Physics in medicine and biology最新文献

Objective.In preclinical research,in vivoimaging of mice and rats is more common than any other animal species, since their physiopathology is very well-known and many genetically altered disease models exist. Animal studies based on small rodents are usually performed using dedicated preclinical imaging systems with high spatial resolution. For studies that require animal models such as mini-pigs or New-Zealand White (NZW) rabbits, imaging systems with larger bore sizes are required. In case of hybrid imaging using positron emission tomography (PET) and magnetic resonance imaging (MRI), clinical systems have to be used, as these animal models do not typically fit in preclinical simultaneous PET-MRI scanners.Approach.In this paper, we present initial imaging results obtained with the Hyperion II^DPET insert which can accommodate NZW rabbits when combined with a large volume MRI RF coil. First, we developed a rabbit-sized image quality phantom of comparable size to a NZW rabbit in order to evaluate the PET imaging performance of the insert under high count rates. For this phantom, radioactive spheres with inner diameters between 3.95 and7.86mm were visible in a warm background with a tracer activity ratio of 4.1 to 1 and with a total¹⁸F activity in the phantom of58MBq at measurement start. Second, we performed simultaneous PET-MR imaging of atherosclerotic plaques in a rabbitin vivousing a single injection containing¹⁸F-FDG for detection of inflammatory activity, and Gd-ESMA for visualization of the aortic vessel wall and plaques with MRI.Main results.The fused PET-MR images reveal¹⁸F-FDG uptake within an active plaques with plaque thicknesses in the sub-millimeter range. Histology showed colocalization of¹⁸F-FDG uptake with macrophages in the aortic vessel wall lesions.Significance.Our initial results demonstrate that this PET insert is a promising system for simultaneous high-resolution PET-MR atherosclerotic plaque imaging studies in NZW rabbits.

Objective.To develop and validate a novel multidimensional readout method that significantly reduces the number of readout channels (NRC) in PET detectors while maintaining high spatial and energy performance.Approach.We arranged a3×3×4SiPM array in multiple dimensions and employed row/column/layer summation with a resistor-based splitting circuit. We then applied denoising methods to enhance the peak-to-valley ratio in the decoding map, ensuring accurate crystal-position determination. Additionally, we investigated the system's energy response at 511 keV and evaluated the suitability for both clinical and research PET systems.Main results.The proposed multidimensional readout method achieved a favorable multiplexing ratio, lowering the total NRCs without compromising energy resolution at 511 keV. Our tests demonstrated that a SiPM bias voltage of 31 V effectively balances gain and saturation effects, resulting in reliable energy measurements.Significance.By reducing system complexity, cost, and power consumption, the multidimensional readout method presents a practical alternative to conventional readout schemes for PET and other large-scale sensor arrays. Additionally, the approach can manage simultaneous multi-layer hits by arranging detector layers and, when needed, uses ICS detection to correct for scatter events. Its adaptable architecture allows scaling to higher dimensions for broader applications (e.g. SPECT, CT, LiDAR). These features make it a valuable contribution toward more efficient, high-performance imaging technologies in both clinical and industrial settings.

Objective: To develop a model that accurately describes the behavior of nanobubbles (NBs) under low-frequency ultrasound (US) insonation (<250 kHz), addressing the limitations of existing numerical models, such as the Rayleigh-Plesset equation and Blake's Threshold model, in predicting NB behavior. Approach. A modified surface tension model, derived from empirical data, was introduced to capture the surface tension behavior of NBs as a function of bubble radius. This model was integrated into the Marmottant framework and combined with the Blake threshold to predict cavitation thresholds at low pressures, providing a comprehensive approach to understanding NB dynamics. Main Results. Experimentally, inertial cavitation for NBs with a radius of 85 nm was observed at peak negative pressures (PNP) of 200 kPa at 80 kHz and 1000 kPa at 250 kHz. The Marmottant model significantly overestimated these thresholds (1600 kPa). The modified surface tension model improved predictions at 250 kHz, while combining it with the Blake threshold accurately aligned cavitation thresholds at both frequencies (~150 kPa at low pressures) with experimental results. Significance. This work bridges a critical gap in understanding the acoustic behavior of NBs at low US frequencies and offers a new theoretical framework for predicting cavitation thresholds of NBs at low US frequencies, advancing their application in biomedical US technologies.

Objective: Accurate prediction of thermal damage extent is essential for effective and precise thermal therapy, especially in brain laser interstitial thermal therapy (LITT). Immediate postoperative contrast-enhanced T1-weighted imaging (CE-T1WI) is the primary method for clinically assessing in vivo thermal damage after image-guided LITT. CE-T1WI reveals a hyperintense enhancing rim surrounding the target lesion, which serves as a key radiological marker for evaluating the thermal damage extent. Although widely used in clinical practice, traditional thermal damage models rely on empirical parameters from in vitro experiments, which can lead to inaccurate predictions of thermal damage in vivo. Additionally, these models predict only two tissue states (damaged or undamaged), failing to capture three tissue states observed on post-CE-T1WI images, highlighting the need for improved thermal damage prediction methods.

Approach: This study proposes a novel Convolutional Long Short-Term Memory (ConvLSTM)-based model that utilizes intraoperative temperature distribution history data measured by magnetic resonance temperature imaging (MRTI) during LITT to predict the enhancing rim on post-CE-T1WI images. This method was implemented and evaluated on retrospective data from 56 patients underwent brain LITT. Main results: The proposed model effectively predicts the enhancing rim on postoperative images, achieving an average Dice Similarity Coefficient (DSC) of 0.82 (±0.063) on the test dataset. Furthermore, it generates real-time predicted thermal damage area variation trends that closely resemble those of the traditional thermal damage model, suggesting potential for real-time prediction of thermal damage extent.

Significance: This method could provide a valuable tool for visualizing and assessing intraoperative thermal damage extent. .

Objective.This study aims to propose a dual-domain network that not only reduces scatter artifacts but also retains structure details in cone-beam computed tomography (CBCT).Approach.The proposed network comprises a projection-domain sub-network and an image-domain sub-network. The projection-domain sub-network utilizes a division residual network to amplify the difference between scatter signals and imaging signals, facilitating the learning of scatter signals. The image-domain sub-network contains dual encoders and a single decoder. The dual encoders extract features from two inputs parallelly, and the decoder fuses the extracted features from the two encoders and maps the fused features back to the final high-quality image. Of the two input images to the image-domain sub-network, one is the scatter-contaminated image analytically reconstructed from the scatter-contaminated projections, and the other is the pre-processed image reconstructed from the pre-processed projections produced by the projection-domain sub-network.Main results.Experimental results on both synthetic and real data demonstrate that our method can effectively reduce scatter artifacts and restore image details. Quantitative analysis using synthetic data shows the mean absolute error was reduced by 74% and peak signal-to-noise ratio increased by 57% compared to the scatter-contaminated ones. Testing on real data found a 38% increase in contrast-to-noise ratio with our method compared to the scatter-contaminated image. Additionally, our method consistently outperforms comparative methods such as U-Net, DSE-Net, deep residual convolution neural network (DRCNN) and the collimator-based method.Significance.A dual-domain network that leverages projection-domain division residual connection and image-domain feature fusion has been proposed for CBCT scatter correction. It has potential applications for reducing scatter artifacts and preserving image details in CBCT.

Objective.Positron emission tomography (PET) is an advanced medical imaging technique that plays a crucial role in non-invasive clinical diagnosis. However, while reducing radiation exposure through low-dose PET scans is beneficial for patient safety, it often results in insufficient statistical data. This scarcity of data poses significant challenges for accurately reconstructing high-quality images, which are essential for reliable diagnostic outcomes.Approach.In this research, we propose a diffusion transformer model (DTM) guided by joint compact prior to enhance the reconstruction quality of low-dose PET imaging. In light of current research findings, we present a pioneering PET reconstruction model that integrates diffusion and transformer models for joint optimization. This model combines the powerful distribution mapping abilities of diffusion model with the capacity of transformers to capture long-range dependencies, offering significant advantages for low-dose PET reconstruction. Additionally, the incorporation of the lesion refining block and alternating direction method of multipliers enhance the recovery capability of lesion regions and preserves detail information, solving blurring problems in lesion areas and texture details of most deep learning frameworks.Main results. Experimental results validate the effectiveness of DTM in reconstructing low-dose PET image quality. DTM achieves state-of-the-art performance across various metrics, including PSNR, SSIM, NRMSE, CR, and COV, demonstrating its ability to reduce noise while preserving critical clinical details such as lesion structure and texture. Compared with baseline methods, DTM delivers best results in denoising and lesion preservation across various low-dose levels, including 10%, 25%, 50%, and even ultra-low-dose level such as 1%. DTM shows robust generalization performance on phantom and patient datasets, highlighting its adaptability to varying imaging conditions.Significance. This approach reduces radiation exposure while ensuring reliable imaging for early disease detection and clinical decision-making, offering a promising tool for both clinical and research applications.

Objective.Bone is a common site for the metastasis of malignant tumors, and single photon emission computed tomography (SPECT) is widely used to detect these metastases. Accurate delineation of metastatic bone lesions in SPECT images is essential for developing treatment plans. However, current clinical practices rely on manual delineation by physicians, which is prone to variability and subjective interpretation. While computer-aided diagnosis systems have the potential to improve diagnostic efficiency, fully automated segmentation approaches frequently suffer from high false positive rates, limiting their clinical utility.Approach.This study proposes an interactive segmentation framework for SPECT images, leveraging the deep convolutional neural networks to enhance segmentation accuracy. The proposed framework incorporates a U-shaped backbone network that effectively addresses inter-patient variability, along with an interactive attention module that enhances feature extraction in densely packed bone regions.Main results.Extensive experiments using clinical data validate the effectiveness of the proposed framework. Furthermore, a prototype tool was developed based on this framework to assist in the clinical segmentation of metastatic bone lesions and to support the creation of a large-scale dataset for bone metastasis segmentation.Significance.In this study, we proposed an interactive segmentation framework for metastatic lesions in bone scintigraphy to address the challenging task of labeling low-resolution, large-size SPECT bone scans. The experimental results show that the model can effectively segment the bone metastases of lung cancer interactively. In addition, the prototype tool developed based on the model has certain clinical application value.

Objective.The exact temporal characteristics of beam delivery affect the efficacy and outcome of ultra-high dose rate (UHDR or 'FLASH') radiotherapy, mainly due to the influence of the beam pulse structure on mean dose rate. Single beams may also be delivered in separate treatment sessions to elevate mean dose rate. This paper therefore describes a model for pulse-by-pulse treatment planning and demonstrates its application by making some generic observations of the characteristics of FLASH radiotherapy with photons and protons.Approach.A beam delivery model was implemented into the AutoBeam (v6.3) inverse treatment planning system, so that the individual pulses of the delivery system could be explicitly described during optimisation. The delivery model was used to calculate distributions of time-averaged and dose-averaged mean dose rate and the dose modifying factor for FLASH was then determined and applied to dose calculated by a discrete ordinates Boltzmann solver. The method was applied to intensity-modulated radiation therapy with photons as well as to passive scattering and pencil beam scanning with protons for the case of a simple phantom geometry with a prescribed dose of 36 Gy in 3 fractions.Main results.Dose and dose rate are highest in the target region, so FLASH sparing is most pronounced around the planning target volume (PTV). When using a treatment session per beam, OAR sparing is possible more peripherally. The sparing with photons is higher than with protons because the dose to OAR is higher with photons.Significance.The framework provides an efficient method to determine the optimal technique for delivering clinical dose distributions using FLASH. The most sparing occurs close to the PTV for hypofractionated treatments.

Using the concept of biologically effective dose (BED), the effect of sublethal DNA damage repair (SLR) on the bio-efficacy of prolonged radiotherapy treatments can be quantified (BED_SLR). Such treatments, lasting more than 20 min, are typically encountered in stereotactic radiosurgery (SRS) applications using the CyberKnife (CK) and Gamma knife systems. Evaluating the plan data from 45 Vestibular Schwannoma (VS) cases treated with single fraction CK-SRS, this work demonstrates a statistically significant correlation between the marginal BED_SLRdelivered to the target (m-BED_SLR) and the ratio of the mean collimator size weighted by the fraction of total beams delivered with each collimator (wmCs), to the tumor volume (Tv). The correlation betweenm-BED_SLRandwmCsTvdatasets was mathematically expressed by the power functionm-BEDSLR=85.21 (±1.7%)⋅(wmCsTv)(0.05±7%) enabling continuousm-BED_SLRpredictions. Using this formula, a specific range ofm-BED_SLRlevels cana prioribe targeted during treatment planning through proper selection of collimator size(s) for a given tumor volume. Inversely, for a selected set of collimators, the optimization range ofm-BED_SLRcan be determined assuming that all beams are delivered with the smallest and largest collimator size. For single collimator cases or when the relative usage of each collimator size is known or estimated, a specificm-BED_SLRlevel can be predicted within 3% uncertainty. The proposed equation is valid for the fixed CK collimators and a physical dose prescription (D_pr) of 13 Gy. For alternateD_prin the range of 11-14 Gy, a linear relationship was found between relative changes ofm-BED_SLR(D_pr) andD_prwith respect tom-BED_SLR(13 Gy) and 13 Gy, respectively. The proposed methodology is simple and easy to implement in the clinical setting allowing for optimization of the treatment's bio-effectiveness, in terms of the delivered BED, during treatment planning.

Objective. Magnetic particle imaging (MPI) is a novel imaging technique that uses magnetic fields to detect tracer materials consisting of magnetic nanoparticles. System matrix (SM) based image reconstruction is essential for achieving high image quality in MPI. However, the time-consuming SM calibrations need to be repeated whenever the magnetic field's or nanoparticle's characteristics change. Accelerating this calibration process is therefore crucial. The most common acceleration approach involves undersampling during the SM calibration procedure, followed by super-resolution methods to recover the high-resolution SM. However, these methods typically require separate training of multiple models for different undersampling ratios, leading to increased storage and training time costs.Approach. We propose an arbitrary-scale SM super-resolution method based on continuous implicit neural representation (INR). Using INR, the SM is modeled as a continuous function in space, enabling arbitrary-scale super-resolution by sampling the function at different densities. A cross-frequency encoder is implemented to share SM frequency information and analyze contextual relationships, resulting in a more intelligent and efficient sampling strategy. Convolutional neural networks (CNNs) are utilized to learn and optimize the grid sampling process in INR, leveraging the advantage of CNNs in learning local feature associations and considering surrounding information comprehensively.Main results. Experimental results on OpenMPI demonstrate that our method outperforms existing methods and enables calibration at any scale with a single model. The proposed method achieves high accuracy and efficiency in SM recovery, even at high undersampling rates.Significance. The proposed method significantly reduces the storage and training time costs associated with SM calibration, making it more practical for real-world applications. By enabling arbitrary-scale super-resolution with a single model, our approach enhances the flexibility and efficiency of MPI systems, paving the way for more widespread adoption of MPI technology.