Predicting which patients may benefit from the hybrid intracavitary+interstitial needle (IC/IS) applicator for advanced cervical cancer: A dosimetric comparison and toxicity benefit analysis.

Purpose: The purpose of this study is to compare the predicted rate of local control and bladder and rectum toxicity rates for image-guided adaptive brachytherapy plans using a tandem and ovoid (T/O) applicator versus using a simulated hybrid intracavitary/interstitial tandem and ring applicator with needles (T/R + N) for patients with locally advanced cervical cancer (LACC).

Methods And Materials: Patients with ≥ FIGO Stage IIB locally advanced cervical cancer treated with T/O from a single institution were included. Simulated treatment plans were created with a T/R + N applicator for the best high-risk clinical target volume (CTV) coverage and minimal dose to organs at risk.

Patch-based generative adversarial neural network models for head and neck MR-only planning.

Purpose: To evaluate pix2pix and CycleGAN and to assess the effects of multiple combination strategies on accuracy for patch-based synthetic computed tomography (sCT) generation for magnetic resonance (MR)-only treatment planning in head and neck (HN) cancer patients.

Materials And Methods: Twenty-three deformably registered pairs of CT and mDixon FFE MR datasets from HN cancer patients treated at our institution were retrospectively analyzed to evaluate patch-based sCT accuracy via the pix2pix and CycleGAN models. To test effects of overlapping sCT patches on estimations, we (a) trained the models for three orthogonal views to observe the effects of spatial context, (b) we increased effective set size by using per-epoch data augmentation, and (c) we evaluated the performance of three different approaches for combining overlapping Hounsfield unit (HU) estimations for varied patch overlap parameters.

Deep-learning-assisted automatic digitization of applicators in 3D CT image-based high-dose-rate brachytherapy of gynecological cancer.

Purpose: Applicator digitization is one of the most critical steps in 3D high-dose-rate brachytherapy (HDRBT) treatment planning. Motivated by recent advances in deep-learning, we propose a deep-learning-assisted applicator digitization method for 3D CT image-based HDRBT. This study demonstrates its feasibility and potential in gynecological cancer HDRBT.

Intelligent inverse treatment planning via deep reinforcement learning, a proof-of-principle study in high dose-rate brachytherapy for cervical cancer.

Inverse treatment planning in radiation therapy is formulated as solving optimization problems. The objective function and constraints consist of multiple terms designed for different clinical and practical considerations. Weighting factors of these terms are needed to define the optimization problem.

Automated high-dose rate brachytherapy treatment planning for a single-channel vaginal cylinder applicator.

High dose rate (HDR) brachytherapy treatment planning is conventionally performed manually and/or with aids of preplanned templates. In general, the standard of care would be elevated by conducting an automated process to improve treatment planning efficiency, eliminate human error, and reduce plan quality variations. Thus, our group is developing AutoBrachy, an automated HDR brachytherapy planning suite of modules used to augment a clinical treatment planning system.

Digital in-line holographic microscopy.

We first briefly review the state of the art of digital in-line holographic microscopy (DIHM) with numerical reconstruction and then discuss some technical issues, such as lateral and depth resolution, depth of field, twin image, four-dimensional tracking, and reconstruction algorithm. We then present a host of examples from microfluidics and biology of tracking the motion of spheres, algae, and bacteria. Finally, we introduce an underwater version of DIHM that is suitable for in situ studies in an ocean environment that show the motion of various plankton species.

Influence of pressure on the lengths of chemical bonds.

An expression to describe the force that a chemical bond exerts on its terminal atoms is proposed, and is used to derive expressions for the bond force constant and bond compressibility. The unknown parameter in this model, the effective charge on the atoms that form the bond, is determined by comparing the derived force constants with those obtained spectroscopically. The resultant bond compressibilities are shown to generally agree well with those determined from high-pressure structure determinations and from the bulk moduli of high-symmetry structures.