Physics in medicine and biology最新文献

Objective. Low-dose computed tomography (LDCT) is an imaging technique that can effectively help patients reduce radiation dose, which has attracted increasing interest from researchers in the field of medical imaging. Nevertheless, LDCT imaging is often affected by a large amount of noise, making it difficult to clearly display subtle abnormalities or lesions. Therefore, this paper proposes a multiple complementary priors CT image reconstruction method by simultaneously considering both the internal prior and external image information of CT images, thereby enhancing the reconstruction quality of CT images.Approach. Specifically, we propose a CT image reconstruction method based on weighted nonconvex low-rank regularized group sparse and deep image priors under hybrid plug-and-play framework by utilizing the weighted nonconvex low rankness and group sparsity of dictionary domain coefficients of each group of similar patches, and a convolutional neural network denoiser. To make the proposed reconstruction problem easier to tackle, we utilize the alternate direction method of multipliers for optimization.Main results. To verify the performance of the proposed method, we conduct detailed simulation experiments on the images of the abdominal, pelvic, and thoracic at projection views of 45, 65, and 85, and at noise levels of1×105and1×106, respectively. A large number of qualitative and quantitative experimental results indicate that the proposed method has achieved better results in texture preservation and noise suppression compared to several existing iterative reconstruction methods.Significance. The proposed method fully considers the internal nonlocal low rankness and sparsity, as well as the external local information of CT images, providing a more effective solution for CT image reconstruction. Consequently, this method enables doctors to diagnose and treat diseases more accurately by reconstructing high-quality CT images.

Objective: To present a long short-term memory (LSTM)-based prompt gamma (PG) emission prediction method for proton therapy.Approach: Computed tomography (CT) scans of 33 patients with a prostate tumor were included in the dataset. A set of 10⁷histories proton pencil beam (PB)s was generated for Monte Carlo (MC) dose and PG simulation. For training (20 patients) and validation (3 patients), over 6000 PBs at 150, 175 and 200 MeV were simulated. 3D relative stopping power (RSP), PG and dose cuboids that included the PB were extracted. Three models were trained, validated and tested based on an LSTM-based network: (1) input RSP and output PG, (2) input RSP with dose and output PG (single-energy), and (3) input RSP/dose and output PG (multi-energy). 540 PBs at each of the four energy levels (150, 175, 200, and 125-210 MeV) were simulated across 10 patients to test the three models. The gamma passing rate (2%/2 mm) and PG range shift were evaluated and compared among the three models.Results: The model with input RSP/dose and output PG (multi-energy) showed the best performance in terms of gamma passing rate and range shift metrics. Its mean gamma passing rate of testing PBs of 125-210 MeV was 98.5% and the worst case was 92.8%. Its mean absolute range shift between predicted and MC PGs was 0.15 mm, where the maximum shift was 1.1 mm. The prediction time of our models was within 130 ms per PB.Significance: We developed a sub-second LSTM-based PG emission prediction method. Its accuracy in prostate patients has been confirmed across an extensive range of proton energies.

Objective: Plastic scintillation dosimeters (PSDs) are highly suitable for real-time dosimetry on the MR-linac. For optimal performance, the primary signal (scintillation) needs to be separated from secondary optical effects (Cerenkov, fluorescence and optical fiber attenuation). This requires a spectral separation approach and careful calibration. Currently, the 'classic' calibration is a multi-step procedure using both kV and MV X-ray sources, requiring an uninterrupted optical connection between the dosimeter and read-out system, complicating efficient use of PSDs. Therefore, we present a more time-efficient and more practical novel calibration technique for PSDs suitable for MR-linac dosimetry. Approach: The novel calibration relies on prior spectral information combined with two 10x10 cm²field irradiations on the 1.5 T MR-linac. Performance of the novel calibration technique was evaluated focusing on its reproducibility, performance characteristics (repeatability, linearity, dose rate dependency, output factors, angular response and detector angle dependency) and IMRT deliveries. To investigate the calibration stability over time, prior spectral information up to 315 days old was used. To quantify the time efficiency, each step of the novel and classic calibration was timed. Main results: The novel calibration showed a high reproducibility with a maximum relative standard deviation of 0.3%. The novel method showed maximum differences of 1.2% compared to the gold-standard calibration, while reusing old classic calibrations after reconnecting fibers showed differences up to 3.0%. The novel calibration improved time efficiency from 105 to 30 minutes compared to the classic method. Significance: The novel calibration method showed a gain in time efficiency and practicality while preserving the dosimetric accuracy. Therefore, this method can replace the traditional method for PSDs suitable for MR-linac dosimetry, using prior spectral information of up to a year. This novel calibration facilitates reconnecting the detector to the read-out system which would lead to unacceptable dosimetric results with the classic calibration method.

The latest developments combining deep learning technology and medical image data have attracted wide attention and provide efficient noninvasive methods for the early diagnosis of breast cancer. The success of this task often depends on a large amount of data annotated by medical experts, which is time-consuming and may not always be feasible in the biomedical field. The lack of interpretability has greatly hindered the application of deep learning in the medical field. Currently, deep stable learning, including causal inference, make deep learning models more predictive and interpretable. In this study, to distinguish malignant tumors in Breast Imaging-Reporting and Data System (BI-RADS) category 3-4A breast lesions, we propose BD-StableNet, a deep stable learning model for the automatic detection of lesion areas. In this retrospective study, we collected 3103 breast ultrasound images (1418 benign and 1685 malignant lesions) from 493 patients (361 benign and 132 malignant lesion patients) for model training and testing. Compared with other mainstream deep learning models, BD-StableNet has better prediction performance (accuracy = 0.952, area under the curve (AUC) = 0.982, precision = 0.970, recall = 0.941, F1-score = 0.955 and specificity = 0.965). The lesion area prediction and class activation map (CAM) results both verify that our proposed model is highly interpretable. The results indicate that BD-StableNet significantly enhances diagnostic accuracy and interpretability, offering a promising noninvasive approach for the diagnosis of BI-RADS category 3-4A breast lesions. Clinically, the use of BD-StableNet could reduce unnecessary biopsies, improve diagnostic efficiency, and ultimately enhance patient outcomes by providing more precise and reliable assessments of breast lesions.

Objective: Positronium lifetime tomography (PLT) is an emerging modality that aims to reconstruct 3D images of positronium lifetime in humans and animals in vivo. The lifetime of ortho-positronium can be influenced by the microstructure and the concentration of bio-active molecules in tissue, providing valuable information for better understanding disease progression and treatment response. However, efficient high-resolution lifetime image reconstruction methods are currently lacking. Existing methods are either computationally intensive or have poor spatial resolution. This paper presents a fast, high-resolution lifetime image reconstruction method for positronium lifetime tomography. Approach: The proposed method, called SIMPLE-Moment (Statistical IMage reconstruction of Positron annihilation LifetimE by Moment weighting), first reconstructs a set of moment images and then estimates the ortho-positronium lifetime image using the method of moments. The implementation of SIMPLE-Moment requires minimal modification to the conventional ordered subset expectation maximization (OSEM) algorithm. Main results: With reasonable assumptions, the proposed method can reconstruct an ortho-positronium lifetime image with a computational cost equivalent to three standard PET image reconstructions. A Monte Carlo simulation study based on an existing time-of-flight (TOF) PET scanner demonstrates that the ortho-positronium lifetime image reconstructed by SIMPLE-Moment is accurate and comparable to results obtained using the more computationally intensive SPLIT method. Significance: The proposed SIMPLE-Moment method provides an efficient approach to high-resolution reconstruction of ortho-positronium lifetime images. By reducing computational costs while enhancing spatial resolution, this method has the potential to make positronium lifetime tomography more accessible and practical for clinical and research applications. .

Objective: The purpose of this study is to analytically derive and validate a novel radiation energy conservation principle for dose mapping via DIR. Approach: A radiation energy conservation principle for the DIR-based dose-deforming process was theoretically derived with a consideration of the volumetric Jacobian and proven using synthetic examples and a patient case. Furthermore, an energy difference error was proposed that can be used to evaluate the DIR-based dose accumulation uncertainty. For the analytical validation of the proposed energy conservation principle, a synthetic isotropic deformation was considered, and artificial deformation uncertainties were introduced. For the validation with a patient case, a ground truth set of CT images and the corresponding deformation was generated. Radiation energy calculation was performed using both the ground truth deformation and another deformation with uncertainty. Main results: The suggested energy conservation principle was preserved with uncertainty-free deformation, but not with error-containing deformations using both the synthetic examples and the patient case. For a synthetic example with a tumor volume reduction of 27.1% (10% reduction in length in all directions), the energy difference error was calculated to be -29.8% and 37.2% for an over-deforming and under-deforming DIR uncertainty of 0.3 cm. The energy difference error for the patient case (tumor volume reduction of 37.6%) was 2.9% for a displacement vector field with a registration error of 2.0 ± 3.2 mm. Significance: A novel energy conservation principle for DIR-based dose deformation and the corresponding energy difference error were mathematically formulated and successfully validated using simple synthetic examples and a patient example. With a consideration of the volumetric Jacobian, this investigation proposed a radiation energy conservation principle which can be met only with uncertainty-free deformations.

Objective: Channelized Hotelling model observers are efficient at simulating the human observer visual performance in medical imaging detection tasks. However, channelized Hotelling observers (CHO) are subject to statistical biases from zero-signal and finite-sample effects. The point estimate of the d' value is also not always symmetric with exact confidence interval (CI) bounds determined for the infinitely trained CHO. A method for correcting these statistical biases and CI asymmetry is studied.

Approach: CHO d' values and CI bounds with hold-out and resubstitution methods were computed for a range of 200x200 pixels images from 20 to 10 000 images for 10, 40 and 96 channels from a set of 20 000 images with gaussian coloured simulated noise and simulated signal. The median of the non-central F cumulative distribution (F'), which is the CHO underlying statistical behaviour for the resubstitution method, was computed, and compared to d' values and CI bounds. A set of experimental data was used to evaluate F' median values.

Main results: The F' median allows to get accurate corrected simulated d' values down to zero-signals. For small d' values, the variation of d' values with the inverse of number of images is not linear while the F' median allows a good correction in such conditions. The F' median is also inherently symmetric with regards to the confidence interval. With experimental data, F' median values in a range of about 1 to 10 d' values were within -0.8% to 4.7% of linearly extrapolated values at an infinite number of images.

Significance: The F' median correction is an effective simultaneous correction of the zero-signal statistical bias and finite-sample statistical bias, and of confidence interval asymmetry of channelized Hotelling observers. .

Objective: In endodontic therapy, 3D Cone Beam Computerized Tomography (CBCT) and oral scan fusion models allow exact root canal channels and guidance. However, the point cloud model from CBCT has few data points and poor model features, limiting 3D fusion with oral scan data. Our aim to build a sub-regional point cloud resampling method and evaluate the precision of merging it with three-dimensional oral scan data. Approach: Two molars and four incisors were resampled for this investigation. Based on point cloud density and curvature, the rebuilt model was separated into the crown and cervical cavities. Using crown surface morphology, Divergence Index (DI) was employed to determine resampling points based on point dispersion. Improved Euclidean Clustering Rule (IECR) downsamples each point using its weight and joins the two halves using Iterative Nearest Neighbour (ICP) to create a complete resampled point cloud. After aligning with the oral scanning model, the maximum error, maximum distance, average distance, and other characteristics are calculated to assess resampling. Additionally, a cross-entropy kernel-based point cloud reconstruction depth selection method is given to determine the appropriate reconstruction depth. Main results: Applying the DI-IECR technique reduces the average distance between the resampled tooth point cloud and the point cloud generated by the dental scanner by around 20%. The maximum error remains same to that of the widely used method. This study also demonstrates that the use of the DI-IECR approach guarantees the complete representation of the coronal characteristics of the resampled reconstructed 3D model, rather than excessively focusing processing resources on pertinent but insignificant areas. Significance: Point cloud data and crown features are balanced using DI-IECR. When registered with the oral scan model, CBCT-generated point clouds are more accurate and timely, making them a better intraoperative navigation model.

Objective: To develop a nonlinear, model-based parameter estimation method directly from incomplete measurements in k-w space for robust spectral analysis in highly accelerated chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI).

Approach: A CEST-specific, separable nonlinear model, which describes spectral decomposition using multi-pool Lorentzian functions (conventional magnetization transfer (MT), direct saturation of water signals (DS), amide, amine, and nuclear Overhauser effect (NOE)) derived from the steady-state Bloch McConnel equation, is incorporated into a measurement model in CEST MRI. Furthermore, signal drop in saturation transfer experiments is formulated by an additional, separable nonlinear spectral prior indicating that the symmetric z-spectra synthesized using conventional MT and DS always remain higher or equal to the whole z-spectra with all pools. Given the above considerations, linear and nonlinear parameters in the proposed method are estimated in an alternating fashion directly from highly incomplete measurements in k-w space by solving a constrained optimization problem with the physics-constrained spectral priors while imposing additional sparsity priors on spatial parameter maps. Main results. Compared with conventional methods, the proposed method yields clearer delineation of tumor-specific CEST maps without apparent artifact and noise.

Significant: We successfully demonstrated the feasibility of the proposed method for CEST MRI with highly incomplete measurements thus enabling high-resolution whole brain CEST MRI in clinically reasonable imaging time.

Objective.To evaluate the feasibility of transspine focused ultrasound using simulation-based phase corrections from a CT-derived ray acoustics model.Approach.Bilateral transspine focusing was performed inex vivohuman vertebrae with a spine-specific ultrasound array. Ray acoustics-derived phase correction was compared to geometric focusing and a hydrophone-corrected gold standard. Planar hydrophone scans were recorded in the spinal canal and three metrics were calculated: target pressure, coronal and sagittal focal shift, and coronal and sagittal Sørensen-Dice similarity to the free-field.Post hocanalysis was performedin silicoto assess the impact of windows between vertebrae on focal shift.Main results.Hydrophone correction reduced mean sagittal plane shift from 1.74 ± 0.82 mm to 1.40 ± 0.82 mm and mean coronal plane shift from 1.07 ± 0.63 mm to 0.54 ± 0.49 mm. Ray acoustics correction reduced mean sagittal plane and coronal plane shift to 1.63 ± 0.83 mm and 0.83 ± 0.60 mm, respectively. Hydrophone correction increased mean sagittal similarity from 0.48 ± 0.22 to 0.68 ± 0.19 and mean coronal similarity from 0.48 ± 0.23 to 0.70 ± 0.19. Ray acoustics correction increased mean sagittal and coronal similarity to 0.53 ± 0.25 and 0.55 ± 0.26, respectively. Target pressure was relatively unchanged across beamforming methods.In silicoanalysis found that, for some targets, unoccluded paths may have increased focal shift.Significance. Gold standard phase correction significantly reduced coronal shift and significantly increased sagittal and coronal Sørensen-Dice similarity (p< 0.05). Ray acoustics-derived phase correction reduced sagittal and coronal shift and increased sagittal and coronal similarity but did not achieve statistical significance. Across beamforming methods, mean focal shift was comparable to MRI resolution, suggesting that transspine focusing is possible with minimal correction in favourable targets. Future work will explore the mitigation of acoustic windows with anti-focus control points.