Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Radiation Pressure: Problem Solving01:09

Radiation Pressure: Problem Solving

450
The radiation pressure applied by an electromagnetic wave on a perfectly absorbing surface equals the energy density of the wave. The wave's momentum also gets transferred to the surface when an electromagnetic wave is entirely absorbed by it. The rate at which momentum is transmitted to an absorbing surface perpendicular to the propagation direction equals the force on the surface.
The average value of the rate of momentum transfer divided by the absorbing area represents the average force...
450
Radiation: Applications01:17

Radiation: Applications

1.2K
The average temperature of Earth is the subject of much current discussion. Earth is in radiative contact with both the Sun and dark space; it receives almost all its energy from the radiation of the Sun and reflects some of it into outer space. Dark space is very cold, about 3 K, so Earth radiates energy into it. For instance, heat transfer occurs from soil and grasses, the rate of which can be so rapid that frost can occur on clear summer evenings, even in warm latitudes.
The average...
1.2K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.7K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.7K
Nuclear Overhauser Enhancement (NOE)01:07

Nuclear Overhauser Enhancement (NOE)

816
Irradiation of a spin-active nucleus causes an increase or decrease in the signal intensity of neighboring nuclei that are not necessarily chemically bonded or involved in J-coupling.  This phenomenon, called the Nuclear Overhauser Enhancement (NOE), results from through-space interactions between the nuclear spins. The NOE effect decreases with increasing internuclear distance and is generally not observed beyond 4 angstroms. In NOE, dipole-dipole interactions between neighboring...
816
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.3K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.3K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

739
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
739

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Investigate gantry position error effect on dynamic spot-scanning proton arc (SPArc) therapy.

Physics in medicine and biology·2025
Same author

A novel energy layer optimization strategy based on machine specific delivery sequence for proton arc therapy.

Physics in medicine and biology·2025
Same author

The first investigation of the dosimetric perturbations from the spot position errors in spot-scanning arc therapy (SPArc).

Physics in medicine and biology·2024
Same author

The first investigation of spot-scanning proton arc (SPArc) delivery time and accuracy with different delivery tolerance window settings.

Physics in medicine and biology·2023
Same author

The first direct method of spot sparsity optimization for proton arc therapy.

Acta oncologica (Stockholm, Sweden)·2023
Same author

Ultra-fast, high spatial resolution single-pulse scintillation imaging of synchrocyclotron pencil beam scanning proton delivery.

Physics in medicine and biology·2023

Related Experiment Video

Updated: Sep 3, 2025

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
08:34

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

20.5K

An evolutionary optimization algorithm for proton arc therapy.

Lewei Zhao1, Gang Liu1,2, Xiaoqiang Li1

  • 1Department of Radiation Oncology, Beaumont Health System, Royal Oak, United States of America.

Physics in Medicine and Biology
|July 25, 2022
PubMed
Summary
This summary is machine-generated.

A new evolutionary algorithm optimizes proton arc plans by balancing plan quality and beam delivery time (BDT). This approach allows for faster treatment delivery without compromising therapeutic effectiveness in various cancer cases.

Keywords:
proton beamsparse optimizationspot numbertreatment delivery time

More Related Videos

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

2.9K
Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

1.5K

Related Experiment Videos

Last Updated: Sep 3, 2025

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
08:34

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

20.5K
Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

2.9K
Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

1.5K

Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Biology

Background:

  • Proton arc therapy plans typically involve numerous beam spots and energy layers, leading to extended beam delivery times (BDT).
  • Optimizing treatment plans requires balancing plan quality with efficient beam delivery to enhance patient throughput and comfort.

Purpose of the Study:

  • To develop and evaluate a novel evolutionary algorithm for optimizing proton arc plans.
  • To directly incorporate beam delivery time (BDT) as a user-defined input to balance plan quality and treatment speed.

Main Methods:

  • A planning platform was created, integrating a plan quality objective, a trust-region-reflective solver-based generator, and a spot selector.
  • The generator and selector were used iteratively to optimize spot sparsity based on user-specified BDTs (15-250s).
  • The framework was tested on clinical datasets for brain, lung, and liver cancer.

Main Results:

  • The evolutionary algorithm successfully optimized proton arc plans according to user-defined BDTs.
  • Plan quality remained optimal until BDTs were reduced to 25s (brain), 50s (lung), and 100s (liver).
  • Degradation in plan quality was observed with excessively short BDTs, attributed to insufficient spot numbers or reduced degrees of freedom.

Conclusions:

  • This study presents the first framework for directly optimizing proton arc plan quality against BDT for next-generation proton therapy systems.
  • The developed platform enables exploration of the trade-off between BDT and plan quality, paving the way for clinical implementation.