|
1
|
Lanini L, Kalyanov A, Ackermann M, Russomanno E, Mata ADC, Wolf M, Jiang J. Time Domain Near-Infrared Optical Tomography Utilizing Full Temporal Data: A Simulation Study. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1438:173-178. [PMID: 37845457 DOI: 10.1007/978-3-031-42003-0_27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2023]
Abstract
The analysis of full temporal data in time-domain near-infrared optical tomography (TD NIROT) measurements enables valuable information to be obtained about tissue properties with good temporal and spatial resolution. However, the large amount of data obtained is not easy to handle in the image reconstruction. The goal of the project is to employ full-temporal data from a TD NIROT modality. We improved TD data-based 3D image reconstruction and compared the performance with other methods using frequency domain (FD) and temporal moments. The iterative reconstruction algorithm was evaluated in simulations with both noiseless and noisy in-silico data. In the noiseless cases, a superior image quality was achieved by the reconstruction using full temporal data, especially when dealing with inclusions at 20 mm and deeper in the tissue. When noise similar to measured data was present, the quality of the recovered image from full temporal data was no longer superior to the one obtained from the analysis of FD data and temporal moments. This indicates that denoising methods for TD data should be developed. In conclusion, TD data contain richer information and yield better image quality.
Collapse
Affiliation(s)
- Letizia Lanini
- Department of Physics, ETH Zürich, Zürich, Switzerland.
- Biomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich, University of Zürich, Zürich, Switzerland.
| | - Alexander Kalyanov
- Biomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Meret Ackermann
- Biomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Emanuele Russomanno
- Biomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Aldo Di Costanzo Mata
- Biomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Martin Wolf
- Biomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Jingjing Jiang
- Biomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| |
Collapse
|
|
2
|
Abstract
Diffuse optical tomography using deep learning is an emerging technology that has found impressive medical diagnostic applications. However, creating an optical imaging system that uses visible and near-infrared (NIR) light is not straightforward due to photon absorption and multi-scattering by tissues. The high distortion levels caused due to these effects make the image reconstruction incredibly challenging. To overcome these challenges, various techniques have been proposed in the past, with varying success. One of the most successful techniques is the application of deep learning algorithms in diffuse optical tomography. This article discusses the current state-of-the-art diffuse optical tomography systems and comprehensively reviews the deep learning algorithms used in image reconstruction. This article attempts to provide researchers with the necessary background and tools to implement deep learning methods to solve diffuse optical tomography.
Collapse
|