# Robust IMPT with automated beam orientation and scanning spot optimization

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2020 · $364,201

## Abstract

Proton beams have emerged as an appealing new modality for cancer therapy. With continuing clinical
adoption and technical advances in the past two decades, intensity modulated proton therapy (IMPT) using
scanning pencil beams has been established as the desired delivery method to fully take advantage of proton
physics. Thus far, IMPT optimization has mainly focused on modulating the scanning spots with manually
selected beam angles. At the same time, for intensity modulated X-ray therapy (IMXT), researchers including
the PI's group have demonstrated that superior dosimetry can be attained with integrated beam orientation
optimization (BOO). Nevertheless, the benefit of BOO has not extended to IMPT due to the paramount
computational challenges of solving the integrated BOO and scanning spot optimization (SSO) problem, which
by itself is a higher-dimensional problem than the fluence map optimization problem in IMXT. Currently, IMPT
BOO is considered a combinatorial problem that is not mathematically tractable with increasing problem size.
Despite the computational challenge, compared with IMXT, BOO is more important for IMPT for the following
reasons. First, the optimal number and orientations of beams for IMPT have not been known. While the BOO
problem in X-ray therapy is often circumvented in practice by using single or multiple arc beams, the same
technique applied to IMPT would increase the volumes of normal tissue being irradiated by the entrance dose
and would therefore start losing its low dose sparing advantage. Furthermore, because IMPT beam time is
restrictive, using many beams in a treatment fraction is operationally impractical. Subsequently, IMPT plan
quality is heavily influenced by each of the few selected beams. Yet, manual IMPT beam orientation selection
in the available non-coplanar solution space is unintuitive and ineffective. Second, IMPT plans are highly
degenerate with different combinations of beams, spots and spot sparsity resulting in similar dosimetry but
vastly different robustness to uncertainties. Existing worst-case optimization methods are a suboptimal
compromise between the dosimetry and robustness. It is hypothesized that both the dosimetry and robustness
will be significantly improved by integrating BOO in IMPT optimization. It is then hypothesized that the
integrated optimization problem can be formulated as a group sparsity optimization problem with efficient
solutions. To test these hypotheses, the following aims are proposed. Aim 1. Develop automated beam
orientation and sparse spot optimization for IMPT. Aim 2. Develop fraction-variant IMPT. Aim 3. Incorporate
sensitivity regularization (SenR) for robust beam orientation and scanning spot optimization. Aim 4. Validation
of the integrated BOO, SSO and robustness optimization framework. The first three aims will be mainly
performed at UCLA with the clinical and physics input from UPENN. The last aim will be mainly performed at
UPENN. Depending on the feedback,...

## Key facts

- **NIH application ID:** 9878075
- **Project number:** 5R01CA230278-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Ke Sheng
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $364,201
- **Award type:** 5
- **Project period:** 2019-03-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9878075

## Citation

> US National Institutes of Health, RePORTER application 9878075, Robust IMPT with automated beam orientation and scanning spot optimization (5R01CA230278-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9878075. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*