Compensating unknown speed of sound in learned fast 3D limited-view photoacoustic tomography

Real-time applications in three-dimensional photoacoustic tomography from planar sensors rely on fast reconstruction algorithms that assume the speed of sound (SoS) in the tissue is homogeneous. Moreover, the reconstruction quality depends on the correct choice for the constant SoS. In this study, we discuss the possibility of ameliorating the problem of unknown or heterogeneous SoS distributions by using learned reconstruction methods. This can be done by modelling the uncertainties in the training data. In addition, a correction term can be included in the learned reconstruction method. We investigate the influence of both and while a learned correction component can improve reconstruction quality further, we show that a careful choice of uncertainties in the training data is the primary factor to overcome unknown SoS. We support our findings with simulated and in vivo measurements in 3D.

Deformable registration of preoperative MR and intraoperative long-length tomosynthesis images for guidance of spine surgery via image synthesis

PURPOSE

Improved integration and use of preoperative imaging during surgery hold significant potential for enhancing treatment planning and instrument guidance through surgical navigation. Despite its prevalent use in diagnostic settings, MR imaging is rarely used for navigation in spine surgery. This study aims to leverage MR imaging for intraoperative visualization of spine anatomy, particularly in cases where CT imaging is unavailable or when minimizing radiation exposure is essential, such as in pediatric surgery.

METHODS

This work presents a method for deformable 3D-2D registration of preoperative MR images with a novel intraoperative long-length tomosynthesis imaging modality (viz., Long-Film [LF]). A conditional generative adversarial network is used to translate MR images to an intermediate bone image suitable for registration, followed by a model-based 3D-2D registration algorithm to deformably map the synthesized images to LF images. The algorithm's performance was evaluated on cadaveric specimens with implanted markers and controlled deformation, and in clinical images of patients undergoing spine surgery as part of a large-scale clinical study on LF imaging.

RESULTS

The proposed method yielded a median 2D projection distance error of 2.0 mm (interquartile range [IQR]: 1.1-3.3 mm) and a 3D target registration error of 1.5 mm (IQR: 0.8-2.1 mm) in cadaver studies. Notably, the multi-scale approach exhibited significantly higher accuracy compared to rigid solutions and effectively managed the challenges posed by piecewise rigid spine deformation. The robustness and consistency of the method were evaluated on clinical images, yielding no outliers on vertebrae without surgical instrumentation and 3% outliers on vertebrae with instrumentation.

CONCLUSIONS

This work constitutes the first reported approach for deformable MR to LF registration based on deep image synthesis. The proposed framework provides access to the preoperative annotations and planning information during surgery and enables surgical navigation within the context of MR images and/or dual-plane LF images.

IOeRT conventional and FLASH treatment planning system implementation exploiting fast GPU Monte Carlo: The case of breast cancer

Partial breast irradiation for the treatment of early-stage breast cancer patients can be performed by means of Intra Operative electron Radiation Therapy (IOeRT). One of the main limitations of this technique is the absence of a treatment planning system (TPS) that could greatly help in ensuring a proper coverage of the target volume during irradiation. An IOeRT TPS has been developed using a fast Monte Carlo (MC) and an ultrasound imaging system to provide the best irradiation strategy (electron beam energy, applicator position and bevel angle) and to facilitate the optimisation of dose prescription and delivery to the target volume while maximising the organs at risk sparing. The study has been performed in silico, exploiting MC simulations of a breast cancer treatment. Ultrasound-based input has been used to compute the absorbed dose maps in different irradiation strategies and a quantitative comparison between the different options was carried out using Dose Volume Histograms. The system was capable of exploring different beam energies and applicator positions in few minutes, identifying the best strategy with an overall computation time that was found to be completely compatible with clinical implementation. The systematic uncertainty related to tissue deformation during treatment delivery with respect to imaging acquisition was taken into account. The potential and feasibility of a GPU based full MC TPS implementation of IOeRT breast cancer treatments has been demonstrated in-silico. This long awaited tool will greatly improve the treatment safety and efficacy, overcoming the limits identified within the clinical trials carried out so far.

Impact of improved dead time correction on the quantification accuracy of a dedicated BrainPET scanner

OBJECTIVE

Quantitative values derived from PET brain images are of high interest for neuroscientific applications. Insufficient DT correction (DTC) can lead to a systematic bias of the output parameters obtained by a detailed analysis of the time activity curves (TACs). The DTC method currently used for the Siemens 3T MR BrainPET insert is global, i.e., differences in DT losses between detector blocks are not considered, leading to inaccurate DTC and, consequently, to inaccurate measurements masked by a bias. However, following careful evaluation with phantom measurements, a new block-pairwise DTC method has demonstrated a higher degree of accuracy compared to the global DTC method.

APPROACH

Differences between the global and the block-pairwise DTC method were studied in this work by applying several radioactive tracers. We evaluated the impact on [11C]ABP688, O-(2-[18F]fluoroethyl)-L-tyrosine (FET), and [15O]H2O TACs.

RESULTS

For [11C]ABP688, a relevant bias of between -0.0034 and -0.0053 ml/ (cm3 • min) was found in all studied brain regions for the volume of distribution (VT) when using the current global DTC method. For [18F]FET-PET, differences of up to 10% were observed in the tumor-to-brain ratio (TBRmax), these differences depend on the radial distance of the maximum from the PET isocenter. For [15O]H2O, differences between +4% and -7% were observed in the GM region. Average biases of -4.58%, -3.2%, and -1.2% for the regional cerebral blood flow (CBF (K1)), the rate constant k2, and the volume of distribution VT were observed, respectively. Conversely, in the white matter region, average biases of -4.9%, -7.0%, and 3.8% were observed for CBF (K1), k2, and VT, respectively.

CONCLUSION

The bias introduced by the global DTC method leads to an overestimation in the studied quantitative parameters for all applications compared to the block-pairwise method.

SIGNIFICANCE

The observed differences between the two DTC methods are particularly relevant for research applications in neuroscientific studies as they affect the accuracy of quantitative Brain PET images.

Compact meta-differentiator for achieving isotropically high-contrast ultrasonic imaging

Ultrasonic imaging is crucial in the fields of biomedical engineering for its deep penetration capabilities and non-ionizing nature. However, traditional techniques heavily rely on impedance differences within objects, resulting in poor contrast when imaging acoustically transparent targets. Here, we propose a compact spatial differentiator for underwater isotropic edge-enhanced imaging, which enhances the imaging contrast without the need for contrast agents or external physical fields. This design incorporates an amplitude meta-grating for linear transmission along the radial direction, combined with a phase meta-grating that utilizes focus and spiral phases with a first-order topological charge. Through theoretical analysis, numerical simulations, and experimental validation, we substantiate the effectiveness of our technique in distinguishing amplitude objects with isotropic edge enhancements. Importantly, this method also enables the accurate detection of both phase objects and artificial biological models. This breakthrough creates new opportunities for applications in medical diagnosis and nondestructive testing.

Hybrid-supervised deep learning for domain transfer 3D protoacoustic image reconstruction

Objective. Protoacoustic imaging showed great promise in providing real-time 3D dose verification of proton therapy. However, the limited acquisition angle in protoacoustic imaging induces severe artifacts, which impairs its accuracy for dose verification. In this study, we developed a hybrid-supervised deep learning method for protoacoustic imaging to address the limited view issue.Approach. We proposed a Recon-Enhance two-stage deep learning method. In the Recon-stage, a transformer-based network was developed to reconstruct initial pressure maps from raw acoustic signals. The network is trained in a hybrid-supervised approach, where it is first trained using supervision by the iteratively reconstructed pressure map and then fine-tuned using transfer learning and self-supervision based on the data fidelity constraint. In the enhance-stage, a 3D U-net is applied to further enhance the image quality with supervision from the ground truth pressure map. The final protoacoustic images are then converted to dose for proton verification.Main results. The results evaluated on a dataset of 126 prostate cancer patients achieved an average root mean squared errors (RMSE) of 0.0292, and an average structural similarity index measure (SSIM) of 0.9618, out-performing related start-of-the-art methods. Qualitative results also demonstrated that our approach addressed the limit-view issue with more details reconstructed. Dose verification achieved an average RMSE of 0.018, and an average SSIM of 0.9891. Gamma index evaluation demonstrated a high agreement (94.7% and 95.7% for 1%/3 mm and 1%/5 mm) between the predicted and the ground truth dose maps. Notably, the processing time was reduced to 6 s, demonstrating its feasibility for online 3D dose verification for prostate proton therapy.Significance. Our study achieved start-of-the-art performance in the challenging task of direct reconstruction from radiofrequency signals, demonstrating the great promise of PA imaging as a highly efficient and accurate tool forinvivo3D proton dose verification to minimize the range uncertainties of proton therapy to improve its precision and outcomes.

Simultaneous photoacoustic and ultrasound imaging: A review

Photoacoustic imaging (PAI) is an emerging biomedical imaging technique that combines the advantages of optical and ultrasound imaging, enabling the generation of images with both optical resolution and acoustic penetration depth. By leveraging similar signal acquisition and processing methods, the integration of photoacoustic and ultrasound imaging has introduced a novel hybrid imaging modality suitable for clinical applications. Photoacoustic-ultrasound imaging allows for non-invasive, high-resolution, and deep-penetrating imaging, providing a wealth of image information. In recent years, with the deepening research and the expanding biomedical application scenarios of photoacoustic-ultrasound bimodal systems, the immense potential of photoacoustic-ultrasound bimodal imaging in basic research and clinical applications has been demonstrated, with some research achievements already commercialized. In this review, we introduce the principles, technical advantages, and biomedical applications of photoacoustic-ultrasound bimodal imaging techniques, specifically focusing on tomographic, microscopic, and endoscopic imaging modalities. Furthermore, we discuss the future directions of photoacoustic-ultrasound bimodal imaging technology.

Investigation of alternative RF power limit control methods for 0.5T, 1.5T, and 3T parallel transmission cardiac imaging: A simulation study

PURPOSE

To investigate safety and performance aspects of parallel-transmit (pTx) RF control-modes for a body coil atB 0 ≤ 3 T $$ {B}_0\le 3\mathrm{T} $$ .

METHODS

Electromagnetic simulations of 11 human voxel models in cardiac imaging position were conducted forB 0 = 0.5 T $$ {B}_0=0.5\mathrm{T} $$ ,1.5 T $$ 1.5\mathrm{T} $$ and3 T $$ 3\mathrm{T} $$ and a body coil with a configurable number of transmit channels (1, 2, 4, 8, 16). Three safety modes were considered: the 'SAR-controlled mode' (SCM), where specific absorption rate (SAR) is limited directly, a 'phase agnostic SAR-controlled mode' (PASCM), where phase information is neglected, and a 'power-controlled mode' (PCM), where the voltage amplitude for each channel is limited. For either mode, safety limits were established based on a set of 'anchor' simulations and then evaluated in 'target' simulations on previously unseen models. The comparison allowed to derive safety factors accounting for varying patient anatomies. All control modes were compared in terms of theB 1 + $$ {B}_1^{+} $$ amplitude and homogeneity they permit under their respective safety requirements.

RESULTS

Large safety factors (approximately five) are needed if only one or two anchor models are investigated but they shrink with increasing number of anchors. The achievableB 1 + $$ {B}_1^{+} $$ is highest for SCM but this advantage is reduced when the safety factor is included. PCM appears to be more robust against variations of subjects. PASCM performance is mostly in between SCM and PCM. Compared to standard circularly polarized (CP) excitation, pTx offers minorB 1 + $$ {B}_1^{+} $$ improvements if local SAR limits are always enforced.

CONCLUSION

PTx body coils can safely be used atB 0 ≤ 3 T $$ {B}_0\le 3\mathrm{T} $$ . Uncertainties in patient anatomy must be accounted for, however, by simulating many models.

Real-time dual-modal photoacoustic and fluorescence small animal imaging

By combining optical absorption contrast and acoustic resolution, photoacoustic imaging (PAI) has broken the barrier in depth for high-resolution optical imaging. Meanwhile, Fluorescence imaging (FLI), owing to advantages of high sensitivity and high specificity with abundant fluorescence agents and proteins, has always been playing a key role in live animal studies. Based on different optical contrast mechanisms, PAI and FLI can provide important complementary information to each other. In this work, we uniquely designed a Photoacoustic-Fluorescence (PA-FL) imaging system that provides real-time dual modality imaging, in which a half-ring ultrasonic array is employed for high quality PA tomography and a specially designed optical window allows simultaneous whole-body fluorescence imaging. The performance of this dual modality system was demonstrated in live animal studies, including real-time monitoring of perfusion and metabolic processes of fluorescent dyes. Our study indicates that the PA-FL imaging system has unique potential for live small animal research.

OIF-Net: An Optical Flow Registration-Based PET/MR Cross-Modal Interactive Fusion Network for Low-Count Brain PET Image Denoising

The short frames of low-count positron emission tomography (PET) images generally cause high levels of statistical noise. Thus, improving the quality of low-count images by using image postprocessing algorithms to achieve better clinical diagnoses has attracted widespread attention in the medical imaging community. Most existing deep learning-based low-count PET image enhancement methods have achieved satisfying results, however, few of them focus on denoising low-count PET images with the magnetic resonance (MR) image modality as guidance. The prior context features contained in MR images can provide abundant and complementary information for single low-count PET image denoising, especially in ultralow-count (2.5%) cases. To this end, we propose a novel two-stream dual PET/MR cross-modal interactive fusion network with an optical flow pre-alignment module, namely, OIF-Net. Specifically, the learnable optical flow registration module enables the spatial manipulation of MR imaging inputs within the network without any extra training supervision. Registered MR images fundamentally solve the problem of feature misalignment in the multimodal fusion stage, which greatly benefits the subsequent denoising process. In addition, we design a spatial-channel feature enhancement module (SC-FEM) that considers the interactive impacts of multiple modalities and provides additional information flexibility in both the spatial and channel dimensions. Furthermore, instead of simply concatenating two extracted features from these two modalities as an intermediate fusion method, the proposed cross-modal feature fusion module (CM-FFM) adopts cross-attention at multiple feature levels and greatly improves the two modalities' feature fusion procedure. Extensive experimental assessments conducted on real clinical datasets, as well as an independent clinical testing dataset, demonstrate that the proposed OIF-Net outperforms the state-of-the-art methods.

On the utilization of the adjoint method in microwave tomography

In microwave imaging, the adjoint method is widely used for the efficient calculation of the update direction, which is then used to update the unknown model parameter. However, the utilization and the formulation of the adjoint method differ significantly depending on the imaging scenario and the applied optimization algorithm. Because of the problem-specific nature of the adjoint formulations, the dissimilarities between the adjoint calculations may be overlooked. Here, we have classified the adjoint method formulations into two groups: the direct and indirect methods. The direct method involves calculating the derivative of the cost function, whereas, in the indirect method, the derivative of the predicted data is calculated. In this review, the direct and indirect adjoint methods are presented, compared, and discussed. The formulations are explicitly derived using the two-dimensional wave equation in frequency and time domains. Finite-difference time-domain simulations are conducted to show the different uses of the adjoint methods for both single source-multiple receiver, and multiple transceiver scenarios. This study demonstrated that an appropriate adjoint method selection is significant to achieve improved computational efficiency for the applied optimization algorithm.

Measurement of the12C(p,n)12N reaction cross section below 150 MeV

Objective. Proton therapy currently faces challenges from clinical complications on organs-at-risk due to range uncertainties. To address this issue, positron emission tomography (PET) of the proton-induced11C and15O activity has been used to provide feedback on the proton range. However, this approach is not instantaneous due to the relatively long half-lives of these nuclides. An alternative nuclide,12N (half-life 11 ms), shows promise for real-timein vivoproton range verification. Development of12N imaging requires better knowledge of its production reaction cross section.Approach. The12C(p,n)12N reaction cross section was measured by detecting positron activity of graphite targets irradiated with 66.5, 120, and 150 MeV protons. A pulsed beam delivery with 0.7-2 × 108protons per pulse was used. The positron activity was measured during the beam-off periods using a dual-head Siemens Biograph mCT PET scanner. The12N production was determined from activity time histograms.Main results. The cross section was calculated for 11 energies, ranging from 23.5 to 147 MeV, using information on the experimental setup and beam delivery. Through a comprehensive uncertainty propagation analysis, a statistical uncertainty of 2.6%-5.8% and a systematic uncertainty of 3.3%-4.6% were achieved. Additionally, a comparison between measured and simulated scanner sensitivity showed a scaling factor of 1.25 (±3%). Despite this, there was an improvement in the precision of the cross section measurement compared to values reported by the only previous study.Significance. Short-lived12N imaging is promising for real-timein vivoverification of the proton range to reduce clinical complications in proton therapy. The verification procedure requires experimental knowledge of the12N production cross section for proton energies of clinical importance, to be incorporated in a Monte Carlo framework for12N imaging prediction. This study is the first to achieve a precise measurement of the12C(p,n)12N nuclear cross section for such proton energies.

Hybrid spherical array for combined volumetric optoacoustic and B-mode ultrasound imaging

Optoacoustic (OA) imaging has achieved tremendous progress with state-of-the-art systems providing excellent functional and molecular contrast, centimeter scale penetration into living tissues, and ultrafast imaging performance, making it highly suitable for handheld imaging in the clinics. OA can greatly benefit from efficient integration with ultrasound (US) imaging, which remains the routine method in bedside clinical diagnostics. However, such integration has not been straightforward since the two modalities typically involve different image acquisition strategies. Here, we present a new, to our knowledge, hybrid optoacoustic ultrasound (OPUS) imaging approach employing a spherical array with dedicated segments for each modality to enable volumetric OA imaging merged with conventional B-mode US. The system performance is subsequently showcased in healthy human subjects. The new OPUS approach hence represents an important step toward establishing OA in point-of-care diagnostic settings.

Comparison of tight-fitting 7T parallel-transmit head array designs using excitation uniformity and local specific absorption rate metrics

PURPOSE

We model the performance of parallel transmission (pTx) arrays with 8, 16, 24, and 32 channels and varying loop sizes built on a close-fitting helmet for brain imaging at 7 T and compare their local specific absorption rate (SAR) and flip-angle performances to that of birdcage coil (used as a baseline) and cylindrical 8-channel and 16-channel pTx coils (single-row and dual-row).

METHODS

We use the co-simulation approach along with MATLAB scripting for batch-mode simulation of the coils. For each coil, we extracted B1 + maps and SAR matrices, which we compressed using the virtual observation points algorithm, and designed slice-selective RF shimming pTx pulses with multiple local SAR and peak power constraints to generate L-curves in the transverse, coronal, and sagittal orientations.

RESULTS

Helmet designs outperformed cylindrical pTx arrays at a constant number of channels in the flip-angle uniformity at a constant local SAR metric: up to 29% for 8-channel arrays, and up to 34% for 16-channel arrays, depending on the slice orientation. For all helmet arrays, increasing the loop diameter led to better local SAR versus flip-angle uniformity tradeoffs, although this effect was more pronounced for the 8-channel and 16-channel systems than the 24-channel and 32-channel systems, as the former have more limited degrees of freedom and therefore benefit more from loop-size optimization.

CONCLUSION

Helmet pTx arrays significantly outperformed cylindrical arrays with the same number of channels in local SAR and flip-angle uniformity metrics. This improvement was especially pronounced for non-transverse slice excitations. Loop diameter optimization for helmets appears to favor large loops, compatible with nearest-neighbor decoupling by overlap.

Multi-Scale Volumetric Dynamic Optoacoustic and Laser Ultrasound (OPLUS) Imaging Enabled by Semi-Transparent Optical Guidance

Major biological discoveries are made by interrogating living organisms with light. However, the limited penetration of un-scattered photons within biological tissues limits the depth range covered by optical methods. Deep-tissue imaging is achieved by combining light and ultrasound. Optoacoustic imaging exploits the optical generation of ultrasound to render high-resolution images at depths unattainable with optical microscopy. Recently, laser ultrasound has been suggested as a means of generating broadband acoustic waves for high-resolution pulse-echo ultrasound imaging. Herein, an approach is proposed to simultaneously interrogate biological tissues with light and ultrasound based on layer-by-layer coating of silica optical fibers with a controlled degree of transparency. The time separation between optoacoustic and ultrasound signals collected with a custom-made spherical array transducer is exploited for simultaneous 3D optoacoustic and laser ultrasound (OPLUS) imaging with a single laser pulse. OPLUS is shown to enable large-scale anatomical characterization of tissues along with functional multi-spectral imaging of chromophores and assessment of cardiac dynamics at ultrafast rates only limited by the pulse repetition frequency of the laser. The suggested approach provides a flexible and scalable means for developing a new generation of systems synergistically combining the powerful capabilities of optoacoustics and ultrasound imaging in biology and medicine.

MCGPU-PET: An Open-Source Real-Time Monte Carlo PET Simulator

Monte Carlo (MC) simulations are commonly used to model the emission, transmission, and/or detection of radiation in Positron Emission Tomography (PET). In this work, we introduce a new open-source MC software for PET simulation, MCGPU-PET, which has been designed to fully exploit the computing capabilities of modern GPUs to simulate the acquisition of more than 100 million coincidences per second from voxelized sources and material distributions. The new simulator is an extension of the PENELOPE-based MCGPU code previously used in cone-beam CT and mammography applications. We validated the accuracy of the accelerated code by comparing it to GATE and PeneloPET simulations achieving an agreement within 10 percent approximately. As an example application of the code for fast estimation of PET coincidences, a scan of the NEMA IQ phantom was simulated. A fully 3D sinogram with 6382 million true coincidences and 731 million scatter coincidences was generated in 54 seconds in one GPU. MCGPU-PET provides an estimation of true and scatter coincidences and spurious background (for positron-gamma emitters such as 124I) at a rate 3 orders of magnitude faster than CPU-based MC simulators. This significant speed-up enables the use of the code for accurate scatter and prompt-gamma background estimations within an iterative image reconstruction process.

ReeGAN: MRI image edge-preserving synthesis based on GANs trained with misaligned data

As a crucial medical examination technique, different modalities of magnetic resonance imaging (MRI) complement each other, offering multi-angle and multi-dimensional insights into the body's internal information. Therefore, research on MRI cross-modality conversion is of great significance, and many innovative techniques have been explored. However, most methods are trained on well-aligned data, and the impact of misaligned data has not received sufficient attention. Additionally, many methods focus on transforming the entire image and ignore crucial edge information. To address these challenges, we propose a generative adversarial network based on multi-feature fusion, which effectively preserves edge information while training on noisy data. Notably, we consider images with limited range random transformations as noisy labels and use an additional small auxiliary registration network to help the generator adapt to the noise distribution. Moreover, we inject auxiliary edge information to improve the quality of synthesized target modality images. Our goal is to find the best solution for cross-modality conversion. Comprehensive experiments and ablation studies demonstrate the effectiveness of the proposed method.

Application of NEMA protocols to verify GATE models based on the Digital Biograph Vision and the Biograph Vision Quadra scanners

PURPOSE

The Monte Carlo method is an effective tool to simulate and verify PET systems. Furthermore, it can help in the design and optimization of new medical imaging devices and algorithms. In this framework, the goal of this work is to verify the GATE toolkit performance when applied to simulate two Siemens Healthineers PET scanners: a standard axial field-of-view Biograph Vision scanner and the new long axial field-of-view Biograph Vision Quadra scanner.

METHODS

The simulation toolkit GATE is based on GEANT4, comprising its main functionalities and a set of domain-specific features in the field of medical physics. To accomplish our purpose, the guidelines described in the NEMA NU 2-2018 protocol are reproduced. Then the simulated results are compared to experimental data available in the literature for both PET scanners. The assessment of the models includes different studies of sensitivity, count rate performances, spatial resolution and image quality. These tests are intended to evaluate the image quality of PET devices.

RESULTS

In the spatial resolution test, relative errors lower than 8% are obtained between the experiments and GATE models. The sensitivity is 17.2 cps/kBq (Vision) and 175.9 cps/kBq (Quadra), representing relative differences with the experiment of 6% and 0.3%, respectively. Deviations from peak NECR are less than 9%. In the Image Quality test, the contrast recovery coefficient for hot spheres, with 8 iterations and 5 subsets, ranges between 57-83% for Vision and 54-86% for Quadra. These values represent a maximum deviation between the simulations and the experiments of 10% for the Quadra scanner. In the case of the Vision scanner, the highest difference is observed for the 10 mm sphere (∼38%) due to the higher contrast recovery of the experiment caused by the Gibbs artifact from the use of PSF reconstruction.

CONCLUSIONS

The results of the simulations have provided evidence of a good agreement between the experimental data and the results obtained with GATE. Thus, this work supports the capability of this MC toolkit to accurately simulate the models of the Vision and Quadra scanners. This study has laid the basis for further research in this field and has identified several areas that could be explored.

On the Importance of Low-Frequency Signals in Functional and Molecular Photoacoustic Computed Tomography

In photoacoustic computed tomography (PACT) with short-pulsed laser excitation, wideband acoustic signals are generated in biological tissues with frequencies related to the effective shapes and sizes of the optically absorbing targets. Low-frequency photoacoustic signal components correspond to slowly varying spatial features and are often omitted during imaging due to the limited detection bandwidth of the ultrasound transducer, or during image reconstruction as undesired background that degrades image contrast. Here we demonstrate that low-frequency photoacoustic signals, in fact, contain functional and molecular information, and can be used to enhance structural visibility, improve quantitative accuracy, and reduce spare-sampling artifacts. We provide an in-depth theoretical analysis of low-frequency signals in PACT, and experimentally evaluate their impact on several representative PACT applications, such as mapping temperature in photothermal treatment, measuring blood oxygenation in a hypoxia challenge, and detecting photoswitchable molecular probes in deep organs. Our results strongly suggest that low-frequency signals are important for functional and molecular PACT.

Feasibility study of a SiPM-fiber detector for non-invasive measurement of arterial input function for preclinical and clinical positron emission tomography

Pharmacokinetic positron emission tomography (PET) studies rely on the measurement of the arterial input function (AIF), which represents the time-activity curve of the radiotracer concentration in the blood plasma. Traditionally, obtaining the AIF requires invasive procedures, such as arterial catheterization, which can be challenging, time-consuming, and associated with potential risks. Therefore, the development of non-invasive techniques for AIF measurement is highly desirable. This study presents a detector for the non-invasive measurement of the AIF in PET studies. The detector is based on the combination of scintillation fibers and silicon photomultipliers (SiPMs) which leads to a very compact and rugged device. The feasibility of the detector was assessed through Monte Carlo simulations conducted on mouse tail and human wrist anatomies studying relevant parameters such as energy spectrum, detector efficiency and minimum detectable activity (MDA). The simulations involved the use of 18F and 68Ga isotopes, which exhibit significantly different positron ranges. In addition, several prototypes were built in order to study the different components of the detector including the scintillation fiber, the coating of the fiber, the SiPMs, and the operating configuration. Finally, the simulations were compared with experimental measurements conducted using a tube filled with both 18F and 68Ga to validate the obtained results. The MDA achieved for both anatomies (approximately 1000 kBq/mL for mice and 1 kBq/mL for humans) falls below the peak radiotracer concentrations typically found in PET studies, affirming the feasibility of conducting non-invasive AIF measurements with the fiber detector. The sensitivity for measurements with a tube filled with 18F (68Ga) was 1.2 (2.07) cps/(kBq/mL), while for simulations, it was 2.81 (6.23) cps/(kBq/mL). Further studies are needed to validate these results in pharmacokinetic PET studies.

UMC-PET: a fast and flexible Monte Carlo PET simulator

Objective.The GPU-based Ultra-fast Monte Carlo positron emission tomography simulator (UMC-PET) incorporates the physics of the emission, transport and detection of radiation in PET scanners. It includes positron range, non-colinearity, scatter and attenuation, as well as detector response. The objective of this work is to present and validate UMC-PET as a a multi-purpose, accurate, fast and flexible PET simulator.Approach.We compared UMC-PET against PeneloPET, a well-validated MC PET simulator, both in preclinical and clinical scenarios. Different phantoms for scatter fraction (SF) assessment following NEMA protocols were simulated in a 6R-SuperArgus and a Biograph mMR scanner, comparing energy histograms, NEMA SF, and sensitivity for different energy windows. A comparison with real data reported in the literature on the Biograph scanner is also shown.Main results.NEMA SF and sensitivity estimated by UMC-PET where within few percent of PeneloPET predictions. The discrepancies can be attributed to small differences in the physics modeling. Running in a 11 GB GeForce RTX 2080 Ti GPU, UMC-PET is ∼1500 to ∼2000 times faster than PeneloPET executing in a single core Intel(R) Xeon(R) CPU W-2155 @ 3.30 GHz.Significance.UMC-PET employs a voxelized scheme for the scanner, patient adjacent objects (such as shieldings or the patient bed), and the activity distribution. This makes UMC-PET extremely flexible. Its high simulation speed allows applications such as MC scatter correction, faster SRM estimation for complex scanners, or even MC iterative image reconstruction.

Point spread function modeling for photoacoustic tomography - I: three-dimensional detection geometries

Photoacoustic computed tomography (PACT) has been under intensive investigation as a promising noninvasive biomedical imaging modality. Various acoustic detector arrays have been developed to achieve enhanced imaging performance. In this paper, we study the effect of the detection geometry on image quality through point spread function (PSF) modeling based on back-projection image reconstruction. Three commonly-used three-dimensional detection geometries, namely, spherical, cylindrical, and planar detector arrays, are investigated. The effect of detector bandwidth and aperture on PSF in these detection geometries is also studied. This work provides a performance evaluation tool for acoustic detector arrays used in PACT and can be helpful in the design and selection of detector arrays in practical imaging applications.

Evaluation of RADIANCE Monte Carlo algorithm for treatment planning in electron based Intraoperative Radiotherapy (IOERT)

PURPOSE

To perform experimental as well as independent Monte Carlo (MC) evaluation of the MC algorithm implemented in RADIANCE version 4.0.8, a dedicated treatment planning system (TPS) for 3D electron dose calculations in intraoperative radiation therapy (IOERT).

METHODS AND MATERIALS

The MOBETRON 2000 (IntraOp Medical Corporation, Sunnyvale, CA) IOERT accelerator was employed. PDD and profiles for five cylindrical plastic applicators with 50-90 mm diameter and 0°, 30° beveling were measured in a water phantom, at nominal energies of 6, 9 and 12 MeV. Additional PDD measurements were performed for all the energies without applicator. MC modeling of the MOBETRON was performed with the user code BEAMnrc and egs_chamber of the MC simulation toolkit EGSnrc. The generated phase space files of the two 0°-bevel applicators (50 mm, 80 mm) and three energies in both RADIANCE and BEAMnrc, were used to determine PDD and profiles in various set-ups of virtual water phantoms with air and bone inhomogeneities. 3D dose distributions were also calculated in image data sets of an anthropomorphic tissue-equivalent pelvis phantom. Image acquisitions were realized with a CT scanner (Philips Big Bore CT, Netherlands). Gamma analysis was applied to quantify the deviations of the RADIANCE calculations to the measurements and EGSnrc calculations. Gamma criteria normalized to the global maximum were investigated between 2%, 2 mm and 3%, 3 mm.

RESULTS

RADIANCE MC calculations satisfied the gamma criteria of 3%, 3 mm with a tolerance limit of 85% passing rate compared to in- water phantom measurements, except for the dose profiles of the 30° beveled applicators. Mismatches lay in surface doses, in umbra regions and in the beveled end of the 30° applicators. A very good agreement to the EGSnrc calculations in heterogeneous media was observed. Deviations were more pronounced for the larger applicator diameter and higher electron energy. In 3D dose comparisons in the anthropomorphic phantom, gamma passing rates were higher than 96 % for both simulated applicators.

CONCLUSIONS

RADIANCE MC algorithm agrees within 3%, 3 mm criteria with in-water phantom measurements and EGSnrc MC dose distributions in heterogeneous media for 0°-bevel applicators. The user should be aware of missing scattering components and the 30° beveled applicators should be used with attention.

Systematic evaluation of human soft tissue attenuation correction in whole-body PET/MR: Implications from PET/CT for optimization of MR-based AC in patients with normal lung tissue

BACKGROUND

Attenuation correction (AC) is an important methodical step in positron emission tomography/magnetic resonance imaging (PET/MRI) to correct for attenuated and scattered PET photons.

PURPOSE

The overall quality of magnetic resonance (MR)-based AC in whole-body PET/MRI was evaluated in direct comparison to computed tomography (CT)-based AC serving as reference. The quantitative impact of isolated tissue classes in the MR-AC was systematically investigated to identify potential optimization needs and strategies.

METHODS

Data of n = 60 whole-body PET/CT patients with normal lung tissue and without metal implants/prostheses were used to generate six different AC-models based on the CT data for each patient, simulating variations of MR-AC. The original continuous CT-AC (CT-org) is referred to as reference. A pseudo MR-AC (CT-mrac), generated from CT data, with four tissue classes and a bone atlas represents the MR-AC. Relative difference in linear attenuation coefficients (LAC) and standardized uptake values were calculated. From the results two improvements regarding soft tissue AC and lung AC were proposed and evaluated.

RESULTS

The overall performance of MR-AC is in good agreement compared to CT-AC. Lungs, heart, and bone tissue were identified as the regions with most deviation to the CT-AC (myocardium -15%, bone tissue -14%, and lungs ±20%). Using single-valued LACs for AC in the lung only provides limited accuracy. For improved soft tissue AC, splitting the combined soft tissue class into muscles and organs each with adapted LAC could reduce the deviations to the CT-AC to < ±1%. For improved lung AC, applying a gradient LAC in the lungs could remarkably reduce over- or undercorrections in PET signal compared to CT-AC (±5%).

CONCLUSIONS

The AC is important to ensure best PET image quality and accurate PET quantification for diagnostics and radiotherapy planning. The optimized segment-based AC proposed in this study, which was evaluated on PET/CT data, inherently reduces quantification bias in normal lung tissue and soft tissue compared to the CT-AC reference.

Signal domain adaptation network for limited-view optoacoustic tomography

Optoacoustic (OA) imaging is based on optical excitation of biological tissues with nanosecond-duration laser pulses and detection of ultrasound (US) waves generated by thermoelastic expansion following light absorption. The image quality and fidelity of OA images critically depend on the extent of tomographic coverage provided by the US detector arrays. However, full tomographic coverage is not always possible due to experimental constraints. One major challenge concerns an efficient integration between OA and pulse-echo US measurements using the same transducer array. A common approach toward the hybridization consists in using standard linear transducer arrays, which readily results in arc-type artifacts and distorted shapes in OA images due to the limited angular coverage. Deep learning methods have been proposed to mitigate limited-view artifacts in OA reconstructions by mapping artifactual to artifact-free (ground truth) images. However, acquisition of ground truth data with full angular coverage is not always possible, particularly when using handheld probes in a clinical setting. Deep learning methods operating in the image domain are then commonly based on networks trained on simulated data. This approach is yet incapable of transferring the learned features between two domains, which results in poor performance on experimental data. Here, we propose a signal domain adaptation network (SDAN) consisting of i) a domain adaptation network to reduce the domain gap between simulated and experimental signals and ii) a sides prediction network to complement the missing signals in limited-view OA datasets acquired from a human forearm by means of a handheld linear transducer array. The proposed method showed improved performance in reducing limited-view artifacts without the need for ground truth signals from full tomographic acquisitions.

Wirelessly interfacing sensor-equipped implants and MR scanners for improved safety and imaging

PURPOSE

To investigate a novel reduced RF heating method for imaging in the presence of active implanted medical devices (AIMDs) which employs a sensor-equipped implant that provides wireless feedback.

METHODS

The implant, consisting of a generator case and a lead, measures RF-inducedE $$ E $$ -fields at the implant tip using a simple sensor in the generator case and transmits these values wirelessly to the MR scanner. Based on the sensor signal alone, parallel transmission (pTx) excitation vectors were calculated to suppress tip heating and maintain image quality. A sensor-based imaging metric was introduced to assess the image quality. The methodology was studied at 7T in testbed experiments, and at a 3T scanner in an ASTM phantom containing AIMDs instrumented with six realistic deep brain stimulation (DBS) lead configurations adapted from patients.

RESULTS

The implant successfully measured RF-inducedE $$ E $$ -fields (Pearson correlation coefficient squared [R2 ] = 0.93) and temperature rises (R2  = 0.95) at the implant tip. The implant acquired the relevant data needed to calculate the pTx excitation vectors and transmitted them wirelessly to the MR scanner within a single shot RF sequence (<60 ms). Temperature rises for six realistic DBS lead configurations were reduced to 0.03-0.14 K for heating suppression modes compared to 0.52-3.33 K for the worst-case heating, while imaging quality remained comparable (five of six lead imaging scores were ≥0.80/1.00) to conventional circular polarization (CP) images.

CONCLUSION

Implants with sensors that can communicate with an MR scanner can substantially improve safety for patients in a fast and automated manner, easing the current burden for MR personnel.

Artificial Intelligence and Deep Learning for Advancing PET Image Reconstruction: State-of-the-Art and Future Directions

Positron emission tomography (PET) is vital for diagnosing diseases and monitoring treatments. Conventional image reconstruction (IR) techniques like filtered backprojection and iterative algorithms are powerful but face limitations. PET IR can be seen as an image-to-image translation. Artificial intelligence (AI) and deep learning (DL) using multilayer neural networks enable a new approach to this computer vision task. This review aims to provide mutual understanding for nuclear medicine professionals and AI researchers. We outline fundamentals of PET imaging as well as state-of-the-art in AI-based PET IR with its typical algorithms and DL architectures. Advances improve resolution and contrast recovery, reduce noise, and remove artifacts via inferred attenuation and scatter correction, sinogram inpainting, denoising, and super-resolution refinement. Kernel-priors support list-mode reconstruction, motion correction, and parametric imaging. Hybrid approaches combine AI with conventional IR. Challenges of AI-assisted PET IR include availability of training data, cross-scanner compatibility, and the risk of hallucinated lesions. The need for rigorous evaluations, including quantitative phantom validation and visual comparison of diagnostic accuracy against conventional IR, is highlighted along with regulatory issues. First approved AI-based applications are clinically available, and its impact is foreseeable. Emerging trends, such as the integration of multimodal imaging and the use of data from previous imaging visits, highlight future potentials. Continued collaborative research promises significant improvements in image quality, quantitative accuracy, and diagnostic performance, ultimately leading to the integration of AI-based IR into routine PET imaging protocols.

Modifying the trajectory of epicardial leads can substantially reduce MRI-induced RF heating in pediatric patients with a cardiac implantable electronic device at 1.5T

PURPOSE

After epicardial cardiac implantable electronic devices are implanted in pediatric patients, they become ineligible to receive MRI exams due to an elevated risk of RF heating. We investigated whether simple modifications in the trajectories of epicardial leads could substantially and reliably reduce RF heating during MRI at 1.5 T, with benefits extending to abandoned leads.

METHODS

Electromagnetic simulations were performed to assess RF heating of two common 35-cm epicardial lead trajectories exhibiting different degrees of coupling with MRI incident electric fields. Experiments in anthropomorphic phantoms implanted with commercial cardiac implantable electronic devices confirmed the findings. Both electromagnetic simulations and experimental measurements were performed using head-first and feet-first positioning and various landmarks. Transfer function approach was used to assess the performance of suggested modifications in realistic body models.

RESULTS

Simulations (head-first, chest landmark) of a 35-cm epicardial lead with a trajectory where the excess length of the lead was looped and placed on the inferior surface of the heart showed an 87-fold reduction in the 0.1 g-averaged specific absorption rate compared with the lead where the excess length was looped on the anterior surface. Repeated experiments with a commercial epicardial device confirmed this. For fully implanted systems following low-specific absorption rate trajectories, there was a 16-fold reduction in the average temperature rise and a 28-fold reduction for abandoned leads. The transfer function method predicted a 7-fold reduction in the RF heating in 336 realistic scenarios.

CONCLUSION

Surgical modification of epicardial lead trajectory can substantially reduce RF heating at 1.5 T, with benefits extending to abandoned leads.

Implant-friendly MRI of deep brain stimulation electrodes at 7 T

PURPOSE

The purpose of this study is to present a strategy to calculate the implant-friendly (IF) excitation modes-which mitigate the RF heating at the contacts of deep brain stimulation (DBS) electrodes-of multichannel RF coils at 7 T.

METHODS

An induced RF current on an implantable electrode generates a scattered magnetic field whose left-handed circularly polarizing component (B 1 + $$ B{1}^{+} $$ ) is approximated using aB 1 + $$ B{1}^{+} $$ -mapping technique and subsequently used as a gauge for the electrode's induced current. Using this approach, the relative induced currents resulting from each channel of a multichannel RF coil on the DBS electrode were calculated. The IF modes of the corresponding multichannel coil were determined by calculating the null space of the relative induced currents. The proposed strategy was tested and validated for unilateral and bilateral commercial DBS electrodes (directional lead; Infinity DBS system, Abbott Laboratories) placed inside a uniform phantom by performing heating and imaging studies on a 7T MRI scanner using a 16-channel transceive RF coil.

RESULTS

Neither individual IF modes nor shim solutions obtained from IF modes induced significant temperature increase when used for a high-power turbo spin-echo sequence. In contrast, shimming with the scanner's toolbox (i.e., based on per-channelB 1 + $$ B{1}^{+} $$ fields) resulted in a more than 2°C temperature increase for the same amount of input power.

CONCLUSION

A strategy for calculating the IF modes of a multichannel RF coil is presented. This strategy was validated using a 16-channel RF coil at 7 T for unilateral and bilateral commercial DBS electrodes inside a uniform phantom.

Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal-to-noise ratio and speed of MRS

BACKGROUND

While magnetic resonance imaging (MRI) provides high resolution anatomical images with sharp soft tissue contrast, magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of millimolar. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient SNR comes at the cost of a long acquisition time.

PURPOSE

We propose to use deep-learning approaches to denoise MRS data without increasing NSA. This method has potential to reduce the acquisition time as well as improve SNR and quality of spectra, which could enhance the diagnostic value and broaden the clinical applications of MRS.

METHODS

The study was conducted using data collected from the brain spectroscopy phantom and human subjects. We utilized a stack auto-encoder (SAE) network to train deep learning models for denoising low NSA data (NSA = 1, 2, 4, 8, and 16) randomly truncated from high SNR data collected with high NSA (NSA = 192), which were also used to obtain the ground truth. We applied both self-supervised and fully-supervised training approaches and compared their performance of denoising low NSA data based on improvement in SNR. To prevent overfitting, the SAE network was trained in a patch-based manner. We then tested the denoising methods on noise-containing data collected from the phantom and human subjects, including data from brain tumor patients. We evaluated their performance by comparing the SNR levels and mean squared errors (MSEs) calculated for the whole spectra against high SNR "ground truth", as well as the value of chemical shift of N-acetyl-aspartate (NAA) before and after denoising.

RESULTS

With the SAE model, the SNR of low NSA data (NSA = 1) obtained from the phantom increased by 28.5% and the MSE decreased by 42.9%. For low NSA data of the human parietal and temporal lobes, the SNR increased by 32.9% and the MSE decreased by 63.1%. In all cases, the chemical shift of NAA in the denoised spectra closely matched with the high SNR spectra without significant distortion to the spectra after denoising. Furthermore, the denoising performance of the SAE model was more effective in denoising spectra with higher noise levels.

CONCLUSIONS

The reported SAE denoising method is a model-free approach to enhance the SNR of MRS data collected with low NSA. With the denoising capability, it is possible to acquire MRS data with a few NSA, shortening the scan time while maintaining adequate spectroscopic information for detecting and quantifying the metabolites of interest. This approach has the potential to improve the efficiency and effectiveness of clinical MRS data acquisition by reducing the scan time and increasing the quality of spectroscopic data.

An 8-channel Tx dipole and 20-channel Rx loop coil array for MRI of the cervical spinal cord at 7 Tesla

The quality of cervical spinal cord images can be improved by the use of tailored radiofrequency (RF) coil solutions for ultrahigh field imaging; however, very few commercial and research 7-T RF coils currently exist for the spinal cord, and in particular, those with parallel transmission (pTx) capabilities. This work presents the design, testing, and validation of a pTx/Rx coil for the human neck and cervical/upper thoracic spinal cord. The pTx portion is composed of eight dipoles to ensure high homogeneity over this large region of the spinal cord. The Rx portion is made up of twenty semiadaptable overlapping loops to produce high signal-to-noise ratio (SNR) across the patient population. The coil housing is designed to facilitate patient positioning and comfort, while also being tight fitting to ensure high sensitivity. We demonstrate RF shimming capabilities to optimize B1 + uniformity, power efficiency, and/or specific absorption rate efficiency. B1 + homogeneity, SNR, and g-factor were evaluated in adult volunteers and demonstrated excellent performance from the occipital lobe down to the T4-T5 level. We compared the proposed coil with two state-of-the-art head and head/neck coils, confirming its superiority in the cervical and upper thoracic regions of the spinal cord. This coil solution therefore provides a convincing platform for producing the high image quality necessary for clinical and research scanning of the upper spinal cord.

Brain injury mobile diagnostic system: Applications in civilian medical service and on the battlefield-General concept and medical aspects

To present the concept of a portable ultrasound tomography device for diagnosing traumatic and vascular brain lesions. The device consisting of multiple transcranial ultrasound probes placed on the surface of the head, specifically but not exclusively in natural acoustic windows. An integral part of the mobile diagnostic system (MDS) is a decision support system based on artificial intelligence algorithms utilizing information from: head images, laboratory data, and assessment of the patient's clinical condition. The MDS can significantly reduce the time from stroke onset to rtPA therapy in civilian medical services and support therapeutic and evacuation strategies in instances of brain and skull trauma on the battlefield.

Meningioma animal models: a systematic review and meta-analysis

BACKGROUND

Animal models are widely used to study pathological processes and drug (side) effects in a controlled environment. There is a wide variety of methods available for establishing animal models depending on the research question. Commonly used methods in tumor research include xenografting cells (established/commercially available or primary patient-derived) or whole tumor pieces either orthotopically or heterotopically and the more recent genetically engineered models-each type with their own advantages and disadvantages. The current systematic review aimed to investigate the meningioma model types used, perform a meta-analysis on tumor take rate (TTR), and perform critical appraisal of the included studies. The study also aimed to assess reproducibility, reliability, means of validation and verification of models, alongside pros and cons and uses of the model types.

METHODS

We searched Medline, Embase, and Web of Science for all in vivo meningioma models. The primary outcome was tumor take rate. Meta-analysis was performed on tumor take rate followed by subgroup analyses on the number of cells and duration of incubation. The validity of the tumor models was assessed qualitatively. We performed critical appraisal of the methodological quality and quality of reporting for all included studies.

RESULTS

We included 114 unique records (78 using established cell line models (ECLM), 21 using primary patient-derived tumor models (PTM), 10 using genetically engineered models (GEM), and 11 using uncategorized models). TTRs for ECLM were 94% (95% CI 92-96) for orthotopic and 95% (93-96) for heterotopic. PTM showed lower TTRs [orthotopic 53% (33-72) and heterotopic 82% (73-89)] and finally GEM revealed a TTR of 34% (26-43).

CONCLUSION

This systematic review shows high consistent TTRs in established cell line models and varying TTRs in primary patient-derived models and genetically engineered models. However, we identified several issues regarding the quality of reporting and the methodological approach that reduce the validity, transparency, and reproducibility of studies and suggest a high risk of publication bias. Finally, each tumor model type has specific roles in research based on their advantages (and disadvantages).

SYSTEMATIC REVIEW REGISTRATION

PROSPERO-ID CRD42022308833.

Dense speed-of-sound shift imaging for ultrasonic thermometry

Objective. Develop a dense algorithm for calculating the speed-of-sound shift between consecutive acoustic acquisitions as a noninvasive means to evaluating temperature change during thermal ablation.Methods. An algorithm for dense speed-of-sound shift imaging (DSI) was developed to simultaneously incorporate information from the entire field of view using a combination of dense optical flow and inverse problem regularization, thus speeding up the calculation and introducing spatial agreement between pixels natively. Thermal ablation monitoring consisted of two main steps: pixel shift tracking using Farneback optical flow, and mathematical modeling of the relationship between the pixel displacement and temperature change as an inverse problem to find the speed-of-sound shift. A calibration constant translates from speed-of-sound shift to temperature change. The method performance was tested inex vivosamples and compared to standard thermal strain imaging (TSI) methods.Main results. Thermal ablation at a frequency of 2 MHz was applied to an agarose phantom that created a speed-of-sound shift measured by an L12-5 imaging transducer. A focal spot was reconstructed by solving the inverse problem. Next, a thermocouple measured the temperature rise during thermal ablation ofex vivochicken breast to calibrate the setup. Temperature changes between 3 °C and 15 °C was measured with high thermometry precision of less than 2 °C error for temperature changes as low as 8 °C. The DSI method outperformed standard TSI in both spatial coherence and runtime in high-intensity focused ultrasound-induced hyperthermia.Significance. Dense ultrasonic speed-of-sound shift imaging can successfully monitor the speed-of-sound shift introduced by thermal ablation. This technique is faster and more robust than current methods, and therefore can be used as a noninvasive, real time and cost-effective thermometry method, with high clinical applicability.

Reconstruction of multi-animal PET acquisitions with anisotropically variant PSF

Among other factors such as random, attenuation and scatter corrections, uniform spatial resolution is key to performing accurate quantitative studies in Positron emission tomography (PET). Particularly in preclinical PET studies involving simultaneous acquisition of multiple animals, the degradation of image resolution due to the depth of interaction (DOI) effect far from the center of the Field of View (FOV) becomes a significant concern. In this work, we incorporated a spatially-variant resolution model into a real time iterative reconstruction code to obtain accurate images of multi-animal acquisition. We estimated the spatially variant point spread function (SV-PSF) across the FOV using measurements and Monte Carlo (MC) simulations. The SV-PSF obtained was implemented in a GPU-based Ordered subset expectation maximization (OSEM) reconstruction code, which includes scatter, attenuation and random corrections. The method was evaluated with acquisitions from two preclinical PET/CT scanners of the SEDECAL Argus family: a Derenzo phantom placed 2 cm off center in the 4R-SuperArgus, and a multi-animal study with 4 mice in the 6R-SuperArgus. The SV-PSF reconstructions showed uniform spatial resolution without significant increase in reconstruction time, with superior image quality compared to the uniform PSF model.

In vivo noninvasive systemic myography of acute systemic vasoactivity in female pregnant mice

Altered vasoactivity is a major characteristic of cardiovascular and oncological diseases, and many therapies are therefore targeted to the vasculature. Therapeutics which are selective for the diseased vasculature are ideal, but whole-body selectivity of a therapeutic is challenging to assess in practice. Vessel myography is used to determine the functional mechanisms and evaluate pharmacological responses of vascularly-targeted therapeutics. However, myography can only be performed on ex vivo sections of individual arteries. We have developed methods for implementation of spherical-view photoacoustic tomography for non-invasive and in vivo myography. Using photoacoustic tomography, we demonstrate the measurement of acute vascular reactivity in the systemic vasculature and the placenta of female pregnant mice in response to two vasodilators. Photoacoustic tomography simultaneously captures the significant acute vasodilation of major arteries and detects selective vasoactivity of the maternal-fetal vasculature. Photoacoustic tomography has the potential to provide invaluable preclinical information on vascular response that cannot be obtained by other established methods.

M-TAG: A modular teaching-aid for Geant4

Geant4 is a versatile Monte Carlo radiation transport simulation toolkit with a steep learning curve. This work introduces a user-code called M-TAG (Modular Radiation Teaching-Aid for Geant4), built on top of Geant4. M-TAG is designed to help gradually introduce the Geant4 toolkit to new users. The goal of Geant4 is to record quantities from the simulated radiation as it is transported through geometries. M-TAG simplifies the inclusion of new geometric elements and detector components in the simulation by including new classes. M-TAG also provides basic validated examples for some common detector development tasks. Geant4 intercom modules, called messenger classes, manage these classes. To validate M-TAG, simulations were performed to calculate the range of positrons in water. One hundred million decays at the center of a water-filled sphere with a radius of 1 m were allowed for fluorine-18, carbon-11, oxygen-15 and gallium-68. These results were compared to literature values. An inexperienced Geant4 user was tasked with creating a simulation model for a plastic scintillator-based detector and conducting basic tests to assess the effectiveness of M-TAG as a teaching tool. The simulation involved calculating the dose to the detector's sensitive volume using a 2x2 cm planar monoenergetic photon source spanning energies from 20 to 100 keV. One billion particles were simulated twice: once with the actual detector geometry and once with the sensitive volume replaced by water. The validity of M-TAG was also verified by computing dose ratios and comparing them with mass-attenuation ratios obtained from NIST XCOM data sets. The mean positron travel distances were within the distribution of literature values. Simulated positron energy spectra means were within 1.8% of literature means. Simulated dose ratios agreed with literature values within uncertainties. We have developed and verified a modular Geant4 teaching aid called M-TAG. It was used to introduce a new user to Geant4, who used it to perform further validation simulations.

A Forward Model Incorporating Elevation-Focused Transducer Properties for 3-D Full-Waveform Inversion in Ultrasound Computed Tomography

Ultrasound computed tomography (USCT) is an emerging medical imaging modality that holds great promise for improving human health. Full-waveform inversion (FWI)-based image reconstruction methods account for the relevant wave physics to produce high spatial resolution images of the acoustic properties of the breast tissues. A practical USCT design employs a circular ring-array comprised of elevation-focused ultrasonic transducers, and volumetric imaging is achieved by translating the ring-array orthogonally to the imaging plane. In commonly deployed slice-by-slice (SBS) reconstruction approaches, the 3-D volume is reconstructed by stacking together 2-D images reconstructed for each position of the ring-array. A limitation of the SBS reconstruction approach is that it does not account for 3-D wave propagation physics and the focusing properties of the transducers, which can result in significant image artifacts and inaccuracies. To perform 3-D image reconstruction when elevation-focused transducers are employed, a numerical description of the focusing properties of the transducers should be included in the forward model. To address this, a 3-D computational model of an elevation-focused transducer is developed to enable 3-D FWI-based reconstruction methods to be deployed in ring-array-based USCT. The focusing is achieved by applying a spatially varying temporal delay to the ultrasound pulse (emitter mode) and recorded signal (receiver mode). The proposed numerical transducer model is quantitatively validated and employed in computer simulation studies that demonstrate its use in image reconstruction for ring-array USCT.

A depth-of-interaction rebinning method based on both geometric and activity weights

BACKGROUND AND OBJECTIVE

For positron emission tomography (PET) scanners with depth-of-interaction (DOI) measurement, the DOI rebinning method that utilizes DOI information to process the projection data is critical to image quality. Current DOI rebinning methods map coincidence events onto the rebinned sinogram based on the correlation of lines of response (LOR). This study aims to incorporate prior radioactivity distribution of the imaging object into DOI rebinning to obtain better image quality.

METHODS

A DOI rebinning method based on both geometric and activity weights was proposed to assign coincidence events to the rebinned sinogram defined by a virtual ring. The geometric weights, representing the correlation between LORs, were calculated based on the areas of intersection. The activity weights, reflecting the activity distribution of the imaging object, were derived from the previous reconstructed image.

RESULTS

Monte Carlo simulation data from four phantoms, including the image quality phantom, Derenzo phantom, and two rat-like ROBY phantoms, was used to evaluate the proposed method. The recovery coefficient (RC), contrast recovery coefficient (CRC), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR) were used as image quality metrics. Compared to other DOI rebinning methods, the proposed method achieved the highest RC (maximum improvement of 32%) and CRC at the same noise level and was also optimal in terms of the SSIM and PSNR. Meanwhile, incorporating the prior activity distribution into DOI rebinning also improved the image reconstruction speed.

CONCLUSIONS

This work developed a new DOI rebinning method combining the correlation of LORs with the prior activity distribution, achieving relatively optimal image quality and reconstruction speed. Furthermore, it still needs to be evaluated on the actual equipment.

Zinc-Doped Iron Oxide Nanoparticles as a Proton-Activatable Agent for Dose Range Verification in Proton Therapy

Proton therapy allows the treatment of specific areas and avoids the surrounding tissues. However, this technique has uncertainties in terms of the distal dose fall-off. A promising approach to studying the proton range is the use of nanoparticles as proton-activatable agents that produce detectable signals. For this, we developed an iron oxide nanoparticle doped with Zn (IONP@Zn-cit) with a hydrodynamic size of 10 nm and stability in serum. Cytotoxicity, defined as half of the surveillance, was 100 μg Zn/mL in the U251 cell line. The effect on clonogenic cell death was tested after X-ray irradiation, which suggested a radioprotective effect of these nanoparticles at low concentrations (1-10 μg Zn/mL). To evaluate the production of positron emitters and prompt-gamma signals, IONP@Zn-cit was irradiated with protons, obtaining prompt-gamma signals at the lowest measured concentration (10 mg Zn/mL). Finally, 67Ga-IONP@Zn-cit showed accumulation in the liver and spleen and an accumulation in the tumor tissue of 0.95% ID/g in a mouse model of U251 cells. These results suggest the possibility of using Zn nanoparticles as proton-activatable agents to verify the range by prompt gamma detection and face the challenges of prompt gamma detection in a specific biological situation, opening different avenues to go forward in this field.

A review of PET attenuation correction methods for PET-MR

Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories.

In vivodosimetry in cancer patients undergoing intraoperative radiation therapy

In vivodosimetry (IVD) is an important tool in external beam radiotherapy (EBRT) to detect major errors by assessing differences between expected and delivered dose and to record the received dose by individual patients. Also, in intraoperative radiation therapy (IORT), IVD is highly relevant to register the delivered dose. This is especially relevant in low-risk breast cancer patients since a high dose of IORT is delivered in a single fraction. In contrast to EBRT, online treatment planning based on intraoperative imaging is only under development for IORT. Up to date, two commercial treatment planning systems proposed intraoperative ultrasound or in-room cone-beam CT for real-time IORT planning. This makes IVD even more important because of the possibility for real-time treatment adaptation. Here, we summarize recent developments and applications of IVD methods for IORT in clinical practice, highlighting important contributions and identifying specific challenges such as a treatment planning system for IORT. HDR brachytherapy as a delivery technique was not considered. We add IVD for ultrahigh dose rate (FLASH) radiotherapy that promises to improve the treatment efficacy, when compared to conventional radiotherapy by limiting the rate of toxicity while maintaining similar tumour control probabilities. To date, FLASH IORT is not yet in clinical use.

Deep Learning Ultrasound Computed Tomography Under Sparse Sampling

Ultrasound computed tomography (USCT) is a fast-emerging imaging modality that is expected to help detect and characterize breast tumors by quantifying the distribution of the speed of sound (SOS) and acoustic attenuation in breast tissue. High-quality quantitative SOS reconstruction in USCT requires a large number of transducers, which incurs high system costs and slow computation. In contrast, sparsely distributed arrays are low-cost and fast but significantly degrade image quality. Thus, we propose a framework to achieve high-quality SOS reconstruction under sparse sampling based on a convolutional neural network (SRSS-Net) with faster computation. We first apply the bent-ray algorithm to sparsely sampled data and then apply the SRSS-Net to efficiently improve the image quality. Experimental results on synthetic and real datasets demonstrate that the proposed SRSS-Net provides reconstructions that are superior to those of state-of-the-art methods in terms of artifact suppression, structural preservation, quantitative restoration, and computational speed. As demonstrated in our experiments, the fine-tuning training strategy is suggested when applying SRSS-Net to real-world circumstances. The imaging and computational performance of SRSS-Net on the inhomogeneous breast phantom further demonstrates that SRSS-Net has great potential in real-time breast cancer detection.

On the importance of low-frequency signals in functional and molecular photoacoustic computed tomography

In photoacoustic computed tomography (PACT) with short-pulsed laser excitation, wideband acoustic signals are generated in biological tissues with frequencies related to the effective shapes and sizes of the optically absorbing targets. Low-frequency photoacoustic signal components correspond to slowly varying spatial features and are often omitted during imaging due to the limited detection bandwidth of the ultrasound transducer, or during image reconstruction as undesired background that degrades image contrast. Here we demonstrate that low-frequency photoacoustic signals, in fact, contain functional and molecular information, and can be used to enhance structural visibility, improve quantitative accuracy, and reduce spare-sampling artifacts. We provide an in-depth theoretical analysis of low-frequency signals in PACT, and experimentally evaluate their impact on several representative PACT applications, such as mapping temperature in photothermal treatment, measuring blood oxygenation in a hypoxia challenge, and detecting photoswitchable molecular probes in deep organs. Our results strongly suggest that low-frequency signals are important for functional and molecular PACT.

Imaging increased metabolism in the spinal cord in mice after middle cerebral artery occlusion

Emerging evidence indicates crosstalk between the brain and hematopoietic system following cerebral ischemia. Here, we investigated metabolism and oxygenation in the spleen and spinal cord in a transient middle cerebral artery occlusion (tMCAO) model. Sham-operated and tMCAO mice underwent [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) to assess glucose metabolism. Naïve, sham-operated and tMCAO mice underwent multispectral optoacoustic tomography (MSOT) assisted by quantitative model-based reconstruction and unmixing algorithms for accurate mapping of oxygenation patterns in peripheral tissues at 24 h after reperfusion. We found increased [18F]FDG uptake and reduced MSOT oxygen saturation, indicating hypoxia in the thoracic spinal cord of tMCAO mice compared with sham-operated mice but not in the spleen. Reduced spleen size was observed in tMCAO mice compared with sham-operated mice ex vivo. tMCAO led to an increase in the numbers of mature T cells in femoral bone marrow tissues, concomitant with a stark reduction in these cell subsets in the spleen and peripheral blood. The combination of quantitative PET and MSOT thus enabled observation of hypoxia and increased metabolic activity in the spinal cord of tMCAO mice at 24 h after occlusion compared to sham-operated mice.

A simulation study on the sensitivity of transcranial ray-tracing ultrasound modeling to skull properties

In transcranial focused ultrasound therapies, such as treating essential tremor via thermal ablation in the thalamus, acoustic energy is focused through the skull using a phased-array transducer. Ray tracing is a computationally efficient method that can correct skull-induced phase aberrations via per-element phase delay calculations using patient-specific computed tomography (CT) data. However, recent studies show that variations in CT-derived Hounsfield unit may account for only 50% of the speed of sound variability in human skull specimens, potentially limiting clinical transcranial ultrasound applications. Therefore, understanding the sensitivity of treatment planning methods to material parameter variations is essential. The present work uses a ray-tracing simulation model to explore how imprecision in model inputs, arising from clinically significant uncertainties in skull properties or considerations of acoustic phenomena, affects acoustic focusing quality through the skull. We propose and validate new methods to optimize ray-tracing skull simulations for clinical treatment planning, relevant for predicting intracranial target's thermal rise, using experimental data from ex-vivo human skulls.

Super-Low-Dose Functional and Molecular Photoacoustic Microscopy

Photoacoustic microscopy can image many biological molecules and nano-agents in vivo via low-scattering ultrasonic sensing. Insufficient sensitivity is a long-standing obstacle for imaging low-absorbing chromophores with less photobleaching or toxicity, reduced perturbation to delicate organs, and more choices of low-power lasers. Here, the photoacoustic probe design is collaboratively optimized and a spectral-spatial filter is implemented. A multi-spectral super-low-dose photoacoustic microscopy (SLD-PAM) is presented that improves the sensitivity by ≈33 times. SLD-PAM can visualize microvessels and quantify oxygen saturation in vivo with ≈1% of the maximum permissible exposure, dramatically reducing potential phototoxicity or perturbation to normal tissue function, especially in imaging of delicate tissues, such as the eye and the brain. Capitalizing on the high sensitivity, direct imaging of deoxyhemoglobin concentration is achieved without spectral unmixing, avoiding wavelength-dependent errors and computational noises. With reduced laser power, SLD-PAM can reduce photobleaching by ≈85%. It is also demonstrated that SLD-PAM achieves similar molecular imaging quality using 80% fewer contrast agents. Therefore, SLD-PAM enables the use of a broader range of low-absorbing nano-agents, small molecules, and genetically encoded biomarkers, as well as more types of low-power light sources in wide spectra. It is believed that SLD-PAM offers a powerful tool for anatomical, functional, and molecular imaging.

Simultaneous quantitative imaging of two PET radiotracers via the detection of positron-electron annihilation and prompt gamma emissions

In conventional positron emission tomography (PET), only one radiotracer can be imaged at a time, because all PET isotopes produce the same two 511 keV annihilation photons. Here we describe an image reconstruction method for the simultaneous in vivo imaging of two PET tracers and thereby the independent quantification of two molecular signals. This method of multiplexed PET imaging leverages the 350-700 keV range to maximize the capture of 511 keV annihilation photons and prompt γ-ray emission in the same energy window, hence eliminating the need for energy discrimination during reconstruction or for signal separation beforehand. We used multiplexed PET to track, in mice with subcutaneous tumours, the biodistributions of intravenously injected [124I]I-trametinib and 2-deoxy-2-[18F]fluoro-D-glucose, [124I]I-trametinib and its nanoparticle carrier [89Zr]Zr-ferumoxytol, and the prostate-specific membrane antigen (PSMA) and infused PSMA-targeted chimaeric antigen receptor T cells after the systemic administration of [68Ga]Ga-PSMA-11 and [124I]I. Multiplexed PET provides more information depth, gives new uses to prompt γ-ray-emitting isotopes, reduces radiation burden by omitting the need for an additional computed-tomography scan and can be implemented on preclinical and clinical systems without any modifications in hardware or image acquisition software.

Towards a barrier-free anthropomorphic brain phantom for quantitative magnetic resonance imaging: Design, first construction attempt, and challenges

Existing magnetic resonance imaging (MRI) reference objects, or phantoms, are typically constructed from simple liquid or gel solutions in containers with specific geometric configurations to enable multi-year stability. However, there is a need for phantoms that better mimic the human anatomy without barriers between the tissues. Barriers result in regions without MRI signal between the different tissue mimics, which is an artificial image artifact. We created an anatomically representative 3D structure of the brain that mimicked the T1 and T2 relaxation properties of white and gray matter at 3 T. While the goal was to avoid barriers between tissues, the 3D printed barrier between white and gray matter and other flaws in the construction were visible at 3 T. Stability measurements were made using a portable MRI system operating at 64 mT, and T2 relaxation time was stable from 0 to 22 weeks. The phantom T1 relaxation properties did change from 0 to 10 weeks; however, they did not substantially change between 10 weeks and 22 weeks. The anthropomorphic phantom used a dissolvable mold construction method to better mimic anatomy, which worked in small test objects. The construction process, though, had many challenges. We share this work with the hope that the community can build on our experience.

Measurement of ultrasound velocity in yolk and blastula of fish embryo in vivo

An acoustic microscopy method for measuring the velocity of ultrasound in the yolk and blastula of bony fish embryos at early stages of development was proposed. The yolk and blastula were approximated as a sphere and a spherical dome, respectively, consisting of a homogeneous liquid. A theoretical model of ultrasonic wave propagation through a spherical liquid drop located on a solid substrate was developed in the ray approximation. The dependence of the wave propagation time on the speed of sound in the drop, its diameter, and the position of the focus of the ultrasonic transducer has been determined. It was shown that the velocity in the drop can be found by solving the inverse problem by minimizing the discrepancy between the experimental and model spatial distributions of the propagation time, assuming that the velocity in the immersion liquid and the radius of the drop are known. The velocities in the yolk and blastula of the loach (Misgurnus fossilis) embryo at the stage of development of the middle blastula were measured in vivo using a pulsed scanning acoustic microscope operating at a central frequency of 50 MHz. The yolk and blastula radii were determined from ultrasound images of the embryo. Acoustic microscopy measurements conducted with four embryos provide velocities of the acoustic longitudinal wave in the yolk and blastula. They were measured to be 1581 ± 5 m/s and 1525 ± 4 m/s when the temperature of the liquid in the water tank was kept at 22 ± 2 °C.