Near-infrared Diffuse Optical Tomography Research Articles

FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction.

Near-infrared diffuse optical tomography (DOT) is a promising functional modality for breast cancer imaging; however, the clinical translation of DOT is hampered by technical limitations. Specifically, conventional finite element method (FEM)-based optical image reconstruction approaches are time-consuming and ineffective in recovering full lesion contrast. To address this, we developed a deep learning-based reconstruction model (FDU-Net) comprised of a Fully connected subnet, followed by a convolutional encoder-Decoder subnet, and a U-Net for fast, end-to-end 3D DOT image reconstruction. The FDU-Net was trained on digital phantoms that include randomly located singular spherical inclusions of various sizes and contrasts. Reconstruction performance was evaluated in 400 simulated cases with realistic noise profiles for the FDU-Net and conventional FEM approaches. Our results show that the overall quality of images reconstructed by FDU-Net is significantly improved compared to FEM-based methods and a previously proposed deep-learning network. Importantly, once trained, FDU-Net demonstrates substantially better capability to recover true inclusion contrast and location without using any inclusion information during reconstruction. The model was also generalizable to multi-focal and irregularly shaped inclusions unseen during training. Finally, FDU-Net, trained on simulated data, could successfully reconstruct a breast tumor from a real patient measurement. Overall, our deep learning-based approach demonstrates marked superiority over the conventional DOT image reconstruction methods while also offering over four orders of magnitude acceleration in computational time. Once adapted to the clinical breast imaging workflow, FDU-Net has the potential to provide real-time accurate lesion characterization by DOT to assist the clinical diagnosis and management of breast cancer.

Method to improve the localization accuracy and contrast recovery of lesions in separately acquired X-ray and diffuse optical tomographic breast imaging.

Near-infrared diffuse optical tomography (DOT) has the potential to improve the accuracy of breast cancer diagnosis and aid in monitoring the response of breast tumors to chemotherapy by providing hemoglobin-based functional imaging. The use of structural lesion priors derived from clinical breast imaging methods, such as mammography, can improve recovery of tumor optical contrast; however, accurate lesion prior placement is essential to take full advantage of prior-guided DOT image reconstruction. Simultaneous optical and anatomical imaging may not always be possible or desired, which can make the accurate registration of the lesion prior challenging. In this paper, we present a three-step lesion prior scanning approach to facilitate improved accuracy in lesion localization based on the optical contrast quantified by the total hemoglobin concentration (HbT) for non-simultaneous multimodal DOT and digital breast tomosynthesis (DBT) imaging. In three challenging breast cancer patient cases, where no clear optical contrast was present initially, we have demonstrated consistent improvement in the recovered HbT lesion contrast by utilizing this method.

Reconstruction and Localization of Tumors in Breast Optical Imaging via Convolution Neural Network Based on Batch Normalization Layers

The Near-Infrared (NIR) Diffuse Optical Tomography (DOT) aims to reconstruct optical-property images of tissue and identify and localize breast tumors. This study has developed an efficient and fast DOT reconstruction method based on Deep Learning (DL) algorithm. The DL has already been applied in DOT application with limitations such as a limited dataset and the reconstruction of absorption coefficients only. We solve the problem of a limited dataset by generating a large dataset with multiple phantoms and inclusions at various positions. Moreover, a single Deep Neural Network (DNN) model has been designed to reconstruct absorption coefficients and scattering coefficients. For evaluation of the proposed DNN models, the phantom experimental dataset has been used where the results of the proposed DNN models outperform the results of the Tikhonov Regularization (TR) method and other Artificial Neural Networks (ANN). Moreover, it is shown that the DNN model with batch normalization layer results in improved spatial resolution, based on Contrast-and-Size Detail (CSD) analysis, as compared to DNN models without batch normalization layers.

A Narrative Review of Ultrasound Technologies for the Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer.

The incidence and mortality rate of breast cancer (BC) in women currently ranks first worldwide, and neoadjuvant chemotherapy (NAC) is widely used in patients with BC. A variety of imaging assessment methods have been used to predict and evaluate the response to NAC. Ultrasound (US) has many advantages, such as being inexpensive and offering a convenient modality for follow-up detection without radiation emission. Although conventional grayscale US is typically used to predict the response to NAC, this approach is limited in its ability to distinguish viable tumor tissue from fibrotic scar tissue. Contrast-enhanced ultrasound (CEUS) combined with a time-intensity curve (TIC) not only provides information on blood perfusion but also reveals a variety of quantitative parameters; elastography has the potential capacity to predict NAC efficiency by evaluating tissue stiffness. Both CEUS and elastography can greatly improve the accuracy of predicting NAC responses. Other US techniques, including three-dimensional (3D) techniques, quantitative ultrasound (QUS) and US-guided near-infrared (NIR) diffuse optical tomography (DOT) systems, also have advantages in assessing NAC response. This paper reviews the different US technologies used for predicting NAC response in BC patients based on the previous literature.

Real-Time Dual-Wavelength Time-Resolved Diffuse Optical Tomography System for Functional Brain Imaging Based on Probe-Hosted Silicon Photomultipliers.

Near-infrared diffuse optical tomography is a non-invasive photonics-based imaging technology suited to functional brain imaging applications. Recent developments have proved that it is possible to build a compact time-domain diffuse optical tomography system based on silicon photomultipliers (SiPM) detectors. The system presented in this paper was equipped with the same eight SiPM probe-hosted detectors, but was upgraded with six injection fibers to shine the sample at several points. Moreover, an automatic switch was included enabling a complete measurement to be performed in less than one second. Further, the system was provided with a dual-wavelength (670 and 820 ) light source to quantify the oxy- and deoxy-hemoglobin concentration evolution in the tissue. This novel system was challenged against a solid phantom experiment, and two in-vivo tests, namely arm occlusion and motor cortex brain activation. The results show that the tomographic system makes it possible to follow the evolution of brain activation over time with a 1-resolution.

Optimal breast cancer diagnostic strategy using combined ultrasound and diffuse optical tomography.

Ultrasound (US)-guided near-infrared diffuse optical tomography (DOT) has demonstrated great potential as an adjunct breast cancer diagnosis tool to US imaging alone, especially in reducing unnecessary benign biopsies. However, DOT data processing and image reconstruction speeds remain slow compared to the real-time speed of US. Real-time or near real-time diagnosis with DOT is an important step toward the clinical translation of US-guided DOT. Here, to address this important need, we present a two-stage diagnostic strategy that is both computationally efficient and accurate. In the first stage, benign lesions are identified in near real-time by use of a random forest classifier acting on the DOT measurements and the radiologists' US diagnostic scores. Any lesions that cannot be reliably classified by the random forest classifier will be passed on to the second stage which begins with image reconstruction. Functional information from the reconstructed hemoglobin concentrations is employed by a Support Vector Machine (SVM) classifier for diagnosis at the end of the second stage. This two-step classification approach which combines both perturbation data and functional features, results in improved classification, as denoted by the receiver operating characteristic (ROC) curve. Using this two-step approach, the area under the ROC curve (AUC) is 0.937 ± 0.009, with a sensitivity of 91.4% and specificity of 85.7%. In comparison, using functional features and US score yields an AUC of 0.892 ± 0.027, with a sensitivity of 90.2% and specificity of 74.5%. Most notably, the specificity is increased by more than 10% due to the implementation of the random forest classifier.

Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging.

Near-infrared diffuse optical tomography (DOT) has demonstrated a great potential as an adjunct modality for differentiation of malignant and benign breast lesions and for monitoring treatment response in patients with locally advanced breast cancers. The path toward commercialization of DOT techniques depends upon the improvement of robustness and user-friendliness of this technique in hardware and software. In this study, we introduce our recently developed ultrasound-guided DOT system, which has been improved in system compactness, robustness, and user-friendliness by custom-designed electronics, automated data preprocessing, and implementation of a new two-step reconstruction algorithm. The system performance has been tested with several sets of solid and blood phantoms and the results show accuracy in reconstructed absorption coefficients as well as blood oxygen saturation. A clinical example of a breast cancer patient, who was undergoing neoadjuvant chemotherapy, is given to demonstrate the system performance.

A spread spectrum approach to time-domain near-infrared diffuse optical imaging using inexpensive optical transceiver modules.

We introduce a compact time-domain system for near-infrared spectroscopy using a spread spectrum technique. The proof-of-concept single channel instrument utilises a low-cost commercially available optical transceiver module as a light source, controlled by a Kintex 7 field programmable gate array (FPGA). The FPGA modulates the optical transceiver with maximum-length sequences at line rates up to 10Gb/s, allowing us to achieve an instrument response function with full width at half maximum under 600ps. The instrument is characterised through a set of detailed phantom measurements as well as proof-of-concept in vivo measurements, demonstrating performance comparable with conventional pulsed time-domain near-infrared spectroscopy systems.

Impact of errors in experimental parameters on reconstructed breast images using diffuse optical tomography.

Near-infrared diffuse optical tomography (NIR-DOT) is an emerging technology that offers hemoglobin based, functional imaging tumor biomarkers for breast cancer management. The most promising clinical translation opportunities are in the differential diagnosis of malignant vs. benign lesions, and in early response assessment and guidance for neoadjuvant chemotherapy. Accurate quantification of the tissue oxy- and deoxy-hemoglobin concentration across the field of view, as well as repeatability during longitudinal imaging in the context of therapy guidance, are essential for the successful translation of NIR-DOT to clinical practice. The ill-posed and ill-condition nature of the DOT inverse problem makes this technique particularly susceptible to model errors that may occur, for example, when the experimental conditions do not fully match the assumptions built into the image reconstruction process. To evaluate the susceptibility of DOT images to experimental errors that might be encountered in practice for a parallel-plate NIR-DOT system, we simulated 7 different types of errors, each with a range of magnitudes. We generated simulated data by using digital breast phantoms derived from five actual mammograms of healthy female volunteers, to which we added a 1-cm tumor. After applying each of the experimental error types and magnitudes to the simulated measurements, we reconstructed optical images with and without structural prior guidance and assessed the overall error in the total hemoglobin concentrations (HbT) and in the HbT contrast between the lesion and surrounding area vs. the best-case scenarios. It is found that slight in-plane probe misalignment and plate rotation did not result in large quantification errors. However, any out-of-plane probe tilting could result in significant deterioration in lesion contrast. Among the error types investigated in this work, optical images were the least likely to be impacted by breast shape inaccuracies but suffered the largest deterioration due to cross-talk between signal channels. However, errors in optical images could be effectively controlled when experimental parameters were properly estimated during data acquisition and accounted for in the image processing procedure. Finally, optical images recovered using structural priors were, in general, less susceptible to experimental errors; however, lesion contrasts were more sensitive to errors when tumor locations were used as a priori info. Findings in this simulation study can provide guidelines for system design and operation in optical breast imaging studies.

Synthesis and characterization of breast-phantom-based gelatine-glutaraldehyde-TiO2 as a test material for the application of breast cancer diagnosis

Synthesis of breast-phantom-based on gelatine-glutaraldehyde-TiO2 as testing material of breast cancer diagnosis using Near Infrared-Diffuse Optical Tomography (NIR-DOT) is presented. Glutaraldehyde (GA) is added to obtain optimum breast phantom which has same elasticity modulus with mammae. First, synthesis is conducted by mixing gelatine with various amounts of 1 g, 2 g and 3 g with saline solution on 40° C temperature for 30 minutes until they become homogenous. Next, GA with concentration of 0.5 and 1.0% is added. The characterization includes FTIR test, physical test, and mechanical test used to identify group of gelatine’s functions. Elasticity modulus of breast phantom of gelatine composition 2 g and 0.5% GA is obtained at 53.46 kPA which is the approximation of mammae culture elasticity. This composition is chosen to synthesise the next step. In the second step, TiO2 is added with variation of 0.01 g, 0.015 g, 0.02 g, 0.025 g, and 0,03 g. With this variation, it is aimed to get a breast phantom providing image with optimum absorption. The test of this material uses Differential Scanning Calorimetry (DSC), homogeneity test, and analysis of coefficient absorption. The result shows the sample has a good thermal property in the range of 40 – 70° C with a good homogeneity and absorption coefficient of 0.4 mm-1.

Overview of diffuse optical tomography and its clinical applications.

Near-infrared diffuse optical tomography (DOT), one of the most sophisticated optical imaging techniques for observations through biological tissue, allows 3-D quantitative imaging of optical properties, which include functional and anatomical information. With DOT, it is expected to be possible to overcome the limitations of conventional near-infrared spectroscopy (NIRS) as well as offering the potential for diagnostic optical imaging. However, DOT has been under development for more than 30 years, and the difficulties in development are attributed to the fact that light is strongly scattered and that diffusive photons are used for the image reconstruction. The DOT algorithm is based on the techniques of inverse problems. The radiative transfer equation accurately describes photon propagation in biological tissue, while, because of its high computation load, the diffusion equation (DE) is often used as the forward model. However, the DE is invalid in low-scattering and/or highly absorbing regions and in the vicinity of light sources. The inverse problem is inherently ill-posed and highly undetermined. Here, we first summarize NIRS and then describe various approaches in the efforts to develop accurate and efficient DOT algorithms and present some examples of clinical applications. Finally, we discuss the future prospects of DOT.

A Comparison of Near-Infrared Diffuse Optical Imaging and 18F-FDG PET/CT for the Early Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy.

In recent years, neoadjuvant chemotherapy (NAC) before surgery has become the standard of care for patients who present with advanced breast cancers or multiple lesions, in order to control distant metastases as well as reduce breast tumor size before surgery ([1][1]). The remaining medical oncology

Abstract P4-03-06: Near-infrared diffuse optical imaging for early prediction to neoadjuvant chemotherapy in patients with primary breast cancer

Abstract Background: Diffuse optical spectroscopic imaging (DOSI) can be exploited as a marker of tumor blood volume quantified by tissue hemoglobin (tHb) concentration. In DOSI, frequent measurement is possible for breast cancer patients because of its non-invasiveness. The tHb concentration determined by DOSI is expected to be a new biomarker for prediction of breast cancer response to neoadjuvant chemotherapy (NAC). Purpose: Our objective is to determine whether early change of tumor tHb concentration predicts pathological complete response (pCR) to NAC in patients with operable breast cancer. Methods: In a prospective study, one hundred patients with primary breast cancer were enrolled for primary objective analysis. The regimens of NAC were according to the standard of care. Patients underwent sequential scans using DOSI at baseline, after 1st course and 2nd course of chemotherapy. The mean value of tHb (tHbmean) concentration of the targeted lesion was measured and the percentage change in tHbmean (ΔtHbmean) concentration was calculated. Receiver operating curve analysis demonstrated diagnostic performance of DOSI for predicting a pCR. Results: In interim analysis, it was regarded as a good outcome that area under the curve (AUC) for ΔtHbmean after 1nd course was 0.797 (SE 0.104, 95%CI 0.633-0.911), and after 2st course was 0.867 (SE 0.06, 95%CI 0.715-0.956). Conclusion: DOSI could predict accurately a pCR to neoadjuvant chemotherapy in patients with primary breast cancer. Citation Format: Ogura H, Yoshizawa N, Ueda S, Hosokawa Y, Matsunuma R, Tochikubo J, Nasu H, Shigekawa T, Takeuchi H, Osaki A, Saeki T, Yoshimoto K, Ohmae E, Suzuki T, Ueda Y, Yamashita Y, Sakahara H. Near-infrared diffuse optical imaging for early prediction to neoadjuvant chemotherapy in patients with primary breast cancer. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P4-03-06.

Characterization of structural-prior guided optical tomography using realistic breast models derived from dual-energy x-ray mammography.

Multi-spectral near-infrared diffuse optical tomography (DOT) is capable of providing functional tissue assessment that can complement structural mammographic images for more comprehensive breast cancer diagnosis. To take full advantage of the readily available sub-millimeter resolution structural information in a multi-modal imaging setting, an efficient x-ray/optical joint image reconstruction model has been proposed previously to utilize anatomical information from a mammogram as a structural prior. In this work, we develop a complex digital breast phantom (available at http://openjd.sf.net/digibreast) based on direct measurements of fibroglandular tissue volume fractions using dual-energy mammographic imaging of a human breast. We also extend our prior-guided reconstruction algorithm to facilitate the recovery of breast tumors, and perform a series of simulation-based studies to systematically evaluate the impact of lesion sizes and contrasts, tissue background, mesh resolution, inaccurate priors, and regularization parameters, on the recovery of breast tumors using multi-modal DOT/x-ray measurements. Our studies reveal that the optical property estimation error can be reduced by half by utilizing structural priors; the minimum detectable tumor size can also be reduced by half when prior knowledge regarding the tumor location is provided. Moreover, our algorithm is shown to be robust to false priors on tumor location.

A fast SPAD-based small animal imager for early-photon diffuse optical tomography.

Photon scatter is the dominant light transport process in biological tissue and is well understood to degrade imaging performance in near-infrared diffuse optical tomography. Measurement of photons arriving at early times following a short laser pulse is considered to be an effective method to improve this limitation, i.e. by systematically selecting photons that have experienced fewer scattering events. Previously, we tested the performance of single photon avalanche photodiode (SPAD) in measurement of early transmitted photons through diffusive media and showed that it outperformed photo-multiplier tube (PMT) systems in similar configurations, principally due to its faster temporal response. In this paper, we extended this work and developed a fast SPAD-based time-resolved diffuse optical tomography system. As a first validation of the instrument, we scanned an optical phantom with multiple absorbing inclusions and measured full time-resolved data at 3240 scan points per axial slice. We performed image reconstruction with very early-arriving photon data and showed significant improvements compared to time-integrated data. Extension of this work to mice in vivo and measurement of time-resolved fluorescence data is the subject of ongoing research.

Near-infrared optical imaging of cancer vascular remodeling after antiangiogenic therapy

Near-infrared diffuse optical imaging (DOSI) has been recently accepted as a method to assess tumor vascularity and oxygenation by measuring tissue hemoglobin (Hb) concentration. It is expected that DOSI could be used to monitor changes in vascularity after antiangiogenic therapy. A patient with advanced breast cancer was treated with single-agent bevacizumab followed by addition of weekly paclitaxel to it. DOSI was performed in the tumor-bearing breast and contralateral normal breast at baseline, 3, 6, 12, 24 h, and then daily for 1 week. Images of a tumor-to-normal ratio of tHb (rtHb) were constructed for identifying a tumor lesion. Serial images of rtHb showed a tumor lesion which corresponded to a hot spot. The level of rtHb rapidly decreased within several hours after administration of single-agent bevacizumab and then persisted at low levels during treatment. From day 2, the value of rtHb gradually increased and peaked on day 5. This trend may be explained considering sequential images of rtHb indicating the inhibition of angiogenesis due to antiangiogenic therapy. We conducted a clinical study using single-agent bevacizumab followed by neoadjuvant chemotherapy in primary breast cancer patients to understand vascular remodeling after antiangiogenic agent at very early time points using DOSI.

Nonquadratic penalization improves near-infrared diffuse optical tomography

A new approach that can easily incorporate any generic penalty function into the diffuse optical tomographic image reconstruction is introduced to show the utility of nonquadratic penalty functions. The penalty functions that were used include quadratic (ℓ2), absolute (ℓ1), Cauchy, and Geman-McClure. The regularization parameter in each of these cases was obtained automatically by using the generalized cross-validation method. The reconstruction results were systematically compared with each other via utilization of quantitative metrics, such as relative error and Pearson correlation. The reconstruction results indicate that, while the quadratic penalty may be able to provide better separation between two closely spaced targets, its contrast recovery capability is limited, and the sparseness promoting penalties, such as ℓ1, Cauchy, and Geman-McClure have better utility in reconstructing high-contrast and complex-shaped targets, with the Geman-McClure penalty being the most optimal one.

Ultrasound-Guided Diffuse Optical Tomography (DOT) of Invasive Breast Carcinoma: Does Tumour Total Haemoglobin Concentration Contribute to the Prediction of Axillary Lymph Node Status?

To prospectively study the ultrasound-guided near-infrared diffuse optical tomography (DOT) findings of the total haemoglobin concentration (THC) detected in invasive breast carcinomas and its contribution to the prediction of axillary lymph node (LN) status.

Spatial-frequency-compression scheme for diffuse optical tomography with dense sampling dataset

Dense sampling of illumination and detection offers an effective way of improving the image-reconstruction performance of near-infrared diffuse optical tomography (DOT) at a cost of lengthy computation times. In this paper, we describe a fast DOT scheme for reconstructing the absorption coefficient image of a slab medium from dense sampling of both illumination and detection in the noncontact DOT. The proposed method is carried out with spatial-frequency encoding in both the source and detection spaces, and involves a spatial-frequency-compression (SFC) strategy for selecting the useful spatial frequency based on the tissue transfer function. The method is expected to considerably reduce the calculation time for reconstruction while improving the quality of the reconstructed images. Results from the simulated data show that the speed for absorption reconstruction with the proposed SFC method is more than 400 times faster than that with the conventional one. A noncontact DOT system for dense sampling of both illumination and detection is developed by using laser raster scanning and CCD-based data acquisition. Experimental measurements on several solid phantoms demonstrate that a high quantitativeness ratio can be obtained from the proposed method thanks to reduction of the ill-posedness of the inverse calculation. It takes less than 20 s for the proposed method to experimentally reconstruct one absorption image from a 256×256-sized dataset, which would take a few hours with the conventional method.

Flexible near-infrared diffuse optical tomography with varied weighting functions of edge-preserving regularization

In this paper, a flexible edge-preserving regularization algorithm based on the finite element method is proposed to reconstruct the optical-property images of near-infrared diffuse optical tomography. This regularization algorithm can easily incorporate with varied weighting functions, such as a generalized Lorentzian function, an exponential function, or a generalized total variation function. To evaluate the performance, results obtained from Tikhonov or edge-preserving regularization are compared with each other. As found, the edge-preserving regularization with the generalized Lorentzian function is more attractive than that with other functions for the estimation of absorption-coefficient images concerning functional tomographic images to discover functional information of tested phantoms/tissues.