Medical physics最新文献

Background: Developing time-of-flight positron emission tomography/magnetic resonance imaging (TOF-PET/MRI) detectors that exploit prompt Cherenkov photons from bismuth germanate (BGO) crystals for estimating 511 keV photon arrival time.

Purpose: To present a low-noise, high-speed electronic readout circuit design for BGO-based TOF-PET detectors that achieves enhanced coincidence time resolution (CTR) in presence of a strong magnetic field.

Methods: The CTR of a BGO-based TOF-PET test detector employing a high-speed, low-noise electronic readout chain was evaluated in a strong magnetic field produced by a permanent magnet placed directly on top of the circuit. For these experiments, which exploit Cherenkov radiation for precise measurement of annihilation photon time arrival time difference, a point source of ²²Na was positioned between a pair of 3 × 3 × 15 mm³ polished BGO crystals wrapped in Teflon tape and optically coupled to 3 × 3 mm² ultra-violet (UV)-sensitive silicon photomultipliers (SiPMs).

Results: By incorporating both Cherenkov (prompt) and standard (slow) luminescence components, 283 ± 8 ps and 275 ± 10 ps full-width-half-maximum (FWHM) CTR were achieved without and with the permanent magnet present, respectfully. These values improved to 236 ± 4 ps and 216 ± 17 ps FWHM when only the Cherenkov components of the timing signal (events with the fastest rise time) were considered.

Conclusions: Results indicate we have designed a high-performance readout circuit that achieves significantly the same CTR in BGO with or without a strong magnetic field present. This further demonstrates that UV SiPMs can effectively operate in a strong magnetic field while remaining highly advantageous for detecting Cherenkov radiation, thus highlighting their potential to be used in BGO-based TOF-PET/MRI scanners.

Background: K-edge subtraction (KES) imaging is a dual-energy imaging technique that enhances contrast by subtracting images taken with x-rays that are above and below the K-edge energy of a specified contrast agent. The resulting reconstruction spatially identifies where the contrast agent accumulates, even when obscured by complex and heterogeneous distributions of human tissue. This method is most successful when x-ray sources are quasimonoenergetic and tunable, conditions that have traditionally only been met at synchrotrons. Laser-Compton x-ray sources (LCSs) are a compact alternative to synchrotron radiation with a quasimonoenergetic x-ray spectrum. One limitation in the clinical application of KES imaging with LCSs has been the extensive time required to tune the x-ray spectrum to two different energies.

Purpose: We introduce an imaging technique called scanning K-edge subtraction (SKES) that leverages the angle-correlated laser-Compton x-ray spectrum in the setting of mammography. The feasibility and utility of this technique will be evaluated through a series of simulation studies. The goal of SKES imaging is to enable rapid K-edge subtraction imaging using a laser-Compton x-ray source. The technique does not rely on the time-consuming process of tuning laser-Compton interaction parameters.

Methods: Laser-Compton interaction physics are modeled using conditions based on an X-band linear electron accelerator architecture currently under development using a combination of 3D particle tracking software and Mathematica. The resulting angle-correlated laser-Compton x-ray beam is propagated through digitally compressed breast phantoms containing iodine contrast-enhanced inserts and then to a digital flat-panel detector using a Matlab Monte Carlo propagation software. This scanning acquisition technique is compared to the direct energy tuning method (DET), as well as to a clinically available dual-energy contrast-enhanced mammography (CEM) system.

Results: KES imaging in a scanning configuration using an LCS was able to generate a KES image of comparable quality to the direct energy tuning method. SKES was able to detect tumors with iodine contrast concentrations lower than what is clinically available today including lesions that are typically obscured by dense fibroglandular tissue. After normalizing to mean glandular dose, SKES is able to generate a KES image with equal contrast to CEM using only 3% of the dose.

Conclusions: By leveraging the unique quasimonochromatic and angle-correlated x-ray spectrum offered by LCSs, a contrast-enhanced subtraction image can be obtained with significantly more contrast and less dose compared to conventional systems, and improve tumor detection in patients with dense breast tissue. The scanning configuration of this technique could accelerate the clinical translation of this technology.

Background: High-resolution brain imaging is crucial in clinical diagnosis and neuroscience, with ultra-high field strength MRI systems ( $\ge 7T$ ) offering significant advantages for imaging neuronal microstructures. However, achieving magnetic field homogeneity is challenging due to engineering faults during the installation of superconducting strip windings and the primary magnet.

Purpose: This study aims to design and optimize active superconducting shim coils for a 7 T animal MRI system, focusing on the impact of safety margin, size, and adjustability of the second-order shim coils on the MRI system's optimization.

Methods: The study employs a nonlinear optimization method to determine the parameters of the shim coils, considering the size of the coil, the level of undesired harmonics, and the whole number approximation of the turns in each coil. The study also conducts a thorough robustness analysis, examining the effects of coil winding accuracy, former processing accuracy, and assembly angle accuracy on the harmonic intensity of each coil.

Results: The optimization design results for the 7 T MRI system's shim coils show that the magnetic field changes are less than 0.5 %. After second-order shimming and the harmonic coupling an, the low-order harmonics are minimized, resulting in an improved magnetic field peak-to-peak uniformity from 254.47 to 8.970 ppm.

Conclusion: The study successfully demonstrates the creation of a set of second-order shim coils for a 7 T animal MRI system through numerical optimization. The design outputs provide essential technological support for the development of a human whole-body 7 T MRI system, ensuring high-quality imaging at the neuronal level. The project also highlights the importance of considering manufacturing and assembly flaws in the shim coil design process to achieve effective shimming in practical engineering scenarios.

Background: The success of embolization, a minimally invasive treatment of liver cancer, could be evaluated in the operational room with cone-beam CT by acquiring a dynamic perfusion scan to inspect the contrast agent flow.

Purpose: The reconstruction algorithm must address the issues of low temporal sampling and higher noise levels inherent in cone-beam CT systems, compared to conventional CT.

Methods: Therefore, a model-based perfusion reconstruction based on the time separation technique (TST) was applied. TST uses basis functions to model time attenuation curves. These functions are either analytical or based on prior knowledge (PK), extracted using singular value decomposition of the classical CT perfusion data of animal subjects. To explore how well the PK can model perfusion dynamics and what the potential limitations are, the dynamic cone-beam CT (CBCT) perfusion scan was simulated from a dynamic CT perfusion scan under different noise levels. The TST method was compared to static reconstruction.

Results: It was demonstrated on this simulated dynamic CBCT perfusion scan that a set consisting of only four basis functions results in perfusion maps that preserve relevant information, denoise the data, and outperform static reconstruction under higher noise levels. TST with PK would not only outperform static reconstruction but also the TST with analytical basis functions. Furthermore, it has been shown that only eight CBCT rotations, unlike previously assumed ten, are sufficient to obtain the perfusion maps comparable to the reference CT perfusion maps. This contributes to saving dose and reconstruction time. The real dynamic CBCT perfusion scan, reconstructed under the same conditions as the simulated scan, shows potential for maintaining the accuracy of the perfusion maps. By visual inspection, the embolized region was matching to that in corresponding CT perfusion maps.

Conclusions: CBCT reconstruction of perfusion scan data using the TST method has shown promising potential, outperforming static reconstructions and potentially saving dose by reducing the necessary number of acquisition sweeps. Further analysis of a larger cohort of patient data is needed to draw final conclusions regarding the expected advantages of the TST.

Background: Diffusing alpha-emitters Radiation Therapy ("Alpha DaRT") is a promising new radiation therapy modality for treating bulky tumors. ²²⁴Ra-carrying sources are inserted intratumorally, producing a therapeutic alpha-dose region with a total size of a few millimeter via the diffusive motion of ²²⁴Ra's alpha-emitting daughters. Clinical studies of Alpha DaRT have reported 100% positive response (30%-100% shrinkage within several weeks), with post-insertion swelling in close to half of the cases. While dosimetry recommendations informed by the effects of edema are standard in some radiation therapy modalities, the effect of edema and tumor shrinkage on the absorbed dose delivered by Alpha DaRT is still unknown.

Purpose: The aim of this work is to develop a simple model for Alpha-DaRT dose deposition in a time-dependent tissue volume in order to study the effect of geometrical changes in source location due to edema and tumor shrinkage on the delivered alpha particle dose.

Methods: We perform FEM-based dose deposition modeling for a single Alpha-DaRT source inside shrinking and swelling tissues. Gradual volume change models were used for shrinkage and swelling, and an additional immediate volume gain model was also used for "worst case" swelling. Volume change rates were estimated from source location data from serial scans acquired at time of insertion and removal for seven patients treated using Alpha DaRT. We calculate absorbed dose profiles under both the high- and low-diffusion regimes described by the Diffusion-Leakage model.

Results: Changes in tissue volume can lead to over- or underestimation of the calculated absorbed dose. In the low-diffusion regime, gradual tissue shrinkage can result in an increase of 100% and gradual swelling can result in a 35% decrease in absorbed dose compared to a calculation in static tissue. Although immediate post-insertion swelling can reduce the absorbed dose by close to 65% for very closely spaced sources, in all cases analyzed the final absorbed dose continues to exceed the 10 Gy target. These effects are less severe in the high-diffusion regime.

Conclusions: These results indicate that tissue swelling and shrinkage can have a non-negligible effect on the tumor absorbed dose. Further study of tissue dynamics during Alpha-DaRT treatment will be necessary for improvements in dosimetry practice.

Background: X-ray grating-based dark-field imaging can sense the small angle scattering caused by object's micro-structures. This technique is sensitive to the porous microstructure of lung alveoli and has the potential to detect lung diseases at an early stage. Up to now, a human-scale dark-field CT (DF-CT) prototype has been built for lung imaging.

Purpose: This study aimed to develop a thorough optimization method for human-scale dark-field lung CT and guide the system design.

Methods: We introduced a task-based metric formulated as the contrast-to-noise ratio (CNR) between normal and lesioned alveoli for system parameter optimization and designed a digital human-thorax phantom to fit the task of lung disease detection. Furthermore, a computational framework was developed to model the signal propagation in DF-CT and established the link between system parameters and the CNR metric.

Results: We showed that for a DF-CT system, its CNR first increases and then decreases with the system auto-correlation length (ACL). The optimal ACL is mostly independent of system's visibility, and is only related to the phantom's properties, that is, its size and absorption. For our phantom, the optimal ACL is about 0.35 µm at the design energy of 60 keV. As for system geometry, increasing source-detector and isocenter-detector distance can extend the system's maximal ACL, making it easier for the system to meet the optimal ACL and relaxing the grating pitches. We proposed a set of parameters for a projective fringe system that can satisfy the simulated optimal ACL.

Conclusion: This study introduced a task-based metric and a process for DF-CT optimization. We demonstrated that for a given phantom, the detection performance of the system is optimized at a specific ACL. The optimization method and design principles are independent from the underlying dark-field imaging method and can be applied to DF-CT system design using different grating-based implementations such as Talbot-Lau interferometer (TLI) or projective fringe method.

Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.

Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.

Methods: This study was based on a publicly available C4KC-KiTS-2019 dataset from the TCIA database, including preoperative computed tomography (CT) images and survival time data of 210 patients. Initially, the radiomics features of the kidney tumor area were extracted using the 3D slicer software. Feature selection was then conducted using ICC, mRMR algorithms, and LASSO regression to calculate radiomics scores. Subsequently, the selected features were input into a pre-trained Transformer model for feature transformation to obtain a higher-dimensional feature set. Then, K-means clustering was performed using this feature set, and the model was evaluated using receiver operating characteristic (ROC) and Kaplan-Meier curves. Finally, the SHAP interpretability algorithm was used for the feature importance analysis of the K-means clustering results.

Results: Eleven important features were selected from 851 radiomics features. The K-means clustering model after Transformer feature transformation showed AUCs of 0.889, 0.841, and 0.926 for predicting 1-, 3-, and 5-year overall survival rates, respectively, thereby outperforming both the K-means model with original feature inputs and the radiomics score method. A clustering analysis revealed survival prognosis differences among different patient groups, and a SHAP analysis provided insights into the features that had the most significant impacts on the model predictions.

Conclusions: The K-means clustering algorithm enhanced by the Transformer feature transformation proposed in this study demonstrates promising accuracy and interpretability in predicting the overall survival rate after kidney tumor resection. This method provides a valuable tool for clinical decision-making and contributes to improved management and treatment strategies for patients with kidney tumors.