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Xue M, Zhang M, Li S, Zou Y, Zhu Q. Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography. BIOMEDICAL OPTICS EXPRESS 2023; 14:6072-6087. [PMID: 38021111 PMCID: PMC10659805 DOI: 10.1364/boe.502244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/11/2023] [Accepted: 10/11/2023] [Indexed: 12/01/2023]
Abstract
Ultrasound (US)-guided diffuse optical tomography (DOT) is a portable and non-invasive imaging modality for breast cancer diagnosis and treatment response monitoring. However, DOT data pre-processing and imaging reconstruction often require labor intensive manual processing which hampers real-time diagnosis. In this study, we aim at providing an automated US-assisted DOT pre-processing, imaging and diagnosis pipeline to achieve near real-time diagnosis. We have developed an automated DOT pre-processing method including motion detection, mismatch classification using deep-learning approach, and outlier removal. US-lesion information needed for DOT reconstruction was extracted by a semi-automated lesion segmentation approach combined with a US reading algorithm. A deep learning model was used to evaluate the quality of the reconstructed DOT images and a two-step deep-learning model developed earlier is implemented to provide final diagnosis based on US imaging features and DOT measurements and imaging results. The presented US-assisted DOT pipeline accurately processed the DOT measurements and reconstruction and reduced the procedure time to 2 to 3 minutes while maintained a comparable classification result with manually processed dataset.
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Affiliation(s)
- Minghao Xue
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Menghao Zhang
- Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Shuying Li
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Yun Zou
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Quing Zhu
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
- Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
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Zhang M, Li S, Xue M, Zhu Q. Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:086002. [PMID: 37638108 PMCID: PMC10457211 DOI: 10.1117/1.jbo.28.8.086002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 07/29/2023] [Accepted: 08/02/2023] [Indexed: 08/29/2023]
Abstract
Significance Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated great potential for breast cancer diagnosis in which real-time or near real-time diagnosis with high accuracy is desired. Aim We aim to use US-guided DOT to achieve an automated, fast, and accurate classification of breast lesions. Approach We propose a two-stage classification strategy with deep learning. In the first stage, US images and histograms created from DOT perturbation measurements are combined to predict benign lesions. Then the non-benign suspicious lesions are passed through to the second stage, which combine US image features, DOT histogram features, and 3D DOT reconstructed images for final diagnosis. Results The first stage alone identified 73.0% of benign cases without image reconstruction. In distinguishing between benign and malignant breast lesions in patient data, the two-stage classification approach achieved an area under the receiver operating characteristic curve of 0.946, outperforming the diagnoses of all single-modality models and of a single-stage classification model that combines all US images, DOT histogram, and imaging features. Conclusions The proposed two-stage classification strategy achieves better classification accuracy than single-modality-only models and a single-stage classification model that combines all features. It can potentially distinguish breast cancers from benign lesions in near real-time.
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Affiliation(s)
- Menghao Zhang
- Washington University in St. Louis, Department of Electrical and Systems Engineering, St. Louis, Missouri, United States
| | - Shuying Li
- Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri, United States
| | - Minghao Xue
- Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri, United States
| | - Quing Zhu
- Washington University in St. Louis, Department of Electrical and Systems Engineering, St. Louis, Missouri, United States
- Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri, United States
- Washington University School of Medicine, Department of Radiology, St. Louis, Missouri, United States
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Jiang J, Ackermann M, Russomanno E, Di Costanzo Mata A, Charbon E, Wolf M, Kalyanov A. Resolution and penetration depth of reflection-mode time-domain near infrared optical tomography using a ToF SPAD camera. BIOMEDICAL OPTICS EXPRESS 2022; 13:6711-6723. [PMID: 36589570 PMCID: PMC9774846 DOI: 10.1364/boe.470985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/29/2022] [Accepted: 09/29/2022] [Indexed: 06/17/2023]
Abstract
In a turbid medium such as biological tissue, near-infrared optical tomography (NIROT) can image the oxygenation, a highly relevant clinical parameter. To be an efficient diagnostic tool, NIROT has to have high spatial resolution and depth sensitivity, fast acquisition time, and be easy to use. Since many tissues cannot be penetrated by near-infrared light, such tissue needs to be measured in reflection mode, i.e., where light emission and detection components are placed on the same side. Thanks to the recent advance in single-photon avalanche diode (SPAD) array technology, we have developed a compact reflection-mode time-domain (TD) NIROT system with a large number of channels, which is expected to substantially increase the resolution and depth sensitivity of the oxygenation images. The aim was to test this experimentally for our SPAD camera-empowered TD NIROT system. Experiments with one and two inclusions, i.e., optically dense spheres of 5mm radius, immersed in turbid liquid were conducted. The inclusions were placed at depths from 10mm to 30mm and moved across the field-of-view. In the two-inclusion experiment, two identical spheres were placed at a lateral distance of 8mm. We also compared short exposure times of 1s, suitable for dynamic processes, with a long exposure of 100s. Additionally, we imaged complex geometries inside the turbid medium, which represented structural elements of a biological object. The quality of the reconstructed images was quantified by the root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and dice similarity. The two small spheres were successfully resolved up to a depth of 30mm. We demonstrated robust image reconstruction even at 1s exposure. Furthermore, the complex geometries were also successfully reconstructed. The results demonstrated a groundbreaking level of enhanced performance of the NIROT system based on a SPAD camera.
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Affiliation(s)
- Jingjing Jiang
- Biomedical Optics Research Laboratory, Dept. of Neonatology, University Hospital Zurich and University of Zurich, Switzerland
| | - Meret Ackermann
- Biomedical Optics Research Laboratory, Dept. of Neonatology, University Hospital Zurich and University of Zurich, Switzerland
| | - Emanuele Russomanno
- Biomedical Optics Research Laboratory, Dept. of Neonatology, University Hospital Zurich and University of Zurich, Switzerland
| | - Aldo Di Costanzo Mata
- Biomedical Optics Research Laboratory, Dept. of Neonatology, University Hospital Zurich and University of Zurich, Switzerland
| | - Edoardo Charbon
- Advanced Quantum Architecture Laboratory, EPFL, 2002 Neuchâtel, Switzerland
| | - Martin Wolf
- Biomedical Optics Research Laboratory, Dept. of Neonatology, University Hospital Zurich and University of Zurich, Switzerland
| | - Alexander Kalyanov
- Biomedical Optics Research Laboratory, Dept. of Neonatology, University Hospital Zurich and University of Zurich, Switzerland
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Li S, Zhang M, Xue M, Zhu Q. Difference imaging from single measurements in diffuse optical tomography: a deep learning approach. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:086003. [PMID: 36008881 PMCID: PMC9403167 DOI: 10.1117/1.jbo.27.8.086003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
SIGNIFICANCE "Difference imaging," which reconstructs target optical properties using measurements with and without target information, is often used in diffuse optical tomography (DOT) in vivo imaging. However, taking additional reference measurements is time consuming, and mismatches between the target medium and the reference medium can cause inaccurate reconstruction. AIM We aim to streamline the data acquisition and mitigate the mismatch problems in DOT difference imaging using a deep learning-based approach to generate data from target measurements only. APPROACH We train an artificial neural network to output data for difference imaging from target measurements only. The model is trained and validated on simulation data and tested with simulations, phantom experiments, and clinical data from 56 patients with breast lesions. RESULTS The proposed method has comparable performance to the traditional approach using measurements without mismatch between the target side and the reference side, and it outperforms the traditional approach using measurements when there is a mismatch. It also improves the target-to-artifact ratio and lesion localization in patient data. CONCLUSIONS The proposed method can simplify the data acquisition procedure, mitigate mismatch problems, and improve reconstructed image quality in DOT difference imaging.
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Affiliation(s)
- Shuying Li
- Washington University in St. Louis, Optical and Ultrasound Imaging Lab, Department of Biomedical Engineering, St. Louis, Missouri, United States
| | - Menghao Zhang
- Washington University in St. Louis, Optical and Ultrasound Imaging Lab, Department of Electrical and Systems Engineering, St. Louis, Missouri, United States
| | - Minghao Xue
- Washington University in St. Louis, Optical and Ultrasound Imaging Lab, Department of Biomedical Engineering, St. Louis, Missouri, United States
| | - Quing Zhu
- Washington University in St. Louis, Optical and Ultrasound Imaging Lab, Department of Biomedical Engineering, St. Louis, Missouri, United States
- Washington University School of Medicine, Department of Radiology, St. Louis, Missouri, United States
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Zou Y, Zeng Y, Li S, Zhu Q. Machine learning model with physical constraints for diffuse optical tomography. BIOMEDICAL OPTICS EXPRESS 2021; 12:5720-5735. [PMID: 34692211 PMCID: PMC8515969 DOI: 10.1364/boe.432786] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 08/06/2021] [Accepted: 08/09/2021] [Indexed: 05/02/2023]
Abstract
A machine learning model with physical constraints (ML-PC) is introduced to perform diffuse optical tomography (DOT) reconstruction. DOT reconstruction is an ill-posed and under-determined problem, and its quality suffers by model mismatches, complex boundary conditions, tissue-probe contact, noise etc. Here, for the first time, we combine ultrasound-guided DOT with ML to facilitate DOT reconstruction. Our method has two key components: (i) a neural network based on auto-encoder is adopted for DOT reconstruction, and (ii) physical constraints are implemented to achieve accurate reconstruction. Both qualitative and quantitative results demonstrate that the accuracy of the proposed method surpasses that of existing models. In a phantom study, compared with the Born conjugate gradient descent (Born-CGD) reconstruction method, the ML-PC method decreases the mean percentage error of the reconstructed maximum absorption coefficient from 16.41% to 13.4% for high contrast phantoms and from 23.42% to 9.06% for low contrast phantoms, with improved depth distribution of the target absorption maps. In a clinical study, better contrast was obtained between malignant and benign breast lesions, with the ratio of the medians of the maximum absorption coefficient improved from 1.63 to 2.22.
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Affiliation(s)
- Yun Zou
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri 63130, USA
| | - Yifeng Zeng
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri 63130, USA
| | - Shuying Li
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri 63130, USA
| | - Quing Zhu
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri 63130, USA
- Department of Radiology, Washington University School of Medicine, St. Louis 63110, USA
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Li S, Zhang M, Zhu Q. Ultrasound segmentation-guided edge artifact reduction in diffuse optical tomography using connected component analysis. BIOMEDICAL OPTICS EXPRESS 2021; 12:5320-5336. [PMID: 34513259 PMCID: PMC8407838 DOI: 10.1364/boe.428107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 05/02/2023]
Abstract
Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential value for breast cancer diagnosis and treatment response assessment. However, in clinical use, the chest wall, poor probe-tissue contact, and tissue heterogeneity can all cause image artifacts. These image artifacts, appearing commonly as hot spots in the non-lesion regions (edge artifacts), can decrease the reconstruction accuracy and cause misinterpretation of lesion images. Here we introduce an iterative, connected component analysis-based image artifact reduction algorithm. A convolutional neural network (CNN) is used to segment co-registered US images to extract the lesion location and size to guide the artifact reduction. We demonstrate its performance using Monte Carlo simulations on VICTRE digital breast phantoms and breast patient images. In simulated tissue mismatch models, this algorithm successfully reduces edge artifacts without significantly changing the reconstructed target absorption coefficients. With clinical data it improves the optical contrast between malignant and benign groups, from 1.55 without artifact reduction to 1.91 with artifact reduction. The proposed algorithm has a broad range of applications in other modality-guided DOT imaging.
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Affiliation(s)
- Shuying Li
- Department of Biomedical Engineering, Washington University in St. Louis, 1 Brookings Dr, St. Louis 63130, USA
| | - Menghao Zhang
- Department of Electrical & Systems Engineering, Washington University in St. Louis, 1 Brookings Dr, St. Louis 63130, USA
| | - Quing Zhu
- Department of Biomedical Engineering, Washington University in St. Louis, 1 Brookings Dr, St. Louis 63130, USA
- Department of Radiology, Washington University School of Medicine, 660 S Euclid Ave, St. Louis 63110, USA
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