4Din vivodosimetry for a FLASH electron beam using radiation-induced acoustic imaging.

Objective. The primary goal of this research is to demonstrate the feasibility of radiation-induced acoustic imaging (RAI) as a volumetric dosimetry tool for ultra-high dose rate FLASH electron radiotherapy (FLASH-RT) in real time. This technology aims to improve patient outcomes by accurate measurements ofin vivodose delivery to target tumor volumes.Approach. The study utilized the FLASH-capable eRT6 LINAC to deliver electron beams under various doses (1.2 Gy pulse-1to 4.95 Gy pulse-1) and instantaneous dose rates (1.55 × 105Gy s-1to 2.75 × 106Gy s-1), for imaging the beam in water and in a rabbit cadaver with RAI. A custom 256-element matrix ultrasound array was employed for real-time, volumetric (4D) imaging of individual pulses. This allowed for the exploration of dose linearity by varying the dose per pulse and analyzing the results through signal processing and image reconstruction in RAI.Main Results. By varying the dose per pulse through changes in source-to-surface distance, a direct correlation was established between the peak-to-peak amplitudes of pressure waves captured by the RAI system and the radiochromic film dose measurements. This correlation demonstrated dose rate linearity, including in the FLASH regime, without any saturation even at an instantaneous dose rate up to 2.75 × 106Gy s-1. Further, the use of the 2D matrix array enabled 4D tracking of FLASH electron beam dose distributions on animal tissue for the first time.Significance. This research successfully shows that 4Din vivodosimetry is feasible during FLASH-RT using a RAI system. It allows for precise spatial (∼mm) and temporal (25 frames s-1) monitoring of individual FLASH beamlets during delivery. This advancement is crucial for the clinical translation of FLASH-RT as enhancing the accuracy of dose delivery to the target volume the safety and efficacy of radiotherapeutic procedures will be improved.

Model-Based X-Ray-Induced Acoustic Computed Tomography.

X-ray-induced acoustic computed tomography (XACT) provides X-ray absorption-based contrast with acoustic detection. For its clinical translation, XACT imaging often has a limited field of view. This can result in image artifacts and overall loss of quantification accuracy. In this article, we aim to demonstrate model-based XACT image reconstruction to address these problems. An efficient matrix-free implementation of the regularized LSQR (MF-LSQR)-based minimization scheme and a noniterative model back-projection (MBP) scheme for computing XACT reconstructions have been demonstrated in this article. The proposed algorithms have been numerically validated and then used to perform reconstructions from experimental measurements obtained from an XACT setup. While the commonly used back-projection (BP) algorithm produces limited-view and noisy artifacts in the region of interest (ROI), model-based LSQR minimization overcomes these issues. The model-based algorithms also reduce the ring artifacts caused due to the nonuniformity response of the multichannel data acquisition. Using the model-based reconstruction algorithms, we are able to obtain reasonable XACT reconstructions for acoustic measurements of up to 120° view. Although the MBP is more efficient than the model-based LSQR algorithm, it provides only the structural information of the ROI. Overall, it has been demonstrated that the model-based image reconstruction yields better image quality for XACT than the standard BP. Moreover, the combination of model-based image reconstruction with different regularization methods can solve the limited-view problem for XACT imaging (in many realistic cases where the full-view dataset is unavailable), and hence pave the way for future clinical translation.

Exploration of Cell Development Pathways through High-Dimensional Single Cell Analysis in Trajectory Space.

High-dimensional single cell profiling coupled with computational modeling is emerging as a powerful tool to elucidate developmental programs directing cell lineages. We introduce tSpace, an algorithm based on the concept of "trajectory space", in which cells are defined by their distance along nearest neighbor pathways to every other cell in a population. Graphical mapping of cells in trajectory space allows unsupervised reconstruction and exploration of complex developmental sequences. Applied to flow and mass cytometry data, the method faithfully reconstructs thymic T cell development and reveals development and trafficking regulation of tonsillar B cells. Applied to the single cell transcriptome of mouse intestine and C. elegans, the method recapitulates development from intestinal stem cells to specialized epithelial phenotypes more faithfully than existing algorithms and orders C. elegans cells concordantly to the associated embryonic time. tSpace profiling of complex populations is well suited for hypothesis generation in developing cell systems.