Physics in medicine and biology最新文献

Objective.Achieving FLASH dose rate with pencil beam scanning intensity modulated proton therapy is challenging. However, utilizing a single energy layer with a ridge filter (RF) can maintain dose rate and conformality. Yet, changes in patient anatomy over the treatment course can render the RF obsolete. Unfortunately, creating a new RF is time-consuming, thus, incompatible with online adaptation. To address this, we propose to re-optimize the spot weights while keeping the same initial RF.Approach.Data from six head and neck cancer patients with a repeated computed tomography (CT₂) were used. FLASH treatment plans were generated with three methods on CT₂: 'full-adaptation' (FA), optimized from scratch with a new RF; 'spot-adaptation only' (SAO), re-using initial RF but adjusting plan spot weights; and 'no adaptation' (NoA) where the dose from initial plans on initial CT (CT₁) was recomputed on CT₂. The prescribed dose per fraction was 9 Gy. Different beam angles were tested for each CT₂(1 beam per fraction). The FA, SAO and NoA plans were then compared on CT₂.Main results.Fractions with SAO showed a median decrease of 0.05 Gy forD98% and a median increase of 0.03 Gy forD2% of CTV when compared to their homologous FA plans on nominal case. Median conformity number decreased by 0.03. Median max dose to spinal cord increased by 0.09 Gy. The largest median increase in mean dose to organs was 0.03 Gy to the mandible. The largest observed median difference in organs receiving a minimal dose rate of 40 Gy s^-1was 0.5% for the mandible. Up to 16 of the 20 evaluated SAO fractions were thus deemed clinically acceptable, with up to 8 NoA plans already acceptable before adaptation.Significance.Proposed SAO workflow showed that for most of our evaluated plans, daily reprinting of RF was not necessary.

Objective.In breast diagnostic imaging, the morphological variability of breast tumors and the inherent ambiguity of ultrasound images pose significant challenges. Moreover, multi-task computer-aided diagnosis systems in breast imaging may overlook inherent relationships between pixel-wise segmentation and categorical classification tasks.Approach.In this paper, we propose a multi-task learning network with deep inter-task interactions that exploits the inherently relations between two tasks. First, we fuse self-task attention and cross-task attention mechanisms to explore the two types of interaction information, location and semantic, between tasks. In addition, a feature aggregation block is developed based on the channel attention mechanism, which reduces the semantic differences between the decoder and the encoder. To exploit inter-task further, our network uses an circle training strategy to refine heterogeneous feature with the help of segmentation maps obtained from previous training.Main results.The experimental results show that our method achieved excellent performance on the BUSI and BUS-B datasets, with DSCs of 81.95% and 86.41% for segmentation tasks, and F1 scores of 82.13% and 69.01% for classification tasks, respectively.Significance.The proposed multi-task interaction learning not only enhances the performance of all tasks related to breast tumor segmentation and classification but also promotes research in multi-task learning, providing further insights for clinical applications.

Objective.Statistical properties of a CdTe photon-counting detector were simulated using a dedicated Monte Carlo model that includes spatial and spectral correlations. A measurement of the same properties was done to validate the simulation and gain further understanding of the detector.Approach.Photon histories were calculated using a Monte Carlo x-ray simulation program using energy dependent interaction probabilities of the incoming photons. Pulse forms corresponding to photon interaction locations were taken from a pre-calculated pulse shape lookup table and were inserted into simulated pulse trains. These pulse trains were evaluated. Measurements were done on a clinical CT scanner equipped with photon-counting detectors. The examined properties of the detector are detected counts, variances, variance-to-mean-ratios, as well as various spectral-spatial correlations connecting different thresholds in neighboring pixels.Main Results.The simulated data reproduced all trends observed in the statistics of the detector. Spectral correlations between threshold in one pixel showed an excellent agreement between simulation and measurement, both for low and higher fluxes. Spatial correlations between lower thresholds were slightly overestimated in simulations.Significance.The comparison of measured and simulated data shows that the simulation models the statistics of the detector well. This allows further investigation of the detector on a simulated basis and allows using the simulation to further optimize the detector design.

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: Fast computation of daily reoptimization is key for an efficient online adaptive proton therapy workflow. Various approaches aim to expedite this process, often compromising daily dose. This study compares MGH's online dose reoptimization approach, PSI's online replanning workflow and a full reoptimization adaptive workflow for head and neck cancer (H&N) patients. Approach:10 H&N patients (PSI:5, MGH:5) with daily CBCTs were included. Synthetic CTs were created by deforming the planning CT to each CBCT. Targets and OARs were deformed on daily images. Three adaptive approaches were investigated: i) an online dose reoptimization approach modifying the fluence of a subset of beamlets, ii) full reoptimization adaptive workflow modifying the fluence of all beamlets, and iii) a full online replanning approach, allowing the optimizer to modify both fluence and position of all beamlets. Two non-adapted (NA) scenarios were simulated by recalculating the original plan on the daily image using: Monte Carlo for NAMGH and raycasting algorithm for NAPSI. Main results:All adaptive scenarios from both institutions achieved the prescribed daily target dose, with further improvements from online replanning. For all patients, low-dose CTV D98% shows mean daily deviations of -2.2%, -1.1%, and 0.4% for workflows i, ii, and iii, respectively. For the online adaptive scenarios, plan optimization averages 2.2 minutes for iii) and 2.4 for i) while the full dose reoptimization requires 72 minutes. The OAMGH20% dose reoptimization approach produced results comparable to online replanning for most patients and fractions. However, for one patient, differences up to 11% in low-dose CTV D98% occurred. Significance:Despite significant anatomical changes, all three adaptive approaches ensure target coverage without compromising OAR sparing. Our data suggests 20% dose reoptimization suffices, for most cases, yielding comparable results to online replanning with a marginal time increase due to Monte Carlo. For optimal daily adaptation, a rapid online replanning is preferable. .

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.