Physics in medicine and biology最新文献

Acoustic holography can be used to construct an arbitrary wavefront at a desired 2D plane or 3D volume by beam shaping an emitted field and is a relatively new technique in the field of biomedical applications. Acoustic holography was first theorized in 1985 following Gabor's work in creating optical holograms in the 1940s. Recent developments in 3D printing have led to an easier and faster way to manufacture monolithic acoustic holographic lenses that can be attached to single-element transducers. As ultrasound passes through the lens material, a phase shift is applied to the waves, causing an interference pattern at the 2D image plane or 3D volume, which forms the desired pressure field. This technology has many applications already in use and has become of increasing interest for the biomedical community, particularly for treating regions that are notoriously difficult to operate on, such as the brain. Acoustic holograms could provide a non-invasive, precise, and patient specific way to deliver drugs, induce hyperthermia, or create tissue cell patterns. However, there are still limitations in acoustic holography, such as the difficulties in creating 3D holograms and the passivity of monolithic lenses. This review aims to outline the biomedical applications of acoustic holograms reported to date and discuss their current limitations and the future work that is needed for them to reach their full potential in the biomedical community.

Objective. Coincidence time resolution (CTR) in time-of-flight positron emission tomography (TOF-PET) has significantly improved with advancements in scintillators, photodetectors, and readout electronics. Achieving a CTR of 100 ps remains challenging due to the need for sufficiently thick scintillators-typically 20 mm-to ensure adequate sensitivity because the photon transit time spread within these thick scintillators impedes achieving 100 ps CTR. Therefore thinner scintillators are preferable for CTR better than 100 ps. To address the trade-off between TOF capability and sensitivity, we propose a readout scheme of PET detectors.Approach. The proposed scheme utilizes two orthogonally stacked one-dimensional PET detectors, enabling the thickness of the scintillators to be reduced to approximately 13 mm without compromising sensitivity. This is achieved by stacking the detectors along the depth-of-interaction (DOI) axis of a PET scanner. We refer to this design as the cross-stacked detector, or xDetector. Furthermore, the xDetector inherently provides DOI information using the same readout scheme.Main results. Experimental evaluations demonstrated that the xDetector achieved the best CTR of 175 ps full width at half maximum (FWHM) and an energy resolution of 11% FWHM at 511 keV with 3 × 3 × 12.8 mm³lutetium oxyorthosilicate crystals, each coupled one-to-one with silicon photomultipliers. The CTRs are between the xDetector and reference detector with a single timing resolution of 111.2 ± 0.8 ps FWHM. In terms ofxy-spatial resolution, the xDetector exhibited an asymmetric resolution due to its readout scheme: one resolution was defined by the 3.2 mm readout pitch, while the other was calculated using the center-of-gravity method.Significance. The xDetector effectively resolves the trade-off between TOF capability and sensitivity while offering scalability and DOI capability. By integrating state-of-the-art scintillators, photodetectors, and readout electronics with the xDetector scheme, achieving a CTR of 100 ps FWHM alongside high DOI resolution becomes a practical possibility.

Objective.To identify suitable combination strategies for treatment planning and beam delivery in scanned carbon ion therapy of moving tumors.Approach. Carbon ion treatment plans for five abdominal tumors were optimized on four-dimensional (4D) computed tomography (CT) data using the following approaches. 4DITV across all phases and within a gating window, single phase uniform dose, and an innovative 4D tracking internal target volume (ITV) strategy. Delivered single-fraction doses were calculated on time-resolved virtual CT images reconstructed from 2D cine-magnetic resonance imaging series, using a deformable image registration pipeline. Treatment plans were combined with various beam delivery techniques: three-dimensional (no motion mitigation), rescanning, gating, beam tracking, and multi-phase 4D delivery with and without residual tracking (MP4D and MP4DRT) to form in total 11 treatment modalities. Single fraction doses were accumulated to simulate a fractionated treatment.Main results. Breath-sampled treatments using the MP4D and MP4DRT delivery techniques were the only to achieveD₉₅> 95% for hypofractionated treatments, with little dependence on the number of fractions. A combination of MP4DRT with the new 4D tracking ITV approach resulting in conformal dose distributions and demonstrated the greatest robustness against irregular motion and anatomical changes.Significance. This study demonstrates, that real-time adaptive beam delivery strategies can deliver conformal doses within single fractions, thereby enabling hypofractionated treatment schemes that are not feasible with conventional strategies.

Background and purpose The small bowel is one of the most radiosensitive organs-at-risk during radiotherapy in the pelvis. This is further complicated due to anatomical and physiological motion. Thus, its accurate tracking becomes of particular importance during therapy delivery, to obtain better dose-toxicity relations and/or to perform safe adaptive treatments. The aim of this work is to simultaneously optimize the MR imaging sequence and motion estimation solution towards improved small bowel tracking precision during radiotherapy delivery. Materials and methods An MRI sequence was optimized, to adhere to the respiratory and peristaltic motion frequencies, by assesing the performance of an image registration algorithm on data acquired on volunteers and patients. In terms of tracking, three registration algorithms, previously-employed in the scope of image-guided radiotherapy, were investigated. The optimized scan was acquired for 7.5 min., in 18 patients and for 15 min., in 10 volunteers at a 1.5T MRL (Unity, Elekta AB). The tracking precision was evaluated and validated by means of three different quality assurance criteria: Structural Similarity Index Metric (SSIM), Inverse Consistency (IC) and Absolute Intensity Difference (AID). Results The optimal sequence was a balanced Fast Field Echo (FFE), which acquired a 3D volume of the abdomen, with a dynamic scan time of 1.8 seconds. An optical flow algorithm performed best and which was able to resolve most of the motion. This was shown by mean IC values of < 1 mm and a mean SSIM >0.9 for the majority of the cases. A strong positive correlation (p<0.001) between the registration performance and visceral fat percentage was found, where a higher visceral fat percentage gave a better registration due to the better image contrast. Conclusions A method for simultaneous optimization of imaging and tracking was presented, which derived an imaging and registration procedure for accurate small bowel tracking on the MR-Linac.

Objective. The study aims to systematically characterize the effect of CT parameter variations on images and lung radiomic and deep features, and to evaluate the ability of different image harmonization methods to mitigate the observed variations.Approach. A retrospective in-house sinogram dataset of 100 low-dose chest CT scans was reconstructed by varying radiation dose (100%, 25%, 10%) and reconstruction kernels (smooth, medium, sharp). A set of image processing, convolutional neural network (CNNs), and generative adversarial network-based (GANs) methods were trained to harmonize all image conditions to a reference condition (100% dose, medium kernel). Harmonized scans were evaluated for image similarity using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS), and for the reproducibility of radiomic and deep features using concordance correlation coefficient (CCC).Main Results. CNNs consistently yielded higher image similarity metrics amongst others; for Sharp/10%, which exhibited the poorest visual similarity, PSNR increased from a mean ± CI of 17.763 ± 0.492 to 31.925 ± 0.571, SSIM from 0.219 ± 0.009 to 0.754 ± 0.017, and LPIPS decreased from 0.490 ± 0.005 to 0.275 ± 0.016. Texture-based radiomic features exhibited a greater degree of variability across conditions, i.e. a CCC of 0.500 ± 0.332, compared to intensity-based features (0.972 ± 0.045). GANs achieved the highest CCC (0.969 ± 0.009 for radiomic and 0.841 ± 0.070 for deep features) amongst others. CNNs are suitable if downstream applications necessitate visual interpretation of images, whereas GANs are better alternatives for generating reproducible quantitative image features needed for machine learning applications.Significance. Understanding the efficacy of harmonization in addressing multi-parameter variability is crucial for optimizing diagnostic accuracy and a critical step toward building generalizable models suitable for clinical use.

Objective: This study introduces a novel method for reconstructing cone beam computed tomography (CBCT) images for arbitrary orbits, addressing the computational and memory challenges associated with traditional iterative reconstruction algorithms.

Approach: The proposed method employs a differentiable shift-variant filtered backprojection neural network, optimized for arbitrary trajectories. By integrating known operators into the learning model, the approach minimizes the number of trainable parameters while enhancing model interpretability. This framework adapts seamlessly to specific orbit geometries, including non-continuous trajectories such as circular-plus-arc or sinusoidal paths, enabling faster and more accurate CBCT reconstructions.

Main results: Experimental validation demonstrates that the method significantly accelerates reconstruction, reducing computation time by over 97% compared to conventional iterative algorithms. It achieves superior or comparable image quality with reduced noise, as evidenced by a 38.6% reduction in mean squared error, a 7.7% increase in peak signal-to-noise ratio, and a 5.0% improvement in the structural similarity index measure. The flexibility and robustness of the approach are confirmed through its ability to handle data from diverse scan geometries.

Significance: This method represents a significant advancement in interventional medical imaging, particularly for robotic C-arm CT systems, enabling real-time, high-quality CBCT reconstructions for customized orbits. It offers a transformative solution for clinical applications requiring computational efficiency and precision in imaging. Code Availability: Code is available at https://github.com/ChengzeYe/Defrise-and-Clack-reconstruction.

Unsupervised medical image translation tasks are challenging due to the difficulty of obtaining perfectly paired medical images. CycleGAN-based methods have proven effective in unpaired medical image translation. However, these methods can produce artifacts in the generated medical images. To address this issue, we propose an unsupervised network based on cycle consistency and hybrid contrastive unpaired translation (CycleH-CUT). CycleH-CUT consists of two CUT (H-CUT) networks. In the H-CUT network, a query-selected attention mechanism is adopted to select queries with important features. The boosted contrastive learning loss is employed to reweight all negative patches via the optimal transport strategy. We further apply spectral normalization to improve training stability, allowing the generator to extract complex features. On the basis of the H-CUT network, a new CycleH-CUT framework is proposed to integrate contrastive learning and cycle consistency. Two H-CUT networks are used to reconstruct the generated images back to the source domain, facilitating effective translation between unpaired medical images. We conduct extensive experiments on three public datasets (BraTS, OASIS3, and IXI) and a private Spinal Column dataset to demonstrate the effectiveness of CycleH-CUT and H-CUT. Specifically, CycleH-CUT achieves an average SSIM of 0.926 in the BraTS dataset, an average SSIM of 0.796 on the OASIS3 dataset, an average SSIM of 0.932 on the IXI dataset, and an average SSIM of 0.890 on the private Spinal Column dataset.

Objective. Artificial intelligence (AI)-generated synthetic CT (sCT) images have become commercially available to provide electron densities and reference anatomies in MR-only radiotherapy workflows. However, hallucinations (false regions of bone or air) introduced in AI-generated sCT images may affect the accuracy of dose calculation and patient setup verification. We developed a tool to detect bone hallucinations and/or inaccuracies in AI-generated pelvic sCT images used in MR-only workflows.Approach. A deep learning auto segmentation (DLAS) model was trained to auto-segment bone on MR images. The model was implemented with a 3D SegResNet network architecture using the MONAI framework with a training dataset of 86 Dixon MR image sets paired with their corresponding ground truth contours derived from planning CT images deformed to the MR images. The model performance was then assessed on an independent testing dataset (n= 10).Main results. The DLAS model-based hallucination screener identified hallucinations in bone structures using daily MR images and accurately flagged these regions on sCT images. The sensitivity of the screener is adjustable based on the distance of discrepancies between bone regions derived from sCT to bone contours generated by the DLAS. The average specificity of the DLAS model was 0.78, 0.93 and 0.98 for distance parameters of 0.8, 1.0 and 1.2 cm, respectively. The screener identified false high-density hallucination regions in the abdomen of AI-generated sCT images for all testing patients, highlighting potential issues with the training data used for the AI sCT model.Significance. A hallucination screener for AI-generated pelvic sCT images was developed and implemented for routine clinical use. The screener serves as an important quality assurance tool for MR-only radiotherapy workflows. By identifying potential AI-generated errors, the hallucination screener may improve the safety and accuracy of sCT images used for dose calculation and image guidance.

Objective.Radiotherapy delivered at an ultra-high dose rate (UHDR) is a promising cancer treatment. In the last years, it has been shown to selectively reduce toxicity in healthy tissue by triggering the so-called FLASH effect achieved through specific temporal dose fractionation. However, the increase of the instantaneous dose rate results in the production of stronger thermoacoustic emissions for microsecond or shorter pulsed ionizing beams, which could potentially impact the treatment outcomes. Focusing on scenarios expected to create the highest acoustic intensities, the objectives of this work were to assess whether acoustic resonance can theoretically occurin vivoand how it could be mitigated in cases where it would influence the biological response.Approach.Thermoacoustic emissions were retrospectively simulated from post-treatment x-ray computed tomography scans of cats irradiated with a single high dose of electron FLASH to treat squamous carcinoma of the nasal planum. The peak dose, pressure intensity and location of the acoustic resonance were assessed for different beam positioning and reproduced for three animals.Main results.Irradiation of nasal planum in cats using a frontal electron beam results in pressure hot spots due to acoustic resonance that are observed in the vicinity of the rostral maxillary bone. The pressure distribution is mostly influenced by the anatomy (i.e. geometry and heterogeneous composition of the irradiated object), whereas its intensity largely depends on the irradiation setup. While further experimental investigation is needed to understand and mitigate potential associated risks, our results underline that acoustic phenomena so far neglected in conventional radiotherapy may need to be accounted for when using UHDR delivery.Significance.We show that specific irradiation scenarios can induce geometry-dependent thermoacoustic resonancesin vivowhich may be of sufficient magnitude to induce biological effects and impact the outcomes of FLASH radiotherapy.

Objective.Accurate dose predictions are crucial to maximizing the benefits of carbon-ion therapy (CIT). Carbon beams incident on the human body cause nuclear interactions with tissues, resulting in changes in the constituent nuclides and leading to dose errors that are conventionally corrected using conventional single-energy computed tomography (SECT). Dual-energy computed tomography (DECT) has frequently been used for stopping power estimation in particle therapy and is well suited for correcting nuclear reactions because of its detailed body-tissue elemental information. This study proposes a correction method for the absolute dose in CIT that considers changes in nuclide composition resulting from nuclear reactions with body tissues, as a novel application of DECT.Approach.The change in dose associated with nuclear reactions is determined by correcting each integrated depth dose component of the carbon beam using a nuclear interaction correction factor. This factor is determined based on the stopping power, mass density, and nuclear interaction cross-section in body tissue. The stopping power and mass density were calculated using established methods, whereas the nuclear interaction cross-section was newly defined through a conversion equation derived from the effective atomic number.Main results.Nuclear interaction correction factors and corrected doses were determined for 85 body tissues with known compositions, comparing them with existing SECT-based methods. The root-mean-square errors of the SECT- and DECT-based nuclear interaction correction factors relative to theoretical values were 0.66% and 0.39%, respectively.Significance.This indicates a notable enhancement in the estimation accuracy with DECT. The dose calculations in uniform body tissues derived from SECT showed slight over-correction in adipose and bone tissues, whereas those based on DECT were almost consistent with theoretical values. Our proposed method demonstrates the potential of DECT for enhancing dose calculation accuracy in CIT, complementing its established role in stopping power estimation.