Numerical Phantom Research Articles

Automated Restarting Fast Proximal Gradient Descent Method for Single-View Cone-Beam X-ray Luminescence Computed Tomography Based on Depth Compensation.

Single-view cone-beam X-ray luminescence computed tomography (CB-XLCT) has recently gained attention as a highly promising imaging technique that allows for the efficient and rapid three-dimensional visualization of nanophosphor (NP) distributions in small animals. However, the reconstruction performance is hindered by the ill-posed nature of the inverse problem and the effects of depth variation as only a single view is acquired. To tackle this issue, we present a methodology that integrates an automated restarting strategy with depth compensation to achieve reconstruction. The present study employs a fast proximal gradient descent (FPGD) method, incorporating L0 norm regularization, to achieve efficient reconstruction with accelerated convergence. The proposed approach offers the benefit of retrieving neighboring multitarget distributions without the need for CT priors. Additionally, the automated restarting strategy ensures reliable reconstructions without the need for manual intervention. Numerical simulations and physical phantom experiments were conducted using a custom CB-XLCT system to demonstrate the accuracy of the proposed method in resolving adjacent NPs. The results showed that this method had the lowest relative error compared to other few-view techniques. This study signifies a significant progression in the development of practical single-view CB-XLCT for high-resolution 3-D biomedical imaging.

Emulating Low-Dose PCCT Image Pairs with Independent Noise for Self-Supervised Spectral Image Denoising.

Photon counting CT (PCCT) acquires spectral measurements and enables generation of material decomposition (MD) images that provide distinct advantages in various clinical situations. However, noise amplification is observed in MD images, and denoising is typically applied. Clean or high-quality references are rare in clinical scans, often making supervised learning (Noise2Clean) impractical. Noise2Noise is a self-supervised counterpart, using noisy images and corresponding noisy references with zero-mean, independent noise. PCCT counts transmitted photons separately, and raw measurements are assumed to follow a Poisson distribution in each energy bin, providing the possibility to create noise-independent pairs. The approach is to use binomial selection to split the counts into two low-dose scans with independent noise. We prove that the reconstructed spectral images inherit the noise independence from counts domain through noise propagation analysis and also validated it in numerical simulation and experimental phantom scans. The method offers the flexibility to split measurements into desired dose levels while ensuring the reconstructed images share identical underlying features, thereby strengthening the model's robustness for input dose levels and capability of preserving fine details. In both numerical simulation and experimental phantom scans, we demonstrated that Noise2Noise with binomial selection outperforms other common self-supervised learning methods based on different presumptive conditions.

Novel Self-Calibrated Threshold-Free Probabilistic Fibrosis Signature Technique for 3D Late Gadolinium Enhancement MRI.

Myocardial fibrosis, marked by excessive collagen buildup in the heart, is a crucial severity marker of heart muscle injury in several heart diseases, such as myocardial infarction, cardiomyopathies, and atrial fibrillation (AF). It is also vital for evaluating the efficacy of induced scarring (dense fibrosis) post-interventions, such as catheter ablation for AF. Cardiac MRI emerged as the gold standard for evaluating myocardial fibrosis and scarring for diagnosis and intervention planning. However, existing 3D cardiac MRI (CMR) fibrosis analysis methods are unreliable as they rely on variable thresholding and suffer from a lack of standardization and high sensitivity to typical MRI uncertainties. Importantly, these methods quantify severity based on fibrosis volume alone while ignoring the unique MRI characteristics of fibrosis distribution, which could better inform on disease severity. To address these limitations, we propose a novel thresholdfree and self-calibrating probabilistic method named "Fibrosis Signatures" for a comprehensive and reliable fibrosis analysis of 3D MRI cardiac images. Through a novel efficient (linear complexity) probabilistic encoding of 'multibillion' MRI intensity disparities into standardized probability density function, our method derives the patient's unique fibrosis signature profile and index (FSI). Our approach goes beyond mere measuring of fibrosis volume; it encodes both the extent and the unique MRI characteristics of fibrosis distribution beyond mere entropy for a more detailed evaluation of fibrosis burden/severity. Our self-calibrating design effectively adjusts for MRI uncertainties like noise, low spatial resolution, and segmentation errors to ensure robust and reproducible fibrosis evaluation pre- and post-intervention. Validated in numerical phantom and 143 in vivo MRI scans of AF patients and compared to five baseline methods, our method showed strong correlations with traditional volume measures of pre-intervention fibrosis and post-intervention scar and was up to 9- times more reliable and reproducible, highlighting its potential to enhance cardiac MRI's utility.

Full-wave Image Reconstruction in Transcranial Photoacoustic Computed Tomography using a Finite Element Method.

Transcranial photoacoustic computed tomography presents challenges in human brain imaging due to skull-induced acoustic aberration. Existing full-wave image reconstruction methods rely on a unified elastic wave equation for skull shear and longitudinal wave propagation, therefore demanding substantial computational resources. We propose an efficient discrete imaging model based on finite element discretization. The elastic wave equation for solids is solely applied to the hard-tissue skull region, while the soft-tissue or coupling-medium region that dominates the simulation domain is modeled with the simpler acoustic wave equation for liquids. The solid-liquid interfaces are explicitly modeled with elastic-acoustic coupling. Furthermore, finite element discretization allows coarser, irregular meshes to conform to object geometry. These factors significantly reduce the linear system size by 20 times to facilitate accurate whole-brain simulations with improved speed. We derive a matched forward-adjoint operator pair based on the model to enable integration with various optimization algorithms. We validate the reconstruction framework through numerical simulations and phantom experiments.

Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) is an emerging imaging modality that holds great promise for breast imaging. Full-waveform inversion (FWI)-based image reconstruction methods incorporate accurate wave physics to produce high spatial resolution quantitative images of speed of sound or other acoustic properties of the breast tissues from USCT measurement data. However, the high computational cost of FWI reconstruction represents a significant burden for its widespread application in a clinical setting. The research reported here investigates the use of a convolutional neural network (CNN) to learn a mapping from USCT waveform data to speed of sound estimates. The CNN was trained using a supervised approach with a task-informed loss function aiming at preserving features of the image that are relevant to the detection of lesions. A large set of anatomically and physiologically realistic numerical breast phantoms (NBPs) and corresponding simulated USCT measurements was employed during training. Once trained, the CNN can perform real-time FWI image reconstruction from USCT waveform data. The performance of the proposed method was assessed and compared against FWI using a hold-out sample of 41 NBPs and corresponding USCT data. Accuracy was measured using relative mean square error (RMSE), structural self-similarity index measure (SSIM), and lesion detection performance (DICE score). This numerical experiment demonstrates that a supervised learning model can achieve accuracy comparable to FWI in terms of RMSE and SSIM, and better performance in terms of task performance, while significantly reducing computational time.

Three-Dimensional Variable Slab-Selective Projection Acquisition Imaging.

Three-dimensional (3D) projection acquisition (PA) imaging has recently gained attention because of its advantages, such as achievability of very short echo time, less sensitivity to motion, and undersampled acquisition of projections without sacrificing spatial resolution. However, larger subjects require a stronger Nyquist criterion and are more likely to be affected by outer-volume signals outside the field of view (FOV), which significantly degrades the image quality. Here, we proposed a variable slab-selective projection acquisition (VSS-PA) method to mitigate the Nyquist criterion and effectively suppress aliasing streak artifacts in 3D PA imaging. The proposed method involves maintaining the vertical orientation of the slab-selective gradient for frequency-selective spin excitation and the readout gradient for data acquisition. As VSS-PA can selectively excite spins only in the width of the desired FOV in the projection direction during data acquisition, the effective size of the scanned object that determines the Nyquist criterion can be reduced. Additionally, unwanted signals originating from outside the FOV (e.g., aliasing streak artifacts) can be effectively avoided. The mitigation of the Nyquist criterion owing to VSS-PA was theoretically described and confirmed through numerical simulations and phantom and human lung experiments. These experiments further showed that the aliasing streak artifacts were nearly suppressed.

Effect of detector placement on joint estimation in X-ray fluorescence emission tomography.

Imaging the spatial distribution of low concentrations of metal is a growing problem of interest with applications in medical and material sciences. X-ray fluorescence emission tomography (XFET) is an emerging metal mapping imaging modality with potential sensitivity improvements and practical advantages over other methods. However, XFET detector placement must first be optimized to ensure accurate metal density quantification and adequate spatial resolution. In this work, we first use singular value decomposition of the imaging model and eigendecomposition of the object-specific Fisher information matrix to study how detector arrangement affects spatial resolution and feature preservation. We then perform joint image reconstructions of a numerical gold phantom. For this phantom, we show that two parallel detectors provide metal quantification with similar accuracy to four detectors, despite the resulting anisotropic spatial resolution in the attenuation map estimate. Two orthogonal detectors provide improved spatial resolution along one axis, but underestimate the metal concentration in distant regions. Therefore, this work demonstrates the minor effect of using fewer, but strategically placed, detectors in the case where detector placement is restricted. This work is a critical investigation into the limitations and capabilities of XFET prior to its translation to preclinical and benchtop uses.

Exploiting gradient-echo frequency evolution: Probing white matter microstructure and extracting bulk susceptibility-induced frequency for quantitative susceptibility mapping.

This work is to investigate the microstructure-induced frequency shift in white matter (WM) with crossing fibers and to separate the microstructure-related frequency shift from the bulk susceptibility-induced frequency shift by model fitting the gradient-echo (GRE) frequency evolution for potentially more accurate quantitative susceptibility mapping (QSM). A hollow-cylinder fiber model (HCFM) with two fiber populations was developed to investigate GRE frequency evolutions in WM voxels with microstructural orientation dispersion. The simulated and experimentally measured TE-dependent local frequency shift was then fitted to a simplified frequency evolution model to obtain a microstructure-related frequency difference parameter ( ) and a TE-independent bulk susceptibility-induced frequency shift ( ). The obtained was then used for QSM reconstruction. Reconstruction performances were evaluated using a numerical head phantom and in vivo data and then compared to other multi-echo combination methods. GRE frequency evolutions and -based tissue parameters in both parallel and crossing fibers determined from our simulations were comparable to those observed in vivo. The TE-dependent frequency fitting method outperformed other multi-echo combination methods in estimating in simulations. The fitted , , and QSM could be improved further by navigator-based B0 fluctuation correction. A HCFM with two fiber populations can be used to characterize microstructure-induced frequency shifts in WM regions with crossing fibers. HCFM-based TE-dependent frequency fitting provides tissue contrast related to microstructure ( ) and in addition may help improve the quantification accuracy of and the corresponding QSM.

A predictor-corrector phase unwrapping algorithm for temporally undersampled gradient-echo MRI.

To develop a method for unwrapping temporally undersampled and nonlinear gradient recalled echo (GRE) phase. Temporal unwrapping is performed as a sequential one step prediction of the echo phase, followed by a correction to the nearest integer wrap-count. A spatio-temporal extension of the 1D predictor corrector unwrapping (PCU) algorithm improves the prediction accuracy, and thereby maintains spatial continuity. The proposed method is evaluated using numerical phantom, physical phantom, and in vivo brain data at both 3 T and 9.4 T. The unwrapping performance is compared with the state-of-the-art temporal and spatial unwrapping algorithms, and the spatio-temporal iterative virtual-echo based Nyquist sampled (iVENyS) algorithm. Simulation results showed significant reduction in unwrapping errors at higher echoes compared with the state-of-the-art algorithms. Similar to the iVENyS algorithm, the PCU algorithm was able to generate spatially smooth phase images for in vivo data acquired at 3 T and 9.4 T, bypassing the use of additional spatial unwrapping step. A key advantage over iVENyS algorithm is the superior performance of PCU algorithm at higher echoes. PCU algorithm serves as a robust phase unwrapping method for temporally undersampled and nonlinear GRE phase, particularly in the presence of high field gradients.

Volumetric and Simultaneous Photoacoustic and Ultrasound Imaging with a Conventional Linear Array in a Multiview Scanning Scheme.

Volumetric, multimodal imaging with precise spatial and temporal co-registration can provide valuable and complementary information for diagnosis and monitoring. Considerable research has sought to combine 3D photoacoustic (PA) and ultrasound (US) imaging in clinically translatable configurations. However, technical compromises currently result in poor image quality either for photoacoustic or ultrasonic modes. This work aims to provide translatable, high quality, simultaneously co-registered dual-mode PA/US 3D tomography. Volumetric imaging based on a synthetic aperture approach was implemented by interlacing PA and US acquisitions during a rotate-translate scan with a 5-MHz linear array (12 angles and 30-mm translation to image a 21-mm diameter, 19 mm long cylindrical volume within 21 seconds). For co-registration, an original calibration method using a specifically designed thread phantom was developed to estimate 6 geometrical parameters and 1 temporal off-set through global optimization of the reconstructed sharpness and superposition of calibration phantom structures. Phantom design and cost function metrics were selected based on analysis of a numerical phantom, and resulted in a high estimation accuracy for the 7 parameters. Experimental estimations validated the calibration repeatability. The estimated parameters were used for bimodal reconstruction of additional phantoms with either identical or distinct spatial distributions of US and PA contrasts. Superposition distance of the two modes was within < 10% of the acoustic wavelength and a wavelength-order uniform spatial resolution was obtained. This dual-mode PA/US tomography should contribute to more sensitive and robust detection and follow-up of biological changes or the monitoring slower-kinetic phenomena in living systems such as the accumulation of nanoagents.

CEST and nuclear Overhauser enhancement imaging withdeep learning-extrapolated semisolid magnetization transfer reference: Scan-rescan reproducibility andreliability studies.

To develop a novel MR physics-driven, deep-learning, extrapolated semisolid magnetization transfer reference (DeepEMR) framework to provide fast, reliable magnetization transfer contrast (MTC) and CEST signal estimations, and to determine the reproducibility and reliability of the estimates from the DeepEMR. A neural network was designed to predict a direct water saturation and MTC-dominated signal at a certain CEST frequency offset using a few high-frequency offset features in the Z-spectrum. The accuracy, scan-rescan reproducibility, and reliability of MTC, CEST, and relayed nuclear Overhauser enhancement (rNOE) signals estimated from the DeepEMR were evaluated on numerical phantoms and in heathy volunteers at 3 T. In addition, we applied the DeepEMR method to brain tumor patients and compared tissue contrast with other CEST calculation metrics. The DeepEMR method demonstrated a high degree of accuracy in the estimation of reference MTC signals at ±3.5 ppm for APT and rNOE imaging, and computational efficiency (˜190-fold) compared with a conventional fitting approach. In addition, the DeepEMR method achieved high reproducibility and reliability (intraclass correlation coefficient = 0.97, intersubject coefficient of variation = 3.5%, and intrasubject coefficient of variation = 1.3%) of the estimation of MTC signals at ±3.5 ppm. In tumor patients, DeepEMR-based amide proton transfer images provided higher tumor contrast than a conventional MT ratio asymmetry image, particularly at higher B1 strengths (>1.5 μT), with a distinct delineation of the tumor core from normal tissue or peritumoral edema. The DeepEMR approach is feasible for measuring clean APT and rNOE effects in longitudinal and cross-sectional studies with low scan-rescan variability.

Evaluation of Electron Tomography Capabilities for Shale Imaging.

Despite the advantageous resolution of electron tomography (ET), reconstruction of three-dimensional (3D) images from multiple two-dimensional (2D) projections presents several challenges, including small signal-to-noise ratios, and a limited projection range. This study evaluates the capabilities of ET for thin sections of shale, a complex nanoporous medium. A numerical phantom with 1.24 nm pixel size is constructed based on the tomographic reconstruction of a Barnett shale. A dataset of 2D projection images is numerically generated from the 3D phantom and studied over a range of conditions. First, common reconstruction techniques are used to reconstruct the shale structure. The reconstruction uncertainty is quantified by comparing overall values of storage and transport metrics, as well as the misclassification of pore voxels compared to the phantom. We then select the most robust reconstruction technique and we vary the acquisition conditions to quantify the effect of artifacts. We find a strong agreement for large pores over the different acquisition workflows, while a wider variability exists for nanometer-scale features. The limited projection range and reconstruction are identified as the main experimental bottlenecks, thereby suggesting that sample thinning, advanced holders, and advanced reconstruction algorithms offer opportunities for improvement.

Bi-component dictionary matching for MR fingerprinting for efficient quantification of fat fraction and water T1 in skeletal muscle.

To propose an efficient bi-component MR fingerprinting (MRF) fitting method using a Variable Projection (VARPRO) strategy, applied to the quantification of fat fraction (FF) and water T1 ( ) in skeletal muscle tissues. The MRF signals were analyzed in a two-step process by comparing them to the elements of separate water and fat dictionaries (bi-component dictionary matching). First, each pair of water and fat dictionary elements was fitted to the acquired signal to determine an optimal FF that was used to merge the fingerprints in a combined water/fat dictionary. Second, standard dictionary matching was applied to the combined dictionary for determining the remaining parameters. A clustering method was implemented to further accelerate the fitting. Accuracy, precision, and matching time of this approach were evaluated on both numerical and in vivo datasets, and compared to the reference dictionary-matching approach that includes FF as a dictionary parameter. In numerical phantoms, all MRF parameters showed high correlation with ground truth for the reference and the bi-component method (R2 > 0.98). In vivo, the estimated parameters from the proposed method were highly correlated with those from the reference approach (R2 > 0.997). The bi-component method achieved an acceleration factor of up to 360 compared to the reference dictionary matching. The proposed bi-component fitting approach enables a significant acceleration of the reconstruction of MRF parameter maps for fat-water imaging, while maintaining comparable precision and accuracy to the reference on FF and estimation.

An edge-preserving total nuclear variation minimization algorithm in EPR image reconstruction

ObjectiveCurrently, slow scanning speed has become a technical bottleneck limiting the development of electron paramagnetic resonance imaging (EPRI), a novel oxygen imaging modality. Image reconstruction from sparse-view projections is an important imaging configuration to achieve fast scanning in EPRI. However, EPR images reconstructed from sparse-view projections using traditional analytic algorithms suffer from sparse artifacts, causing severe image degradation. In this work, we aim to use an optimization-based approach to achieve high-precision sparse reconstruction for EPRI. Methods. Inspired by the success of total nuclear variation (TVN) for denoising multi-channel spectral CT images and adaptive-weighted total variation (awTV) for improving the edge-preserving performance of TV, we proposed an edge-preserving total nuclear variation (EPTVN) minimization algorithm for sparse reconstruction in EPRI. The EPTVN combines the advantage of TVN in modelling structural similarity and low-rank prior between multi-channel images for denoising with the advantage of awTV for edge-preserving, which can better protect the edge structure of images while suppressing sparse artifacts. Results. We designed two numerical phantoms with different properties, a piecewise-constant phantom and a gradient phantom, and a physical phantom, and then performed inverse crime, simulation study and real-data study. Experimental results show that the EPTVN minimization algorithm can reconstruct more accurate EPR images from sparse-view projections for both the numerical and physical phantoms. Significance. The proposed method can effectively suppress sparse artifacts and protect edge structures of reconstructed images, thus achieving high-precision sparse-view reconstruction for EPRI. The insights of this work can also be applied to multi-channel image denoising tasks.

Photoacoustic tomography with a model-based approach involving realistic detector properties

A computational and experimental study is conducted to examine how directivity associated with a finite aperture sensor affects photoacoustic tomography (PAT) image reconstruction. Acoustic signals for the simulation work were computed using a discrete particle approach from three numerical phantoms including a vasculature. The theoretical framework and a Monte Carlo approach for construction of a tissue configuration are discussed in detail. While simulating forward data, the directivity of the sensor was taken into account. The image reconstruction was accomplished using system matrix based methods like l2 norm Tikhonov regularization, l1 norm regularization and total variation (TV) minimization. Accordingly, two different system matrices were constructed- (i) assuming transducer as a point detector (PD) and (ii) retaining properties of a finite detector with directivity (FDWD). Image reconstruction was also performed utilizing experimentally measured PA signals. Both the computational and experimental results demonstrate that blur-free PAT imaging can be achieved with the FDWD method. Additionally, TV minimization provides marginally better image reconstruction compared to the other schemes.

A Machine Learning-Based Classification Method for Monitoring Alzheimer’s Disease Using Electromagnetic Radar Data

Alzheimer’s and Parkinson’s diseases are two neurodegenerative brain disorders affecting more than 50 million people globally. Early diagnosis and appropriate assessment of disease progression are critical for treatment and improving patients’ health. Currently, the diagnosis of these neurodegenerative diseases is based primarily on mental status exams and neuroimaging scans, which are costly, time-consuming, and sometimes erroneous. Novel, cost-effective, and precise diagnostic tools and techniques are, thus, urgently required, particularly for early detection and prediction. In the recent decade, electromagnetic imaging has evolved as a cost-effective and noninvasive alternative approach for studying brain diseases. These studies focus on wearable and portable devices and imaging algorithms. However, microwave imaging cannot detect minimal changes in the brain at early stages accurately due to its lower resolution. This article investigates machine learning (ML) techniques for the early diagnosis of acute neurological diseases, especially Alzheimer’s disease (AD). A machine-learning-based classification method is proposed. Simulations are performed on realistic numerical brain phantoms using the CST studio suite to get the scattered signals. A novel data augmentation method is proposed to generate synthetic data required for ML algorithms. A deep neural network-based autoencoder extracts features to train various ML algorithms. The classification results are compared with raw data and manual feature extraction. The study shows that the proposed ML-based method could be used to monitor AD at its early stages.

Microwave Tomographic Imaging of Anatomically Realistic Numerical Phantoms with Debye Dispersion for Breast Cancer Detection Using a Regularized Inverse Scattering Technique in the Time Domain

Microwave Tomographic Imaging of Anatomically Realistic Numerical Phantoms with Debye Dispersion for Breast Cancer Detection Using a Regularized Inverse Scattering Technique in the Time Domain

Enabling strain imaging in realistic Eulerian ultrasound simulation methods

Cardiovascular strain imaging is continually improving due to ongoing advances in ultrasound acquisition and data processing techniques. The phantoms used for validation of new methods are often burdensome to make and lack flexibility to vary mechanical and acoustic properties. Simulations of US imaging provide an alternative with the required flexibility and ground truth strain data. However, the current Lagrangian US strain imaging models cannot simulate heterogeneous speed of sound distributions and higher-order scattering, which limits the realism of the simulations. More realistic Eulerian modelling techniques exist but have so far not been used for strain imaging. In this research, a novel sampling scheme was developed based on a band-limited interpolation of the medium, which enables accurate strain simulation in Eulerian methods. The scheme was validated in k-Wave using various numerical phantoms and by a comparison with Field II. The method allows for simulations with a large range in strain values and was accurate with errors smaller than −60 dB. Furthermore, an excellent agreement with the Fourier theory of US scattering was found. The ability to perform simulations with heterogeneous speed of sound distributions was demonstrated using a pulsating artery model. The developed sampling scheme contributes to more realistic strain imaging simulations, in which the effect of heterogenous acoustic properties can be taken into account.

Mapping MRI Intensity to the Dielectric Properties of Body Tissues Using Microwave Imaging

Mapping a nonlinear relationship between magnetic resonance imaging (MRI) intensity and the dielectric properties of body tissues is proposed using the microwave imaging. The main assumption in our approach is that in a specific tissue area a nonlinear mapping can be considered between the MRI intensity and the electromagnetic properties. Once this relationship is obtained, the MRI intensity information can be converted directly to the dielectric properties of tissues. The performance of the proposed method is evaluated using anatomically-realistic numerical breast phantoms. The results show that in the presence of different levels of noise, the relationship between MRI intensity and dielectric properties of tissues can be accurately retrieved.

Implants talk to each-other: RF heating changes when two DBS leads are present simultaneously during MRI.

Deep brain stimulation (DBS) has proven to be an effective treatment for Parkinson's disease and other brain disorders. The procedure often involves implanting two elongated leads aimed at specific brain nuclei in both the left and right hemispheres. However, evaluating the safety of MRI in patients with such implants has only been done on an individual lead basis, ignoring the possibility of crosstalk between the leads. This study evaluates the impact of crosstalk on power deposition at the lead tip through numerical simulation and phantom experiments. We used CT images to obtain patient-specific lead trajectories and compared the power deposition at the lead tip in cases with bilateral and unilateral DBS implants. Our results indicate that the RF power deposition at the lead tip can vary by up to 6-fold when two DBS leads are present together compared to when only one lead is present. Experimental measurements in a simplified case of two lead-only DBS systems confirmed the existence of crosstalk.Clinical Relevance-Our results indicate that RF heating of implanted leads during MRI can be affected by the presence of another lead in the body, which may increase or decrease the power deposition in the tissue depending on the position and configuration of the leads.