-covariance, which are under exploited
by the standard modules from computer vision. Consequently, current DL-based CT image formation has no
direct characterization, let alone prospective control, of image resolution and noise variance-covariance. These
properties can only be assessed after an image is generated, but resolution/noise has no direct influence
during the image formation process. In this proposal, we challenge this established paradigm and propose an
innovative DL framework named GradDNN to (1) characterize the resolution and noise properties of a DNN’s
output during network training or parameter fine-tuning, and to (2) guide the image formation process so that
the output has the desired resolution/noise properties. GradDNN (which stands for gradient + DNN) applies
network linearization, i.e., gradient computation, to a mother DNN to extract local resolution and noise
properties of the images generated by the mother, and make these properties available during network training
and parameter fine tuning. The linearization method for analyzing nonlinear systems such as a DNN has never
been attempted before. Conceptually, GradDNN associates a daughter module to any mother DNN for
noise/resolution characterization, thereby making the resulting network CT-specific. We will develop GradDNN
and demonstrate its capability in the context of two important clinical tasks: (1) mitigation of calcium blooming
in coronary CT angiography, and (2) low contrast lesion detection in abdominal CT, both of which have high
requirements on resolution and noise. Data for DL network training will be generated using digitally augmented
patient data prospectively collected at two sites. Image quality comparison between the mother DNN alone and
the mother+daughter duo, using a number of effective mother DNN architectures, will be carried out to
demonstrate the additional gain of DL with joint resolution/noise learning. The comparison will use both digitally
augmented patient data and real patient data to further establish robustness and generalizability. In this
exploratory proposal, we focus on DL networks that perform image-to-image transformation. However, the
learning framework using GradDNN is general and can be applied to DL networks that perform direct
projection-to-ima...

## Key facts

- **NIH application ID:** 10911914
- **Project number:** 5R21EB033426-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** JINGYAN XU
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $200,788
- **Award type:** 5
- **Project period:** 2023-09-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10911914

## Citation

> US National Institutes of Health, RePORTER application 10911914, DL-based CT image formation with characterization and control of resolution and noise (5R21EB033426-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10911914. Licensed CC0.

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