Realistic Phantom Research Articles

A realistic human head phantom for electromagnetic detection of brain diseases.

The research on electromagnetic detection technology for brain diseases requires precise simulation of the human head. This article combines high-precision computed tomography (CT) images and magnetic resonance imaging (MRI) images to establish an electromagnetic numerical model of the human head with a real anatomical structure. (1) It had Asian characteristics and encompassed 14 different structures, including skin, muscles, cranial bones, cerebrospinal fluid, cerebral veins, cerebral arteries, gray matter, white matter of the brain, basal ganglia, thalamus, cerebellum, brainstem, eyeballs, and vertebrae. (2) The model used a combination of 0.625 mm-resolution CT and 1 mm-resolution MRI image data for reconstruction, with a smooth surface and high accuracy. (3) Within the simulation environment, this model enabled the generation of various brain disease scenarios, such as different types and degrees of cerebral hemorrhage and cerebral ischemia. It proved valuable for studying the distribution of electromagnetic fields in the human head and for investigating novel electromagnetic detection techniques exploiting brain tissue dielectric properties. (4) The created physical model and the numerical model were derived from the same person, which provided a good continuity between simulation experiments and physical experiments, and provided a realistic verification platform for the research of electromagnetic detection technology for brain diseases, such as differentiating the kind of stroke, monitoring brain edema, brain tumor microwave imaging, and diagnosis of Alzheimer's disease.

Low-cost microvascular phantom for photoacoustic imaging using loofah.

Existing photoacoustic phantoms are unable to mimic complex microvascular structures with varying sizes and distributions. A suitable material with structures that mimic intricate microvascular networks is needed. Our aim is to introduce loofah as a natural phantom material with complex fiber networks ranging from 50 to , enabling the fabrication of phantoms with controlled optical properties comparable to those of human microvasculature. By introducing a controllable chromophore into the loofah material, we controlled its absorption properties. The loofah's vasculature-mimetic capabilities and stability in photoacoustic signal generation were evaluated using co-registered ultrasound, acoustic-resolution photoacoustic microscopy (ARPAM), and optical-resolution photoacoustic microscopy (ORPAM). ORPAM results confirmed the loofah's ability to control chromophore distribution, leading to consistent and regulated photoacoustic signals. ARPAM results demonstrated that the loofah phantom effectively replicates vascular structures, exhibiting superior performance in mimicking microvascular networks compared with commonly used tissue-mimetic phantoms. The dominant diameter range of the phantom's microvasculature was between 100 and , aligning well with the targeted range and facilitating meaningful comparisons with human vascular structures. The loofah material provides a low-cost and effective method for creating submillimeter microvascular phantoms for photoacoustic imaging. Its exceptional morphology and customizability allow it to be shaped into various vascular network configurations, enhancing the fidelity of phantom imaging and assisting in system calibration and validation. In addition, data obtained from this realistic microvascular phantom can offer greater opportunities for training machine learning models.

Simulation and quantitative evaluation of three surgical techniques of endoscopic enucleation of prostate on a realistic phantom model.

To evaluate the impact of three different AEEP techniques on the training performance of novices using a realistic hydrogel prostate phantom model. The experimental setup utilized realistic prostate phantom model provided by the Max Planck Institute for Intelligent Systems, Germany. For the enucleation, we utilized a new solid-state pulsed thulium laser (Thulio®, Dornier MedTech, Weßling, Germany). We explored three different AEEP techniques-bilobar, trilobar, and en-bloc-repeated ten times each, totaling 30 procedures. Median enucleation time was 9.5 min (range: 6-16), median laser time was 4.29 min (3.21-6.34), median total energy used was 25.8 kJ (19.4-38.1), and median number of laser pulses was 12.8 thousand (9.7-17). There were no significant differences in operation time, laser time, pulses, or joules among the en-bloc, two-lobe, and three-lobe techniques (p = 0.113, 0.143, 0.148, 0.141 respectively). Ultrasound evaluations showed the one-lobe technique to be superior in accuracy, smoothness, and circularity (p = 0.0002, 0.012, 0.00005 respectively) (Figs. 9, 10, 11), despite having the highest perforation rate, which was not statistically significant compared to other techniques (p = 1.4). The one-lobe technique's higher accuracy may increase the risk of perforation. In contrast, the three-lobe technique had the lowest perforation rate and removal efficiency due to its lower accuracy. The en-bloc, bilobar, and trilobar enucleation techniques exhibited comparable operation times. The one-lobe method emerged as superior in terms of accuracy, smoothness, and circularity. However, it also presented the highest rate of perforation.

A Novel Method for the Generation of Realistic Lung Nodules Visualized Under X-Ray Imaging.

Image-guided diagnosis and treatment of lung lesions is an active area of research. With the growing number of solutions proposed, there is also a growing need to establish a standard for the evaluation of these solutions. Thus, realistic phantom and preclinical environments must be established. Realistic study environments must include implanted lung nodules that are morphologically similar to real lung lesions under X-ray imaging. Various materials were injected into a phantom swine lung to evaluate the similarity to real lung lesions in size, location, density, and grayscale intensities in X-ray imaging. A combination of n-butyl cyanoacrylate (n-BCA) and ethiodized oil displayed radiopacity that was most similar to real lung lesions, and various injection techniques were evaluated to ensure easy implantation and to generate features mimicking malignant lesions. The techniques used generated implanted nodules with properties mimicking solid nodules with features including pleural extensions and spiculations, which are typically present in malignant lesions. Using only n-BCA, implanted nodules mimicking ground glass opacity were also generated. These results are condensed into a set of recommendations that prescribe the materials and techniques that should be used to reproduce these nodules. Generated recommendations on the use of n-BCA and ethiodized oil can help establish a standard for the evaluation of new image-guided solutions and refinement of algorithms in phantom and animal studies with realistic nodules.

Development, characterization and radiation dosimetry evaluation of Bovine gelatin crosslinked with Gum Arabic Aldehyde as brain phantom gel material in radiation therapy

Brain phantom gel materials are of critical importance for accuracy of dosimetry, treatment planning and quality assurance in radiotherapy. Development of reliable and realistic phantoms enhances improved radiotherapy practices. Gel dosimeters have unique property to record radiation dose distribution in three dimensions. Polymer gels also have added advantages for brachytherapy dosimetry. Objective of the study was to develop an alternative polymeric gel material equivalent to human brain for conducting radiation dosimetry studies in radiotherapy. Hydrogel prepared from gelatin and gum arabic aldehyde was used to develop a brain phantom gel material for radiation dosimetry in radiotherapy. The results of comparative studies with G-GAAB gel strongly suggest, the gel can successfully be utilized as a brain equivalent material for radiation dosimetry in place of standard water phantom by measuring (a) CT number values (b) mass attenuation coefficient values at two X-ray energies used in radiation treatment (c) absorbed dose values at different depths for various setup conditions. The midrange CT number values inside gel material and that of human brain measured from CT images were 25.5HU and 25HU respectively with a variation of 2%. The mass attenuation coefficients for brain, water and gel material at 6MV photons were 0.0275cm2/g, 0.0280cm2/g,0.0270cm2/g and for 15MV photons, the attenuation coefficients were 0.0192cm2/g, 0.0190cm2/g and 0.0180cm2/g respectively. The percentage deviation of all measurements of absorbed dose values in gel phantom material, compared with the values of standard water phantom at various radiation dosimetry setup parameters, is less than 2%.

Construction of a realistic voxel phantom of the Japanese red fox (Vulpes vulpes japonica) based on MRI imaging and estimation of its background external radiation dose rate from environmental radionuclides.

In this study, we selected the Japanese red fox (Vulpes vulpes japonica) as a representative mid-size mammal from the forests near the spent nuclear fuel reprocessing plant at Rokkasho. A fox voxel phantom was constructed based on magnetic resonance imaging of a female red fox caught in Rokkasho. This phantom consisted of 264×321×383 voxels (each voxel size: 0.78×0.78×2mm) with internal organs. The external radiation dose rate to the voxel phantom by beta and gamma rays from environmental radionuclides was estimated using a Monte Carlo code (EGS4 and UCPIXEL). We estimated the dose rates to the phantom on the ground and in an average fox burrow in the Rokkasho forest, which were 11 and 27 nGy h-1, respectively. Assuming that the animal on the ground was irradiated by cosmic rays of 27 nGy h-1, the total external dose rate was evaluated to be 38 nGy h-1. Based on the assumption that the fox lives on the ground for 12h and in the burrow for 12h, the dose rate was estimated to be 33 nGy h-1.

Imaging lung tumor motion using integrated-mode proton radiography-A phantom study towards tumor tracking in proton radiotherapy.

Motion of lung tumors during radiotherapy leads to decreased accuracy of the delivered dose distribution. This is especially true for proton radiotherapy due to the finite range of the proton beam. Methods for mitigating motion rely on knowing the position of the tumor during treatment. Proton radiography uses the treatment beam, at an energy high enough to traverse the patient, to produce a radiograph. This work shows the first results of using an integrated-mode proton radiography system to track the position of moving objects in an experimental phantom study; demonstrating the potential of using this method for measuring tumor motion. Proton radiographs of an anthropomorphic lung phantom, with a motor-driven tumor insert, were acquired approximately every 1s, using tumor inserts of 10, 20, and 30mm undergoing a known periodic motion. The proton radiography system used a monolithic scintillator block and digital cameras to capture the residual range of each pencil beam passing through the phantom. These ranges were then used to produce a water equivalent thickness map of the phantom. The centroid of the tumor insert in the radiographs was used to determine its position. This measured position was then compared to the known motion of the phantom to determine theaccuracy. Submillimeter accuracy on the measurement of the tumor insert was achieved when using a 30mm tumor insert with a period of 24s and was found to be improved for decreasing motion amplitudes with a mean absolute error (MAE) of 1.0, 0.9, and 0.7mm for 20, 15, and 10mm respectively. Using smaller tumor inserts reduced the accuracy with a MAE of 1.8 and 1.9mm for a 20 and 10mm insert respectively undergoing a periodic motion with an amplitude of 20mm and a period of 24s. Using a shorter period resulted in significant motion artifacts reducing the accuracy to a MAE of 2.2mm for a 12s period and 3.1mm for a 6s period for the 30mm insert with an amplitude of20mm. This work demonstrates that the position of a lung tumor insert in a realistic anthropomorphic phantom can be measured with high accuracy using proton radiographs. Results show that the accuracy of the position measurement is the highest for slower tumor motions due to a reduction in motion artifacts. This indicates that the primary obstacle to accurate measurement is the speed of the radiograph acquisition. Although the slower tumor motions used in this study are not clinically realistic, this work demonstrates the potential for using proton radiography for measuring tumor motion with an increased scanning speed that results in a decreased acquisitiontime.

In silico dosimetry for a prostate cancer treatment using 198Au nanoparticles

Objective. To estimate dose rates delivered by using radioactive198Au nanoparticles for prostate cancer nanobrachytherapy, identifying contribution by photons and electrons emmited from the source.Approach. Utilizingin silicomodels, two different anatomical representations were compared: a mathematical model and a unstructured mesh model based on the International Commission on Radiological Protection (ICRP) Publication 145 phantom. Dose rates by activity were calculated to the tumor and nearby healthy tissues, including healthy prostate tissue, urinary bladder wall and rectum, using Monte Carlo code MCNP6.2.Main results. Results indicate that both models provide dose rate estimates within the same order of magnitude, with the mathematical model overestimating doses to the prostate and bladder by approximately 20% compared to the unstructured mesh model. The discrepancies for the tumor and rectum were below 4%. Photons emmited from the source were defined as the primary contributors to dose to other organs, while 97.9% of the dose to the tumor was due to electrons emmited from the source.Significance. Our findings emphasize the importance of model selection in dosimetry, particularly the advantages of using realistic anatomical phantoms for accurate dose calculations. The study demonstrates the feasibility and effectiveness of198Au nanoparticles in achieving high dose concentrations in tumor regions while minimizing exposure to surrounding healthy tissues. Beta emissions were found to be predominantly responsible for tumor dose delivery, reinforcing the potential of198Au nanoparticles in localized radiation therapy. We advocate for using realistic body phantoms in further research to enhance reliability in dosimetry for nanobrachytherapy, as the field still lacks dedicated protocols.

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

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

Automated, Reproducible, and Reconfigurable Human Head Phantom for Experimental Testing of Microwave Systems for Stroke Classification

ABSTRACTMicrowave systems for prehospital stroke classification are currently being developed. In the future, these systems should enable rapid recognition of the type of stroke, shorten the time to start treatment, and thus significantly improve the prognosis of patients. In this study, we realized a realistic and reconfigurable 3D human head phantom for the development, testing, and validation of these newly developed diagnostic methods. The phantom enables automated and reproducible measurements for different positions of the stroke model. The stroke model itself is also interchangeable, so measurements can be made for different types, sizes, and shapes of strokes. Furthermore, an extensive series of measurements was performed at a frequency of 1 GHz, and an SVM classification algorithm was deployed, which successfully identified ischemic stroke in 80% of the corresponding measured data. If similar classification accuracy could be achieved in patients, it would lead to a dramatic reduction in the consequences of strokes.

Automated assessment of task-based performance of digital mammography and tomosynthesis systems using an anthropomorphic breast phantom and deep learning-based scoring.

Conventional metrics used for assessing digital mammography (DM) and digital breast tomosynthesis (DBT) image quality, including noise, spatial resolution, and detective quantum efficiency, do not necessarily predict how well the system will perform in a clinical task. A number of existing phantom-based methods have their own limitations, such as unrealistic uniform backgrounds, subjective scoring using humans, and regular signal patterns unrepresentative of common clinical findings. We attempted to address this problem with a realistic breast phantom with random hydroxyapatite microcalcifications and semi-automated deep learning-based image scoring. Our goal was to develop a methodology for objective task-based assessment of image quality for tomosynthesis and DM systems, which includes an anthropomorphic phantom, a detection task (microcalcification clusters), and automated performance evaluation using a convolutional neural network. Experimental 2D and pseudo-3D mammograms of an anthropomorphic inkjet-printed breast phantom with inserted microcalcification clusters were collected on clinical mammography systems to train a signal-present/signal-absent image classifier based on Resnet-18 architecture. In a separate validation study using simulations, this Resnet-18 classifier was shown to approach the performance of an ideal observer. Microcalcification detection performance was evaluated as a function of four dose levels using receiver operating characteristic (ROC) analysis [i.e., area under the ROC curve (AUC)]. To demonstrate the use of this evaluation approach for assessing different technologies, the method was applied to two different mammography systems, as well as to mammograms with re-binned pixels emulating a lower-resolution X-ray detector. Microcalcification detectability, as assessed by the deep learning classifier, was observed to vary with the exposure incident on the breast phantom for both DM and tomosynthesis. At full dose, experimental AUC was 0.96 (for DM) and 0.95 (for DBT), whereas at half dose, it dropped to 0.85 and 0.71, respectively. AUC performance on DM was significantly decreased with an effective larger pixel size obtained with re-binning. The task-based assessment approach also showed the superiority of a newer mammography system compared with an older system. An objective task-based methodology for assessing the image quality of mammography and tomosynthesis systems is proposed. Possible uses for this tool could be quality control, acceptance, and constancy testing, assessing the safety and effectiveness of new technology for regulatory submissions, and system optimization. The results from this study showed that the proposed evaluation method using a deep learning model observer can track differences in microcalcification signal detectability with varied exposure conditions.

Gradient descent optimization of acoustic holograms for transcranial focused ultrasound

Acoustic holographic lenses, also known as acoustic holograms, can change the phase of a transmitted wavefront in order to shape and construct complex ultrasound pressure fields, often for focusing the acoustic energy on a target region. These lenses have been proposed for transcranial focused ultrasound (tFUS) to create diffraction-limited focal zones that target specific brain regions while compensating for skull aberration. Holograms are currently designed using time-reversal approaches in full-wave time-domain numerical simulations. Such simulations need time-consuming computations, which severely limits the adoption of iterative optimization strategies. In the time-reversal method, the number and distribution of virtual sources can significantly influence the final sound field. Because of the computational constraints, predicting these effects and determining the optimal arrangement is challenging. This study introduces an efficient method for designing acoustic holograms using a volumetric holographic technique to generate focused fields inside the skull. The proposed method combines a modified mixed-domain method for ultrasonic propagation with a gradient descent iterative optimization algorithm. The findings are further validated in underwater experiments with a realistic 3D-printed skull phantom. This approach enables substantially faster holographic computation than previously reported techniques. The iterative process uses explicitly defined loss functions to bias the ultrasound field’s optimization parameters to specific desired characteristics, such as axial resolution, transversal resolution, coverage, and focal region uniformity, while eliminating the uncertainty associated with virtual sources in time-reversal techniques. The proposed techniques enable more rapid hologram computation and more flexibility in tailoring ultrasound fields for specific therapeutic requirements.

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

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

Development of a novel 3D-printed dynamic anthropomorphic thorax phantom for evaluation of four-dimensional computed tomography

Background and purposeIn radiotherapy, the image quality of four-dimensional computed tomography (4DCT) is often degraded by artifacts resulting from breathing irregularities. Quality assurance mostly employ simplistic phantoms, not fully representing complexities and dynamics in patients. 3D-printing allows for design of highly customized phantoms. This study aims to validate the proof-of-concept of a realistic dynamic thorax phantom and its 4DCT application. Materials and methodsUsing 3D-printing, a realistic thorax phantom was produced with tissue-equivalent materials for soft tissue, bone, and compressible lungs, including bronchi and tumors. Lung compression was facilitated by motors simulating customized breathing curves with an added platform for application of monitoring systems. The phantom contained three tumors which were assessed in terms of tumor motion amplitude. Three 4DCT sequences and repeated static images for different lung compression levels were acquired to evaluate the reproducibility. Moreover, more complex patient-specific breathing patterns with irregularities were simulated. ResultsThe phantom showed a reproducibility of ±0.2 mm and ±0.4 mm in all directions for static 3DCT images and 4DCT images, respectively. Furthermore, the tumor close to the diaphragm showed higher amplitudes in the inferior/superior direction (13.9 mm) than lesions higher in the lungs (8.1 mm) as observed in patients. The more complex breathing patterns demonstrated commonly seen 4DCT artifacts. ConclusionThis study developed a dynamic 3D-printed thorax phantom, which simulated customized breathing patterns. The phantom represented a realistic anatomy and 4DCT scanning of it could create realistic artifacts, making it beneficial for 4DCT quality assurance or protocol optimization.

A comprehensive numerical procedure for high-intensity focused ultrasound ablation of breast tumour on an anatomically realistic breast phantom.

High-Intensity Focused Ultrasound (HIFU) as a promising and impactful modality for breast tumor ablation, entails the precise focalization of high-intensity ultrasonic waves onto the tumor site, culminating in the generation of extreme heat, thus ablation of malignant tissues. In this paper, a comprehensive three-dimensional (3D) Finite Element Method (FEM)-based numerical procedure is introduced, which provides exceptional capacity for simulating the intricate multiphysics phenomena associated with HIFU. Furthermore, the application of numerical procedures to an anatomically realistic breast phantom (ARBP) has not been explored before. The integrity of the present numerical procedure has been established through rigorous validation, incorporating comparative assessments with previous two-dimensional (2D) simulations and empirical data. For ARBP ablation, the administration of a 0.1 MPa pressure input pulse at a frequency of 1.5 MHz, sustained at the focal point for 10 seconds, manifests an ensuing temperature elevation to 80°C. It is noteworthy that, in contrast, the prior 2D simulation using a 2D phantom geometry reached just 72°C temperature under the identical treatment regimen, underscoring the insufficiency of 2D models, ascribed to their inherent limitations in spatially representing acoustic energy, which compromises their overall effectiveness. To underscore the versatility of this numerical platform, a simulation of a more clinically relevant HIFU therapy procedure has been conducted. This scenario involves the repositioning of the ultrasound focal point to three separate lesions, each spaced at 3 mm intervals, with ultrasound exposure durations of 6 seconds each and a 5-second interval for movement between focal points. This approach resulted in a more uniform high-temperature distribution at different areas of the tumour, leading to the ablation of almost all parts of the tumour, including its verges. In the end, the effects of different abnormal tissue shapes are investigated briefly as well. For solid mass tumors, 67.67% was successfully ablated with one lesion, while rim-enhancing tumors showed only 34.48% ablation and non-mass enhancement tumors exhibited 20.32% ablation, underscoring the need for multiple lesions and tailored treatment plans for more complex cases.

Dual-Domain Collaborative Diffusion Sampling for Multi-Source Stationary Computed Tomography Reconstruction.

The multi-source stationary CT, where both the detector and X-ray source are fixed, represents a novel imaging system with high temporal resolution that has garnered significant interest. Limited space within the system restricts the number of X-ray sources, leading to sparse-view CT imaging challenges. Recent diffusion models for reconstructing sparse-view CT have generally focused separately on sinogram or image domains. Sinogram-centric models effectively estimate missing projections but may introduce artifacts, lacking mechanisms to ensure image correctness. Conversely, image-domain models, while capturing detailed image features, often struggle with complex data distribution, leading to inaccuracies in projections. Addressing these issues, the Dual-domain Collaborative Diffusion Sampling (DCDS) model integrates sinogram and image domain diffusion processes for enhanced sparse-view reconstruction. This model combines the strengths of both domains in an optimized mathematical framework. A collaborative diffusion mechanism underpins this model, improving sinogram recovery and image generative capabilities. This mechanism facilitates feedback-driven image generation from the sinogram domain and uses image domain results to complete missing projections. Optimization of the DCDS model is further achieved through the alternative direction iteration method, focusing on data consistency updates. Extensive testing, including numerical simulations, real phantoms, and clinical cardiac datasets, demonstrates the DCDS model's effectiveness. It consistently outperforms various state-of-the-art benchmarks, delivering exceptional reconstruction quality and precise sinogram.

Time-Reversion Fast-Sampling Score-Based Model for Limited-Angle CT Reconstruction.

The score-based generative model (SGM) has received significant attention in the field of medical imaging, particularly in the context of limited-angle computed tomography (LACT). Traditional SGM approaches achieved robust reconstruction performance by incorporating a substantial number of sampling steps during the inference phase. However, these established SGM-based methods require large computational cost to reconstruct one case. The main challenge lies in achieving high-quality images with rapid sampling while preserving sharp edges and small features. In this study, we propose an innovative rapid-sampling strategy for SGM, which we have aptly named the time-reversion fast-sampling (TIFA) score-based model for LACT reconstruction. The entire sampling procedure adheres steadfastly to the principles of robust optimization theory and is firmly grounded in a comprehensive mathematical model. TIFA's rapid-sampling mechanism comprises several essential components, including jump sampling, time-reversion with re-sampling, and compressed sampling. In the initial jump sampling stage, multiple sampling steps are bypassed to expedite the attainment of preliminary results. Subsequently, during the time-reversion process, the initial results undergo controlled corruption by introducing small-scale noise. The re-sampling process then diligently refines the initially corrupted results. Finally, compressed sampling fine-tunes the refinement outcomes by imposing regularization term. Quantitative and qualitative assessments conducted on numerical simulations, real physical phantom, and clinical cardiac datasets, unequivocally demonstrate that TIFA method (using 200 steps) outperforms other state-of-the-art methods (using 2000 steps) from available [0°, 90°] and [0°, 60°]. Furthermore, experimental results underscore that our TIFA method continues to reconstruct high-quality images even with 10 steps. Our code at https://github.com/tianzhijiaoziA/TIFADiffusion.

Sacral neuromodulation update in 2024: Insights from the ICS workshop 2023

Purpose:This review is based on the International Continence Society (ICS) Workshop on Hands-On Sacral Neuromodulation (SNM)- Ideal Lead Placement presented at the Annual Congress in 2023. This workshop briefly reviewed appropriate patient selection, focused most of the time on hands on skills to teach appropriate needle and lead placement techniques. Six very experienced experts worked with attendees on lifelike models to teach them how to achieve lead placement efficiently and appropriately. Tips and tricks for getting the best responses were reviewed. Materials and methods: The review follows the structure adopted by workshop, where SNM-Ideal Lead Placement is discussed in four sections: main/extended indications and ideal patient profile, sacral neuromodulation techniques included basic evaluation (PNE), advanced evaluation, ideal lead placement and novel technology. Results:The learning objectives were achieved at the end of the workshop. These were: understanding the role of sacral neuromodulation, recognize the latest technological developments, their clinical implications and getting the best outcome with SNM, stress the importance of patient selection process and current SNM indications, reviewing standard surgical technique for optimal lead placement and gain understanding of choices for recharge versus fixed battery (as well as use in neurological disease). Conclusions:Hands-on training workshops on sacral neuromodulation is efficient training modules to ensure learning objectives for participants. Accurate needle/temporary lead placement, lead placement with permanent lead, smart programming with programmers are the main parts of hands-on training session with using realistic phantom and 3D printed models in workshop.

3D printing of realistic body phantoms: Comparison of measured and simulated organ doses on the example of a CT scan on a pregnant woman.

Medical examinations or treatment of pregnant women using ionizing radiation are sometimes unavoidable. In such cases, the risk of harm to the embryo and fetus after exposure to ionizing radiation must be carefully estimated. However, no commercially available anthropomorphic body phantoms of pregnant women are available for dose measurements. A promising possibility for the production of body phantoms for patient groups that are not adequately represented by the phantoms of reference persons is 3D printing. However, this approach is still in the evaluation phase. To print the abdomen of a woman in the late stage of pregnancy and compare the dose distribution measured using thermoluminescence dosimeters (TLDs) in the printed phantom for two different computed tomography (CT) protocols with the corresponding results of Monte Carlo simulations on voxel models of the pregnant woman. The physical phantom was produced through multi-material extrusion printing using different print materials identified in previous studies to simulate homogeneous soft tissues and the mean compositions of maternal and fetal bones. The 3D printed abdomen was combined with a conventionally produced anthropomorphic female phantom to obtain a whole-body phantom of a pregnant woman. Dose values resulting from two different CT scans acquired at tube voltages of 80 and 120kV were measured using TLDs positioned in the physical phantom and cross-validated with the results of Monte Carlo simulations performed for two different voxel models. The first was a voxelized model of the produced phantom itself and the second a realistic digital model of a pregnant woman. Representative CT values of the materials used in the printed phantom were determined from the acquired CT images. The CT values of maternal and fetal tissue structures in the phantom are comparable to CT values of real human tissues. The difference between most organ doses measured in the 3D printed phantom and simulated in the voxel models was below 20% and equivalent within the measurement uncertainties. Only the dose to the fetal head was up to 50% higher and not equivalent for the realistic model and the 80kV-protocol. As expected, the agreement was better for the voxelized than for the realistic model. For both models a slight energy dependence was observed, with larger deviations for the 80-kV protocol especially for organs located in the pelvic region. Individualized physical body phantoms, such as that of a pregnant woman, can be produced using 3D printing. The good agreement between measured and simulated doses to the fetus cross-validates both dosimetric methods. Therefore, this study demonstrates the suitability of 3D printing phantoms for patients not adequately represented by commercially available body phantoms of reference persons.

Artificial intelligence improves bronchoscopy performance: a randomised crossover trial.

Flexible bronchoscopy is an operator-dependent procedure. An automatic bronchial identification system based on artificial intelligence (AI) could help bronchoscopists to perform more complete and structured procedures through automatic guidance. 101 participants were included from six different continents at the European Respiratory Society annual conference in Milan, 9-13 September 2023. Participants were split into three groups based on experience: novices (0 bronchoscopies), intermediates (1-249 bronchoscopies) and experienced (≥250 bronchoscopies). The participants performed two bronchoscopies on a realistic physical phantom, one with AI (AmbuBronchoSimulatorTrainingGUIDEv.0.0.1, Prototype version, Ambu) and one Standard procedure. The F1-group received AI guidance for their first procedure, the F2-group for their second. A crossover randomisation controlled for learning by testing. All procedures were automatically rated according to the outcome measures: inspected segments, structured progressions and procedure time. AI guidance caused the participants to inspect more segments (mean difference, paired t-test: +6.0 segments, p<0.001), perform more structured progressions (+5.2 progressions, p<0.001) and spend more time on the procedure (+72 s, p<0.001) compared to their standard procedures. The effects of AI guidance on inspected segments and structured progression were highest for novices but significant for all experience groups: novices (+8.2 segments, p=0.012 and +6.6 progressions, p<0.001), intermediates (+5.7 segments, p=0.006 and +5.1 progressions, p<0.001) and experienced (+4.3 segments, p=0.006 and +3.8 progressions, p<0.016). AI guidance helped bronchoscopists of all experience levels to inspect more segments in a more structured order. Clinical implementation of AI guidance could help ensure and document more complete bronchoscopy procedures in the future.