Numerical Phantom Research Articles

Evaluation of dose distributions and respiratory motion tolerance for layer-stacking conformal carbon-ion radiotherapy.

While layer-stacking irradiation provides a conformal dose distribution, it is vulnerable to respiratory motion. Considering that the motion tolerance has not yet been demonstrated, this study aimed to determine the tolerance level for the amount of target motion. Dose distributions considering motion were simulated for a numerical water phantom using in-house software. Comparisons with measured and simulated physical dose distributions confirmed the validity of the simulation, with gamma analysis showing almost 90% or greater agreement under all conditions with a criterion of 3%/3mm. The variation in physical dose from static conditions followed a similar trend. Based on the evaluation of the simulated clinical dose uniformity, motion tolerance was derived. The acceptable motion amounts in the lateral direction were 11mm in respiratory-ungated condition and at least 20mm with 30% lateral gating at 4Gy (RBE). In the longitudinal (beam upstream) direction, the acceptable target motion amounts were 3mm without gating and 6mm with gating. These results employed worst-case scenarios considering multiple respiratory cycles. In both lateral and longitudinal directions, the motion amounts of 3mm for non-gating and 5mm for gating were acceptable. The acceptable target motion amounts improved by 1-9mm with gating and increased prescribed doses. The dose uniformity and motion tolerance under multiple conditions, although based on a simple system, may be useful for treatment involving target motion in layer-stacking irradiation.

Ultrasound-assisted aberration correction of transcranial photoacoustic imaging based on angular spectrum theory

To correct the refraction aberration induced by the skull in photoacoustic imaging, a method for phase distortion compensation is proposed based on the angular spectrum theory with the aid of ultrasonic signals. This method first updates the speed of sound distribution by iteratively performing aberration correction in the ultrasonic reconstruction. Then the speed of sound distribution obtained with ultrasound-assisted serves as prior knowledge to address phase distortion compensation by adjusting the phase shift factor of the wavefront in different media. Finally, the aberration-corrected ultrasonic-photoacoustic dual-modality image can be obtained. Numerical simulations and phantom experiments confirm the effectiveness of this method. Specifically, in simulations, the position error of the proposed method is reduced from −13.61% to 1.27% in depth compared to the method based on the reconstruction with constant speed. Moreover, a real ex-vivo rabbit skull experiment illustrates the potential biological application of the proposed method in transcranial photoacoustic imaging.

Eliminating metal artifacts in dental computed tomography using an elaborate sinogram normalization interpolation method with CNR-based metal segmentation

Metal artifact reduction (MAR) in dental cone-beam computed tomography (CBCT) is critical for improving its clinical usefulness and remains a challenging problem. This study presents an elaborate sinogram normalization interpolation (SNI) method with contrast-to-noise ratio (CNR)-based metal segmentation to eliminate metal artifacts in dental CBCT. The proposed MAR method involves three main steps: (1) CNR-based segmentation of a metal trace in the sinogram domain; (2) generation of a residual artifact-reduced prior sinogram; and (3) sinogram completion using the prior sinogram, followed by filtered-backprojection (FBP)-based CBCT reconstruction and metal insertion processes. To verify the efficacy of the proposed method, simulations and experiments were performed using numerical and physical dental phantoms with several metal inserts. The quality of the resulting CBCT images was quantitatively evaluated using the structural similarity (SSIM) metric and compared to those obtained with other interpolation-based MAR methods. A tabletop setup for the micro-CT system was used in the experiment, which comprised an X-ray tube operated under tube conditions of 70 kV p and 5 mA and a flat-panel detector with a pixel size of 198 μm. Our results indicate that the CNR-based metal segmentation method precisely identified traces of metallic objects on the sinogram, and thereby, the proposed SNI-based MAR method considerably reduced metal artifacts in dental CBCT images without introducing any contrast anomalies. The SSIM values of the CBCT images obtained with the proposed MAR method were 0.99 and 0.95 for the simulation and experiment, respectively, which are approximately 11% and 9% greater than those with a simple threshold-based linear interpolation method, respectively, improving the image quality. The proposed MAR method can be applied to reduce metal artifacts in real-world dental CBCT systems.

Contrast-to-noise ratio comparison between X-ray fluorescence emission tomography and computed tomography.

We provide a comparison of X-ray fluorescence emission tomography (XFET) and computed tomography (CT) for detecting low concentrations of gold nanoparticles (GNPs) in soft tissue and characterize the conditions under which XFET outperforms energy-integrating CT (EICT) and photon-counting CT (PCCT). We compared dose-matched Monte Carlo XFET simulations and analytical fan-beam EICT and PCCT simulations. Each modality was used to image a numerical mouse phantom and contrast-depth phantom containing GNPs ranging from 0.05% to 4% by weight in soft tissue. Contrast-to-noise ratios (CNRs) of gold regions were compared among the three modalities, and XFET's detection limit was quantified based on the Rose criterion. A partial field-of-view (FOV) image was acquired for the phantom region containing 0.05% GNPs. For the mouse phantom, XFET produced superior CNR values ( , 21.6, and 3.4) compared with CT images obtained with both energy-integrating ( , 4.6, and 1.5) and photon-counting ( , 7.7, and 2.0) detection systems. More generally, XFET outperformed CT for superficial imaging depths ( ) for gold concentrations at and above 0.5%. XFET's surface detection limit was quantified as 0.44% for an average phantom dose of 16mGy compatible with in vivo imaging. XFET's ability to image partial FOVs was demonstrated, and 0.05% gold was easily detected with an estimated dose of to a localized region of interest. We demonstrate a proof of XFET's benefit for imaging low concentrations of gold at superficial depths and the feasibility of XFET for in vivo metal mapping in preclinical imaging tasks.

A deep learning approach for non-invasive Alzheimer’s monitoring using microwave radar data

Over 50 million people globally suffer from Alzheimer’s disease (AD), emphasizing the need for efficient, early diagnostic tools. Traditional methods like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans are expensive, bulky, and slow. Microwave-based techniques offer a cost-effective, non-invasive, and portable solution, diverging from conventional neuroimaging practices. This article introduces a deep learning approach for monitoring AD , using realistic numerical brain phantoms to simulate scattered signals via the CST Studio Suite. The obtained data is preprocessed using normalization, standardization, and outlier removal to ensure data integrity. Furthermore, we propose a novel data augmentation technique to enrich the dataset across various AD stages. Our deep learning approach combines Recursive Feature Elimination (RFE) with Principal Component Analysis (PCA) and Autoencoders (AE) for optimal feature selection. Convolution Neural Network (CNN) is combined with Gated Recurrent Unit (GRU), Bidirectional Long Short Term Memory (Bidirectional-LSTM), and Long Short-Term Memory (LSTM) to improve classification performance. The integration of RFE-PCA-AE significantly elevates performance, with the CNN+GRU model achieving an 87% accuracy rate, thus outperforming existing studies.

Enhanced breast tumor localization with DRA antenna backscattering and GPR algorithm in microwave imaging

With the rapid rate of increasing breast cancer cases across the globe and restrictions of existing early stage detection techniques, microwave imaging based cancer and malignant cell diagnosing methodology is finding its way. Microwave imaging technology has higher accuracy of detection in the variation of tissue properties. In this article, a dielectric resonator based wide band antenna is designed and investigated for microwave imaging (MWI) of early breast cancer detection due to its improved accuracy. The designed antenna has compact size, broad frequency spectrum, high gain and broadside radiation characteristics. To attain the wider bandwidth from (6.5 GHz–12.5 GHz), two rectangular shaped dielectric slabs are used. Wide band microwave frequency band supports the high resolution images. A C-shaped defected ground structure is used to tune the impedance matching. Dielectric resonator based antenna has higher gain and reduces the squints in antenna impedance. Two-asymmetrical dielectric slabs contribute in the excitation of different resonating modes which in turn enhance the bandwidth. It also contributes to excite multiple resonance mode that in turn enhance the bandwidth. Antenna performance is first numerically and experimentally verified in free space. Then, simulation based performance is examined for the microwave imaging. Numerical phantom with equivalent tissue properties is designed with and without the tumor. Unhealthy cells have higher water percentage and larger dielectric constant as compared to healthy cells. It causes the different in the back scattered signal of antenna, which is analyzed to diagnose the tumor or cancer. Here, a maximum deviation of 16 dB is observed in the backscattered signal. Furthermore, surface of the breast tissue is scanned along the x-axis and y-axis at different angels. The collected signals are used to reconstruct the microwave image of the interior. Mean value data of backscattered signals is used to locate the existence of unhealthy cells and GPR algorithm is used to calculate the depth of a 4 mm tumor in breast tissue. Proposed structure has shown efficient performance for microwave imaging.

POSE: POSition Encoding for accelerated quantitative MRI

Quantitative MRI utilizes multiple acquisitions with varying sequence parameters to sufficiently characterize a biophysical model of interest, resulting in undesirable scan times. Here we propose, validate and demonstrate a new general strategy for accelerating MRI using subvoxel shifting as a source of encoding called POSition Encoding (POSE). The POSE framework applies unique subvoxel shifts along the acquisition parameter dimension, thereby creating an extra source of encoding. Combining with a biophysical signal model of interest, accelerated and enhanced resolution maps of biophysical parameters are obtained. This has been validated and demonstrated through numerical Bloch equation simulations, phantom experiments and in vivo experiments using the variable flip angle signal model in 3D acquisitions as an application example. Monte Carlo simulations were performed using in vivo data to investigate our method's noise performance. POSE quantification results from numerical Bloch equation simulations of both a numerical phantom and realistic digital brain phantom concur well with the reference method, validating our method both theoretically and for realistic situations. NIST phantom experiment results show excellent overall agreement with the reference method, confirming our method's applicability for a wide range of T1 values. In vivo results not only exhibit good agreement with the reference method, but also show g-factors that significantly outperforms conventional parallel imaging methods with identical acceleration. Furthermore, our results show that POSE can be combined with parallel imaging to further accelerate while maintaining superior noise performance over parallel imaging that uses lower acceleration factors.

Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) quantifies acoustic tissue properties such as the speed-of-sound (SOS). Although full-waveform inversion (FWI) is an effective method for accurate SOS reconstruction, it can be computationally challenging for large-scale problems. Deep learning-based image-to-image learned reconstruction (IILR) methods can offer computationally efficient alternatives. This study investigates the impact of the chosen input modalities on IILR methods for high-resolution SOS reconstruction in USCT. The selected modalities are traveltime tomography (TT) and reflection tomography (RT), which produce a low-resolution SOS map and a reflectivity map, respectively. These modalities have been chosen for their lower computational cost relative to FWI and their capacity to provide complementary information: TT offers a direct SOS measure, while RT reveals tissue boundary information. Systematic analyses were facilitated by employing a virtual USCT imaging system with anatomically realistic numerical breast phantoms. Within this testbed, a supervised convolutional neural network (CNN) was trained to map dual-channel (TT and RT images) to a high-resolution SOS map. Single-input CNNs were trained separately using inputs from each modality alone (TT or RT) for comparison. The accuracy of the methods was systematically assessed using normalized root mean squared error (NRMSE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). For tumor detection performance, receiver operating characteristic analysis was employed. The dual-channel IILR method was also tested on clinical human breast data. Ensemble average of the NRMSE, SSIM, and PSNR evaluated on this clinical dataset were 0.2355, 0.8845, and 28.33 dB, respectively.

Complex Pseudo 3D Auto-Correlation Network for High-Quality Single-Angle Plane Wave Imaging

Abstract Deep learning has shown potential as an effective beamformer for improving the image quality of plane wave imaging (PWI). But most existing deep learning methods cannot directly handle the complex in-phase and quadrature (IQ) data. And noise in ultrasound signals would significantly damage the performance of regular convolution networks. To address these challenges, we proposed the Complex Pseudo 3D Auto-Correlation Network (CP3AN) which combined complex convolution and pseudo 3D auto-correlation blocks (P3AB) to directly map delayed IQ data from 0° plane wave into PWI pixels. The complex convolution could fully utilize the envelope and phase information of IQ data, while the P3AB used 1D convolution to extract noise in channel and space dimensions, allowing the network to prioritize valid signals with extremely low computational cost. We evaluated the performance of CP3AN through numerical simulations, phantom experiments, and in-vivo experiments, which showed comparable metrics with minimum variance (MV), including contrast (CR), contrast-to-noise ratio (CNR), generalized contrast-to-noise ratio (GCNR), lateral, and axial full-width half maximum (FWHM) at -9.60 dB, 1.12, 0.65, 319 um, and 344 um, respectively. The CP3AN achieved a low computational cost of 0.11 M floating-point operations (FLOPs), significantly lower than the MV or other compared deep learning-based methods. Our proposed method provided a promising solution for improving single-angle PWI imaging, particularly in situations where high frame rates are necessary.

Fat Suppression in Distal Extremity 3-T MRI Using Spectral Heterogeneity Adaptive Radiofrequency Pulses.

Background Conventional chemical shift selective (CHESS) fat suppression may fail in distal extremity MRI due to sensitivity to field inhomogeneities. Purpose To develop a patient-specific fat-suppression method for distal extremity 3-T MRI by exploiting the spectral heterogeneity adaptive radiofrequency pulse (SHARP) technique and to compare it to fat suppression with CHESS. Materials and Methods SHARP uses the routinely acquired frequency spectrum at MRI calibration to adapt the frequency range and time-bandwidth product of the fat-suppression pulse. In this prospective study, fat suppression by SHARP was assessed by numerical simulations, phantom experiments, and imaging in 15 asymptomatic participants who underwent ankle, foot, and hand (in superman and hand-by-the-side positions) MRI using SHARP, CHESS, and reference standard (short-tau inversion recovery or Dixon) techniques. Three readers ranked the MRI scans from 1 (best) to 3 (worst) regarding fat-suppression homogeneity. The added value of SHARP was defined as the difference between the proportions of images where SHARP outranked CHESS and where CHESS outranked SHARP. Friedman, Wilcoxon signed rank, and χ2 tests were used to compare in vivo data. Results At numerical simulations, SHARP showed 0% water and 62%-70% fat suppression, whereas CHESS showed 2% water and 57% fat suppression. Phantom data demonstrated lower fat-suppression inhomogeneity indexes with Dixon (1.0%) and SHARP (2.4%) compared with CHESS (10.7%). In 15 participants (mean age, 38.5 years ± 12.8 [SD]; six female participants), mean ranking by readers of fat homogeneity in the reference technique (ankle, foot, hand in superman position, and hand-by-the-side position: 1.02, 1.02, 1.03, and 1.06, respectively) was higher than those with SHARP (1.39, 1.46, 1.50, and 1.66, respectively), which were higher than those with CHESS (1.64, 1.80, 1.61, and 1.80, respectively) (all P < .001). The added value of SHARP was highest for images in the foot (389 of 1158; 33.6%; P < .001 vs other joints), followed by the ankle (247 of 971 [25%]; P < .001 vs both hand positions), and lowest for hand-by-the-side and hand in superman positions (158 of 1223; [13%] and 133 of 1193 [11%], respectively; P = .18). Conclusion SHARP provided more homogeneous fat suppression than CHESS. © RSNA, 2024 Supplemental material is available for this article.

Novel spin-lock time sampling strategies for improved reproducibility in quantitative T1ρ mapping.

This study aimed to optimize the sampling of spin-lock times (TSLs) in quantitative T1ρ mapping for improved reproducibility. Two new TSL sampling schemes were proposed: (i) reproducibility-guided random sampling (RRS) and (ii) reproducibility-guided optimal sampling (ROS). They were compared to the existing linear sampling (LS) and precision-guided sampling (PS) schemes for T1ρ reproducibility through numerical simulations, phantom experiments, and volunteer studies. Each study evaluated the four sampling schemes with three commonly used T1ρ preparations based on composite and balanced spin-locking. Additionally, the phantom and volunteer studies investigated the impact of B0 and B1 field inhomogeneities on T1ρ reproducibility, respectively. The reproducibility was assessed using the coefficient of variation (CoV) by repeating the T1ρ measurements eight times for phantom experiments and five times for volunteer studies. Numerical simulations resulted in lower mean CoVs for the proposed RRS (1.74%) and ROS (0.68%) compared to LS (2.93%) and PS (3.68%). In the phantom study, the mean CoVs were also lower for RRS (2.7%) and ROS (2.6%) compared to LS (4.1%) and PS (3.1%). Furthermore, the mean CoVs of the proposed RRS and ROS were statistically lower (P < 0.001) compared to existing LS and PS schemes at a B1 offset of 20%. In the volunteer study, consistently lower mean CoVs were observed in bilateral thigh muscles for RRS (9.3%) and ROS (9.2%) compared to LS (10.9%) and PS (10.2%), and the difference was more prominent at B0 offsets higher than 50 Hz. The proposed sampling schemes improve the reproducibility of quantitative T1ρ mapping by optimizing the selection of TSLs. This improvement is especially beneficial for longitudinal studies that track and monitor disease progression and treatment response.

Improved myelin water imaging using B1+ correction and data-driven global feature extraction: Application on people with MS

Abstract The predominant technique for quantifying myelin content in the white matter is multicompartment analysis of MRI’s T2 relaxation times (mcT2 analysis). The process of resolving the T2 spectrum at each voxel, however, is highly ill-posed and remarkably susceptible to noise and to inhomogeneities of the transmit field (B1+). To address these challenges, we employ a preprocessing stage wherein a spatially global data-driven analysis of the tissue is performed to identify a set of mcT2 configurations (motifs) that best describe the tissue under investigation, followed by using this basis set to analyze the signal in each voxel. This procedure is complemented by a new algorithm for correcting B1+ inhomogeneities, lending the overall fitting process with improved robustness and reproducibility. Successful validations are presented using numerical and physical phantoms vs. ground truth, showcasing superior fitting accuracy and precision compared with conventional (non-data-driven) fitting. In vivo application of the technique is presented on 26 healthy subjects and 29 people living with multiple sclerosis (MS), revealing substantial reduction in myelin content within normal-appearing white matter regions of people with MS (i.e., outside obvious lesions), and confirming the potential of data-driven myelin values as a radiological biomarker for MS.

Free-breathing, fat-corrected T1 mapping of the liver with stack-of-stars MRI, and joint estimation of T1, PDFF, , and .

Quantitative T1 mapping has the potential to replace biopsy for noninvasive diagnosis and quantitative staging of chronic liver disease. Conventional T1 mapping methods are confounded by fat and inhomogeneities, resulting in unreliable T1 estimations. Furthermore, these methods trade off spatial resolution and volumetric coverage for shorter acquisitions with only a few images obtained within a breath-hold. This work proposes a novel, volumetric (3D), free-breathing T1 mapping method to account for multiple confounding factors in a single acquisition. Free-breathing, confounder-corrected T1 mapping was achieved through the combination of non-Cartesian imaging, magnetization preparation, chemical shift encoding, and a variable flip angle acquisition. A subspace-constrained, locally low-rank image reconstruction algorithm was employed for image reconstruction. The accuracy of the proposed method was evaluated through numerical simulations and phantom experiments with a T1/proton density fat fraction phantom at 3.0 T. Further, the feasibility of the proposed method was investigated through contrast-enhanced imaging in healthy volunteers, also at 3.0 T. The method showed excellent agreement with reference measurements in phantoms across a wide range of T1 values (200 to 1000 ms, slope = 0.998 (95% confidence interval (CI) [0.963 to 1.035]), intercept = 27.1 ms (95% CI [0.4 54.6]), r2 = 0.996), and a high level of repeatability. In vivo imaging studies demonstrated moderate agreement (slope = 1.099 (95% CI [1.067 to 1.132]), intercept = -96.3 ms (95% CI [-82.1 to -110.5]), r2 = 0.981) compared to saturation recovery-based T1 maps. The proposed method produces whole-liver, confounder-corrected T1 maps through simultaneous estimation of T1, proton density fat fraction, and in a single, free-breathing acquisition and has excellent agreement with reference measurements in phantoms.

Ultrasound Speckle Decorrelation Analysis-Based Velocimetry for 3D-Velocity-Components Measurement Using a 1D Transducer Array.

Ultrasound velocimetry has been widely used for blood flow imaging. However, the flow measurements are constrained to resolve the in-plane 2D flow components when using a 1D transducer array. In this work, an ultrasound speckle decorrelation analysis-based velocimetry (3C-vUS) is proposed for 3D velocity components measurement using a 1D transducer array. The 3C-vUS theory is first derived and validated with numerical simulations and phantom experiments. The in vivo testing results show that 3C-vUS can accurately measure the blood flow 3D-velocity-components of the human carotid artery at arbitrary probe-to-vessel angles throughout the cardiac cycle. With such capability, the 3C-vUS will alleviate the requirement of operators and promote disease screening for blood flow-related disorders.

Efficient 3D cone trajectory design for improved combined angiographic and perfusion imaging using arterial spin labeling.

To improve the spatial resolution and repeatability of a non-contrast MRI technique for simultaneous time resolved 3D angiography and perfusion imaging by developing an efficient 3D cone trajectory design. A novel parameterized 3D cone trajectory design incorporating the 3D golden angle was integrated into 4D combined angiography and perfusion using radial imaging and arterial spin labeling (CAPRIA) to achieve higher spatial resolution and sampling efficiency for both dynamic angiography and perfusion imaging with flexible spatiotemporal resolution. Numerical simulations and physical phantom scanning were used to optimize the cone design. Eight healthy volunteers were scanned to compare the original radial trajectory in 4D CAPRIA with our newly designed cone trajectory. A locally low rank reconstruction method was used to leverage the complementary k-space sampling across time. The improved sampling in the periphery of k-space obtained with the optimized 3D cone trajectory resulted in improved spatial resolution compared with the radial trajectory in phantom scans. Improved vessel sharpness and perfusion visualization were also achieved in vivo. Less dephasing was observed in the angiograms because of the short TE of our cone trajectory and the improved k-space sampling efficiency also resulted in higher repeatability compared to the original radial approach. The proposed 3D cone trajectory combined with 3D golden angle ordering resulted in improved spatial resolution and image quality for both angiography and perfusion imaging and could potentially benefit other applications that require an efficient sampling scheme with flexible spatial and temporal resolution.

Improved quantification in CEST-MRI by joint spatial total generalized variation.

In this work, the use of joint Total Generalized Variation (TGV) regularization to improve Multipool-Lorentzian fitting of chemical exchange saturation transfer (CEST) Spectra in terms of stability and parameter signal-to-noise ratio (SNR) was investigated. The joint TGV term was integrated into the nonlinear parameter fitting problem. To increase convergence and weight the gradients, preconditioning using a voxel-wise singular value decomposition was applied to the problem, which was then solved using the iteratively regularized Gauss-Newton method combined with a Primal-Dual splitting algorithm. The TGV method was evaluated on simulated numerical phantoms, 3T phantom data and 7T in vivo data with respect to systematic errors and robustness. Three reference methods were also implemented: The standard nonlinear fitting, a method using a nonlocal-means filter for denoising and the pyramid scheme, which uses downsampled images to acquire accurate start values. The proposed regularized fitting method showed significantly improved robustness (compared to the reference methods). In testing, over a range of SNR values the TGV fit outperformed the other methods and showed accurate results even for large amounts of added noise. Parameter values found were closer or comparable to the ground truth. For in vivo datasets, the added regularization increased the parameter map SNR and prevented instabilities. The proposed fitting method using TGV regularization leads to improved results over a range of different data-sets and noise levels. Furthermore, it can be applied to all Z-spectrum data, with different amounts of pools, where the improved SNR and stability can increase diagnostic confidence.

A deep-learning-based scatter correction with water equivalent path length map for digital radiography.

We proposed a new deep learning (DL) model for accurate scatter correction in digital radiography. The proposed network featured a pixel-wise water equivalent path length (WEPL) map of subjects with diverse sizes and 3D inner structures. The proposed U-Net model comprises two concatenated modules: one for generating a WEPL map and the other for predicting scatter using the WEPL map as auxiliary information. First, 3D CT images were used as numerical phantoms for training and validation, generating observed and scattered images by Monte Carlo simulation, and WEPL maps using Siddon's algorithm. Then, we optimised the model without overfitting. Next, we validated the proposed model's performance by comparing it with other DL models. The proposed model obtained scatter-corrected images with a peak signal-to-noise ratio of 44.24 ± 2.89dB and a structural similarity index measure of 0.9987 ± 0.0004, which were higher than other DL models. Finally, scatter fractions (SFs) were compared with other DL models using an actual phantom to confirm practicality. Among DL models, the proposed model showed the smallest deviation from measured SF values. Furthermore, using an actual radiograph containing an acrylic object, the contrast-to-noise ratio (CNR) of the proposed model and the anti-scatter grid were compared. The CNR of the images corrected using the proposed model are 16% and 82% higher than those of the raw and grid-applied images, respectively. The advantage of the proposed method is that no actual radiography system is required for collecting training dataset, as the dataset is created from CT images using Monte Carlo simulation.

A non-invasive approach to skin cancer diagnosis via graphene electrical tattoos and electrical impedance tomography

Objective. Making up one of the largest shares of diagnosed cancers worldwide, skin cancer is also one of the most treatable. However, this is contingent upon early diagnosis and correct skin cancer-type differentiation. Currently, methods for early detection that are accurate, rapid, and non-invasive are limited. However, literature demonstrating the impedance differences between benign and malignant skin cancers, as well as between different types of skin cancer, show that methods based on impedance differentiation may be promising. Approach. In this work, we propose a novel approach to rapid and non-invasive skin cancer diagnosis that leverages the technologies of difference-based electrical impedance tomography (EIT) and graphene electronic tattoos (GETs). Main results. We demonstrate the feasibility of this first-of-its-kind system using both computational numerical and experimental skin phantom models. We considered variations in skin cancer lesion impedance, size, shape, and position relative to the electrodes and evaluated the impact of using individual and multi-electrode GET (mGET) arrays. The results demonstrate that this approach has the potential to differentiate based on lesion impedance, size, and position, but additional techniques are needed to determine shape. Significance. In this way, the system proposed in this work, which combines both EIT and GET technology, exhibits potential as an entirely non-invasive and rapid approach to skin cancer diagnosis.

Robust residual-guided iterative reconstruction for sparse-view CT in small animal imaging

Objective. We introduce a robust image reconstruction algorithm named residual-guided Golub–Kahan iterative reconstruction technique (RGIRT) designed for sparse-view computed tomography (CT), which aims at high-fidelity image reconstruction from a limited number of projection views. Approach. RGIRT utilizes an inner-outer dual iteration framework, with a flexible least square QR (FLSQR) algorithm implemented in the inner iteration and a restarted iterative scheme applied in the outer iteration. The inner FLSQR employs a flexible Golub–Kahan bidiagonalization method to reduce the size of the inverse problem, and a weighted generalized cross-validation method to adaptively estimate the regularization hyper-parameter. The inner iteration efficiently yields the intermediate reconstruction result, while the outer iteration minimizes the residual and refines the solution by using the result obtained from the inner iteration. Main results. The reconstruction performance of RGIRT is evaluated and compared to other reference methods (FBPConvNet, SART-TV, and FLSQR) using projection data from both numerical phantoms and real experimental Micro-CT data. The experimental findings, from testing various numbers of projection views and different noise levels, underscore the robustness of RGIRT. Meanwhile, theoretical analysis confirms the convergence of residual for our approach. Significance. We propose a robust iterative reconstruction algorithm for x-ray CT scans with sparse views, thereby shortening scanning time and mitigating excessive ionizing radiation exposure to small animals.

In vivo T2 measurements of the fetal brain using single-shot fast spin echo sequences.

We propose a quantitative framework for motion-corrected T2 fetal brain measurements in vivo and validate the single-shot fast spin echo (SS-FSE) sequence to perform these measurements. Stacks of two-dimensional SS-FSE slices are acquired with different echo times (TE) and motion-corrected with slice-to-volume reconstruction (SVR). The quantitative T2 maps are obtained by a fit to a dictionary of simulated signals. The sequence is selected using simulated experiments on a numerical phantom and validated on a physical phantom scanned on a 1.5T system. In vivo quantitative T2 maps are obtained for five fetuses with gestational ages (GA) 21-35 weeks on the same 1.5T system. The simulated experiments suggested that a TE of 400 ms combined with the clinically utilized TEs of 80 and 180 ms were most suitable for T2 measurements in the fetal brain. The validation on the physical phantom confirmed that the SS-FSE T2 measurements match the gold standard multi-echo spin echo measurements. We measured average T2s of around 200 and 280 ms in the fetal brain grey and white matter, respectively. This was slightly higher than fetal T2* and the neonatal T2 obtained from previous studies. The motion-corrected SS-FSE acquisitions with varying TEs offer a promising practical framework for quantitative T2 measurements of the moving fetus.