Physics in medicine and biology最新文献

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.

Objective.x-ray photon-counting detectors have recently gained popularity due to their capabilities in energy discrimination power, noise suppression, and resolution refinement. The latest extremity photon-counting computed tomography (PCCT) scanner leverages these advantages for tissue characterization, material decomposition, beam hardening correction, and metal artifact reduction. However, technical challenges such as charge splitting and pulse pileup can distort the energy spectrum and compromise image quality. Also, there is a clinical need to balance radiation dose and imaging speed for contrast-enhancement and other studies. This paper aims to address these challenges by developing a dual-domain correction approach to enhance PCCT reconstruction quality quantitatively and qualitatively.Approach.We propose a novel correction method that operates in both projection and image domains. In the projection domain, we employ a residual-based Wasserstein generative adversarial network to capture local and global features, suppressing pulse pileup, charge splitting, and data noise. This is facilitated with traditional filtering methods in the image domain to enhance signal-to-noise ratio while preserving texture across each energy channel. To address GPU memory constraints, our approach utilizes a patch-based volumetric refinement network.Main results.Our dual-domain correction approach demonstrates significant fidelity improvements across both projection and image domains. Experiments on simulated and real datasets reveal that the proposed model effectively suppresses noise and preserves intricate details, outperforming the state-of-the-art methods.Significance.This approach highlights the potential of dual-domain PCCT data correction to enhance image quality for clinical applications, showing promise for advancing PCCT image fidelity and applicability in preclinical/clinical environments.

Objective.This study demonstrates high volume rate bistatic 3-D vascular strain imaging, to overcome well-known challenges caused by the anisotropic resolution and contrast inherent to ultrasound imaging.Approach.Using two synchronized 32 × 32 element matrix arrays (3.5 MHz), coherent 3-D ultrasound images ofex vivoporcine aortas were acquired at 90 Hz during pulsation in a mock circulation loop. The image data of interleaved transmissions were coherently compounded on one densely sampled Cartesian grid to estimate frame-to-frame displacements using 3-D block matching. The radial displacement components were projected onto mesh nodes of the aortic wall, after which local circumferential and radial strain estimates were calculated with a 3-D least squares strain estimator.Main results.The additional reflection content and high-resolution phase information along the axis of the second transducer added more distinctive features for block matching, resulting in an increased coverage of high correlation values and more accurate lateral displacements. Compared to single array results, the mean motion tracking error for one inflation cycle was reduced by a factor 5-8 and circumferential elastographic signal-to-noise ratio increased by 5-10 dB. Radial strain remains difficult to estimate at the transmit frequency used at these imaging depths, but may benefit from more research into strain regularization and sub-pixel interpolation techniques.Significance.These results suggest that multi-aperture ultrasound acquisition sequences can advance the field of vascular strain imaging and elastography by addressing challenges related to estimating local-scale deformation on an acquisition level. Future research into 3-D aberration correction and probe localization techniques is important to extend the method's applicability towardsin vivouse and for a wider range of applications.

Objective.Integrating time-of-flight (ToF) measurements in radiography and computed tomography (CT) enables an approach for scatter rejection in imaging systems that eliminates the need for anti-scatter grids, potentially increasing system sensitivity and image quality. However, present hardware dedicated to the time-correlated measurement of x-rays is limited to a single pixel physically too large for the desired spatial resolution. A switch to highly integrated electronics and detectors is needed to progress towards detector arrays capable of acquiring images, while offering a timing resolution below 300 ps FWHM to achieve scatter rejection comparable to current anti-scatter grids.Approach.Using off-the-shelf scintillators, photodetectors and readouts designed for ToF positron emission tomography (PET) provides a preliminary evaluation of available highly integrated readout systems supporting detector arrays for ToF scatter rejection. The TOFPET2c ASIC from PETSys offers an established development platform necessary for fast and reliable results, with no known limitation regarding time-correlated detection of medical imaging x-rays (20-140 keV).Main results.Reliable photon detection down to 31 keV was achieved, reaching energy resolutions from 23% to 92% FWHM throughout the desired energy range. Optimal detector timing resolution (DTR) from 250 ps FWHM at 130 keV to 678 ps FWHM at 30 keV was reached. Strong time walk effects were observed, showing a time shift of 642 ps up to 1740 ps between events spanning the energies used in x-ray medical imaging.Significance.The TOFPET2c ASIC has shown its potential for ToF scatter rejection, but meets the time resolution requirement of 300 ps FWHM only for limited energies (110-140 keV). This significant timing degradation observed at lower energies limits the use of the TOFPET2c ASIC for ToF scatter rejection, but offers significant advancements regarding the understanding of the phenomenon arising from the time-correlated detection of medical imaging x-rays.

Photoacoustic imaging (PAI), by integrating optical and ultrasound modalities, combines high spatial resolution with deep tissue penetration, making it a transformative tool in biomedical research. This review presents a comprehensive analysis of the current status of dual photoacoustic/ultrasound (PA/US) imaging technologies, emphasising their applications in preclinical research. It details advancements in light excitation strategies, including tomographic and microscopic modalities, innovations in pulsed laser and alternative light sources, and ultrasound instrumentation. The review further explores preclinical methodologies, encompassing dedicated instrumentation, signal processing, and data analysis techniques essential for PA/US systems. Key applications discussed include the visualisation of blood vessels, micro-circulation, and tissue perfusion; diagnosis and monitoring of inflammation; evaluation of infections, atherosclerosis, burn injuries, healing, and scar formation; assessment of liver and renal diseases; monitoring of epilepsy and neurodegenerative conditions; studies on brain disorders and preeclampsia; cell therapy monitoring; and tumour detection, staging, and recurrence monitoring. Challenges related to imaging depth, resolution, cost, and the translation of contrast agents to clinical practice are analysed, alongside advancements in high-speed acquisition, artificial intelligence-driven reconstruction, and innovative light-delivery methods. While clinical translation remains complex, this review underscores the crucial role of preclinical studies in unravelling fundamental biomedical questions and assessing novel imaging strategies. Ultimately, this review delves into the future trends of dual PA/US imaging, highlighting its potential to bridge preclinical discoveries with clinical applications and drive advances in diagnostics, therapeutic monitoring, and personalised medicine.