Session Q18: Innovations in Medical Physics

Lattice Boltzmann method based computations for gold nanorods mediated thermal ablation during plasmonic photothermal therapy

Use of lattice Boltzmann method (LBM) is gaining importance for solving thermal transport within biological tissue during thermal therapy such as plasmonic photothermal therapy (PPTT) due to its advantages like parallel computation, high scalability, ease of solving complex boundaries etc. over conventional numerical techniques. Commonly, during PPTT, near infrared (NIR) irradiation is used for a tumor embedded with nanoparticles having plasmon response in NIR wavelength range. Gold nanorods (GNRs), due to their biocompatibility and having plasmon response in NIR band, are widely considered nanoparticle during PPTT. LBM provides advantages of real time computing and faster computations. Herein, D3Q15 scheme of LBM is reported to solve thermal transport using Pennes' bioheat model and thermal ablation using Arrhenius equation within a 3D tumor-tissue domain during PPTT involving GNRs and on NIR irradiation. The absorption of incident irradiation by GNRs is calculated using Beer-Lambert's law. The temperature obtained by the proposed 3D algorithm of LBM is first validated with the finite element method. Then, the effects of GNRs concentration and irradiation intensity are computed which reveal that higher GNR concentrations leads to higher surface temperature while lower concentrations provide more uniform temperature profile within whole of the tumor. This study suggests that LBM is useful for pre-treatment planning of PPTT for faster computations.   

Ion Type Dependence of DNA Electronic Excitation in Water under Proton, α-particle, and Carbon Ion Irradiation: A First-Principles Simulation Study

Understanding how the electronic excitation response of DNA changes with different irradiating ions is central to advancing ion beam cancer therapy and other related therapies, such as boron neutron capture therapy. Unlike photon-based radiation, high-energy ions show a highly localized energy deposition profile and can more precisely target tumor cells without damaging surrounding healthy cells. While protons have been the predominant ion of choice in ion beam cancer therapy, heavier ions, particularly carbon ions, have drawn significant attention over the past decade. However, the molecular-level details of the electronic excitation under higher-Z ion irradiation remain unknown. Here we use real-time time-dependent density functional theory (RT-TDDFT) to simulate the non-equilibrium energy transfer excitation process in solvated DNA under proton, α-particle, and carbon ion irradiation^[1-2]. Our results show that the energy transfer rate does indeed increase for the heavier ions, albeit differently from the behavior predicted by linear response theory. The simulations also reveal that increased hole formation on DNA, together with the formation of large numbers of highly energetic holes, both contribute significantly to the increase.

[1]       C. Shepard, D. C. Yost, Y. Kanai, Physical Review Letters 2023, 130, 118401.

[2]       C. Shepard, R. Zhou, D. C. Yost, Y. Yao, Y. Kanai, The Journal of Chemical Physics 2021, 155, 100901.   

Bridging the Gap Between Mechanistic and Phenomenological Models of Cell Survival via the Manifold Boundary Approximation Method (MBAM)

We apply the Manifold Boundary Approximation Method (MBAM) to systematically compress the parameter space of Mechanistic DNA Repair and Survival (MEDRAS), a complex mechanistic radiobiological model for cell survival in the presence of radiation. We first apply MBAM to a simplified analytic version of MEDRAS, where the cell survival probability can be determined in closed-form. This explicit representation renders the Fisher Information Matrix (FIM) calculable through automatic differentiation techniques. The complete set of fifteen parameters in the analytic MEDRAS model is observed to display a sloppy hierarchy of parameter sensitivity, such that essentially all characteristics of the dose-response are encapsulated in a reduced three-parameter model, which has a natural interpretation in terms of the phenomenological target theory of radiobiology, but with parameters that can now be directly related to more fundamental mechanistic parameters of DNA damage and misrepair. We conclude by discussing efforts to extend our approach to more general stochastic MEDRAS simulations, where the cell survival probability does not usually have a closed-form representation.   

Single module of a Compton camera detector for online beam range monitoring in proton therapy

The key advantage of proton therapy over conventional radiotherapy is the dose deposition pattern: unlike X-rays, protons are fully stopped in patient's tissues with a distinct maximum at the end of their range: the Bragg peak. This enables precise coverage of a tumor volume while sparing the nearby healthy tissues. However, accurate control of the proton beam range is still considered a challenge. The SiFi-CC (SiPM and scintillating Fiber based Compton Camera) group develops a method of in vivo proton range monitoring with a Compton camera. Such a detector exploits the Compton effect and registers prompt gamma rays emitted in interactions of protons with the tissues' nuclei. Our design is a trade-off between the camera performance and its cost. In our approach, both detector modules (scatterer and absorber) will consist of stacked scintillating fibers with dual readout via silicon photomultipliers. Our simulation studies have shown that such a solution is feasible and appropriate for online range monitoring in proton therapy. The scintillating material and fiber coating were chosen based on an extensive study of the fiber properties. I am going to present the overview of the SiFi-CC project and discuss the performance of a single module of a Compton camera in proton beam conditions.   

X-Ray Attenuation and X-ray Fluorescence Measurements of a Primary Incisor Tooth Slice

Primary teeth are accessible human tissues whose analysis can reveal developmental health issues such as toxic exposures, malnutrition, and dietary changes. X-ray fluorescence (XRF) elemental analysis is nondestructive, fast, and inexpensive. Modern table-top systems can probe microscopic elemental distributions and may become a viable alternative to synchrotron-based studies. A primary incisor tooth was cleaned by sonication in distilled water for 2-3 hours, embedded in resin, and cut in thin slices with a diamond saw blade. A 0.63 mm-thick slice was selected for analysis by the microbeam from an integrated polycapillary x-ray lens and x-ray tube unit. XRF and linear attenuation coefficient (μ) measurements consisted of photon energy spectra acquired by an x-ray detector in backscatter and transmission geometries, respectively. A linear scan of 0.05 mm steps measured μ of dentin, enamel-dentin junction, and enamel regions. Two locations in the dentin and enamel layers were probed by XRF. Average μ values for photon energy range 9.6-15.5 keV, were in the 0.59 to 5.3 mm^-1, and 2.0 to 9.8 mm^-1 ranges for dentin and enamel, respectively. These results are consistent with reported density differences: 1.8-2.1 g/cm³ for dentin and 2.6-3.0 g/cm³ for enamel, as well as enamel’s higher Ca and P content. The fundamental parameter method was applied to convert XRF measurements into concentrations. In dentin, P, Ca, Cu, Zn, and Sr concentrations in mg/g units were estimated to be 140±40, 157±3, 1.76±0.05, 0.18±0.02, and 0.026±0.003, respectively. In enamel, the same elemental concentrations in mg/g were 80±30, 168±3, 0.70±0.05, 0.57±0.03, and 0.015±0.002, respectively. Sr/Ca ratios agreed with reported measurements, but Zn/Ca ratios were 10 times larger. This result is consistent with past observations of Zn and Cu XRF peaks from metallic parts of the experimental setup. Future work will further investigate the accuracy of XRF results and subtraction of XRF signals not originating in the sample.   

Fundamental Parameter Method Application to Determinations of Absolute Elemental Concentrations in a Thin Lamb Bone Slice From Microscopic X-ray Fluorescence Measurements at the Canadian Light Source Synchrotron

In vitro x-ray fluorescence (XRF) studies using synchrotron radiation (SR) typically probe microscopic 2D elemental distributions in human tissues by employing XRF raster scanning techniques with intense small x-ray beams. In most SR studies, however, elemental concentrations are not estimated and 2D maps are linked to normal or abnormal histology and physiology. Spatial inhomogeneity of trace and bulk elements in tissues implies an inhomogeneous linear attenuation coefficient (µ). If tissue slice thickness is much smaller than µ^-1 or the mean-free-path (mfp) of characteristic x-rays, 2D XRF maps are accurate representations of elemental distributions. The mfp of Ca K-shell XRF photons in the human cortical bone is about 35 µm. Making ultrathin bone slices is labor intensive, time consuming, and prone to elemental contaminations. Therefore, developing a data analysis method which accounts for x-ray attenuation in SR XRF microscopy studies of dense tissues is a more practical alternative. In our study, a thin (0.2 mm) semicircular slice was cut from a larger lamb leg bone. Inner and outer equal rectangular areas (0.4 mm x 0.6 mm) were examined employing a raster scan by a 6 μm lateral size x-ray beam from the VESPERS beamline at the Canadian Light Source synchrotron. Four 2D maps of P, Ca, Fe, Ni, Cu, Zn, and Sr distributions in the bone slice were obtained for each of the two areas by raster scans using four different incident photon energies: 12, 15, 18.6, and 20.0 keV. Fundamental Parameter (FP) method in which measured XRF peak areas, µ of human cortical bone, and experimental setup geometry parameters served as input data was employed to compute 2D maps of elemental concentrations. Concentrations of bulk and trace elemental agreed with expected and literature values. Approximations and implications to future SR XRF studies were discussed.   

Surgical Planning: a path planning approach with differentiable simulations and overdamped Langevin equation

Surgical planning based on medical images presents specific challenges. These include ambiguities in medical images, navigation in dynamically changing soft environments, penetrating obstacles, and the need to sample multiple paths. Additionally, simulations must account for various sources of uncertainty. These uncertainties may arise from the imaging process itself or from expected variances in a surgeon's movements. Furthermore, since planning is tailored to each patient, simulations should efficiently run on limited computing resources. They should also be adaptive to changes in the patient's condition or unforeseen changes during surgeries.

We introduce a streamlined method for quick path planning. This method utilizes differentiable simulations, eliminating the need to fully simulate all aspects of surgery. The issue is simplified to a random walk in an environment. In this environment, diffusion and forces are trained to pinpoint the optimal path under various constraints. Optimization is achieved by directly sampling paths based on the current fields and constraints, and differentiating the simulation. Non-local constraints can be incorporated to consider the mechanical limitations of surgical tools. Once the optimal path is found, our framework still offers the flexibility to sample multiple routes to the target by adjusting diffusion values. We showcase our method's effectiveness in navigating through the lungs, ear, and heart   

A Novel TCM-based AI Large Model Framework toward Human diseases and Drug-Diseases Associations

Traditional Chinese Medicine (TCM), which originated in ancient China with a history of thousands of years, characterizes and addresses human physiology, pathology, and diseases diagnosis and prevention using TCM theories and Chinese herbal products. Many research works have been devoted to revealing the effectiveness and efficacy of Chinese herbs for new drug discovery in a bottom-up manner. However, the pharmacological principles in TCM theory, the core treasure house of TCM, have rarely been systematically investigated in a top-down manner, which hinders the modernization and standardization of TCM. To bridge the gap, we propose a novel TCM-based artificial intelligence (AI) framework to unravel general patterns and principles of human disease and investigate potential drug-diseases associations. We collect and refine extensive TCM data, as well as biological, chemical, and clinical data, to establish an integrated multi-modal TCM database. Subsequently, we construct a TCM pharmacological network to reveals the underlying structure and patterns within the TCM data. An attention-based AI model is trained to embed multi-modal TCM data into an interpretable pharmacological space, allowing for quantitative and personalized analysis of complex interactions among diseases, symptoms, herbs, compounds, and genes. The pharmacological embedding space with biological significance provides new perspectives toward modern medicine issues from the view of TCM. Our work aims to promote the quantitative underpinning of TCM pharmacological principles, provide a basis for the objectification of the diagnosis and treatment process of TCM, and pave the way for the knowledge fusion of TCM evidence-based medicine and modern biology.