Hybrid time-domain and continuous-wave diffuse optical tomography instrument with concurrent, clinical magnetic resonance imaging for breast cancer imaging

Enhanced model iteration algorithm with graph neural network for diffuse optical tomography

Diffuse optical tomography (DOT) employs near-infrared light to reveal the optical parameters of biological tissues. Due to the strong scattering of photons in tissues and the limited surface measurements, DOT reconstruction is severely ill-posed. The Levenberg-Marquardt (LM) is a popular iteration method for DOT, however, it is computationally expensive and its reconstruction accuracy needs improvement. In this study, we propose a neural model based iteration algorithm which combines the graph neural network with Levenberg-Marquardt (GNNLM), which utilizes a graph data structure to represent the finite element mesh. In order to verify the performance of the graph neural network, two GNN variants, namely graph convolutional neural network (GCN) and graph attention neural network (GAT) were employed in the experiments. The results showed that GCNLM performs best in the simulation experiments within the training data distribution. However, GATLM exhibits superior performance in the simulation experiments outside the training data distribution and real experiments with breast-like phantoms. It demonstrated that the GATLM trained with simulation data can generalize well to situations outside the training data distribution without transfer training. This offers the possibility to provide more accurate absorption coefficient distributions in clinical practice.

Self-Guided Algorithm for Fast Image Reconstruction in Photo-Magnetic Imaging: Artificial Intelligence-Assisted Approach

Previously, we introduced photomagnetic imaging (PMI) that synergistically utilizes laser light to slightly elevate the tissue temperature and magnetic resonance thermometry (MRT) to measure the induced temperature. The MRT temperature maps are then converted into absorption maps using a dedicated PMI image reconstruction algorithm. In the MRT maps, the presence of abnormalities such as tumors would create a notable high contrast due to their higher hemoglobin levels. In this study, we present a new artificial intelligence-based image reconstruction algorithm that improves the accuracy and spatial resolution of the recovered absorption maps while reducing the recovery time. Technically, a supervised machine learning approach was used to detect and delineate the boundary of tumors directly from the MRT maps based on their temperature contrast to the background. This information was further utilized as a soft functional a priori in the standard PMI algorithm to enhance the absorption recovery. Our new method was evaluated on a tissue-like phantom with two inclusions representing tumors. The reconstructed absorption map showed that the well-trained neural network not only increased the PMI spatial resolution but also improved the accuracy of the recovered absorption to as low as a 2% percentage error, reduced the artifacts by 15%, and accelerated the image reconstruction process approximately 9-fold.

Fluorescent Graphitic Carbon Nitride (g-C3N4)-Embedded Hyaluronic Acid Microgel Composites for Bioimaging and Cancer-Cell Targetability as Viable Theragnostic

Fluorescent graphitic carbon nitride (g-C3N4) doped with various heteroatoms, such as B, P, and S, named Bg-C3N4, Pg-C3N4, and Sg-C3N4, were synthesized with variable band-gap values as diagnostic materials. Furthermore, they were embedded within hyaluronic acid (HA) microgels as g-C3N4@HA microgel composites. The g-C3N4@HA microgels had a 0.5-20 μm size range that is suitable for intravenous administration. Bare g-C3N4 showed excellent fluorescence ability with 360 nm excitation wavelength and 410-460 emission wavelengths for possible cell imaging application of g-C3N4@HA microgel composites as diagnostic agents. The g-C3N4@HA-based microgels were non-hemolytic, and no clotting effects on blood cells or cell toxicity on fibroblasts were observed at 1000 μg/mL concentration. In addition, approximately 70% cell viability for SKMEL-30 melanoma cells was seen with Sg-C3N4 and its HA microgel composites. The prepared g-C3N4@HA and Sg-C3N4@HA microgels were used in cell imaging because of their excellent penetration capability for healthy fibroblasts. Furthermore, g-C3N4-based materials did not interact with malignant cells, but their HA microgel composites had significant penetration capability linked to the binding function of HA with the cancerous cells. Flow cytometry analysis revealed that g-C3N4 and g-C3N4@HA microgel composites did not interfere with the viability of healthy fibroblast cells and provided fluorescence imaging without any staining while significantly decreasing the viability of cancerous cells. Overall, heteroatom-doped g-C3N4@HA microgel composites, especially Sg-C3N4@HA microgels, can be safely used as multifunctional theragnostic agents for both diagnostic as well as target and treatment purposes in cancer therapy because of their fluorescent nature.

Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography

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.

One-dimensional convolutional neural network for Jacobian in Diffuse Optical Tomography

Non-linear least square minimization algorithms are often employed to solve Diffuse Optical Tomography (DOT) inverse problem. However, it is time-consuming to calculate the Jacobian matrix. This work has proposed a data-driven neural network method to improve computational efficiency. The singular value decomposition is employed to compute the updated Jacobian and a mapping from boundary measurements to the singular values based on a convolutional neural network (CNN) is learned to obtain the singular values. The method is validated with 3D numerical simulation data. We have demonstrated that the approach can save computation time compared to Adjoint method, and reconstructed absorption coefficient close to Adjoint method.Clinical Relevance- These results are not focused on clinical relevance currently, but in the future may be helpful to accelerant DOT reconstruction in clinic.

A Model-Based Iterative Learning Approach for Diffuse Optical Tomography

Diffuse optical tomography (DOT) utilises near-infrared light for imaging spatially distributed optical parameters, typically the absorption and scattering coefficients. The image reconstruction problem of DOT is an ill-posed inverse problem, due to the non-linear light propagation in tissues and limited boundary measurements. The ill-posedness means that the image reconstruction is sensitive to measurement and modelling errors. The Bayesian approach for the inverse problem of DOT offers the possibility of incorporating prior information about the unknowns, rendering the problem less ill-posed. It also allows marginalisation of modelling errors utilising the so-called Bayesian approximation error method. A more recent trend in image reconstruction techniques is the use of deep learning, which has shown promising results in various applications from image processing to tomographic reconstructions. In this work, we study the non-linear DOT inverse problem of estimating the (absolute) absorption and scattering coefficients utilising a 'model-based' learning approach, essentially intertwining learned components with the model equations of DOT. The proposed approach was validated with 2D simulations and 3D experimental data. We demonstrated improved absorption and scattering estimates for targets with a mix of smooth and sharp image features, implying that the proposed approach could learn image features that are difficult to model using standard Gaussian priors. Furthermore, it was shown that the approach can be utilised in compensating for modelling errors due to coarse discretisation enabling computationally efficient solutions. Overall, the approach provided improved computation times compared to a standard Gauss-Newton iteration.

Compact fiber-free parallel-plane multi-wavelength diffuse optical tomography system for breast imaging

To facilitate the clinical applicability of the diffuse optical inspection device, a compact multi-wavelength diffuse optical tomography system for breast imaging (compact-DOTB) with a fiber-free parallel-plane structure was designed and fabricated for acquiring three-dimensional optical properties of the breast in continuous-wave mode. The source array consists of 56 surface-mounted micro light-emitting diodes (LEDs), each integrating three wavelengths (660, 750, and 840 nm). The detector array is arranged with 56 miniaturized surface-mounted optical sensors, each encapsulating a high-sensitivity photodiode (PD) and a low-noise current amplifier with a gain of 24×. The system provides 3,136 pairs of source-detector measurements at each wavelength, and the fiber-free design largely ensures consistency between source/detection channels while effectively reducing the complexity of system operation and maintenance. We have evaluated the compact-DOTB system's characteristics and demonstrated its performance in terms of reconstruction positioning accuracy and recovery contrast with breast-sized phantom experiments. Furthermore, the breast cancer patient studies have been carried out, and the quantitative results indicate that the compact-DOTB system is able to observe the changes in the functional tissue components of the breast after receiving the neoadjuvant chemotherapy (NAC), demonstrating the great potential of the proposed compact system for clinical applications, while its cost and ease of operation are competitive with the existing breast-DOT devices.

Multiwavelength photo-magnetic imaging algorithm improved for direct chromophore concentration recovery using spectral constraints

Multiwavelength photo-magnetic imaging (PMI) is a novel combination of diffuse optics and magnetic resonance imaging, to the best of our knowledge, that yields tissue chromophore concentration maps with high resolution and quantitative accuracy. Here, we present the first experimental results, to the best of our knowledge, obtained using a spectrally constrained PMI image reconstruction method, where chromophore concentration maps are directly recovered, unlike the conventional two-step approach that requires an intermediate step of reconstructing wavelength-dependent absorption coefficient maps. The imposition of the prior spectral information into the PMI inverse problem improves the reconstructed image quality and allows recovery of highly quantitative concentration maps, which are crucial for effective cancer detection and characterization. The obtained results demonstrate the higher performance of the direct reconstruction method. Indeed, the reconstructed concentration maps are not only of higher quality but also obtained approximately 2 times faster than the conventional method.

Objective Numerical Evaluation of Diffuse, Optically Reconstructed Images Using Structural Similarity Index

Diffuse optical tomography is emerging as a non-invasive optical modality used to evaluate tissue information by obtaining the optical properties' distribution. Two procedures are performed to produce reconstructed absorption and reduced scattering images, which provide structural information that can be used to locate inclusions within tissues with the assistance of a known light intensity around the boundary. These methods are referred to as a forward problem and an inverse solution. Once the reconstructed image is obtained, a subjective measurement is used as the conventional way to assess the image. Hence, in this study, we developed an algorithm designed to numerically assess reconstructed images to identify inclusions using the structural similarity (SSIM) index. We compared four SSIM algorithms with 168 simulated reconstructed images involving the same inclusion position with different contrast ratios and inclusion sizes. A multiscale, improved SSIM containing a sharpness parameter (MS-ISSIM-S) was proposed to represent the potential evaluation compared with the human visible perception. The results indicated that the proposed MS-ISSIM-S is suitable for human visual perception by demonstrating a reduction of similarity score related to various contrasts with a similar size of inclusion; thus, this metric is promising for the objective numerical assessment of diffuse, optically reconstructed images.

Chromophore reconstruction at depth in bilayered media: a method for quantification

We report a method for deriving the absolute value of absorption coefficients at depth in bilayered media. The method was simplified from that of time-resolved diffuse optical tomography (TR-DOT) into one dimension to validate and set up the main parameters with the help of simulations, and to test it in an easy preclinical model. The method was applied to buried flaps as used in reconstructive surgery, and absolute chromophore concentrations in the flap and in the upper (skin and fat) layer were derived. The encouraging results obtained lay a foundation for developing more complex multidimensional models.

Effect and correction of optode coupling errors in breast imaging using diffuse optical tomography

In diffuse optical tomography (DOT) and spectroscopy (DOS) using handheld probes, tissue curvature can cause bad fiber-to-tissue contact. Understanding and minimizing image artifacts caused by these coupling errors would significantly improve DOT and DOS image quality. In this work, we utilized Monte Carlo simulations and experiments with gelatin-Intralipid phantoms to systematically study the influence of source or detector (optode) coupling errors. Optode coupling errors can increase the amplitude and decrease the phase of the measured diffuse reflectance, creating artifacts in the reconstructed absorption maps, such as hot spots on the edges. We propose an outlier removal algorithm that can correct these image artifacts, and we demonstrate its performance using simulations, phantom experiments, and breast patient data acquired with bad probe contact due to a dense or small breast. Further, we designed and implemented a new resistance-type thin-film force sensor array that provides real-time optode coupling feedback and guides the outlier removal to minimize optode coupling errors. Our approaches and study results have significant implications for reducing image artifacts arising from handheld probes, which are commonly used with mobile and wearable DOT and DOS devices.

Evaluation of temporal moments and Fourier transformed data in time-domain diffuse optical tomography

Time-domain diffuse optical tomography (TD-DOT) uses near-infrared pulsed lasers as light sources to measure time-varying exitance on the boundary of the target. These are used to estimate optical properties of the imaged target. Several integral-transform-based moments of the time-resolved data have been utilized in TD-DOT, the most common being the mean time of flight and variance. Recently, it has been shown that Fourier transforming the time-domain data to frequency domain enables utilization of these data at one or several frequencies, producing equally as good estimates as the whole time-domain data. In this work, we present a systematic comparison of the usage of the temporal moments and Fourier transformed data in TD-DOT. Both absolute and difference imaging are evaluated using numerical simulations. The simulations show that utilizing temporal moments and Fourier transformed data in TD-DOT provides good quality reconstructions with a good estimation accuracy. These estimates are improved if more than one data type is used. Furthermore, the simulations show that the frequency-domain computations enable computationally cheaper and straightforward implementation of the inverse solver when compared to the temporal moments.

Camera-based CW Diffuse Optical Tomography for obtaining 3D absorption maps by means of digital tomosynthesis

We present a novel method for obtaining a 3D absorption map of a tissue-like turbid slab in the near-infrared spectral range by tomosynthesis. Transmittance data are obtained for a large number of oblique projection directions by scanning a cw laser source across the surface of the slab and by using a CCD camera for spatially resolved light detection. A perturbation model of light transport is used to convert the intensity maps for the different projections into absorption maps. By applying the tomosynthesis approach to these new maps, 3D absorption information on embedded inclusions has been obtained for the first time. The number and the positions of the lateral offset detectors have been optimized by employing a structural similarity index for comparison of the reconstructed with the true absorption data. We present 3D reconstruction of absorption maps using both Monte Carlo simulations and experiments on phantoms with breast-like optical properties. A comparison with conventional 3D reconstruction by a finite element approach shows the superior location performance of tomosynthesis.

Development of digital breast tomosynthesis and diffuse optical tomography fusion imaging for breast cancer detection

Diffuse optical tomography (DOT) non-invasively measures the functional characteristics of breast lesions using near infrared light to probe tissue optical properties. This study aimed to evaluate a new digital breast tomosynthesis (DBT)/DOT fusion imaging technique and obtain preliminary data for breast cancer detection. Twenty-eight women were prospectively enrolled and underwent both DBT and DOT examinations. DBT/DOT fusion imaging was created after acquisition of both examinations. Two breast radiologists analyzed DBT and DOT images independently, and then finally evaluated the fusion images. The diagnostic performance of each reading session was compared and interobserver agreement was assessed. The technical success rate was 96.4%, with one failure due to an error during DOT data storage. Among the 27 women finally included in the analysis, 13 had breast cancer. The areas under the receiver operating characteristic curve (AUCs) for DBT were 0.783 and 0.854 for readers 1 and 2, respectively. DOT showed comparable diagnostic performance to DBT for both readers. The AUCs were significantly improved (P = 0.004) when the DBT/DOT fusion images were used. Interobserver agreements were highest for the DBT/DOT fusion images. In conclusion, this study suggests that DBT/DOT fusion imaging technique appears to be a promising tool for breast cancer diagnosis.

Convolutional neural network-based approach to estimate bulk optical properties in diffuse optical tomography

Deep learning has been actively investigated for various applications such as image classification, computer vision, and regression tasks, and it has shown state-of-the-art performance. In diffuse optical tomography (DOT), the accurate estimation of the bulk optical properties of a medium is paramount because it directly affects the overall image quality. In this work, we exploit deep learning to propose a novel, to the best of our knowledge, convolutional neural network (CNN)-based approach to estimate the bulk optical properties of a highly scattering medium such as biological tissue in DOT. We validated the proposed method by using experimental, as well as, simulated data. For performance assessment, we compared the results of the proposed method with those of existing approaches. The results demonstrate that the proposed CNN-based approach for bulk optical property estimation outperforms existing methods in terms of estimation accuracy, with lower computation time.

Time-domain diffuse optical tomography utilizing truncated Fourier series approximation

Diffuse optical tomography (DOT) uses near infrared light for in vivo imaging of spatially varying optical parameters in biological tissues. It is known that time-resolved measurements provide the richest information on soft tissues, among other measurement types in DOT such as steady-state and intensity-modulated measurements. Therefore, several integral-transform-based moments of the time-resolved DOT measurements have been considered to estimate spatially distributed optical parameters. However, the use of such moments can result in low-contrast images and cross-talks between the reconstructed optical parameters, limiting their accuracy. In this work, we propose to utilize a truncated Fourier series approximation in time-resolved DOT. Using this approximation, we obtained optical parameter estimates with accuracy comparable to using whole time-resolved data that uses low computational time and resources. The truncated Fourier series approximation based estimates also displayed good contrast and minimal parameter cross-talk, and the estimates further improved in accuracy when multiple Fourier frequencies were used.

Real-time optical properties and oxygenation imaging using custom parallel processing in the spatial frequency domain

The development of real-time, wide-field and quantitative diffuse optical imaging methods is becoming increasingly popular for biological and medical applications. Recent developments introduced a novel approach for real-time multispectral acquisition in the spatial frequency domain using spatio-temporal modulation of light. Using this method, optical properties maps (absorption and reduced scattering) could be obtained for two wavelengths (665 nm and 860 nm). These maps, in turn, are used to deduce oxygen saturation levels in tissues. However, while the acquisition was performed in real-time, processing was performed post-acquisition and was not in real-time. In the present article, we present CPU and GPU processing implementations for this method with special emphasis on processing time. The obtained results show that the proposed custom direct method using a General Purpose Graphic Processing Unit (GPGPU) and C CUDA (Compute Unified Device Architecture) implementation enables 1.6 milliseconds processing time for a 1 Mega-pixel image with a maximum average error of 0.1% in extracting optical properties.

Special Section Guest Editorial: Celebration of the Britton Chance Legacy

This guest editorial introduces the Special Section on Metabolic Imaging and Spectroscopy: Britton Chance 105th Birthday Commemorative.