# Optimizing Mobile Photon-Counting CT Image Quality via Deep Learning for Neuro Intensive Care Unit

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $450,632

## Abstract

Mobile CT scanners are routinely used in the neuro intensive care unit (ICU) for critically ill patients to avoid
morbidity and adverse events associated with patient transport. The image quality of mobile CT is inferior to a
fixed MDCT in terms of image noise, spatial resolution, soft tissue contrast, and susceptibility to artifacts from
beam hardening, motion, metallic implants, and truncation. Reduced image quality may compromise care and
necessitate transport to a fixed scanner. For example, on a mobile CT scanner, it is difficult to diagnose small
infarcts or hemorrhage, or to differentiate between intracranial hemorrhage and contrast extravasation after
endovascular processes.
 In this project, we will leverage the benefits from an FDA-approved mobile photon counting CT (PCCT), as
well as deep learning-based image reconstruction algorithms to improve the image quality of the mobile PCCT
to or beyond that of a fixed scanner. The multi-spectral and high-resolution features of the mobile PCCT will be
explored and combined with deep learning algorithms for noise and artifacts reduction.
 In this project, the following aims will be investigated to achieve our goal to match the image quality of a
mobile PCCT to a fixed scanner: (1) We will develop low-dose, high-resolution deep learning-based
reconstruction algorithms to reduce the noise and improve gray-white matter contrast in the mobile PCCT; (2)
We will develop methods for material decomposition with reduced noise amplification and spectral optimization
to overcome beam hardening artifacts and achieve discrimination between calcium/contrast/hemorrhage; (3) We
will develop deep learning-based algorithms for image artifacts correction to tackle artifacts that are more
frequent on a mobile CT, including motion and metal artifacts; (4) To validate the methods, the optimized mobile
PCCT images will be compared with fixed CT images by trained radiologists.

## Key facts

- **NIH application ID:** 10979477
- **Project number:** 1R01EB035394-01A1
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Rajiv Gupta
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $450,632
- **Award type:** 1
- **Project period:** 2024-07-01 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10979477

## Citation

> US National Institutes of Health, RePORTER application 10979477, Optimizing Mobile Photon-Counting CT Image Quality via Deep Learning for Neuro Intensive Care Unit (1R01EB035394-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10979477. Licensed CC0.

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