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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Multi-scale perceptual modulation network for low-dose computed tomography denoising.

Jiexing Huang¹, Anni Zhong², Yujian Liu¹

¹Department of Radiation Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

Quantitative Imaging in Medicine and Surgery

|December 19, 2024

Summary

This summary is machine-generated.

This study introduces a novel deep learning network for low-dose computed tomography (LDCT) denoising. The developed multi-scale perceptual modulation network (MSPMnet) effectively reduces noise and artifacts while preserving image quality, outperforming existing methods.

Keywords:

Low-dose computed tomography (LDCT)decomposable convolution denoising modulation multi-scale perceptual

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Low-dose computed tomography (LDCT) minimizes radiation exposure but introduces noise and artifacts, compromising diagnostic accuracy.
Existing convolutional neural networks (CNNs) for LDCT denoising have limited receptive fields, hindering performance.
Increasing kernel size in CNNs improves performance but significantly raises computational costs.

Purpose of the Study:

To develop a novel LDCT denoising CNN with an enlarged receptive field and reduced computational complexity.
To introduce a multi-scale perceptual modulation network (MSPMnet) capable of efficient receptive field expansion and multi-scale feature capture.

Main Methods:

Developed a multi-head decomposable convolution (MHDC) module to efficiently expand the receptive field and capture multi-scale features.
MHDC couples maximum-pooling with depth-wise convolutions and decomposes large 2D kernels into 1D kernels for computational efficiency.
Introduced a receptive field-ramp mechanism to progressively model long-range dependencies with increasing network depth.

Main Results:

Evaluated on the Mayo Clinic dataset, MSPMnet demonstrated superior visual and quantitative denoising performance compared to conventional algorithms, CNNs, and Transformers.
MSPMnet effectively reduced noise and artifacts while preserving crucial image structures, edges, and textures.
Achieved the lowest RMSE (8.3094±1.9325) and highest PSNR (33.8525±1.8213 dB), SSIM (0.9309±0.0272), and FSIM (0.9699±0.0113).

Conclusions:

The proposed MSPMnet offers a significant advancement in LDCT denoising.
It effectively removes noise and artifacts while maintaining high image fidelity, outperforming current state-of-the-art methods.
MSPMnet presents a computationally efficient solution for high-quality LDCT image reconstruction.