Physical Phantom Research Articles

Low-dose spectral CT reconstruction based on image-gradient L0-norm and adaptive spectral PICCS

The photon-counting detector based spectral computed tomography (CT) is promising for lesion detection, tissue characterization, and material decomposition. However, the lower signal-to-noise ratio within multi-energy projection dataset can result in poorly reconstructed image quality. Recently, as prior information, a high-quality spectral mean image was introduced into the prior image constrained compressed sensing (PICCS) framework to suppress noise, leading to spectral PICCS (SPICCS). In the original SPICCS model, the image gradient L1-norm is employed, and it can cause blurred edge structures in the reconstructed images. Encouraged by the advantages in edge preservation and finer structure recovering, the image gradient L0-norm was incorporated into the PICCS model. Furthermore, due to the difference of energy spectrum in different channels, a weighting factor is introduced and adaptively adjusted for different channel-wise images, leading to an L0-norm based adaptive SPICCS (L0-ASPICCS) algorithm for low-dose spectral CT reconstruction. The split-Bregman method is employed to minimize the objective function. Extensive numerical simulations and physical phantom experiments are performed to evaluate the proposed method. By comparing with the state-of-the-art algorithms, such as the simultaneous algebraic reconstruction technique, total variation minimization, and SPICCS, the advantages of our proposed method are demonstrated in terms of both qualitative and quantitative evaluation results.

Low Dose CT Perfusion With K-Space Weighted Image Average (KWIA).

CTP (Computed Tomography Perfusion) is widely used in clinical practice for the evaluation of cerebrovascular disorders. However, CTP involves high radiation dose (≥ ~200mGy) as the X-ray source remains continuously on during the passage of contrast media. The purpose of this study is to present a low dose CTP technique termed K-space Weighted Image Average (KWIA) using a novel projection view-shared averaging algorithm with reduced tube current. KWIA takes advantage of k-space signal property that the image contrast is primarily determined by the k-space center with low spatial frequencies and over-sampled projections. KWIA divides each 2D Fourier transform (FT) or k-space CTP data into multiple rings. The outer rings are averaged with neighboring time frames to achieve adequate signal-to-noiseratio (SNR), while the center region of k-space remains unchanged to preserve high temporal resolution. Reduced dose sinogram data were simulated by adding Poisson distributed noise with zero mean on digital phantom and clinical CTP scans. A physical CTP phantom study was also performed with different X-ray tube currents. The sinogram data with simulated and real low doses were then reconstructed with KWIA, and compared with those reconstructed by standard filtered back projection (FBP) and simultaneous algebraic reconstruction with regularization of total variation (SART-TV). Evaluation of image quality and perfusion metrics using parameters including SNR, CNR (contrast-to-noise ratio), AUC (area-under-the-curve), and CBF (cerebral blood flow) demonstrated that KWIA is able to preserve the image quality, spatial and temporal resolution, as well as the accuracy of perfusion quantification of CTP scans with considerable (50–75%) dose-savings.

Head phantoms for bioelectromagnetic applications: a material study

BackgroundAssessments of source reconstruction procedures in electroencephalography and computations of transcranial electrical stimulation profiles require verification and validation with the help of ground truth configurations as implemented by physical head phantoms. For these phantoms, synthetic materials are needed, which are mechanically and electrochemically stable and possess conductivity values similar to the modeled human head tissues. Three-compartment head models comprise a scalp layer with a conductivity range of 0.137 S/m to 2.1 S/m, a skull layer with conductivity values between 0.066 S/m and 0.00275 S/m, and an intracranial volume with an often-used average conductivity value of 0.33 S/m. To establish a realistically shaped physical head phantom with a well-defined volume conduction configuration, we here characterize the electrical conductivity of synthetic materials for modeling head compartments. We analyzed agarose hydrogel, gypsum, and sodium chloride (NaCl) solution as surrogate materials for scalp, skull, and intracranial volume. We measured the impedance of all materials when immersed in NaCl solution using a four-electrode setup. The measured impedance values were used to calculate the electrical conductivity values of each material. Further, the conductivities in the longitudinal and transverse directions of reed sticks immersed in NaCl solution were measured to test their suitability for mimicking the anisotropic conductivity of white matter tracts.ResultsWe obtained conductivities of 0.314 S/m, 0.30 S/m, 0.311 S/m (2%, 3%, 4% agarose), 0.0425 S/m and 0.0017 S/m (gypsum with and without NaCl in the compound), and 0.332 S/m (0.17% NaCl solution). These values are within the range of the conductivity values used for EEG and TES modeling. The reed sticks showed anisotropic conductivity with a ratio of 1:2.8.ConclusionWe conclude that agarose, gypsum, and NaCl solution can serve as stable representations of the three main conductivity compartments of the head, i.e., scalp, skull, and intracranial volume. An anisotropic conductivity structure such as a fiber track in white matter can be modeled using tailored reed sticks inside a volume conductor.

Person-specific calibration of a partial body counter used for individualised Am241 skull measurements

Incorporation of bone seeking alpha-emitting radionuclides such as 241Am are of special concern, due to the potential of alpha particles to damage the extremely radiation-sensitive bone marrow. In the case of an internal contamination with 241Am, direct in vivo measurements using Gamma-detectors are typically used to quantify the incorporated activity. Such detectors need to be calibrated with an anatomical phantom, for example of the skull, of known 241Am activity that reproduces the anatomy of the measured individual as closely as possible. Any difference in anatomy and material composition between phantom and individual will bias the estimation of the incorporated activity. Consequently, in this work the impact of the most important anatomical parameters on detection efficiency of one of the germanium detectors of the Helmholtz Center Munich (HMGU) partial body counter were systematically studied. For that a detailed model of the germanium detector was implemented in the Monte Carlo codes GEANT4 and MCNPX. To simulate the detector efficiency, various skull voxel phantoms were used. By changing the phantom dimensions and geometry the impact of parameters such as shape and size of the skull, thickness of tissue covering the skull bone, distribution of 241Am across the scull and within the skull bone matrix, on the detector efficiency was studied. Approaches to correct for these parameters were specifically developed for three physical skull phantoms for which Voxel phantoms were available: Case 102 USTUR phantom, Max-06 phantom, BfS phantom. Based on the impact of each parameter, correction factors for an ‘individual-specific’ calibration were calculated and applied to a real 241Am contamination case reported in 2014. It was found that the incorporated 241Am activity measured with the HMGU partial body counter was about twice as large as that estimated when using the BfS skull phantom without applying any correction factor for person-specific parameters. It is concluded that the approach developed in the present study should in the future be applied routinely for skull phantom measurements, because it allows for a considerably improved reconstruction of incorporated 241Am using partial body counters.

A synthesizing method for signal-enhanced and artifact-reduced mammogram from digital breast tomosynthesis

In this paper, we propose a method for compositing a synthetic mammogram (SM) from digital breast tomosynthesis (DBT) slice images. The method consists of four parts. The first part is image reconstruction of DBT from the acquired projection data by use of backprojection-filtration (BPF) algorithm with a low-frequency boosting scheme and a high-density object reduction technique embedded. Also, a few expectation-maximization (EM) iterations have been additively implemented on top of the BPF algorithm to prepare a separate volume image. The second is generating three kinds of intermediate SMs. A forward projection image and a linear structure weighted forward projection image were computed. A maximum intensity projection of the BPF reconstructed volume image was also generated. The third part is integrating three intermediate SMs. The last is the post-processing of the SM. We scanned two physical phantoms in a prototype DBT scanner, and we have evaluated the performance of the proposed method. We also performed a clinical data study by use of 30 patient data who went through both DBT and digital mammography (DM) scans. Three experienced radiologists have read the SMs generated by several component techniques and also read the DM of each patient, and evaluated the generated SMs. The experimental phantom study and the clinical reader study consistently demonstrated the usefulness of the proposed method.

Impact of the phantom geometry on the evaluation of the minimum detectable activity following a radionuclide intake: From physical to numerical phantoms

The establishment of an in vivo internal monitoring programme requires the use of phantoms to represent an activity distribution of an incorporated radionuclide within the body. The aim of this study was to quantify the impact of the phantom geometry on the minimum detectable activity (MDA) of an incorporated radionuclide. The MDA was assessed for two instruments: a conventional radiation protection instrument and a portable gamma spectrometer. Four phantoms were considered: two physical phantoms, a simplified torso phantom and a commercial whole body phantom, as well as two numerical phantoms, the reference adult male and female voxel phantoms published by the International Commission on Radiological Protection (ICRP). The phantoms were loaded with activity at the level of the thorax and abdomen using reference sources of 57Co, 133Ba, 137Cs, 60Co and 152Eu. The MDA for both instruments was experimentally assessed using the two physical phantoms. The experimental setup was modelled in GEANT4 and the simulated instrument responses were validated by the experimental data. The Monte Carlo model was then used to compute the instruments response and corresponding MDA when using the ICRP voxel phantoms. The simplified torso phantom provided one of the highest MDA estimates, up to a factor of 5 higher than the ones obtained with the voxel phantoms when considering a 57Co source. Depending on the considered source distribution within the phantoms, physical phantoms may lead to an underestimation of the MDA when compared to more complex and anatomically accurate numerical phantoms. This work presents a quantitative comparison between the MDA obtained with different phantoms and radionuclide distributions.

A digital viscoelastic liver phantom for investigation of elastographic measurements

To develop elastography imaging technologies and implement image reconstruction algorithms, testing is done with phantoms. Although the validation step is usually taken using real data and physical phantoms, their geometry as well as composition, biomechanical parameters, and details of applying stress cannot be modified readily. Such considerations have gained increasing importance with the growth of elastography techniques as one of the non-invasive medical imaging modalities, which can map the elastic properties and stiffness of soft tissues. In this article, we develop a digital viscoelastic phantom using computed tomography (CT) imaging data and several application software tools based on illustrations of normal liver anatomy so as to investigate the biomechanics of elastography via finite element modeling (FEM). Here we discuss how to create this phantom step by step, demonstrate typical shear wave elastography (SWE) experiments of applying transient stress to the liver model, and calculate quantitative measurements. In particular, shear wave velocities are investigated through a parametric study designed based on tissue stiffness and distance from the applied stress. According to the results of FEM analysis, low errors were obtained for shear wave velocity estimation for both mechanical stress (~2–5%) and acoustic radiation force (~3–7%). Results show that our model is a powerful framework and benchmark for simulating and implementing different algorithms in shear wave elastography, which can serve as a guide for upcoming researches and assist scientists to optimize their subsequent experiments in terms of design.

Fabrication of a pediatric torso phantom with multiple tissues represented using a dual nozzle thermoplastic 3D printer.

PurposeTo demonstrate an on‐demand and nearly automatic method for fabricating tissue‐equivalent physical anthropomorphic phantoms for imaging and dosimetry applications using a dual nozzle thermoplastic three‐dimensional (3D) printer and two types of plastic.MethodsTwo 3D printing plastics were investigated: (a) Normal polylactic acid (PLA) as a soft tissue simulant and (b) Iron PLA (PLA‐Fe), a composite of PLA and iron powder, as a bone simulant. The plastics and geometry of a 1‐yr‐old computational phantom were combined with a dual extrusion 3D printer to fabricate an anthropomorphic imaging phantom. The volumetric fill density of the 3D‐printed parts was varied to approximate tissues of different radiographic density using a calibration curve relating the printer infill density setting to measured CT number. As a demonstration of our method we printed a 10 cm axial cross‐section of the computational phantom’s torso at full scale. We imaged the phantom on a CT scanner and compared HU values to those of a 1‐yr‐old patient and a commercial 5‐yr‐old physical phantom.ResultsThe phantom was printed in six parts over the course of a week. The printed phantom included 30 separate anatomical regions including soft tissue remainder, lungs (left and right), heart, esophagus, rib cage (left and right ribs 1 to 10), clavicles (left and right), scapulae (left and right), thoracic vertebrae (one solid object defining thoracic vertebrae T1 to T9). CT scanning of the phantom showed five distinct radiographic regions (heart, lung, soft tissue remainder, bone, and air cavity) despite using only two types of plastic. The 3D‐printed phantom demonstrated excellent similarity to commercially available phantoms, although key limitations in the printer and printing materials leave opportunity for improvement.ConclusionPatient‐specific anthropomorphic phantoms can be 3D printed and assembled in sections for imaging and dosimetry applications. Such phantoms will be useful for dose verification purposes when commercial phantoms are unavailable for purchase in the specific anatomies of interest.

Deep learning approaches for thermographic imaging

In this paper, we investigate two deep learning approaches to recovering initial temperature profiles from thermographic images in non-destructive material testing. First, we trained a deep neural network (DNN) in an end-to-end fashion by directly feeding the surface temperature measurements to the DNN. Second, we turned the surface temperature measurements into virtual waves (a recently developed concept in thermography), which we then fed to the DNN. To demonstrate the effectiveness of these methods, we implemented a data generator and created a dataset comprising a total of 100 000 simulated temperature measurement images. With the objective of determining a suitable baseline, we investigated several state-of-the-art model-based reconstruction methods, including Abel transformation, curvelet denoising, and time- and frequency-domain synthetic aperture focusing techniques. Additionally, a physical phantom was created to support evaluation on completely unseen real-world data. The results of several experiments suggest that both the end-to-end and the hybrid approach outperformed the baseline in terms of reconstruction accuracy. The end-to-end approach required the least amount of domain knowledge and was the most computationally efficient one. The hybrid approach required extensive domain knowledge and was more computationally expensive than the end-to-end approach. However, the virtual waves served as meaningful features that convert the complex task of the end-to-end reconstruction into a less demanding undertaking. This in turn yielded better reconstructions with the same number of training samples compared to the end-to-end approach. Additionally, it allowed more compact network architectures and use of prior knowledge, such as sparsity and non-negativity. The proposed method is suitable for non-destructive testing (NDT) in 2D where the amplitudes along the objects are considered to be constant (e.g., for metallic wires). To encourage the development of other deep-learning-based reconstruction techniques, we release both the synthetic and the real-world datasets along with the implementation of the deep learning methods to the research community.

Measurement of PET Quantitative Bias In Vivo.

Quantitative imaging biomarkers are widely used in PET for both research and clinical applications, yet bias in the underlying image data has not been well characterized. In the absence of a readily available reference standard for in vivo quantification, bias in PET images has been inferred using physical phantoms, even though arrangements of this sort provide only a poor approximation of the imaging environment in real patient examinations. In this study, we used data acquired from patient volunteers to assess PET quantitative bias in vivo. Image-derived radioactivity concentrations in the descending aorta were compared with blood samples counted on a calibrated γ-counter. Methods: Ten patients with prostate cancer were studied using 2-(3-(1-carboxy-5-[(6-18F-fluoro-pyridine-3-carbonyl)-amino]-pentyl)-ureido)-pentanedioic acid PET/CT. For each patient, 3 whole-body PET/CT image series were acquired after a single administration of the radiotracer: shortly after injection as well as approximately 1 and 4 h later. Venous blood samples were obtained at 8 time points over an 8-h period, and whole blood was counted on a NaI γ-counter. A 10-mm-diameter, 20-mm-long cylindric volume of interest was positioned in the descending thoracic aorta to estimate the PET-derived radioactivity concentration in blood. A triexponential function was fit to the γ-counter blood data and used to estimate the radioactivity concentration at the time of each PET acquisition. Results: The PET-derived and γ-counter-derived radioactivity concentrations were linearly related, with an R2 of 0.985, over a range of relevant radioactivity concentrations. The mean difference between the PET and γ-counter data was 4.8% ± 8.6%, with the PET measurements tending to be greater. Conclusion: Human image data acquired on a conventional whole-body PET/CT system with a typical clinical protocol differed by an average of around 5% from blood samples counted on a calibrated γ-counter. This bias may be partly attributable to residual uncorrected scatter or attenuation correction error. These data offer an opportunity for the assessment of PET bias in vivo and provide additional support for the use of quantitative imaging biomarkers.

Characterization of a multi-pinhole molecular breast tomosynthesis scanner

In recent years, breast imaging using radiolabelled molecules has attracted significant interest. Our group has proposed a multi-pinhole molecular breast tomosynthesis (MP-MBT) scanner to obtain 3D functional molecular breast images at high resolutions. After conducting extensive optimisation studies using simulations, we here present a first prototype of MP-MBT and evaluate its performance using physical phantoms. The MP-MBT design is based on two opposing gamma cameras that can image a lightly compressed pendant breast. Each gamma camera consists of a 250 × 150 mm2 detector equipped with a collimator with multiple pinholes focusing on a line. The NaI(Tl) gamma detector is a customised design with 3.5 mm intrinsic spatial resolution and high spatial linearity near the edges due to a novel light-guide geometry and the use of square PMTs. A volume-of-interest is scanned by translating the collimator and gamma detector together in a sequence that optimises count yield from the scan region. Derenzo phantom images showed that the system can reach 3.5 mm resolution for a clinically realistic 99mTc activity concentration in an 11-minute scan, while in breast phantoms the smallest spheres visible were 6 mm in diameter for the same scan time. To conclude, the experimental results of the novel MP-MBT scanner showed that the setup had sub-centimetre breast tumour detection capability which might facilitate 3D molecular breast cancer imaging in the future.

Toward improved 3D carotid artery imaging with Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT).

Interventional treatments of aneurysms in the carotid artery are increasingly being supplemented with three-dimensional (3D) x-ray imaging. The 3D imaging provides additional information on device sizing and stent malapposition during the procedure. Standard 3D x-ray image acquisition is a one-size fits all model, exposing patients to additional radiation and results in images that may have cardiac-induced motion blur around the artery. Here, we investigate the potential of a novel dynamic imaging technique Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT) to personalize image acquisition by adapting the gantry velocity and projection rate in real-time to changes in the patient's electrocardiogram (ECG) trace. We compared the total number of projections acquired, estimated carotid artery widths and image quality between ACROBEAT and conventional (single rotation fixed gantry velocity and acquisition rate, no ECG-gating) scans in a simulation study and a proof-of-concept physical phantom experimental study. The simulation study dataset consisted of an XCAT digital software phantom programmed with five patient-measured ECG traces and artery motion curves. The ECG traces had average heart rates of 56, 64, 76, 86, and 100bpm. To validate the concept experimentally, we designed and manufactured the physical phantom from an 8-mm diameter silicon rubber tubing cast into Phytagel. An artery motion curve and the ECG trace with an average heart rate of 56bpm was passed through the phantom. To implement ACROBEAT on the Siemens ARTIS pheno angiography system for the proof-of-concept experimental study, the Siemens Test Automation Control System was used. The total number of projections acquired and estimated carotid artery widths were compared between the ACROBEAT and conventional scans. As the ground truth was available for the simulation studies, the image quality metrics of Root Mean Square Error (RMSE) and Structural Similarity Index (SSIM) were also utilized to assess image quality. In the simulation study, on average, ACROBEAT reduced the number of projections acquired by 63%, reduced carotid width estimation error by 65%, reduced RMSE by 11% and improved SSIM by 27% compared to conventional scans. In the proof-of-concept experimental study, ACROBEAT enabled a 60% reduction in the number of projections acquired and reduced carotid width estimation error by 69% compared to a conventional scan. A simulation and proof-of-concept experimental study was completed applying a novel dynamic imaging protocol, ACROBEAT, to imaging the carotid artery. The ACROBEAT results showed significantly improved image quality with fewer projections, offering potential applications to intracranial interventional procedures negatively affected by cardiac motion.

Validation of a novel cardiac motion correction algorithm for x-ray computed tomography: From phantom experiments to initial clinical experience.

A novel cardiac motion correction algorithm has been introduced recently. Unlike other segmentation-based approaches it is fully automatic and capable of correcting motion artifacts of myocardial wall and other moving structures as well as coronary arteries of the heart. In addition, it requires raw data of only less than a single rotation for motion estimation and correction, which is a significant advantage from the perspective of x-ray exposure and workflow. The aim of this study is to explore the capability of the proposed method through phantoms and in-vivo experiments. Motion correction of coronary arteries and other heart structures including myocardial wall is the main focus of the evaluation. First, we provide a brief introduction to the concept of the motion correction algorithm. Next we address the procedure of our studies using an XCAT phantom and commercially available physical phantoms. Results of XCAT phantom demonstrate that our solution significantly improves the structural similarity of coronary arteries compared to FBP (proposed: 0.94, FBP: 0.77, p<0.001). Besides, it provides significantly lower root mean square error (proposed: 20.27, FBP: 25.33, p = 0.01) of the whole heart image. Mocomo phantom study shows that the proposed method improves the visualization of coronary arteries estimated based on motion score (1: worst, 5: best) from two experienced radiologists (proposed: 3.5, FBP: 2.1, p<0.001). The results of these phantom studies reveal that the proposed has a great potential in handling motion artifacts of other heart structures as well as coronary arteries. Finally, we provide the results of in-vivo animal and human studies. The 3D and 4D heart images show a consistently superior performance in the visualization of coronary arteries along with myocardial wall and other cardiothoracic structures. Based on these findings of our studies, we are of the opinion that our solution has a considerable potential to improve temporal resolution of cardiac CT imaging. This would open the door to innovations in structural or functional diagnosis of the heart.

Influence of phosphate concentration on amine, amide, and hydroxyl CEST contrast.

To evaluate the influence of phosphate on amine, amide, and hydroxyl CEST contrast using Bloch-McConnell simulations applied to physical phantom data. Phantom solutions of 4 representative metabolites with exchangeable protons-glycine (α-amine protons), Cr (η-amine protons), egg white protein (amide protons), and glucose (hydroxyl protons)-were prepared at different pH levels (5.6 to 8.9) and phosphate concentrations (5 to 80 mM). CEST images of the phantom were collected with CEST-EPI sequence at 3 tesla. The CEST data were then fitted to full Bloch-McConnell equation simulations to estimate the exchange rate constants. With the fitted parameters, simulations were performed to evaluate the intracellular and extracellular contributions of CEST signals in normal brain tissue and brain tumors, as well as in dynamic glucose-enhanced experiments. The exchange rates of α-amine and hydroxyl protons were found to be highly dependent on both pH and phosphate concentrations, whereas the exchange rates of η-amine and amide protons were pH-dependent, albeit not catalyzed by phosphate. With phosphate being predominantly intracellular, CEST contrast of α-amine exhibited a higher sensitivity to changes in the extracellular microenvironment. Simulations of dynamic glucose-enhanced signals demonstrated that the contrast between normal and tumor tissue was mostly due to the extracellular CEST effect. The proton exchange rates in some metabolites can be greatly catalyzed by the presence of phosphate at physiological concentrations, which substantially alters the CEST contrast. Catalytic agents should be considered as confounding factors in future CEST-MRI research. This new dimension may also benefit the development of novel phosphate-sensitive imaging methods.

LEGO-compatible modular mapping phantom for magnetic resonance imaging

Physical phantoms have been widely used for performance evaluation of magnetic resonance imaging (MRI). Although there are many kinds of physical phantoms, most MRI phantoms use fixed configurations with specific sizes that may fit one or a few different types of radio frequency (RF) coils. Therefore, it has limitations for various image quality assessments of scanning areas. In this article, we report a novel design for a truly customizable MRI phantom called the LEGO-compatible Modular Mapping (MOMA) phantom, which not only serves as a general quality assurance phantom for a wide range of RF coils, but also a flexible calibration phantom for quantitative imaging. The MOMA phantom has a modular architecture which includes individual assessment functionality of the modules and LEGO-type assembly compatibility. We demonstrated the feasibility of the MOMA phantom for quantitative evaluation of image quality using customized module assembly compatible with head, breast, spine, knee, and body coil features. This unique approach allows comprehensive image quality evaluation with wide versatility. In addition, we provide detailed MOMA phantom development and imaging characteristics of the modules.

Realistic Dynamic Numerical Phantom for MRI of the Upper Vocal Tract.

Dynamic and real-time MRI (rtMRI) of human speech is an active field of research, with interest from both the linguistics and clinical communities. At present, different research groups are investigating a range of rtMRI acquisition and reconstruction approaches to visualise the speech organs. Similar to other moving organs, it is difficult to create a physical phantom of the speech organs to optimise these approaches; therefore, the optimisation requires extensive scanner access and imaging of volunteers. As previously demonstrated in cardiac imaging, realistic numerical phantoms can be useful tools for optimising rtMRI approaches and reduce reliance on scanner access and imaging volunteers. However, currently, no such speech rtMRI phantom exists. In this work, a numerical phantom for optimising speech rtMRI approaches was developed and tested on different reconstruction schemes. The novel phantom comprised a dynamic image series and corresponding k-space data of a single mid-sagittal slice with a temporal resolution of 30 frames per second (fps). The phantom was developed based on images of a volunteer acquired at a frame rate of 10 fps. The creation of the numerical phantom involved the following steps: image acquisition, image enhancement, segmentation, mask optimisation, through-time and spatial interpolation and finally the derived k-space phantom. The phantom was used to: (1) test different k-space sampling schemes (Cartesian, radial and spiral); (2) create lower frame rate acquisitions by simulating segmented k-space acquisitions; (3) simulate parallel imaging reconstructions (SENSE and GRAPPA). This demonstrated how such a numerical phantom could be used to optimise images and test multiple sampling strategies without extensive scanner access.

End-to-end Res-Unet based reconstruction algorithm for photoacoustic imaging.

Recently, deep neural networks have attracted great attention in photoacoustic imaging (PAI). In PAI, reconstructing the initial pressure distribution from acquired photoacoustic (PA) signals is a typically inverse problem. In this paper, an end-to-end Unet with residual blocks (Res-Unet) is designed and trained to solve the inverse problem in PAI. The performance of the proposed algorithm is explored and analyzed by comparing a recent model-resolution-based regularization algorithm (MRR) with numerical and physical phantom experiments. The improvement obtained in the reconstructed images was more than 95% in pearson correlation and 39% in peak signal-to-noise ratio in comparison to the MRR. The Res-Unet also achieved superior performance over the state-of-the-art Unet++ architecture by more than 18% in PSNR in simulation experiments.

An efficient lattice Boltzmann method for fluorescent diffuse optical tomography on GPUs

Fluorescent diffuse optical tomography (FDOT) is an emerging imaging modality, with great prospects in areas such as biology and medicine. However, current FDOT encounters difficulty in simulating photon propagation in biological tissue, i.e., the forward problem, which limits its further application in biomedical research. This paper presents a lattice Boltzmann method (LBM) on the GPU to greatly improve the computational efficiency in the forward problem realization. This method separated the LBM simulating the propagation of photon in tissues into collision, streaming and boundary processing processes on GPUs, which are local computational processes and inefficient on CPU, so that we can perform the LBM efficiently. Both the numerical phantom and the physical phantom experiments were carried out to evaluate the performance of the proposed method. The experimental results showed that the proposed method achieved the best performance of 2471 mega lattice-updates per second (MLUPS) and a 118-fold speedup under the precondition of simulation accuracy, compared to the diffusion equation implemented by finite element method (FEM) on CPU. Thus, the LBM on the GPU has the potential for efficiently solving the forward problem in FDOT.

Effects of acoustic nonlinearity on pulse-echo attenuation coefficient estimation from tissue-mimicking phantoms.

The ultrasonic attenuation coefficient (ACE) can be used to classify tissue state. Pulse-echo spectral-based attenuation estimation techniques, such as the spectral-log-difference method (SLD), account for beam diffraction effects using a reference phantom having a sound speed close to the sound speed of the sample. Methods like SLD assume linear propagation of ultrasound and do not account for potential acoustic nonlinear distortion of the backscattered power spectra in both sample and reference. In this study, the ACE of a sample was computed and compared using the SLD with two independent references (high attenuating and low attenuating phantoms but with similar B/A values) and over several pressure levels. Both numerical and physical tissue-mimicking phantoms were used in the study. The results indicated that the biases in ACE increased when using a reference having low attenuation, whereas the high attenuating reference produced more consistent ACE. Furthermore, increments in ACE vs input pressure were correlated to the log-ratio of Gol'dberg numbers between the sample and reference (R2=0.979 in simulations and R2=0.734 in experiments). Therefore, the results suggest that to reduce bias in ACE using spectral-based methods, both the sound speed and the Gol'dberg number of the reference phantom should be matched to the sample.

FPGA Acceleration for 3-D Low-Dose Tomographic Reconstruction

X-ray computed tomography (CT) is commonly used to obtain vivo images to characterize diseases but results in radiation exposure to patients. Low-dose CT (LDCT) provides CT images of clinical quality with reduced cumulative radiation dose. Iterative image reconstruction methods with effective regularization are used for LDCT but generally require more computing resources and induce higher computational load than the conventional filtered backprojection (FBP) methods. The high computational demand of the iterative reconstruction (IR) with notably increased reconstruction time precludes its routine clinical application. In this work, we focus on the FPGA acceleration of a compute-intensive full IR (full-IR) algorithm based on the Mumford–Shah regularization. At the algorithmic level, we propose a beam-based asynchronous update algorithm to reduce the computational cost and alleviate the conflicts. At the hardware-level, we first present pipeline-friendly optimization for the original algorithm to increase the computation throughput. We then apply the LDCT-specific tiling strategy to improve the data reuse rate. The experimental results show that our implementation takes 8.5 min to reconstruct a typical physical phantom with the image quality comparable with the vendor’s result. The FPGA implementation achieves $11.6\times $ throughput against the state-of-the-art GPU version.