Physics in medicine and biology最新文献

Objective. In magneto-acousto-electrical tomography (MAET), linearly frequency-modulated (LFM) signal stimulation uses much lower peak voltage than the spike pulse stimulation, lengthening the operation life of the transducer. However, due to the uneven frequency responses of the transducer, the low-noise amplifier (LNA), and the bandpass filter (BPF), MAET using LFM signal stimulation suffers from longitudinal resolution loss. In this paper, frequency response compensated linearly frequency-modulated (FRC-LFM) signal stimulation is investigated to resolve the problem.Approach. The physical model of measurement of the frequency responses of the transducer and the cascading module of the detection electrodes, the LNA, and the BPF is constructed. The frequency responses are approximated by fitting a curve to the measurement data. The frequency response compensation function is set to the reciprocal of the product of the frequency responses. The digital FRC-LFM signal is generated in MATLAB and converted to analog signal through an arbitrary waveform generator. Two groups of MAET experiments are designed to confirm the performance of the FRC-LFM signal stimulation. Pure agar phantom with rectangular through-holes and agar phantom with pork tissue inclusion serve as the samples.Main results. The pulse-compressed magneto-acousto-electrical signal obtained using FRC-LFM stimulation has narrower main-lobe than that obtained using LFM excitation, although the signal to noise pulse interference ratio of the former is little lower than that of the latter, which is due to the limitation of the power amplifier. FRC-LFM also proves to be an effective method to utilize the frequency outside the working band of the transducer in MAET.Significance. The method in this study compensates for the longitudinal resolution loss due to the uneven frequency responses. Combining with high-capability power amplifier and high-performance LNA, the MAET using FRC-LFM signal stimulation can potentially achieve high longitudinal resolution and high sensitivity, advancing MAET toward the clinical application.

Objective. This work aims to investigate the iso-effectiveness of conventional and FLASH radiotherapy on tumors through in-silico mathematical models. We focused on the role of radiolytic oxygen depletion (ROD), which has been argued as a possible factor to explain the FLASH effect.Approach. We used a spatiotemporal reaction-diffusion model, including ROD, to simulate tumor oxygenation and response. From those oxygen distributions we obtained surviving fractions (SFs) using the linear-quadratic (LQ) model with the oxygen enhancement ratios (OERs). We then employed the calculated SFs to describe the evolution of preclinical tumor volumes through a mathematical model of tumor response, and we also extrapolated those results to calculate tumor control probabilities (TCPs) using the Poisson-LQ approach.Main results. Our study suggests that the ROD effect may cause differences in SF between FLASH and conventional radiotherapy, especially in lowα/βandpoorly oxygenatedcells. However, a statistical analysis showed that these changes in SF generally do not result in significant differences in the evolution of preclinical tumor growth curves when the sample size is small, because such differences in SF may not be noticeable in the heterogeneity of the population of animals. Nonetheless, when extrapolating this effect to TCP curves, we observed important differences between both techniques (TCP is lower in FLASH radiotherapy). When analyzing the response of tumors with heterogeneous oxygenations, differences in TCP are more important forwell oxygenatedtumors. This apparent contradiction with the results obtained for homogeneously oxygenated cells is explained by the complex interplay between the heterogeneity of tumor oxygenation, the OER effect, and the ROD effect.Significance. This study supports the experimentally observed iso-effectiveness of FLASH and conventional radiotherapy when analyzing the volume evolution of preclinical tumors (that are far from control). However, this study also hints that tumor growth curves may be less sensitive to small variations in SF than tumor control probability: ROD may lead to increased SF in FLASH radiotherapy, which while not large enough to cause significant differences in tumor growth curves, could lead to important differences in clinical TCPs. Nonetheless, it cannot be discarded that other effects not modeled in this work, like radiation-induced immune effects, can contribute to tumor control and maintain the iso-effectiveness of FLASH radiotherapy. The study of tumor growth curves may not be the ideal experiment to test the iso-effectiveness of FLASH, and experiments reporting TCP orD₅₀may be preferred.

Objective.Fluorescence molecular tomography (FMT) holds promise for early tumor detection by mapping fluorescent agents in three dimensions non-invasively with low cost. However, since ill-posedness and ill-condition due to strong scattering effects in biotissues and limited measurable data, current FMT reconstruction is still up against unsatisfactory accuracy, including location prediction and morphological preservation.Approach.To strike the above challenges, we propose a novel Sparse-Laplace hybrid graph manifold (SLHGM) model. This model integrates a hybrid Laplace norm-based graph manifold learning term, facilitating a trade-off between sparsity and preservation of morphological features. To address the non-convexity of the hybrid objective function, a fixed-point equation is designed, which employs two successive resolvent operators and a forward operator to find a converged solution.Main results.Through numerical simulations andin vivoexperiments, we demonstrate that the SLHGM model achieves an improved performance in providing accurate spatial localization while preserving morphological details.Significance.Our findings suggest that the SLHGM model has the potential to advance the application of FMT in biological research, not only in simulation but also inin vivostudies.

Objective Achieving ultra-precise temporal resolution in ionizing radiation detection is essential, particularly in positron emission tomography, where precise timing enhances signal-to-noise ratios and may enable reconstruction-less imaging. A promising approach involves utilizing ultrafast modulation of the complex refractive index, where sending probe pulses to the detection crystals will result in changes in picoseconds (ps), and thus a sub - 10 ps coincidence time resolution can be realized. Towards this goal, here, we aim to first measure the ps changes in probe pulses using an ionizing radiation source with high time resolution. Approach We used relativistic, ultrafast electrons to induce complex refractive index and use probe pulses in the near-infrared (800 nm) and terahertz (THz, 300 µm) regimes to test the hypothesized wavelength-squared increase in absorption coefficient in the Drude free-carrier absorption model. We measured BGO, ZnSe, BaF2, ZnS, PBG, and PWO with 1 mm thickness to control the deposited energy of the 3 MeV electrons, simulating ionization energy of the 511 keV photons. Main results Both with the 800 nm and THz probe pulses, transmission decreased across most samples, indicating the free carrier absorption, with an induced signal change of 11% in BaF2, but without the predicted Drude modulation increase. To understand this discrepancy, we simulated ionization tracks and examined the geometry of the free carrier distribution, attributing the mismatch in THz modulations to the sub-wavelength diameter of trajectories, despite the lengths reaching 500 µm to 1 mm. Additionally, thin samples truncated the final segments of the ionization tracks, and the measured initial segments have larger inter-inelastic collision distances due to lower stopping power (dE/dx) for high-energy electrons, exacerbating diffraction-limited resolution. Significance Our work offers insights into ultrafast radiation detection using complex refractive index modulation and highlights critical considerations in sample preparation, probe wavelength, and probe-charge carrier coupling scenarios.

Diffusion magnetic resonance imaging (dMRI) currently stands as the foremost noninvasive method for quantifying brain tissue microstructure and reconstructing white matter fiber pathways. However, the inherent free diffusion motion of water molecules in dMRI results in signal decay, diminishing the signal-to-noise ratio (SNR) and adversely affecting the accuracy and precision of microstructural data. In response to this challenge, we propose a novel method known as the Multiscale Fast Attention-Multibranch Irregular Convolutional Neural Network for dMRI image denoising. In this work, we introduce Multiscale Fast Channel Attention, a novel approach for efficient multiscale feature extraction with attention weight computation across feature channels. This enhances the model's capability to capture complex features and improves overall performance. Furthermore, we propose a multi-branch irregular convolutional architecture that effectively disrupts spatial noise correlation and captures noise features, thereby further enhancing the denoising performance of the model. Lastly, we design a novel loss function, which ensures excellent performance in both edge and flat regions. Experimental results demonstrate that the proposed method outperforms other state-of-the-art deep learning denoising methods in both quantitative and qualitative aspects for dMRI image denoising with fewer parameters and faster operational speed.

Objective.To investigate magnetic field effects on the dose distribution and ionization chambers response in carbon ion reference fields and determine magnetic field correction factors for chambers of different volumes.Approach.The response of six Farmer-type chambers with varying radii (1-6 mm, termed as R1-R6) was measured in magnetic fields up to 1 T in 0.1 T increments using an experimental electromagnet and compared with Monte Carlo simulations. Chamber readings were measured in the entrance region of a monoenergetic carbon ion beam of 390.75 MeV u^-1. A lower energy of 200.28 MeV u^-1was applied to chamber R3 for comparison. Polarity and recombination corrections were investigated for the R3 chamber. The local dose change induced by the magnetic field was calculated by Monte Carlo, which together with change of the chamber's response, was used to calculate the final magnetic field correction factors.Main results.The dependence of the chamber response on the magnetic field was non-linear and volume-dependent. Maximum changes ranged from 0.30% (R4) to 0.62% (R5) at 0.2 T. For R3, the response for the lower energy was systematically decreased by 0.2% in the range of 0.2 T to 0.7 T. No significant effect of the magnetic field on polarity and ion recombination correction was found. The maximum variation of the local dose was found to be (0.03 ± 0.08) % at 0.2 T for beam energy of 390.75 MeV u^-1. Magnetic field correction factors for the different chambers ranged from 0.28% (R4) to 0.60% (R5).Significance.This study provides the first detailed analysis of chambers' response to magnetic flux densities of up to 1 T using chambers of different radii and comparison with simulations. By combining the chamber response alterations with local dose changes magnetic field correction factors were calculated for all six chambers, including the commercial Farmer-type chamber.

Objective.In this study, we present a model to correct the progressive post-irradiation darkening of EBT3 films. The model allows for a clinical use of EBT3 using application and calibration films scanned with different post-irradiation times.Approach.The model is a post-irradiation time- and dose-dependent power-law function, projecting the scanned transmittance of application films to the transmittance matching the same post-irradiation time of calibration films. The model was characterized for two EBT3 production lots within the dose range 0.1-12.8 Gy. A first characterization was performed utilizing calibration films scanned repeatedly for 54 d post-irradiation (lot 1), while a fast re-characterization of a second lot used three post-irradiation times (lot 2). For a long-term validation of the model, 16 film strips were irradiated at 2 Gy on different time points starting from the day of film calibration up to 43 d afterwards (lot 1). For the multiple-dose validation of the model, 8 strips were irradiated with dose levels ranging 0-12 Gy deposited 25 d after the calibration (lot 2). As a proof of principle, the model was applied to four clinical patient-specific quality assurance film measurements with prescribed dose/fraction ranging 2.66 Gy-8 Gy.Main results. The post-irradiation transmittance decreased for higher doses up to -2.5% at 12.8 Gy, and 54 d post-irradiation. With a lot-specific model correction, the mean dose accuracy of validation strips that ranged from initial -3.4% (triple-channel) and -9.90% (blue-channel) reduced to within 3.0% (all colour channels) for doses above 1 Gy. The median dose difference with the planned dose improved from -3.5% to -1.1%, and the 3%/2 mm local gamma ranged from (48.5-92.5)% to (81.2-99.2)%.Significance.A film darkening model corrects the transmittance of EBT3 films and increases the flexibility of existing dosimetry protocols. The correction ensures dose accuracies within 3%.

Objectives.Magnetic resonance imaging (MRI) is pivotal in diagnosing brain injuries in infants. However, the dynamic development of the brain introduces variability in infant MRI characteristics, posing challenges for MRI-based classification in this population. Furthermore, manual data selection in large-scale studies is labor-intensive, and existing algorithms often underperform with thick-slice MRI data. To enhance research efficiency and classification accuracy in large datasets, we propose an advanced classification model.Approach.We introduce the Dual-Branch Attention Information Interactive Neural Network (DBAII-Net), a cutting-edge model inspired by radiologists' use of multiple MRI sequences. DBAII-Net features two innovative modules: (1) the convolutional enhancement module (CEM), which leverages advanced convolutional techniques to aggregate multi-scale features, significantly enhancing information representation; and (2) the cross-modal attention module (CMAM), which employs state-of-the-art attention mechanisms to fuse data across branches, dramatically improving positional and channel feature extraction. Performances (accuracy, sensitivity, specificity, area under the curve (AUC), etc) of DBAII-Net were compared with eight benchmark models for brain MRI classification in infants aged 6 months to 2 years.Main results.Utilizing a self-constructed dataset of 240 thick-slice brain MRI scans (122 with brain injuries, 118 without), DBAII-Net demonstrated superior performance. On a test set of approximately 50 cases, DBAII-Net achieved average performance metrics of 92.53% accuracy, 90.20% sensitivity, 94.93% specificity, and an AUC of 0.9603. Ablation studies confirmed the effectiveness of CEM and CMAM, with CMAM significantly boosting classification metrics.Significance.DBAII-Net with CEM and CMAM outperforms existing benchmarks in enhancing the precision of brain MRI classification in infants, significantly reducing manual effort in infant brain research. Our code is available athttps://github.com/jiazhen4585/DBAII-Net.

Objective. The implementation of deep learning in magnetic resonance imaging (MRI) has significantly advanced the reduction of data acquisition times. However, these techniques face substantial limitations in scenarios where acquiring fully sampled datasets is unfeasible or costly.Approach. To tackle this problem, we propose a fusion enhanced contrastive self-supervised learning (FCSSL) method for parallel MRI reconstruction, eliminating the need for fully sampledk-space training dataset and coil sensitivity maps. First, we introduce a strategy based on two pairs of re-undersampling masks within a contrastive learning framework, aimed at enhancing the representational capacity to achieve higher quality reconstruction. Subsequently, a novel adaptive fusion network, trained in a self-supervised learning manner, is designed to integrate the reconstruction results of the framework.Results. Experimental results on knee datasets under different sampling masks demonstrate that the proposed FCSSL achieves superior reconstruction performance compared to other self-supervised learning methods. Moreover,the performance of FCSSL approaches that of the supervised methods, especially under the 2DRU and RADU masks, but no need for fully sample data. The proposed FCSSL, trained under the 3× 1DRU and 2DRU masks, can effectively generalize to unseen 1D and 2D undersampling masks, respectively. For target domain data that exhibit significant differences from source domain data, the proposed model, fine-tuned with just a few dozen instances of undersampled data in the target domain, achieves reconstruction performance comparable to that achieved by the model trained with the entire set of undersampled data.Significance. The novel FCSSL model offers a viable solution for reconstructing high-quality MR images without needing fully sampled datasets, thereby overcoming a major hurdle in scenarios where acquiring fully sampled MR data is difficult.

Objective.Cavitation dose monitoring plays a key role in ultrasound drug delivery to the brain. The use of capacitive micromachined ultrasonic transducer (CMUT) technology has a great potential for passive cavitation detection (PCD).Approach.Here, a circular (diameter 7 mm) CMUT centered at 5 MHz was designed to be inserted into a therapeutic transducer (1.5 MHz) used for ultrasound-induced blood-brain barrier (BBB) disruption on mice. CMUT-based real-time cavitation detection was performed during the ultrasound procedure (50μl intravenous injection of SonoVue microbubbles, frequency 1.5 MHz, PNP 480 kPa, duty Cycle 10%, PRF 10 Hz, duration 60 s). BBB disruption were confirmed by contrast-enhanced 7T-MRI.Main results.The CMUT device has a fractional bandwidth of 140%, almost twice a conventional piezocomposite PCD transducer. As expected, the CMUT device was able to detect the occurrence of harmonic, subharmonic and ultraharmonic frequencies as well as the increase of broadband signal indicating inertial cavitation in a wide frequency range (from 0.75 to 6 MHz). Signal-to-noise ratio was high enough (>40 dB) to perform ultrafast monitoring and follow the subtle intrapulse variations of frequency components at a rate of 10 kHz.Significance. This firstin vivoproof of concept demonstrates the interest of CMUT for PCD and encourages us to develop devices for PCD in larger animals by integrating an amplifier directly to the CMUT front-end to considerably increase the signal-to-noise ratio.