Improving Dose-Response Correlations for Locally Advanced NSCLC Patients Treated with IMRT or PSPT

The standard of care for locally advanced non-small cell lung cancer (NSCLC) is concurrent chemo-radiotherapy. Despite recent advancements in radiation delivery methods, the median survival time of NSCLC patients remains below 28 months. Higher tumor dose has been found to increase survival but also a higher rate of radiation pneumonitis (RP) that affects breathing capability. In fear of such toxicity, less-aggressive treatment plans are often clinically preferred, leading to metastasis and recurrence. Therefore, accurate RP prediction is crucial to ensure tumor coverage to improve treatment outcome. Current models have associated RP with increased dose but with limited accuracy as they lack spatial correlation between accurate dose representation and quantitative RP representation. These models represent lung tissue damage with radiation dose distribution planned pre-treatment, which assumes a fixed patient geometry and inevitably renders imprecise dose delivery due to intra-fractional breathing motion and inter-fractional anatomy response. Additionally, current models employ whole-lung dose metrics as the contributing factor to RP as a qualitative, binary outcome but these global dose metrics discard microscopic, voxel-(3D pixel)-level information and prevent spatial correlations with quantitative RP representation. To tackle these limitations, we developed advanced deformable image registration (DIR) techniques that registered corresponding anatomical voxels between images for tracking and accumulating dose throughout treatment. DIR also enabled voxel-level dose-response correlation when CT image density change (IDC) was used to quantify RP. We hypothesized that more accurate estimates of biologically effective dose distributions actually delivered, achieved through (a) dose accumulation using deformable registration of weekly 4DCT images acquired over the course or radiotherapy and (b) the incorporation of variable relative biological effectiveness (RBE), would lead to statistically and clinically significant improvement in the correlation of RP with biologically effective dose distributions. Our work resulted in a robust intra-4DCT and inter-4DCT DIR workflow, with the accuracy meeting AAPM TG-132 recommendations for clinical implementation of DIR. The automated DIR workflow allowed us to develop a fully automated 4DCT-based dose accumulation pipeline in RayStation (RaySearch Laboratories, Stockholm, Sweden). With a sample of 67 IMRT patients, our results showed that the accumulated dose was statistically different than the planned dose across the entire cohort with an average MLD increase of ~1 Gy and clinically different for individual patients where 16% resulted in difference in the score of the normal tissue complication probability (NTCP) using an established, clinically used model, which could qualify the patients for treatment planning re-evaluation. Lastly, we associated dose difference with accuracy difference by establishing and comparing voxel-level dose-IDC correlations and concluded that the accumulated dose better described the localized damage, thereby a closer representation of the delivered dose. Using the same dose-response correlation strategy, we plotted the dose-IDC relationships for both photon patients (N = 51) and proton patients (N = 67), we measured the variable proton RBE values to be 3.07–1.27 from 9–52 Gy proton voxels. With the measured RBE values, we fitted an established variable proton RBE model with pseudo-R2 of 0.98. Therefore, our results led to statistically and clinically significant improvement in the correlation of RP with accumulated and biologically effective dose distributions and demonstrated the potential of incorporating the effect of anatomical change and biological damage in RP prediction models

Consistency Regularization for Generalizable Source-free Domain Adaptation

Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset, making it applicable in a variety of real-world scenarios. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. This oversight leads to overfitting issues and constrains the model's generalization ability. In this paper, we propose a consistency regularization framework to develop a more generalizable SFDA method, which simultaneously boosts model performance on both target training and testing datasets. Our method leverages soft pseudo-labels generated from weakly augmented images to supervise strongly augmented images, facilitating the model training process and enhancing the generalization ability of the adapted model. To leverage more potentially useful supervision, we present a sampling-based pseudo-label selection strategy, taking samples with severer domain shift into consideration. Moreover, global-oriented calibration methods are introduced to exploit global class distribution and feature cluster information, further improving the adaptation process. Extensive experiments demonstrate our method achieves state-of-the-art performance on several SFDA benchmarks, and exhibits robustness on unseen testing datasets.Comment: Accepted by ICCV 2023 worksho

Structured Sparsity Learning for Efficient Video Super-Resolution

The high computational costs of video super-resolution (VSR) models hinder their deployment on resource-limited devices, (e.g., smartphones and drones). Existing VSR models contain considerable redundant filters, which drag down the inference efficiency. To prune these unimportant filters, we develop a structured pruning scheme called Structured Sparsity Learning (SSL) according to the properties of VSR. In SSL, we design pruning schemes for several key components in VSR models, including residual blocks, recurrent networks, and upsampling networks. Specifically, we develop a Residual Sparsity Connection (RSC) scheme for residual blocks of recurrent networks to liberate pruning restrictions and preserve the restoration information. For upsampling networks, we design a pixel-shuffle pruning scheme to guarantee the accuracy of feature channel-space conversion. In addition, we observe that pruning error would be amplified as the hidden states propagate along with recurrent networks. To alleviate the issue, we design Temporal Finetuning (TF). Extensive experiments show that SSL can significantly outperform recent methods quantitatively and qualitatively. We will release codes and models

A step towards true delivered dose with dose accumulation in radiotherapy

https://openworks.mdanderson.org/sumexp21/1188/thumbnail.jp

Rationally Designed Sodium Chromium Vanadium Phosphate Cathodes with Multi-Electron Reaction for Fast-Charging Sodium-Ion Batteries

Sodium super-ionic conductor (NASICON)-structured phosphates are emerging as rising stars as cathodes for sodium-ion batteries. However, they usually suffer from a relatively low capacity due to the limited activated redox couples and low intrinsic electronic conductivity. Herein, a reduced graphene oxide supported NASICON Na3Cr0.5V1.5(PO4)3 cathode (VC/C-G) is designed, which displays ultrafast (up to 50 C) and ultrastable (1 000 cycles at 20 C) Na+ storage properties. The VC/C-G can reach a high energy density of ≈470 W h kg−1 at 0.2 C with a specific capacity of 176 mAh g−1 (equivalent to the theoretical value); this corresponds to a three-electron transfer reaction based on fully activated V5+/V4+, V4+/V3+, V3+/V2+ couples. In situ X-ray diffraction (XRD) results disclose a combination of solid-solution reaction and biphasic reaction mechanisms upon cycling. Density functional theory calculations reveal a narrow forbidden-band gap of 1.41 eV and a low Na+ diffusion energy barrier of 0.194 eV. Furthermore, VC/C-G shows excellent fast-charging performance by only taking ≈11 min to reach 80% state of charge. The work provides a widely applicable strategy for realizing multi-electron cathode design for high-performance SIBs

“Mn-locking” effect by anionic coordination manipulation stabilizing Mn-rich phosphate cathodes

High-voltage cathodes with high power and stable cyclability are needed for high-performance sodium-ion batteries. However, the low kinetics and inferior capacity retention from structural instability impede the development of Mn-rich phosphate cathodes. Here, we propose light-weight fluorine (F) doping strategy to decrease the energy gap to 0.22 eV from 1.52 eV and trigger a “Mn-locking” effect—to strengthen the adjacent chemical bonding around Mn as confirmed by density functional theory calculations, which ensure the optimized Mn ligand framework, suppressed Mn dissolution, improved structural stability and enhanced electronic conductivity. The combination of in situ and ex situ techniques determine that the F dopant has no influence on the Na+ storage mechanisms. As a result, an outstanding rate performance up to 40C and an improved cycling stability (1000 cycles at 20C) are achieved. This work presents an effective and widely available light-weight anion doping strategy for high-performance polyanionic cathodes

Brain SPECT Collimator Design

A multi-pinhole (MPH) collimator is designed to pair with an existing fan-beam collimator for single-photon emission computed tomography (SPECT). A mechanical design has been developed for constructing a brain-dedicated MPH collimator that will replace a commercial single pinhole collimator for general imaging. The spatial and weight constraints are satisfied. Material deformation during operation is simulated and used to ensure safety and imaging accuracy. Monte-Carlo simulation of the gamma-ray interaction is performed to simulate brain imaging and validate the model geometry. The student has also determined the operation type of the shutter mechanism for specific apertures that will allow or restrict the passage of photons to adapt the imaging characteristics of the collimator

Stock Market Simulation

In this Interactive Qualifying Project (IQP), the group conducted a 14-week stock market simulation using three different trading strategies: technical, swing, and position trading. The team researched the fundamentals of the stock market and the basics of trading using tools and resources gathered from the Internet. Each member managed a portfolio using one trading strategy with an initial $500,000 to invest. Trading decisions were supported by market analysis techniques and results were exchanged in weekly conventions. The project gave the team members a valuable beginning stock trading experience and helped them to gain a better knowledge and understanding of the stock market. This IQP has built a strong foundation for potential investment in the future

SERS and machine learning based effective feature extraction for detection and identification of amphetamine analogs

Surface-enhanced Raman spectroscopy (SERS) is extensively researched in diverse disciplines due to its sensitivity and non-destructive nature. It is particularly considered a potential and promising technology for rapid on-site screening in drug detection. In this investigation, a technique was developed for fabricating nanocrystals of Ag@Au SNCs. Ag@Au SNCs, as the basic material of SERS, can detect amphetamine at concentrations as low as 1 μg/mL. The Ag@Au SNCs exhibits a strong surface plasmon resonance effect, which amplifies molecular signals. The SERS spectra of ten substances, including amphetamine and its analogs, showed a strong peak signal. To establish a qualitative distinction, we examined the Raman spectra and conducted density functional theory (DFT) calculations on the ten aforementioned species. The DFT calculation enabled us to determine the vibrational frequency and assign normal modes, thereby facilitating the qualitative differentiation of amphetamines and its analogs. Furthermore, the SERS spectrum of the ten mentioned substances was analysed using the support vector machine learning algorithm, which yielded a discrimination accuracy of 98.0 %