Simulated Phantom Research Articles

Impact of gadolinium concentration and cell oxygen levels on radiobiological characteristics of gadolinium neutron capture therapy technique in brain tumor treatment.

Neutron capture therapy (NCT) with various concentrations of gadolinium (157Gd) is one of the treatment modalities for glioblastoma (GBM) tumors. Current study aims to evaluate how variations of 157Gd concentration and cell oxygen levels can affect the relative biological effectiveness (RBE) of gadolinium neutron capture therapy (GdNCT) technique through a hybrid Monte Carlo (MC) simulation approach. At first, Snyder phantom including a spherical tumor was simulated by Geant4 MC code and relevant energy electron spectra to different 157Gd concentrations including 100, 250, 500, and 1000 ppm were calculated following the neutron irradiation of simulated phantom. Scored energy electron spectra were then imported to Monte Carlo damage simulation (MCDS) code to estimate RBE values (both RBESSB and RBEDSB) at different gadolinium concentrations and oxygen levels from 10 to 100%. The results indicate that variations of 157Gd can affect the energy spectrum of released secondary electrons including Auger electrons. Variation of gadolinium concentration from 100 to 1000 ppm in tumor region can change RBESSB and RBEDSB values by about 0.1% and 0.5%, respectively. Besides, maximum variations of 4.3% and 2% were calculated for RBEDSB and RBESSB when cell oxygen level changed from 10 to 100%. From the results, variations of considered gadolinium and oxygen concentrations during GdNCT can influence RBE values. Nevertheless, due to the not remarkable changes in the intensity of Auger electrons, a slight difference in RBE values would be expected at various 157Gd concentrations, although considerable RBE changes were calculated relevant to the oxygen alternations inside tumor tissue.

Synthetization of high-dose images using low-dose CT scans.

Radiation dose reduction has been the focus of many research activities in x-ray CT. Various approaches were taken to minimize the dose to patients, ranging from the optimization of clinical protocols, refinement of the scanner hardware design, and development of advanced reconstruction algorithms. Although significant progress has been made, more advancements in this area are needed to minimize the radiation risks to patients. Reconstruction algorithm-based dose reduction approaches focus mainly on the suppression of noise in the reconstructed images while preserving detailed anatomical structures. Such an approach effectively produces synthesized high-dose images (SHD) from the data acquired with low-dose scans. A representative example is the model-based iterative reconstruction (MBIR). Despite its widespread deployment, its full adoption in a clinical environment is often limited by an undesirable image texture. Recent studies have shown that deep learning image reconstruction (DLIR) can overcome this shortcoming. However, the limited availability of high-quality clinical images for training and validation is often the bottleneck for its development. In this paper, we propose a novel approach to generate SHD with existing low-dose clinical datasets that overcomes both the noise texture issue and the data availability issue. Our approach is based on the observation that noise in the image can be effectively reduced by performing image processing orthogonal to the imaging plane. This process essentially creates an equivalent thick-slice image (TSI), and the characteristics of TSI depend on the nature of the image processing. An advantage of this approach is its potential to reduce impact on the noise texture. The resulting image, however, is likely corrupted by the anatomical structural degradation due to partial volume effects. Careful examination has shown that the differential signal between the original and the processed image contains sufficient information to identify regions where anatomical structures are modified. The differential signal, unfortunately, contains significant noise and has to be removed. The noise removal can be accomplished by performing iterative noise reduction to preserve structural information. The processed differential signal is subsequently subtracted from TSI to arrive at SHD. The algorithm was evaluated extensively with phantom and clinical datasets. For better visual inspection, difference images between the original and SHD were generated and carefully examined. Negligible residual structure could be observed. In addition to the qualitative inspection, quantitative analyses were performed on clinical images in terms of the CT number consistency and the noise reduction characteristics. Results indicate that no CT number bias is introduced by the proposed algorithm. In addition, noise reduction capability is consistent across different patient anatomical regions. Further, simulated water phantom scans were utilized in the generation of the noise power spectrum (NPS) to demonstrate the preservation of the noise-texture. We present a method to generate SHD datasets from regularly acquired low-dose CT scans. Images produced with the proposed approach exhibit excellent noise-reduction with the desired noise-texture. Extensive clinical and phantom studies have demonstrated the efficacy and robustness of our approach. Potential limitations of the current implementation are discussed and further research topics are outlined.

Abstract 191: Simulating Intracranial Stenosis: A Methodological Approach In An In‐Vitro Neurovascular Model

Introduction Intracranial atherosclerotic disease (ICAD) induces the luminal narrowing of an intracranial vessel and represents one of the major causes of ischemic strokes [1, 2, 3, 4]. In vitro 3D printed models have gained popularity in the stroke research field as they are morphologically accurate and offer the possibility to simulate clinical scenarios for training purposes or device testing. Despite being a challenge for interventionalists, to date, clinically accurate ICAD in vitro models have not been developed. We aimed to develop a 3D printed ICAD model including realistic features to provide an optimal simulation phantom for research and training purposes. Methods Stereolithography 3D printing technique was used to create a resin neurovascular model based on vascular anatomies extracted from anonymized CTA images. The phantom includes the aortic arch, all supraoptic cervical arteries and a complete circle of Willis up to the M2‐MCA, A2‐ACA and P2‐PCA segments. 3% sodium alginate solution was cast into a stenosis mold and crosslinked in a 40% calcium chloride. The deformable gel constituted an 8mm long replaceable stenotic segment at the level of M1‐MCA simulating an atherosclerotic plaque with a 0.5mm internal diameter. A flow sensor (ME‐8PXL, Transonic) was used to measure the lesion flow rate before and after endovascular treatment was performed under fluoroscopy. Results The baseline angiographic run showed an irregular 80% stenosis at the level of M1‐MCA that generated a substantial delay of contrast arrival to the distal branches. After angioplasty (Gateway PTA Balloon Catheter 3.5x20mm, Boston Scientific) and stenting (Wingspan 2.5mmx15mm, Stryker) were performed the residual stenosis was <10% (Figure 1). An initial stenosis flow rate was registered with a value of 8.5 ± 5.33 mL/min. Following the completion of the endovascular procedure, the flow sensor detected a post‐procedural stenosis flow rate of 160 ± 3.45 mL/min. Conclusion The developed ICAD model is anatomically accurate and offers realistic physiological and procedural features. The phantom represents an ideal tool for training purposes and a platform to test different devices for the endovascular treatment of ICAD. The methodology and materials could be applied to simulate stenotic lesions at different levels in the cervical and intracranial arteries.

How to evaluate the quality of ultrasound images for B-mode and Doppler?

The present study evaluated the performance of the Ultrasound (US) and Doppler devices. The methodology was based on the recommendations of the American Association of Medical Physics (AAPM). The study used an image simulator phantom manufactured by Sun Nuclear/Gammex (model 403). The performance evaluation was applied for B-mode and Doppler. The study analyzed 182 devices with 546 transducers from different models and manufacturers. The evaluation parameters were: a) spatial resolution; b) depth of view c) image contrast; d) visualization of anechoic objects; d) velocity; e) sensitivity for (Doppler Mode); and f) artifacts in the images. The evaluation indicated that 43,95% of the probes showed some type of non-compliance, directly affecting the accuracy of the medical diagnosis. The present investigation indicates the need to carry out image quality evaluation to verify the degradation of the probes to minimizing possible errors in diagnostic imaging due to inadequate performance of the US equipments.

Multidimensional compressed sensing to advance 23 Na multi-quantum coherences MRI.

Sodium (23 Na) multi-quantum coherences (MQC) MRI was accelerated using three-dimensional (3D) and a dedicated five-dimensional (5D) compressed sensing (CS) framework for simultaneous Cartesian single (SQ) and triple quantum (TQ) sodium imaging of in vivo human brain at 3.0 and 7.0 T. 3D 23 Na MQC MRI requires multi-echo paired with phase-cycling and exhibits thus a multidimensional space. A joint reconstruction framework to exploit the sparsity in all imaging dimensions by extending the conventional 3D CS framework to 5D was developed. 3D MQC images of simulated brain, phantom and healthy brain volunteers obtained from 3.0 T and 7.0 T were retrospectively and prospectively undersampled. Performance of the CS models were analyzed by means of structural similarity index (SSIM), root mean squared error (RMSE), signal-to-noise ratio (SNR) and signal quantification of tissue sodium concentration and TQ/SQ ratio. It was shown that an acceleration of three-fold, leading to less than min of scan time with a resolution of at 3.0 T, are possible. 5D CS improved SSIM by 3%, 5%, 1% and reduced RMSE by 50%, 30%, 8% for in vivo SQ, TQ, and TQ/SQ ratio maps, respectively. Furthermore, for the first time prospective undersampling enabled unprecedented high resolution from to MQC images of in vivo human brain at 7.0 T without extending acquisition time. 5D CS proved to allow up to three-fold acceleration retrospectively on 3.0 T data. 2-fold acceleration was demonstrated prospectively at 7.0 T to reach higher spatial resolution of 23 Na MQC MRI.

Compressed sensing photoacoustic imaging based on the gradient projection Newton pursuit algorithm

Photoacoustic imaging (PAI) is a unique non-invasive biomedical imaging technique that illuminates biological tissue with short-pulsed laser light. This process generates acoustic waves, which in turn enable high-resolution and high-contrast imaging of deep tissue. However, the complexity of PAI imaging data presents challenges in achieving rapid and optimal reconstruction results due to the time and resource demands of traditional Nyquist sampling. On the contrary, compressive sensing (CS) overcomes the limitations of Nyquist sampling by recovering the original signal with a lower amount of sampled data, resulting in significant cost savings in signal transmission and processing. Additionally, CS-based hard-thresholding iterative algorithms possess various advantages in sparse optimization, including sparsity control and fast computation. Moreover, the assumptions of the Gradient Projection Newton Pursuit (GPNP) for CS problems are relatively weaker compared to many state-of-the-art algorithms. Therefore, we investigated the performance of the GPNP algorithm in PAI. We compared our algorithm with improved iterative hard thresholding (IIHT), conjugate gradient iterative hard thresholding (CGIHT), compressive sampling matching pursuit (CoSaMP), SPGL1, SALSA and elastic net algorithms using phantom, Vasculature, and SGERF simulation images. The simulation results demonstrate that the GPNP algorithm achieves excellent image reconstruction performance when evaluating both the quality of reconstruction and the reconstruction time. Taking the simulation results of Vasculature with a noise level of 40 dB and 40 channels as an example, the PNSR index of GPNP has improved by 4.09, 11.03, 12.89, 13.14, 13.15, and 11.75 compared to IIHT, CGIHT, elastic net algorithms, SPGL1, SALSA and CoSaMP, respectively.

Polarization differential interference contrast microscopy with physics-inspired plug-and-play denoiser for single-shot high-performance quantitative phase imaging.

We present single-shot high-performance quantitative phase imaging with a physics-inspired plug-and-play denoiser for polarization differential interference contrast (PDIC) microscopy. The quantitative phase is recovered by the alternating direction method of multipliers (ADMM), balancing total variance regularization and a pre-trained dense residual U-net (DRUNet) denoiser. The custom DRUNet uses the Tanh activation function to guarantee the symmetry requirement for phase retrieval. In addition, we introduce an adaptive strategy accelerating convergence and explicitly incorporating measurement noise. After validating this deep denoiser-enhanced PDIC microscopy on simulated data and phantom experiments, we demonstrated high-performance phase imaging of histological tissue sections. The phase retrieval by the denoiser-enhanced PDIC microscopy achieves significantly higher quality and accuracy than the solution based on Fourier transforms or the iterative solution with total variance regularization alone.

Dual energy CT reconstruction using the constrained one step spectral image reconstruction algorithm.

The constrained one-step spectral CT Image Reconstruction method (cOSSCIR) has been developed to estimate basis material maps directly from spectral CT data using a model of the polyenergetic x-ray transmissions and incorporating convex constraints into the inversion problem. This 'one-step' approach has been shown to stabilize the inversion in the case of photon-counting CT, and may provide similar benefits to dual-kV systems that utilize integrating detectors. Since the approach does not require the same rays be acquired for every spectral measurement, cOSSCIR can apply to dual energy protocols and systems used clinically, such as fast and slow kV switching systems and dual sourcescanning. The purpose of this study is to investigate the use of cOSSCIR applied to dual-kV data, using both registered and unregistered spectral acquisitions, specifically slow and fast kV switching imaging protocols. For this application, cOSSCIR is investigated using inverse crime simulations and dual-kV experiments. This study is the first demonstration of cOSSCIR on the dual-kV reconstructionproblem. An integrating detector model was developed for the purpose of reconstructing dual-kV data, and an inverse crime study was used to validate the detector model within the cOSSCIR framework using a simulated pelvic phantom. Experiments were also used to evaluate cOSSCIR on the dual energy problem. Dual-kV data was obtained from a physical phantom containing analogs of adipose, bone, and liver tissues, with the aim of recovering the material coefficients in the bone and adipose basis material maps. cOSSCIR was applied to acquisitions where all rays performed both spectral measurements (registered) and fast and slow kV switching acquisitions (unregistered). cOSSCIR was also compared to two image-domain decomposition approaches, where image-domain methods are the conventional approach for decomposing unregistered spectraldata. Simulations demonstrate the application of cOSSCIR to the dual-kV inversion problem by successfully recovering the material basis maps on ideal data, while further showing that unregistered data presents a more challenging inversion problem. In our experimental reconstructions, the recovered basis material coefficient errors were found to be less than 6.5% in the bone, adipose, and liver regions for both registered and unregistered protocols. Similarly, the errors were less than 4% in the 50 keV virtual mono-energetic images, and the recovered material decomposition vectors nearly overlap their corresponding ground-truth vectors. Additionally, a preliminary two material decomposition study of iodine quantification recovered an average concentration of 9.2 mg/mL from a 10 mg/mL experimental iodineanalog. Using our integrating detector and spectral models, cOSCCIR is capable of accurately recovering material basis maps from dual-kV data for both registered and unregistered data. The material decomposition quantification compare favorably to the image domain approaches, and our results were not affected by the imaging protocol. Our results also suggest the extension of cOSSCIR to iodine quantification using two materialdecomposition.

Exposure computational models with voxel phantoms coupled to EGSnrc Monte Carlo code

In computational dosimetry of ionizing radiation, the energy deposited in radiosensitive organs and tissues is evaluated when an anthropomorphic simulator (phantom) is irradiated using Exposure Computational Models (ECMs). An ECM is a virtual scene with a phantom positioned mathematically relative to a radioactive source. The initial state includes information like the type of primary particle, its energy, starting point coordinates, and direction. Subsequently, robust Monte Carlo (MC) codes are used to simulate the particle's mean free path, interaction with the medium's atoms, and energy deposition. These are common steps for simulations involving photons and/or primary electrons. The GDN (Research Group on Numerical Dosimetry and the Research Group on Computational Dosimetry and Embedded Systems) has published ECMs with voxel phantoms irradiated by photons using the MC code EGSnrc. This work has led to specific computational tools development for various numerical dosimetry stages, including input file preparation, ECM execution, and result analysis. Since 2004, the GDN developed in-house applications like FANTOMAS, CALDose_X, DIP, and MonteCarlo. Certain previously used phantoms are reintroduced to provide historical context in the ECMs' production timeline, emphasizing additive modifications inherent in systematic theme studies. The dosimetric evaluations used the binary version of the MASH (Male Adult mesh) phantom, converted to the SID (Dosimetric Information System) text file type. This format has been used by the group since 2021 to couple a voxel phantom to the EGSnrc user code. The ECM included an environmental dosimetry problem simulation. Most of these tools are accessible on the GDN page (http://dosimetrianumerica.org).

Optimized Virtual Sources Distributions for 3-D Ultrafast Diverging Wave Compounding Imaging: A Simulation Study.

Ultrafast ultrasound imaging allows observing rapid phenomena; combined with 3-D imaging it has the potential to provide more accurate analysis on organs which leads, at the end, to better diagnosis. Coherent compounding using diverging waves is commonly used to reconstruct high quality images on large volumes while keeping frame rate high enough to allow dynamic analysis. In practice, the virtual sources that drive the diverging waves are often distributed by a deterministic way: following a regular grid, concentric rings and spirals. Even though those deterministic distributions can offer various trade-off in terms of imaging performance, other distributions can be considered to improve imaging performance. It is herein suggested to look at alternative virtual sources distributions for optimizing the lateral resolution and the secondary lobes level on several PSFs by means of a multiobjective genetic algorithm. The optimization framework leads to seven pseudo-irregular distributions of virtual source distributions that have not yet been found in the literature. An analysis on the imaging performance with a simulated phantom shows that these new distributions offer different trade-offs between lateral resolution and contrast, respectively measured on point-like reflectors and anechoic cysts. As an example, one of these optimized distributions improves the lateral resolution by 16% and gives equivalent contrast values on cysts, when compared to a concentric-rings-based distribution.

Verification of relationship between transmission/reception conditions of high-frequency plane wave compounding and evaluation accuracy of extended amplitude envelope statistics

Amplitude envelopment statistics is one of the effective analysis method for quantitative ultrasound. In the studies using a single transducer of around 15–25 MHz and the Double-Nakagami (DN) model, it was possible to evaluate fat mass in rat livers. In order to realize clinical applications, it is essential to study with array probes, especially to understand the influence of the sound field characteristics on the amplitude envelope statistics. In this study, fatty liver simulated phantoms which were containing two types of scatterers was observed with five types of high-frequency(15–40 MHz) linear array probes, and the amplitude enveloping characteristics were analyzed using the DN model to evaluate the fat constitution. As a result, it was possible to evaluate fatty components with high accuracy under conditions in which fat is distributed in low density, such as in mild fatty liver. No significant differences in evaluation systems were observed among the probes, probably due to the effect of parallel plane wave transmission and reception.

DEASA studies and applications to space physics and muon tomography

The high energy cosmic rays entering the Earth’s atmosphere throw light upon many aspects of Astroparticle and Particle Physics. This work outlines investigative learning about these high energy primaries based on a mini array DEASA in Agra,India. DEASA (Dayalbagh Educational Air Shower Array) consists of eight plastic scintillators each with an area of 1 square meter. This array covers an area of 260 square meters and is the first array in the northern part of our country. A real-life application of these highly energetic particles has been to find the best material to protect the astronaut from them in form of galactic cosmic rays (GCR). Geant4 based hadronic binary model was used in simulation of phantom, vehicle, SEP (Solar Energetic Particles) and GCR shield. The SEP shielding material was fixed as water and GCR shield was varied among aluminum, Polystyrene and Polyethylene. The poly materials were found to be the best due to large amount of hydrogen (H) and low atomic number (Z). In this work the equivalent dose deposited in the phantom with Polystyrene material for GCR shield was calculated to be minimum (107 sievert) as compared to the other materials.In the second application, the high energy muons have been studied to image nuclear caskets through muon tomography.In this Monte Carlo based simulation, a dry cask container containing different number of the UO2 rods have been bombarded with definite energy muons to measure the muon scattering .The parameters computed in this work are energy loss,radiation length and scattering angle which can calibrate these containers for correct identification of nuclear wastage.

Robotics in Screening, Diagnosis and Treatment of Breast Cancer: A Perspective View

This perspective article aims to summarize and provide an outlook for developments around the use of robotics in the screening, diagnosis and treatment of breast cancer. We searched existing literature on the design and development of new systems and the current use of pre-existing surgical robotic systems. Robotic interventions for breast palpation and biopsy under ultrasound and MRI guidance are being developed and tested on simulated breast phantoms. Results are comparable to those achieved by clinicians; however, there are yet to be any human trials. Existing robotic surgical systems have been evaluated in human trials to perform nipple-sparing mastectomy and harvesting of autologous tissue for breast reconstruction. Results are comparable to traditional NSM and demonstrate positive short-term outcomes for patients. Robotic devices could revolutionize the clinical workflow around breast cancer through less invasive surgery, greater accuracy in biopsies and microsurgery and a potential reduction in clinicians’ workload. However, more research into the practical deployment of these devices and concrete scientific evidence of better patient outcomes is needed.

Evaluation of tractogram filtering methods using human-like connectome phantoms

Tractography algorithms are prone to reconstructing spurious connections. The set of streamlines generated with tractography can be post-processed to retain the streamlines that are most biologically plausible. Several microstructure-informed filtering algorithms are available for this purpose, however, the comparative performance of these methods has not been extensively evaluated. In this study, we aim to evaluate streamline filtering and post-processing algorithms using simulated connectome phantoms. We first establish a framework for generating connectome phantoms featuring brain-like white matter fiber architectures. We then use our phantoms to systematically evaluate the performance of a range of streamline filtering algorithms, including SIFT, COMMIT, and LiFE. We find that all filtering methods successfully improve connectome accuracy, although filter performance depends on the complexity of the underlying white matter fiber architecture. Filtering algorithms can markedly improve tractography accuracy for simple tubular fiber bundles (F-measure deterministic– unfiltered: 0.49 and best filter: 0.72; F-measure probabilistic– unfiltered: 0.37 and best filter: 0.81), but for more complex brain-like fiber architectures, the improvement is modest (F-measure deterministic– unfiltered: 0.53 and best filter: 0.54; F-measure probabilistic– unfiltered: 0.46 and best filter: 0.50). Overall, filtering algorithms have the potential to improve the accuracy of connectome mapping pipelines, particularly for weighted connectomes and pipelines using probabilistic tractography methods. Our results highlight the need for further advances tractography and streamline filtering to improve the accuracy of connectome mapping.

Image Reconstruction Using Supervised Learning in Wearable Electrical Impedance Tomography of the Thorax.

Electrical impedance tomography (EIT) is a non-invasive technique for visualizing the internal structure of a human body. Capacitively coupled electrical impedance tomography (CCEIT) is a new contactless EIT technique that can potentially be used as a wearable device. Recent studies have shown that a machine learning-based approach is very promising for EIT image reconstruction. Most of the studies concern models containing up to 22 electrodes and focus on using different artificial neural network models, from simple shallow networks to complex convolutional networks. However, the use of convolutional networks in image reconstruction with a higher number of electrodes requires further investigation. In this work, two different architectures of artificial networks were used for CCEIT image reconstruction: a fully connected deep neural network and a conditional generative adversarial network (cGAN). The training dataset was generated by the numerical simulation of a thorax phantom with healthy and illness-affected lungs. Three kinds of illnesses, pneumothorax, pleural effusion, and hydropneumothorax, were modeled using the electrical properties of the tissues. The thorax phantom included the heart, aorta, spine, and lungs. The sensor with 32 area electrodes was used in the numerical model. The ECTsim custom-designed toolbox for Matlab was used to solve the forward problem and measurement simulation. Two artificial neural networks were trained with supervision for image reconstruction. Reconstruction quality was compared between those networks and one-step algebraic reconstruction methods such as linear back projection and pseudoinverse with Tikhonov regularization. This evaluation was based on pixel-to-pixel metrics such as root-mean-square error, structural similarity index, 2D correlation coefficient, and peak signal-to-noise ratio. Additionally, the diagnostic value measured by the ROC AUC metric was used to assess the image quality. The results showed that obtaining information about regional lung function (regions affected by pneumothorax or pleural effusion) is possible using image reconstruction based on supervised learning and deep neural networks in EIT. The results obtained using cGAN are strongly better than those obtained using a fully connected network, especially in the case of noisy measurement data. However, diagnostic value estimation showed that even algebraic methods allow us to obtain satisfactory results.

Development and Evaluation of Doppler Ultrasound Training Phantom for Human Vessel Simulation

The purpose of this study was to create a Doppler ultrasound training phantom aimed at aiding beginners in comprehending and effectively utilizing critical parameters during the learning process. Our designed training phantom does not require the use of a water pump or an automated injector. The fabrication of the vessel-mimicking phantom was accomplished using agarose gel. We utilized LEGO blocks to introduce a height difference that simulated blood flow within the phantom. The imitation blood material was prepared using glycerin. Ultrasound images were obtained using an Accuvix V10 device. This study utilized a Doppler ultrasound training phantom to facilitate stable imaging for beginners during scanning, due to its secure fixation. Furthermore, the fabricated vessel-mimicking phantom offers the advantage of adjusting the diameter of vessels during the fabrication process. Additionally, the easy adaptability, to tailor the phantom specifically for certain conditions by modifying only the vascular components, is another notable advantage. The experimental values for parameters such as the color box, scale, and color gain were collected. The spectral Doppler was used for a rough assessment of blood flow velocity. Color Doppler images, acquired via adjusting the color box to the left and right, displayed blood flow information in blue on the left, and red on the right. At a scale setting of 4 kHz and 0.6 kHz for color Doppler imaging, aliasing was absent at 4 kHz, but appeared at 0.6 kHz. Experiments involving various gain settings (2 dB, 5 dB, 10 dB, 35 dB, 60 dB, and 100 dB) demonstrated that the blood flow information was diminished at 2 dB, and exaggerated at 100 dB.

Importance sampling-accelerated simulation of full-spectrum backscattered diffuse reflectance.

The Monte Carlo (MC) method is one of the most widely used numerical tools to model the light interaction with tissue. However, due to the low photon collection efficiency and the need to simulate the entire emission spectrum, it is computationally expensive to simulate the full-spectrum backscattered diffuse reflectance (F-BDR). Here, we propose an acceleration scheme based on importance sampling (IS). We derive the biasing sampling function tailored for simulating BDR based on the two-term scattering phase function (TT). The parameters of the TT function at different wavelengths are directly obtained by fitting the Mie scattering phase function. Subsequently, we incorporate the TT function and its corresponding biased function into the redefined IS process and realize the accelerated simulation of F-BDR. Phantom simulations based on the Fourier-domain optical coherence tomography (FD-OCT) are conducted to demonstrate the efficiency of the proposed method. Compared to the original simulator without IS, our proposed method achieves a 373× acceleration in simulating the F-BDR of the multi-layer phantom with a relative mean square error (rMSE) of less than 2%. Besides, by parallelly computing A-lines, our method enables the simulation of an entire B-scan in less than 0.4 hours. To our best knowledge, it is the first time that a volumetric OCT image of a complex phantom is simulated. We believe that the proposed acceleration method can be readily applied to fast simulations of various F-BDR-dependent applications. The source codes of this manuscript are also publicly available online.

3D CEST MRI with an unevenly segmented RF irradiation scheme: A feasibility study in brain tumor imaging.

To integrate 3D CEST EPI with an unevenly segmented RF irradiation module and preliminarily demonstrate it in the clinical setting. A CEST MRI with unevenly segmented RF saturation was implemented, including a long primary RF saturation to induce the steady-state CEST effect, maintained with repetitive short secondary RF irradiation between readouts. This configuration reduces relaxation-induced blur artifacts during acquisition, allowing fast 3D spatial coverage. Numerical simulations were performed to select parameters such as flip angle (FA), short RF saturation duration (Ts2), and the number of readout segments. The sequence was validated experimentally with data from a phantom, healthy volunteers, and a brain tumor patient. Based on the numerical simulation and l-carnosine gel phantom experiment, FA, Ts2, and the number of segments were set to 20°, 0.3 s, and the range from 4 to 8, respectively. The proposed method minimized signal modulation in the human brain images in the kz direction during the acquisition and provided the blur artifacts-free CEST contrast over the whole volume. Additionally, the CEST contrast in the tumor tissue region is higher than in the contralateral normal tissue region. It is feasible to implement a highly accelerated 3D EPI CEST imaging with unevenly segmented RF irradiation.

Ray theory-based compounded plane wave ultrasound imaging for aberration corrected transcranial imaging: Phantom experiments and simulations

Compounded plane wave imaging (CPWI) allows high-frame-rate measurement and has been one of the most promising modalities for real-time brain imaging. However, ultrasonic brain imaging using the CPWI modality is usually performed with a worn thin or removal of the skull layer. Otherwise, the skull layer is expected to distort the ultrasonic wavefronts and significantly decrease intracranial imaging quality. The motivation of this study is to investigate a CPWI method for transcranial brain imaging with the skull layer. A coordinate transformation ray-tracing (CTRT) approach was proposed to track the distorted ultrasonic wavefronts and calculate the time delays for the ultrasound plane wave passing through the skull layer. With an accurate correction for the time delays in beamforming, the CTRT-based CPWI could achieve high-quality intracranial images with the presence of skulls. The proposed CTRT-based CPWI method was verified using a simplified three-layer transcranial model. The full-wave simulation demonstrated that CTRT could accurately (i.e., relative percentage error less than0.18%) track the distorted transmitting wavefront through skull. Compared with the CPWI without aberration correction, the CTRT-based CPWI provided high-quality intracranial imaging and could accurately localize intracranial point scatterers; specifically, positioning error decreases from 0.5 mm to 0.1 mm on average in the axial direction and from 0.7 mm to 0.1 mm on average in the lateral direction. As the compounded angles increased in the CTRT-based CPWI, the contrast improved by 16.2 dB on average for the region of interest, and the array performance indicator (representing resolution) decreased by 4.0 on average for the intracranial point scatterers. The CTRT is of low computational cost compared with full wave simulation. This study suggested that the proposed CTRT-based CPWI might have the potential for real-time and non-invasive transcranial aberration-corrected imaging.

Three-dimensional ultrasound to investigate synovitis in first carpometacarpal osteoarthritis: A feasibility study.

Synovitis is one of the defining characteristics of osteoarthritis (OA) in the carpometacarpal (CMC1) joint of the thumb. Quantitative characterization of synovial volume is important for furthering our understanding of CMC1 OA disease progression, treatment response, and monitoring strategies. In previous studies, three-dimensional ultrasound (3-D US) has demonstrated the feasibility of being a point-of-care system for monitoring knee OA. However, 3-D US has not been tested on the smaller joints of the hand, which presents unique physiological and imaging challenges. To develop and validate a novel application of 3-D US to monitor soft-tissue characteristics of OA in a CMC1 OA patient population compared to the current gold standard, magnetic resonance imaging (MRI). A motorized submerged transducer moving assembly was designed for this device specifically for imaging the joints of the hands and wrist. The device used a linear 3-D scanning approach, where a 14L5 2-D transducer was translated over the region of interest. Two imaging phantoms were used to test the linear and volumetric measurement accuracy of the 3-D US device. To evaluate the accuracy of the reconstructed 3-D US geometry, a multilayer monofilament string-grid phantom (10mm square grid) was scanned. To validate the volumetric measurement capabilities of the system, a simulated synovial tissue phantom with an embedded synovial effusion was fabricated and imaged. Ten CMC1 OA patients were imaged by our 3-D US and a 3.0 T MRI system to compare synovial volumes. The synovial volumes were manually segmented by two raters on the 2D slices of the 3D US reconstruction and MR images, to assess the accuracy and precision of the device for determining synovial tissue volumes. The Standard Error of Measurement and Minimal Detectable Change was used to assess the precision and sensitivity of the volume measurements. Paired sample t-tests were used to assess statistical significance. Additionally, rater reliability was assessed using Intra-Class Correlation (ICC) coefficients. The largest percent difference observed between the known physical volume of synovial extrusion in the phantom and the volume measured by our 3D US was 1.1% (p-value=0.03). The mean volume difference between the 3-D US and the gold standard MRI was 1.78% (p-value=0.48). The 3-D US synovial tissue volume measurements had a Standard Error Measurement (SEm ) of 11.21mm3 and a Minimal Detectible Change (MDC) of 31.06mm3 , while the MRI synovial tissue volume measurements had an SEM of 16.82mm3 and an MDC of 46.63mm3 . Excellent inter- and intra-rater reliability (ICCs=0.94-0.99) observed across all imaging modalities and raters. Our results indicate the feasibility of applying 3-D US technology to provide accurate and precise CMC1 synovial tissue volume measurements, similar to MRI volume measurements. Lower MDC and SEm values for 3-D US volume measurements indicate that it is a precise measurement tool to assess synovial volume and that it is sensitive to variation between volume segmentations. The application of this imaging technique to monitor OA pathogenesis and treatment response over time at the patient's bedside should be thoroughly investigated in future studies.