Phantom Experiments Research Articles

Phase-sensitive deep reconstruction method for rapid multiparametric MR fingerprinting and quantitative susceptibility mapping in the brain

IntroductionThis study explores the potential of Magnetic Resonance Fingerprinting (MRF) with a novel Phase-Sensitivity Deep Reconstruction Network (PS-DRONE) for simultaneous quantification of T1, T2, Proton Density, B1+, phase and quantitative susceptibility mapping (QSM). MethodsData were acquired at 3 T in vitro and in vivo using an optimized EPI-based MRF sequence. Phantom experiments were conducted using a standardized phantom for T1 and T2 maps and a custom-made agar-based gadolinium phantom for B1 and QSM maps. In vivo experiments included five healthy volunteers and one patient diagnosed with brain metastasis. PSDRONE maps were compared to reference maps obtained through standard imaging sequences. ResultsTotal scan time was 2 min for 32 slices and a resolution of [1 mm, 1 mm, 4.5 mm]. The reconstruction of T1, T2, Proton Density, B1+ and phase maps were reconstructed within 1 s. In the phantoms, PS-DRONE analysis presented accurate and strongly correlated T1 and T2 maps (r = 0.99) compared to the reference maps. B1 maps from PS-DRONE showed slightly higher values, though still correlated (r = 0.6) with the reference. QSM values showed a small bias but were strongly correlated (r = 0.99) with reference data. In the in vivo analysis, PS-DRONE-derived T1 and T2 values for gray and white matter matched reference values in healthy volunteers. PS-DRONE B1 and QSM maps showed strong correlations with reference values. ConclusionThe PS-DRONE network enables concurrent acquisition of T1, T2, PD, B1+, phase and QSM maps, within 2 min of acquisition time and 1 s of reconstruction time.

Fast and accessible T2 mapping using off-resonance corrected DESPOT2 with application to 3D prostate

PurposeMost T1 and T2 mapping take long acquisitions or needs specialized sequences not widely accessible on clinical scanners. An available solution is DESPOT1/T2 (Driven equilibrium single pulse observation of T1/T2). DESPOT1/T2 uses Spoiled gradient-echo (SPGR) and balanced Steady-State Free Precession (bSSFP) sequences, offering an accessible and reliable way for 3D accelerated T1/T2 mapping. However, bSSFP is prone to off-resonance artifacts, limiting the application of DESPOT2 in regions with high susceptibility contrasts, like the prostate. Our proposal, DESPO+, employs the full bSSFP and SPGR models with a dictionary-based method to reconstruct 3D T1/T2 maps in the prostate region without off-resonance banding. MethodsDESPO+ modifies the bSSFP acquisition of the original variable flip angle DESPOT2. DESPO+ uses variable repetition and echo times, employing a dictionary-based method of the full bSSFP and SPGR models to reconstruct T1, T2, and Proton Density (PD) simultaneously. The proposed DESPO+ method underwent testing through simulations, T1/T2 phantoms, and on fourteen healthy subjects. ResultsThe results reveal a significant reduction in T2 map banding artifacts compared to the original DESPOT2 method. DESPO+ approach reduced T2 errors by up to seven times compared to DESPOT2 in simulations and phantom experiments. We also synthesized in-vivo T1-weighted/T2-weighted images from the acquired maps using a spin-echo model to verify the map's quality when lacking a reference. For in-vivo imaging, the synthesized images closely resemble those from the clinical MRI protocol, reducing scan time by around 50% compared to traditional spin-echo T1-weighted/T2-weighted acquisitions. ConclusionDESPO+ provides an off-resonance insensitive and clinically available solution, enabling high-resolution 3D T1/T2 mapping and synthesized T1-weighted/T2-weighted images for the entire prostate, all achieved within a short scan time of 3.6 min, similar to DESPOT1/T2.

Sub-Nyquist sampling-based high-frequency photoacoustic computed tomography.

High-frequency (greater than 30 MHz) photoacoustic computed tomography (PACT) provides the opportunity to reveal finer details of biological tissues with high spatial resolution. To record photoacoustic signals above 30 MHz, sampling rates higher than 60 MHz are required according to the Nyquist sampling criterion. However, the highest sampling rates supported by existing PACT systems are typically within the range of 40-60 MHz. Herein, we propose a novel PACT imaging method based on sub-Nyquist sampling. The results of numerical simulation, phantom experiment, and in vivo experiment demonstrate that the proposed imaging method can achieve high-frequency PACT imaging with a relatively low sampling rate. An axial resolution of 22 μm is achieved with a 30-MHz transducer and a 41.67-MHz sampling rate. To the best of our knowledge, this is the highest axial resolution ever achieved in PACT based on a sampling rate of not greater than 60 MHz. This work is expected to provide a practical way for high-frequency PACT imaging with limited sampling rates.

Iterative Pulse-Echo Tomography for Ultrasonic Image Correction.

Acoustic aberration, caused by the uneven distribution of tissue speed-of-sound (SoS), significantly reduces the quality of ultrasound imaging. An important approach to mitigate this issue is imaging correction based on local SoS estimation. Computed ultrasound tomography in echo mode (CUTE) is an SoS estimation method that utilizes phase-shift information from ultrasound pulse-echo signals, offering both practical utility and computational efficiency. However, the traditional single-pass CUTE often suffers from poor accuracy and robustness. In this paper, an advanced approach known as iterative CUTE is introduced, which refines SoS estimates through iterative correction of errors and noise, addressing the limitations of traditional single-pass methods. It is argued that traditional precision indicators like root mean square error (RMSE) fall short in adequately reflecting the quality of SoS estimates for imaging correction, and coherence factor (CF) is proposed as a more indicative metric. Performance validation of the iterative CUTE algorithm was conducted using a simulation and agar phantom experiment. The results indicated that the iterative CUTE approach surpasses the single-pass approach, enhancing the average CF for SoS estimates by up to 18.2%. In phantom experiments, imaging corrected with SoS estimates from iterative CUTE reduced the Array Performance Index (API) by up to 40% compared to traditional methods.

System characterization of a human-sized 3D real-time magnetic particle imaging scanner for cerebral applications

Since the initial patent in 2001, the Magnetic Particle Imaging community has endeavored to develop a human-applicable Magnetic Particle Imaging scanner, incorporating contributions from various research fields. Here we present an improved head-sized Magnetic Particle Imaging scanner with low power consumption, operated by open-source software and characterize it with an emphasis on human safety. The focus is on the evaluation of the technical components and on phantom experiments for brain perfusion. We achieved 3D single- and multi-contrast imaging at 4 Hz frame rate. The system characterization includes sensitivity, resolution, perfusion and multi-contrast experiments as well as field measurements and sequence analysis. Images were acquired with a clinically approved tracer and within human peripheral nerve stimulation thresholds. This advanced scanner holds potential as a tomographic imager for diagnosing conditions such as ischemic stroke (different stages) or intracranial hemorrhage in environments lacking electromagnetic shielding, such as the intensive care unit.

Comparing fluorescent contrast agents for fluorescence guided surgery using 3-D cryo-imaging.

Fluorescence cryo-imaging is a high-resolution optical imaging technique that produces 3-D whole-body biodistributions of fluorescent molecules within an animal specimen. To accomplish this, animal specimens are administered a fluorescent molecule or reporter and are frozen to be autonomously sectioned and imaged at a temperature of -20°C or below. Thus, to apply this technique effectively, administered fluorescent molecules should be relatively invariant to low temperature conditions for cryo-imaging and ideally the fluorescence intensity should be stable and consistent in both physiological and cryo-imaging conditions. Herein, we assessed the mean fluorescence intensity of 11 fluorescent contrast agents as they are frozen in a tissue-simulating phantom experiment and show an example of a tested fluorescent contrast agent in a cryo-imaged whole pig brain. Most fluorescent contrast agents were stable within ~25% except for FITC and PEGylated FITC derivatives, which showed a dramatic decrease in fluorescence intensity when frozen.

Development of an augmented reality system for tracheal intubation guidance of airway management.

Tracheal intubation is a crucial procedure performed in airway management to sustain life during various procedures. However, difficult airways can make intubation challenging, which is associated with increased mortality and morbidity. This is particularly important for children who undergo intubation where the situation is difficult. Improved airway management will decrease incidences of repeated attempts, decrease hypoxic injuries in patients, and decrease hospital stays, resulting in better clinical outcomes and reduced costs. Currently, 3D printed models based on CT scans and ultrasound-guided intubation are being used or tested for device fitting and procedure guidance to increase the success rate of intubation, but both have limitations. Maintaining a 3D printing facility can be logistically inconvenient, and it can be time consuming and expensive. Ultrasound-guided intubation can be hindered by operator dependence, limited two-dimensional visualization, and potential artifacts. In this study, we developed an augmented reality (AR) system that allows the overlay of intubation tools and internal airways, providing real-time guidance during the procedure. A child manikin was used to develop and test the AR system. Three-dimensional CT images were acquired from the manikin. Different tissues were segmented to generate the 3D models that were imported into Unity to build the holograms. Phantom experiments demonstrated the AR-guided system for potential applications in tracheal intubation guidance.

Photoacoustic microscopy for real-time monitoring of near-infrared optical absorbers inside biological tissue.

We developed a high-speed optical-resolution photoacoustic microscopy (OR-PAM) system using a high-repetition-rate supercontinuum (SC) light source and a two-axes Galvano scanner. The OR-PAM system enabled real-time imaging of optical absorbers inside biological tissues with excellent excitation wavelength tunability. In the near-infrared (NIR) wavelength range, high-speed OR-PAM faces limitations due to the lack of wavelength-tunable light sources. Our study aimed to enable high-speed OR-PAM imaging of various optical absorbers, including NIR contrast agents, and validate the performance of high-speed OR-PAM in the detection of circulating tumor cells (CTCs). A high-repetition nanosecond pulsed SC light source was used for OR-PAM. The excitation wavelength was adjusted by bandpass filtering of broadband light pulses produced by an SC light source. Phantom and in vivo experiments were performed to detect tumor cells stained with an NIR contrast agent within flowing blood samples. The newly developed high-speed OR-PAM successfully detected stained cells both in the phantom and in vivo. The phantom experiment confirmed the correlation between the tumor cell detection rate and tumor cell concentration in the blood sample. The high-speed OR-PAM effectively detected stained tumor cells. Combining high-speed OR-PAM with molecular probes that stain tumor cells in vivo enables in vivo CTC detection.

Image reconstruction method for incomplete CT projection based on self-guided image filtering.

In some fields of medical diagnosis or industrial nondestructive testing, it is difficult to obtain complete computed tomography (CT) data due to the limitation of radiation dose or other factors. Therefore, image reconstruction of incomplete projection data is the focus of this paper. In this paper, a new image reconstruction model based on self-guided image filtering (SGIF) term is proposed for few-view and segmental limited-angle (SLA) CT reconstruction. Then the alternating direction method (ADM) is used to solve this model. For simplicity, we call it ADM-SGIF method. The key idea of ADM-SGIF method is to use the reconstructed image itself as a reference and utilize its structural features to guide CT reconstruction. This method can effectively preserve image structures and remove shading artifacts. To validate the effectiveness of the proposed reconstruction method, we conduct digital phantom and real CT data experiments. The results indicate that ADM-SGIF method outperforms competing methods, including total variation (TV), relative total variation (RTV), and L0-norm minimization solved by ADM (ADM-L0) methods, in both subjective and objective evaluations.

Facial augmented reality based on hierarchical optimization of similarity aspect graph

BackgroundThe existing face matching method requires a point cloud to be drawn on the real face for registration, which results in low registration accuracy due to the irregular deformation of the patient's skin that makes the point cloud have many outlier points. MethodsThis work proposes a non-contact pose estimation method based on similarity aspect graph hierarchical optimization. The proposed method constructs a distance-weighted and triangular-constrained similarity measure to describe the similarity between views by automatically identifying the 2D and 3D feature points of the face. A mutual similarity clustering method is proposed to construct a hierarchical aspect graph with 3D pose as nodes. A Monte Carlo tree search strategy is used to search the hierarchical aspect graph for determining the optimal pose of the facial 3D model, so as to realize the accurate registration of the facial 3D model and the real face. ResultsThe proposed method was used to conduct accuracy verification experiments on the phantoms and volunteers, which were compared with four advanced pose calibration methods. The proposed method obtained average fusion errors of 1.13 ± 0.20 mm and 0.92 ± 0.08 mm in head phantom and volunteer experiments, respectively, which exhibits the best fusion performance among all comparison methods. ConclusionsOur experiments proved the effectiveness of the proposed pose estimation method in facial augmented reality.

Probing Layered Tissues by Backscattering Mueller Matrix Imaging and Tissue Optical Clearing

Polarization imaging is a label-free and non-invasive technique that is sensitive to microstructure and suitable for probing the microstructure of living tissues. However, obtaining deep-layer information from tissues has been a challenge for optical techniques. In this work, we used tissue optical clearing (TOC) to increase optical penetration depth and characterize the layered structures of tissue samples. Different tissue phantoms were constructed to examine changes in the polarization features of the layered structure during the TOC process. We found that depolarization and anisotropy parameters were able to distinguish between single-layer and double-layer phantoms, reflecting microstructural information from each layer. We observed changes in polarization parameter images during the TOC process and, by analyzing different regions of the images, explained the sensitivity of these parameters to double-layer structures and analyzed the influence of oblique incident illumination. Finally, we conducted TOC experiments on living skin samples, leveraging the experience gained from phantom experiments to identify the double-layer structure of the skin and extract features related to layered structures. The results show that the combination of backscattering polarization imaging and tissue optical clearing provides a powerful tool for the characterization of layered samples.

Optimizing dual-energy CT technique for iodine-based contrast-to-noise ratio, a theoretical study.

Dual-energy CT (DECT) systems provide valuable material-specific information by simultaneously acquiring two spectral measurements, resulting in superior image quality and contrast-to-noise ratio (CNR) while reducing radiation exposure and contrast agent usage. The selection of DECT scan parameters, including x-ray tube settings and fluence, is critical for the stability of the reconstruction process and hence the overall imagequality. The goal of this study is to propose a systematic theoretical method for determining the optimal DECT parameters for minimal noise and maximum CNR in virtual monochromatic images (VMIs) for fixed subject size and total radiationdose. The noise propagation in the process of projection based material estimation from DECT measurements is analyzed. The main components of the study are the mean pixel variances for the sinogram and monochromatic image and the CNR, which were shown to depend on the Jacobian matrix of the sinograms-to-DECT measurementsmap. Analytic estimates for the mean sinogram and monochromatic image pixel variances and the CNR as functions of tube potentials, fluence, and VMI energy are derived, and then used in a virtual phantom experiment as an objective function for optimizing the tube settings and VMI energy to minimize the image noise and maximize theCNR. It was shown that DECT measurements corresponding to kV settings that maximize the square of Jacobian determinant values over a domain of interest lead to improved stability of basis material reconstructions. Instances of non-uniqueness in DECT were addressed, focusing on scenarios where the Jacobian determinant becomes zero within the domain of interest despite significant spectral separation. The presence of non-uniqueness can lead to singular solutions during the inversion of sinograms-to-DECT measurements, underscoring the importance of considering uniqueness properties in parameter selection. Additionally, the optimal VMI energy and tube potentials for maximal CNR was determined. When the x-ray beam filter material was fixed at 2 mm of aluminum and the photon fluence for low and high kV scans were considered equal, the tube potential pair of 60/120 kV led to the maximal iodine CNR in the VMI at 53keV. Optimizing DECT scan parameters to maximize the CNR can be done in a systematic way. Also, choosing the parameters that maximize the Jacobian determinant over the set of expected line integrals leads to more stable reconstructions due to the reduced amplification of the measurement noise. Since the values of the Jacobian determinant depend strongly on the imaging task, careful consideration of all of the relevant factors is needed when implementing the proposedframework.

Moving Beyond Simulation: Data-Driven Quantitative Photoacoustic Imaging Using Tissue-Mimicking Phantoms.

Accurate measurement of optical absorption coefficients from photoacoustic imaging (PAI) data would enable direct mapping of molecular concentrations, providing vital clinical insight. The ill-posed nature of the problem of absorption coefficient recovery has prohibited PAI from achieving this goal in living systems due to the domain gap between simulation and experiment. To bridge this gap, we introduce a collection of experimentally well-characterised imaging phantoms and their digital twins. This first-of-a-kind phantom data set enables supervised training of a U-Net on experimental data for pixel-wise estimation of absorption coefficients. We show that training on simulated data results in artefacts and biases in the estimates, reinforcing the existence of a domain gap between simulation and experiment. Training on experimentally acquired data, however, yielded more accurate and robust estimates of optical absorption coefficients. We compare the results to fluence correction with a Monte Carlo model from reference optical properties of the materials, which yields a quantification error of approximately 20%. Application of the trained U-Nets to a blood flow phantom demonstrated spectral biases when training on simulated data, while application to a mouse model highlighted the ability of both learning-based approaches to recover the depth-dependent loss of signal intensity. We demonstrate that training on experimental phantoms can restore the correlation of signal amplitudes measured in depth. While the absolute quantification error remains high and further improvements are needed, our results highlight the promise of deep learning to advance quantitative PAI.

Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches.

Quantitative ultrasound (QUS) analyzes the ultrasound (US) backscattered data to find the properties of scatterers that correlate with the tissue microstructure. Statistics of the envelope of the backscattered radio frequency (RF) data can be utilized to estimate several QUS parameters. Different distributions have been proposed to model envelope data. The homodyned K-distribution (HK-distribution) is one of the most comprehensive distributions that can model US backscattered envelope data under diverse scattering conditions (varying scatterer number density and coherent scattering). The scatterer clustering parameter ( α ) and the ratio of the coherent to diffuse scattering power ( k ) are the parameters of this distribution that have been used extensively for tissue characterization in diagnostic US. The estimation of these two parameters (which we refer to as HK parameters) is done using optimization algorithms in which statistical features such as the envelope point-wise signal-to-noise ratio (SNR), skewness, kurtosis, and the log-based moments have been utilized as input to such algorithms. The optimization methods minimize the difference between features and their theoretical value from the HK model. We propose that the true value of these statistical features is a hyperplane that covers a small portion of the feature space. In this article, we follow two approaches to reduce the effect of sample features' error. We propose a model projection neural network based on denoising autoencoders to project the noisy features into this space based on this assumption. We also investigate if the noise distribution can be learned by the deep estimators. We compare the proposed methods with conventional methods using simulations, an experimental phantom, and data from an in vivo animal model of hepatic steatosis. The network weight and a demo code are available online at ht.tp://code.sonography.ai.

Photoacoustic/ultrasound dual-modality imaging for marker clip localization in neoadjuvant chemotherapy of breast cancer.

To ensure precise tumor localization and subsequent pathological examination, a metal marker clip (MC) is placed within the tumor or lymph node prior to neoadjuvant chemotherapy for breast cancer. However, as tumors decrease in size following treatment, detecting the MC using ultrasound imaging becomes challenging in some patients. Consequently, a mammogram is often required to pinpoint the MC, resulting in additional radiation exposure, time expenditure, and increased costs. Dual-modality imaging, combining photoacoustic (PA) and ultrasound (US), offers a promising solution to this issue. Our objective is to localize the MC without radiation exposure using PA/US dual-modality imaging. A PA/US dual-modality imaging system was developed. Utilizing this system, both phantom and clinical experiments were conducted to demonstrate that PA/US dual-modality imaging can effectively localize the MC. The PA/US dual-modality imaging can identify and localize the MC. In clinical trials encompassing four patients and five MCs, the recognition rate was . Three experiments to verify the accuracy of marker position recognition were successful. We effectively localized the MC in real time using PA/US dual-modality imaging. Unlike other techniques, the new method enables surgeons to pinpoint nodules both preoperatively and intraoperatively. In addition, it boasts non-radioactivity and is comparatively cost-effective.

An Adaptive Harmonic Separation Technique for Ultrasound Harmonic Imaging

ObjectiveAn adaptive harmonic separation (HS) technique is proposed to overcome the limitations in conventional filtering techniques for ultrasound (US) tissue harmonic imaging (THI). MethodsBased on expectation-maximization source separation, the proposed HS technique adaptively models the depth-varying fundamental and harmonic components in the frequency domain and separates the two by applying their calculated posterior probabilities. Phantom experiments with a Tx center frequency of 2 MHz are conducted to evaluate the proposed HS-based US THI schemes. ResultsThe phantom images show that the proposed single-pulse THI scheme utilizing the HS technique provides not only an average improvement of 19.2% in axial resolution compared to the conventional bandpass filtering scheme but also similar image quality to that of the conventional pulse-inversion (PI) scheme which requires two Tx/Rx sequences for each scan line. Furthermore, when combined with the PI technique, the HS technique provides a uniform axial resolution over the entire 170 mm imaging depth with an average improvement of 17.1% compared to the conventional PI scheme. ConclusionThese results show that the proposed adaptive HS technique is capable of improving both the frame rate and the image quality of US THI.

Endoscopic Hyperspectral Imaging System to Discriminate Tissue Characteristics in Tissue Phantom and Orthotopic Mouse Pancreatic Tumor Model.

In this study, we developed an endoscopic hyperspectral imaging (eHSI) system and evaluated its performance in analyzing tissues within tissue phantoms and orthotopic mouse pancreatic tumor models. Our custom-built eHSI system incorporated a liquid crystal tunable filter. To assess its tissue discrimination capabilities, we acquired images of tissue phantoms, distinguishing between fat and muscle regions. The system underwent supervised training using labeled samples, and this classification model was then applied to other tissue phantom images for evaluation. In the tissue phantom experiment, the eHSI effectively differentiated muscle from fat and background tissues. The precision scores regarding fat tissue classification were 98.3% for the support vector machine, 97.7% for the neural network, and 96.0% with a light gradient-boosting machine algorithm, respectively. Furthermore, we applied the eHSI system to identify tumors within an orthotopic mouse pancreatic tumor model. The F-score of each pancreatic tumor-bearing model reached 73.1% for the KPC tumor model and 63.1% for the Pan02 tumor models. The refined imaging conditions and optimization of the fine-tuning of classification algorithms enhance the versatility and diagnostic efficacy of eHSI in biomedical applications.

Correction of high-rate motion for photoacoustic microscopy by orthogonal cross-correlation.

Photoacoustic imaging is a promising technology for in vivo imaging. However, its imaging performance can be hampered by motion artifacts, especially when dealing with high-rate motion. In this paper, we propose an orthogonal motion correction method that utilizes cross-correlation along orthogonal scan directions to extract accurate motion displacements from the photoacoustic data. The extracted displacements are then applied to remove artifacts and compensate for motion-induced distortions. Phantom experiments demonstrate that the proposed method can extract the motion information and the structural similarity index measurement after correction is increased by 26.5% and 11.2% compared to no correction and the previous correction method. Then the effectiveness of our method is evaluated in vivo imaging of a mouse brain. Our method shows a stable and effective performance under high-rate motion. The high accuracy of the motion correction method makes it valuable in improving the accuracy of photoacoustic imaging.

Machine learning algorithm to extract properties of ATE phantoms from microwave measurements

Abstract The Muscle Analyzer System (MAS) project wants to create a standalone microwave device that can assess the muscle quality, called the MAS device. To achieve that an algorithm that can derive the properties of skin, fat and muscle from the measurements is needed. This paper presents a machine learning algorithm that aims to do precisely that. The algorithm relies on first predicting the skin using the data from the MAS device, then predicting the fat again using the data from the MAS but also the predicted skin value and lastly the muscle is predicted using the microwave data together with the skin and fat predictions. Data have been collected in phantom experiments, materials that mimick the dielectric properties of human tissues. The algorithm is trained to predict the properties of said phantoms. The results show that the prediction for skin thickness works well, the fat thickness prediction is okay but the muscle prediction struggles. This is partly due to the error from the skin and fat layers are propagated to the muscle layer and partly because the muscle layer is farthest away from the sensor, which makes getting information from that layer harder.

Wide-field illumination diffuse optical tomography within a framework of single-pixel time-domain spatial frequency domain imaging.

We present a wide-field illumination time-domain (TD) diffusion optical tomography (DOT) for three-dimensional (3-D) reconstruction within a shallow region under the illuminated surface of the turbid medium. The methodological foundation is laid on the single-pixel spatial frequency domain (SFD) imaging that facilitates the adoption of the well-established time-correlated single-photon counting (TCSPC)-based TD detection and generalized pulse spectrum techniques (GPST)-based reconstruction. To ameliorate the defects of the conventional diffusion equation (DE) in the forward modeling of TD-SFD-DOT, mainly the low accuracy in the near-field region and in profiling early-photon migration, we propose a modified model employing the time-dependent δ-P1 approximation and verify its improved accuracy in comparison with both the Monte Carlo and DE-based ones. For a simplified inversion process, a modified GPST approach is extended to TD-SFD-DOT that enables the effective separation of the absorption and scattering coefficients using a steady-state equivalent strategy. Furthermore, we set up a single-pixel TD-SFD-DOT system that employs the TCSPC-based TD detection in the SFD imaging framework. For assessments of the reconstruction approach and the system performance, phantom experiments are performed for a series of scenarios. The results show the effectiveness of the proposed methodology for rapid 3-D reconstruction of the absorption and scattering coefficients within a depth range of about 5 mean free pathlengths.