Thoracic motion-compensated cone-beam computed tomography in under 20 seconds on a fast-rotating linac: A simulation study

BACKGROUND

Rapid kV cone-beam computed tomography (CBCT) scans are achievable in under 20 s on select linear accelerator systems to generate volumetric images in three dimensions (3D). Daily pre-treatment four-dimensional CBCT (4DCBCT) is recommended in image-guided lung radiotherapy to mitigate the detrimental effects of respiratory motion on treatment quality.

PURPOSE

To demonstrate the potential for thoracic 4DCBCT reconstruction using projection data that was simulated using a clinical rapid 3DCBCT acquisition protocol.

METHODS

We simulated conventional (1320 projections over 4 min) and rapid (491 projections over 16.6 s) CBCT acquisitions using 4D computed tomography (CT) volumes of 14 lung cancer patients. Conventional acquisition data were reconstructed using the 4D Feldkamp-Davis-Kress (FDK) algorithm. Rapid acquisition data were reconstructed using 3DFDK, 4DFDK, and Motion-Compensated FDK (MCFDK). Image quality was evaluated using Contrast-to-Noise Ratio (CNR), Tissue Interface Width (TIW), Root-Mean-Square Error (RMSE), and Structural SIMilarity (SSIM).

RESULTS

The conventional acquisition 4DFDK reconstructions had median phase averaged CNR, TIW, RMSE, and SSIM of 2.96, 8.02 mm, 83.5, and 0.54, respectively. The rapid acquisition 3DFDK reconstructions had median CNR, TIW, RMSE, and SSIM of 2.99, 13.6 mm, 112, and 0.44 respectively. The rapid acquisition MCFDK reconstructions had median phase averaged CNR, TIW, RMSE, and SSIM of 2.98, 10.2 mm, 103, and 0.46, respectively. Rapid acquisition 4DFDK reconstruction quality was insufficient for any practical use due to sparse angular projection sampling.

CONCLUSIONS

Results suggest that 4D motion-compensated reconstruction of rapid acquisition thoracic CBCT data are feasible with image quality approaching conventional acquisition CBCT data reconstructed using standard 4DFDK.

BRAVEHeart: a randomised trial comparing the accuracy of Breathe Well and RPM for deep inspiration breath hold breast cancer radiotherapy

BACKGROUND

Deep inspiration breath hold (DIBH) reduces radiotherapy cardiac dose for left-sided breast cancer patients. The primary aim of the BRAVEHeart (Breast Radiotherapy Audio Visual Enhancement for sparing the Heart) trial is to assess the accuracy and usability of a novel device, Breathe Well, for DIBH guidance for left-sided breast cancer patients. Breathe Well will be compared to an adapted widely available monitoring system, the Real-time Position Management system (RPM).

METHODS

BRAVEHeart is a single institution prospective randomised trial of two DIBH devices. BRAVEHeart will assess the DIBH accuracy for Breathe Well and RPM during left-sided breast cancer radiotherapy. After informed consent has been obtained, 40 patients will be randomised into two equal groups, the experimental arm (Breathe Well) and the control arm (RPM with in-house modification of an added patient screen). The primary hypothesis of BRAVEHeart is that the accuracy of Breathe Well in maintaining the position of the chest during DIBH is superior to the RPM system. Accuracy will be measured by comparing chest wall motion extracted from images acquired of the treatment field during breast radiotherapy for patients treated using the Breathe Well system and those using the RPM system.

DISCUSSION

The Breathe Well device uses a depth camera to monitor the chest surface while the RPM system monitors a block on the patient's abdomen. The hypothesis of this trial is that the chest surface is a better surrogate for the internal chest wall motion used as a measure of treatment accuracy. The Breathe Well device aims to deliver an easy-to-use implementation of surface monitoring. The findings from the study will help inform the technology choice for other centres performing DIBH.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02881203 . Registered on 26 August 2016.

A method for beam's eye view breath-hold monitoring during breast volumetric modulated arc therapy

Background and purpose

Deep inspiration breath-hold (DIBH) is a technique that is widely utilised to spare the heart and lungs during breast radiotherapy. In this study, a method was developed to validate directly the intrafraction accuracy of DIBH during breast volumetric modulated arc therapy (VMAT) via internal chest wall (CW) monitoring.

Materials and methods

In-house software was developed to automatically extract and compare the treatment position of the CW in cine-mode electronic portal image device (EPID) images with the planned CW position in digitally reconstructed radiographs (DRR) for breast VMAT treatments. Feasibility of this method was established by evaluating the percentage of total dose delivered to the target volume when the CW was sufficiently visible for monitoring. Geometric accuracy of the approach was quantified by applying known displacements to an anthropomorphic thorax phantom. The software was used to evaluate (offline) the geometric treatment accuracy for ten patients treated using real-time position management (RPM)-guided DIBH.

Results

The CW could be monitored within the tangential sub-arcs which delivered a median 89% (range 73% to 97%) of the dose to target volume. The phantom measurements showed a geometric accuracy within 1 mm, with visual inspection showing good agreement between the software-derived and user-determined CW positions. For the RPM-guided DIBH treatments, the CW was found to be within ±5 mm of the planned position in 97% of EPID frames in which the CW was visible.

Conclusion

An intrafraction monitoring method with sub-millimetre accuracy was successfully developed to validate target positioning during breast VMAT DIBH.

Nanoparticles for MRI-guided radiation therapy: a review

The development of nanoparticle agents for MRI-guided radiotherapy is growing at an increasing pace, with clinical trials now underway and many pre-clinical evaluation studies ongoing. Gadolinium and iron-oxide-based nanoparticles remain the most clinically advanced nanoparticles to date, although several promising candidates are currently under varying stages of development. Goals of current and future generation nanoparticle-based contrast agents for MRI-guided radiotherapy include achieving positive signal contrast on T1-weighted MRI scans, local radiation enhancement at clinically relevant concentrations and, where applicable, avoidance of uptake by the reticuloendothelial system. Exploiting the enhanced permeability and retention effect or the use of active targeting ligands on nanoparticle surfaces is utilised to promote tumour uptake. This review outlines the current status of promising nanoparticle agents for MRI-guided radiation therapy, including several platforms currently undergoing clinical evaluation or at various stages of the pre-clinical development process. Challenges facing nanoparticle agents and possible avenues for current and future development are discussed.

Investigating the use of machine learning to generate ventilation images from CT scans

Background

Radiotherapy treatment planning incorporating ventilation imaging can reduce the incidence of radiation‐induced lung injury. The gold‐standard of ventilation imaging, using nuclear medicine, has limitations with respect to availability and cost.

Purpose

An alternative type of ventilation imaging to nuclear medicine uses 4DCT (or breath‐hold CT [BHCT] pair) with deformable image registration (DIR) and a ventilation metric to produce a CT ventilation image (CTVI). The purpose of this study is to investigate the application of machine learning as an alternative to DIR‐based methods when producing CTVIs.

Methods

A patient dataset of 15 inhale and exhale BHCTs and Galligas PET ventilation images were used to train and test a 2D U‐Net style convolutional neural network. The neural network established relationships between axial input BHCT image pairs and axial labeled Galligas PET images and was evaluated using eightfold cross‐validation. Once trained, the neural network could produce a CTVI from an input BHCT image pair. The CTVIs produced by the neural network were qualitatively assessed visually and quantitatively compared to a Galligas PET ventilation image using a Spearman correlation and Dice similarity coefficient (DSC). The DSC measured the spatial overlap between three segmented equal lung volumes by ventilation (high, medium, and low functioning lung [LFL]).

Results

The mean Spearman correlation between the CTVIs and the Galligas PET ventilation images was 0.58 ± 0.14. The mean DSC over high, medium, and LFL between the CTVIs and Galligas PET ventilation images was 0.55 ± 0.06. Visually, a systematic overprediction of ventilation within the lung was observed in the CTVIs with respect to the Galligas PET ventilation images, with jagged regions of ventilation in the sagittal and coronal planes.

Conclusions

A convolutional neural network was developed that could produce a CTVI from a BHCT image pair, which was then compared with a Galligas PET ventilation image. The performance of this machine learning method was comparable to previous benchmark studies investigating a DIR‐based CTVI, warranting future development, and investigation of applying machine learning to a CTVI.

Roadmap for metal nanoparticles in radiation therapy: current status, translational challenges, and future directions

This roadmap outlines the potential roles of metallic nanoparticles (MNPs) in the field of radiation therapy. MNPs made up of a wide range of materials (from Titanium, Z = 22, to Bismuth, Z = 83) and a similarly wide spectrum of potential clinical applications, including diagnostic, therapeutic (radiation dose enhancers, hyperthermia inducers, drug delivery vehicles, vaccine adjuvants, photosensitizers, enhancers of immunotherapy) and theranostic (combining both diagnostic and therapeutic), are being fabricated and evaluated. This roadmap covers contributions from experts in these topics summarizing their view of the current status and challenges, as well as expected advancements in technology to address these challenges.

Dose enhancement effects to the nucleus and mitochondria from gold nanoparticles in the cytosol

Gold nanoparticles (GNPs) have shown potential as dose enhancers for radiation therapy. Since damage to the genome affects the viability of a cell, it is generally assumed that GNPs have to localise within the cell nucleus. In practice, however, GNPs tend to localise in the cytoplasm yet still appear to have a dose enhancing effect on the cell. Whether this effect can be attributed to stress-induced biological mechanisms or to physical damage to extra-nuclear cellular targets is still unclear. There is however growing evidence to suggest that the cellular response to radiation can also be influenced by indirect processes induced when the nucleus is not directly targeted by radiation. The mitochondrion in particular may be an effective extra-nuclear radiation target given its many important functional roles in the cell. To more accurately predict the physical effect of radiation within different cell organelles, we measured the full chemical composition of a whole human lymphocytic JURKAT cell as well as two separate organelles; the cell nucleus and the mitochondrion. The experimental measurements found that all three biological materials had similar ionisation energies  ∼70 eV, substantially lower than that of liquid water  ∼78 eV. Monte Carlo simulations for 10-50 keV incident photons showed higher energy deposition and ionisation numbers in the cell and organelle materials compared to liquid water. Adding a 1% mass fraction of gold to each material increased the energy deposition by a factor of  ∼1.8 when averaged over all incident photon energies. Simulations of a realistic compartmentalised cell show that the presence of gold in the cytosol increases the energy deposition in the mitochondrial volume more than within the nuclear volume. We find this is due to sub-micron delocalisation of energy by photoelectrons, making the mitochondria a potentially viable indirect radiation target for GNPs that localise to the cytosol.

The cytoplasm as a radiation target: an in silico study of microbeam cell irradiation

We performed in silico microbeam cell irradiation modelling to quantitatively investigate ionisations resulting from soft x-ray and alpha particle microbeams targeting the cytoplasm of a realistic cell model. Our results on the spatial distribution of ionisations show that as x-rays are susceptible to scatter within a cell that can lead to ionisations in the nucleus, soft x-ray microbeams may not be suitable for investigating the DNA damage response to radiation targeting the cytoplasm alone. In contrast, ionisations from an ideal alpha microbeam are tightly confined to the cytoplasm, but a realistic alpha microbeam degrades upon interaction with components upstream of the cellular target. Thus it is difficult to completely rule out a contribution from alpha particle hits to the nucleus when investigating DNA damage response to cytoplasmic irradiation. We find that although the cytoplasm targeting efficiency of an alpha microbeam is better than that of a soft x-ray microbeam (the probability of stray alphas hitting the nucleus is 0.2% compared to 3.6% for x-rays), stray alphas produce more ionisations in the nucleus and thus have greater potential for initiating damage responses therein. Our results suggest that observed biological responses to cytoplasmic irradiation include a small component that can be attributed to stray ionisations in the nucleus resulting from the stochastic nature of particle interactions that cause out-of-beam scatter. This contribution is difficult to isolate experimentally, thus demonstrating the value of the in silico approach.