The role of imaging in radiation therapy planning: past, present, and future

Prior-image-based low-dose CT reconstruction for adaptive radiation therapy

Objective. The study aims to reduce the imaging radiation dose in Adaptive Radiotherapy (ART) while maintaining high-quality CT images, critical for effective treatment planning and monitoring.Approach. We developed the Prior-aware Learned Primal-Dual Network (pLPD-UNet), which uses prior CT images to enhance reconstructions from low-dose scans. The network was separately trained on thorax and abdomen datasets to accommodate the unique imaging requirements of each anatomical region.Main results. The pLPD-UNet demonstrated improved reconstruction accuracy and robustness in handling sparse data compared to traditional methods. It effectively maintained image quality essential for precise organ delineation and dose calculation, while achieving a significant reduction in radiation exposure.Significance. This method offers a significant advancement in the practice of ART by integrating prior imaging data, potentially setting a new standard for balancing radiation safety with the need for high-resolution imaging in cancer treatment planning.

A cross-national investigation of CT, MRI, PET, mammography, and radiation therapy resources and utilization

PURPOSE

This study aimed to analyze the domestic and international landscape of imaging diagnostics and treatments, focusing on Japan, to provide current insights for policymaking, clinical practice enhancement, and international collaboration.

METHODS

Data from 1996 to 2021 were collected from Japan's Ministry of Health, Labor and Welfare database for medical device counts of CT, MRI, PET, mammography, and radiotherapy. The National Database of Health Insurance Claims and Specific Health Checkups of Japan was utilized for examination numbers. An international comparison was made with data from 41 countries using the Organization for Economic Cooperation and Development (OECD) database.

RESULTS

The data included a total of 108,596 CT devices, 47,233 MRI devices, 2998 PET devices, 20,641 MMG devices, and 8023 RT devices during the survey period. Upon international comparison, Japan ranked first in CT and MRI devices per million people and second in examination numbers per 1000 people. The number of PET devices per million people exceeded OECD averages; however, the number of examinations per 1000 people was below the OECD average in 2020 (Japan: 4.0, OECD: 4.9). Although Japan exceeded OECD averages in mammography device counts (Japan: 33.8, OECD: 24.5 in 2020), radiotherapy device counts were similar to OECD averages (Japan: 8.3, OECD: 7.9 in 2020).

CONCLUSION

We have analyzed the utilization of equipment in the context of diagnostic imaging and radiotherapy in Japan. Since the initial survey year, all devices have shown an upward trend. However, it is essential not only to increase the number of devices and examinations but also to address the chronic shortage of radiologists and allied health professionals. Based on the insights gained from this study, understanding the actual status of diagnostic imaging and radiation therapy equipment is critical for grasping the domestic situation and may contribute to improving the quality of healthcare in Japan.

Radio-miRs: a comprehensive view of radioresistance-related microRNAs

Radiotherapy is a key treatment option for a wide variety of human tumors, employed either alone or alongside with other therapeutic interventions. Radiotherapy uses high-energy particles to destroy tumor cells, blocking their ability to divide and proliferate. The effectiveness of radiotherapy is due to genetic and epigenetic factors that determine how tumor cells respond to ionizing radiation. These factors contribute to the establishment of resistance to radiotherapy, which increases the risk of poor clinical prognosis of patients. Although the mechanisms by which tumor cells induce radioresistance are unclear, evidence points out several contributing factors including the overexpression of DNA repair systems, increased levels of reactive oxygen species, alterations in the tumor microenvironment, and enrichment of cancer stem cell populations. In this context, dysregulation of microRNAs or miRNAs, critical regulators of gene expression, may influence how tumors respond to radiation. There is increasing evidence that miRNAs may act as sensitizers or enhancers of radioresistance, regulating key processes such as the DNA damage response and the cell death signaling pathway. Furthermore, expression and activity of miRNAs have shown informative value in overcoming radiotherapy and long-term radiotoxicity, revealing their potential as biomarkers. In this review, we will discuss the molecular mechanisms associated with the response to radiotherapy and highlight the central role of miRNAs in regulating the molecular mechanisms responsible for cellular radioresistance. We will also review radio-miRs, radiotherapy-related miRNAs, either as sensitizers or enhancers of radioresistance that hold promise as biomarkers or pharmacological targets to sensitize radioresistant cells.

Deep Learning Auto-Segmentation Network for Pediatric Computed Tomography Data Sets: Can We Extrapolate From Adults?

PURPOSE

Artificial intelligence (AI)-based auto-segmentation models hold promise for enhanced efficiency and consistency in organ contouring for adaptive radiation therapy and radiation therapy planning. However, their performance on pediatric computed tomography (CT) data and cross-scanner compatibility remain unclear. This study aimed to evaluate the performance of AI-based auto-segmentation models trained on adult CT data when applied to pediatric data sets and explore the improvement in performance gained by including pediatric training data. It also examined their ability to accurately segment CT data acquired from different scanners.

METHODS AND MATERIALS

Using the nnU-Net framework, segmentation models were trained on data sets of adult, pediatric, and combined CT scans for 7 pelvic/thoracic organs. Each model was trained on 290 to 300 cases per category and organ. Training data sets included a combination of clinical data and several open repositories. The study incorporated a database of 459 pediatric (0-16 years) CT scans and 950 adults (>18 years), ensuring all scans had human expert ground-truth contours of the selected organs. Performance was evaluated based on Dice similarity coefficients (DSC) of the model-generated contours.

RESULTS

AI models trained exclusively on adult data underperformed on pediatric data, especially for the 0 to 2 age group: mean DSC was below 0.5 for the bladder and spleen. The addition of pediatric training data demonstrated significant improvement for all age groups, achieving a mean DSC of above 0.85 for all organs in every age group. Larger organs like the liver and kidneys maintained consistent performance for all models across age groups. No significant difference emerged in the cross-scanner performance evaluation, suggesting robust cross-scanner generalization.

CONCLUSIONS

For optimal segmentation across age groups, it is important to include pediatric data in the training of segmentation models. The successful cross-scanner generalization also supports the real-world clinical applicability of these AI models. This study emphasizes the significance of data set diversity in training robust AI systems for medical image interpretation tasks.

Artificial neural network-assisted prediction of radiobiological indices in head and neck cancer

Background and purpose

We proposed an artificial neural network model to predict radiobiological parameters for the head and neck squamous cell carcinoma patients treated with radiation therapy. The model uses the tumor specification, demographics, and radiation dose distribution to predict the tumor control probability and the normal tissue complications probability. These indices are crucial for the assessment and clinical management of cancer patients during treatment planning.

Methods

Two publicly available datasets of 31 and 215 head and neck squamous cell carcinoma patients treated with conformal radiation therapy were selected. The demographics, tumor specifications, and radiation therapy treatment parameters were extracted from the datasets used as inputs for the training of perceptron. Radiobiological indices are calculated by open-source software using dosevolume histograms from radiation therapy treatment plans. Those indices were used as output in the training of a single-layer neural network. The distribution of data used for training, validation, and testing purposes was 70, 15, and 15%, respectively.

Results

The best performance of the neural network was noted at epoch number 32 with the mean squared error of 0.0465. The accuracy of the prediction of radiobiological indices by the artificial neural network in training, validation, and test phases were determined to be 0.89, 0.87, and 0.82, respectively. We also found that the percentage volume of parotid inside the planning target volume is the significant parameter for the prediction of normal tissue complications probability.

Conclusion

We believe that the model has significant potential to predict radiobiological indices and help clinicians in treatment plan evaluation and treatment management of head and neck squamous cell carcinoma patients.

Synthetic CT generation from MRI using 3D transformer-based denoising diffusion model

BACKGROUND AND PURPOSE

Magnetic resonance imaging (MRI)-based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error-prone image registration, ultimately reducing patient radiation dose and setup uncertainty. In this work, we propose a MRI-to-CT transformer-based improved denoising diffusion probabilistic model (MC-IDDPM) to translate MRI into high-quality sCT to facilitate radiation treatment planning.

METHODS

MC-IDDPM implements diffusion processes with a shifted-window transformer network to generate sCT from MRI. The proposed model consists of two processes: a forward process, which involves adding Gaussian noise to real CT scans to create noisy images, and a reverse process, in which a shifted-window transformer V-net (Swin-Vnet) denoises the noisy CT scans conditioned on the MRI from the same patient to produce noise-free CT scans. With an optimally trained Swin-Vnet, the reverse diffusion process was used to generate noise-free sCT scans matching MRI anatomy. We evaluated the proposed method by generating sCT from MRI on an institutional brain dataset and an institutional prostate dataset. Quantitative evaluations were conducted using several metrics, including Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), Multi-scale Structure Similarity Index (SSIM), and Normalized Cross Correlation (NCC). Dosimetry analyses were also performed, including comparisons of mean dose and target dose coverages for 95% and 99%.

RESULTS

MC-IDDPM generated brain sCTs with state-of-the-art quantitative results with MAE 48.825 ± 21.491 HU, PSNR 26.491 ± 2.814 dB, SSIM 0.947 ± 0.032, and NCC 0.976 ± 0.019. For the prostate dataset: MAE 55.124 ± 9.414 HU, PSNR 28.708 ± 2.112 dB, SSIM 0.878 ± 0.040, and NCC 0.940 ± 0.039. MC-IDDPM demonstrates a statistically significant improvement (with p < 0.05) in most metrics when compared to competing networks, for both brain and prostate synthetic CT. Dosimetry analyses indicated that the target dose coverage differences by using CT and sCT were within ± 0.34%.

CONCLUSIONS

We have developed and validated a novel approach for generating CT images from routine MRIs using a transformer-based improved DDPM. This model effectively captures the complex relationship between CT and MRI images, allowing for robust and high-quality synthetic CT images to be generated in a matter of minutes. This approach has the potential to greatly simplify the treatment planning process for radiation therapy by eliminating the need for additional CT scans, reducing the amount of time patients spend in treatment planning, and enhancing the accuracy of treatment delivery.

Dual-energy computed tomography imaging with megavoltage and kilovoltage X-ray spectra

Purpose

Single-energy computed tomography (CT) often suffers from poor contrast yet remains critical for effective radiotherapy treatment. Modern therapy systems are often equipped with both megavoltage (MV) and kilovoltage (kV) X-ray sources and thus already possess hardware for dual-energy (DE) CT. There is unexplored potential for enhanced image contrast using MV-kV DE-CT in radiotherapy contexts.

Approach

A single-line integral toy model was designed for computing basis material signal-to-noise ratio (SNR) using estimation theory. Five dose-matched spectra (3 kV, 2 MV) and three variables were considered: spectral combination, spectral dose allocation, and object material composition. The single-line model was extended to a simulated CT acquisition of an anthropomorphic phantom with and without a metal implant. Basis material sinograms were computed and synthesized into virtual monoenergetic images (VMIs). MV-kV and kV-kV VMIs were compared with single-energy images.

Results

The 80 kV-140 kV pair typically yielded the best SNRs, but for bone thicknesses > 8    cm , the detunedMV-80 kV pair surpassed it. Peak MV-kV SNR was achieved with ∼ 90 % dose allocated to the MV spectrum. In CT simulations of the pelvis with a steel implant, MV-kV VMIs yielded a higher contrast-to-noise ratio (CNR) than single-energy CT and kV-kV DE-CT. Without steel, the MV-kV VMIs produced higher contrast but lower CNR than single-energy CT.

Conclusions

This work analyzes MV-kV DE-CT imaging and assesses its potential advantages. The technique may be used for metal artifact correction and generation of VMIs with higher native contrast than single-energy CT. Improved denoising is generally necessary for greater CNR without metal.

Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography

Purpose

Meticulous manual delineations of the prostate and the surrounding organs at risk are necessary for prostate cancer radiation therapy to avoid side effects to the latter. This process is time consuming and hampered by inter- and intraobserver variability, all of which could be alleviated by artificial intelligence (AI). This study aimed to evaluate the performance of AI compared with manual organ delineations on computed tomography (CT) scans for radiation treatment planning.

Methods and Materials

Manual delineations of the prostate, urinary bladder, and rectum of 1530 patients with prostate cancer who received curative radiation therapy from 2006 to 2018 were included. Approximately 50% of those CT scans were used as a training set, 25% as a validation set, and 25% as a test set. Patients with hip prostheses were excluded because of metal artifacts. After training and fine-tuning with the validation set, automated delineations of the prostate and organs at risk were obtained for the test set. Sørensen-Dice similarity coefficient, mean surface distance, and Hausdorff distance were used to evaluate the agreement between the manual and automated delineations.

Results

The median Sørensen-Dice similarity coefficient between the manual and AI delineations was 0.82, 0.95, and 0.88 for the prostate, urinary bladder, and rectum, respectively. The median mean surface distance and Hausdorff distance were 1.7 and 9.2 mm for the prostate, 0.7 and 6.7 mm for the urinary bladder, and 1.1 and 13.5 mm for the rectum, respectively.

Conclusions

Automated CT-based organ delineation for prostate cancer radiation treatment planning is feasible and shows good agreement with manually performed contouring.

The influence of patient positioning and immobilization equipment on MR image quality and image registration in radiation therapy

INTRODUCTION

MRI is preferred for brain tumor assessment, while CT is used for radiotherapy simulation. This study evaluated immobilization equipment's impact on CT-MRI registration accuracy and MR image quality in RT setup.

METHODS

We included CT and MR images from 11 patients with high-grade glioma, all of whom were immobilized with a thermoplastic mask and headrest. T1- and T2-weighted MR images were acquired using an MR head coil in a diagnostic setup (DS) and a body matrix coil in RT setup. To assess MR image quality, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were considered in some dedicated regions of interest. We also evaluated the impact of immobilization equipment on CT-MRI rigid registration using line profile and external contour methods.

RESULTS

The CNR and SNR reduction was in the RT setup of imaging. This was more evident in T1-weighted images than in T2-weighted ones. The SNR decreased by 14.91% and 12.09%, while CNR decreased by 25.12% and 20.15% in T1- and T2-weighted images, respectively. The immobilization equipment in the RT setup decreased the mean error in rigid registration by 1.02 mm. The external contour method yielded Dice similarity coefficients (DSC) of 0.84 and 0.92 for CT-DS MRI and CT-RT MRI registration, respectively.

CONCLUSION

The image quality reduction in the RT setup was due to the imaged region's anatomy and its position relative to the applied coil. Furthermore, optimizing the pulse sequence is crucial for MR imaging in RT applications. Although the use of immobilization equipment may decrease the image quality in the RT setup, it does not affect organ delineation, and the image quality is still satisfactory for this purpose. Also, the use of immobilization equipment in the RT setup has increased registration accuracy.

How Imaging Advances Are Defining the Future of Precision Radiation Therapy

Radiation therapy is fundamental in the treatment of cancer. Imaging has always played a central role in radiation oncology. Integrating imaging technology into irradiation devices has increased the precision and accuracy of dose delivery and decreased the toxic effects of the treatment. Although CT has become the standard imaging modality in radiation therapy, the development of recently introduced next-generation imaging techniques has improved diagnostic and therapeutic decision making in radiation oncology. Functional and molecular imaging techniques, as well as other advanced imaging modalities such as SPECT, yield information about the anatomic and biologic characteristics of tumors for the radiation therapy workflow. In clinical practice, they can be useful for characterizing tumor phenotypes, delineating volumes, planning treatment, determining patients' prognoses, predicting toxic effects, assessing responses to therapy, and detecting tumor relapse. Next-generation imaging can enable personalization of radiation therapy based on a greater understanding of tumor biologic factors. It can be used to map tumor characteristics, such as metabolic pathways, vascularity, cellular proliferation, and hypoxia, that are known to define tumor phenotype. It can also be used to consider tumor heterogeneity by highlighting areas at risk for radiation resistance for focused biologic dose escalation, which can impact the radiation planning process and patient outcomes. The authors review the possible contributions of next-generation imaging to the treatment of patients undergoing radiation therapy. In addition, the possible roles of radio(geno)mics in radiation therapy, the limitations of these techniques, and hurdles in introducing them into clinical practice are discussed. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Dose optimization of 2D X-ray image acquisition protocols in image-guided radiotherapy

PURPOSE

In contemporary radiotherapy, patient positioning accuracy relies on kV imaging. This study aims at optimizing planar kV image acquisition protocols regarding patient dose without degrading image quality.

MATERIALS AND METHODS

An image quality test-object was placed in-between PMMA plates, suitably arranged to model head or pelvis. Constructed phantoms were imaged using default protocols, the resultant image quality was assessed and the corresponding radiation dose was measured. The process was repeated using numerous kV/mAs combinations to identify those acquisition settings providing images at lower dose than the default protocols but without deterioration in image quality. Default and dose-optimized protocols were then tested on an anthropomorphic phantom and on 51 patients during two successive treatment sessions. Image quality was independently assessed by two readers. Organ and effective doses were estimated using a Monte Carlo simulation software.

RESULTS

Low-contrast detectability exhibited a stronger dependence on kV/mAs settings, compared to high-contrast resolution. Dose-optimized protocols resulted in significant dose reductions (anteroposterior-head 48.0 %, lateral-head 30.0 %, anteroposterior-pelvis 28.4 %, lateral-pelvis 27.0 %) compared to the default ones, without compromising image quality. Optimized protocols decreased effective doses by 54 % and 29.6 % in head and pelvic acquisitions, respectively. Regarding image quality, anthropomorphic and patient images acquired using the dose-optimized protocols were subjectively evaluated equivalent to those obtained with the corresponding default settings, indicating that the proposed protocols may be routinely used.

CONCLUSIONS

Given the potentially large number of radiotherapy fractions and the pertinent image acquisitions, dose-optimized protocols could significantly reduce patient dose associated with planar imaging without compromising positioning accuracy.

Measurements of target volumes and organs at risk using DW‑MRI in patients with central lung cancer accompanied with atelectasis

Accurate imaging-based tumor delineation is crucial for guiding the radiotherapy treatments of various solid tumors. Currently, several imaging procedures, including diffusion-weighted magnetic resonance imaging (DW-MRI), intensified computed tomography and positron emission tomography are routinely used for targeted tumor delineation. However, the performance of these imaging procedures has not yet been comprehensively evaluated. In order to address this matter, the present study was conducted in an aim to assess the use of DW-MRI in guiding radiotherapy treatments, by comparing its performance to that of other imaging procedures. Specifically, the exposure dosages to organs at risk, including the lungs, heart and spinal mencord, were evaluated using various radiotherapy regimes. The findings of the present study demonstrated that DW-MRI is a non-invasive and cost-effective imaging procedure that can be used to reduce lung exposure doses, minimizing the risk of radiation pneumonitis. The data further demonstrate the immense potential of the DW-MRI procedure in the precision radiotherapy of lung cancers.

A Promising Glass Type in Electronic and Laser Applications: Elastic Moduli, Mechanical, and Photon Transmission Properties of WO3 Reinforced Ternary-Tellurite Glasses

We report the symmetry of mechanical and gamma-ray attenuation properties for some tellurite glasses through elastic moduli, mechanical, and transmission properties as a function of varied WO3 amount in glass configuration. Four glass samples, along with different molar compositions as well as WO3/GdF3 substitution ratios, are investigated. Transmission properties using several essential parameters, such as attenuation coefficients, half-value layers, effective atomic numbers, effective conductivity, and buildup factors, are calculated in the 0.015–15 MeV energy range. Moreover, elastic moduli and Poisson’s ratios (σ) of the studied glass are calculated using the Makishima–Mackenzie model. The M4 sample with the highest WO3 addition is found with superior photon attenuation properties among the glasses investigated. Poisson’s ratio (σ) is increased, while all elastic moduli are decreased. Young’s modulus is reported as 62.23 GPa and 36.45.37 GPa at the highest and lowest WO3 mol%, respectively. It can be concluded that WO3 is a functional and monotonic tool in ternary-tellurite glasses for multiple modifications and enhancement purposes on gamma-ray attenuation, elastic moduli, and mechanical properties. It can also be concluded that increasing the WO3 amount in tellurite glasses may be considered a tool in terms of providing symmetry for mechanical and gamma-ray attenuation properties.

Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)

Objective. Dynamic cone-beam CT (CBCT) imaging is highly desired in image-guided radiation therapy to provide volumetric images with high spatial and temporal resolutions to enable applications including tumor motion tracking/prediction and intra-delivery dose calculation/accumulation. However, dynamic CBCT reconstruction is a substantially challenging spatiotemporal inverse problem, due to the extremely limited projection sample available for each CBCT reconstruction (one projection for one CBCT volume).Approach. We developed a simultaneous spatial and temporal implicit neural representation (STINR) method for dynamic CBCT reconstruction. STINR mapped the unknown image and the evolution of its motion into spatial and temporal multi-layer perceptrons (MLPs), and iteratively optimized the neuron weightings of the MLPs via acquired projections to represent the dynamic CBCT series. In addition to the MLPs, we also introduced prior knowledge, in the form of principal component analysis (PCA)-based patient-specific motion models, to reduce the complexity of the temporal mapping to address the ill-conditioned dynamic CBCT reconstruction problem. We used the extended-cardiac-torso (XCAT) phantom and a patient 4D-CBCT dataset to simulate different lung motion scenarios to evaluate STINR. The scenarios contain motion variations including motion baseline shifts, motion amplitude/frequency variations, and motion non-periodicity. The XCAT scenarios also contain inter-scan anatomical variations including tumor shrinkage and tumor position change.Main results. STINR shows consistently higher image reconstruction and motion tracking accuracy than a traditional PCA-based method and a polynomial-fitting-based neural representation method. STINR tracks the lung target to an average center-of-mass error of 1-2 mm, with corresponding relative errors of reconstructed dynamic CBCTs around 10%.Significance. STINR offers a general framework allowing accurate dynamic CBCT reconstruction for image-guided radiotherapy. It is a one-shot learning method that does not rely on pre-training and is not susceptible to generalizability issues. It also allows natural super-resolution. It can be readily applied to other imaging modalities as well.

Investigation of Computed Tomography Numbers on Multiple Imaging Systems using Single and Multislice Methods

Aim

The aim of this study is to determine the variation in Hounsfield values with single and multi-slice methods using in-house software on fan-beam computed tomography (FCT), linear accelerator (linac) cone-beam computed tomography (CBCT), and Icon-CBCT datasets acquired using Gammex and advanced electron density (AED) phantoms.

Materials and Methods

The AED phantom was scanned on a Toshiba computed tomography (CT) scanner, five linac-based CBCT X-ray volumetric imaging systems, and Leksell Gamma Knife Icon. The variation between single and multi-slice methods was assessed by comparing scans acquired using Gammex and AED phantoms. The variation in Hounsfield units (HUs) between seven different clinical protocols was assessed using the AED phantom. A CIRS Model 605 Radiosurgery Head Phantom (TED) phantom was scanned on all three imaging systems to assess the target dosimetric changes due to HU variation. An in-house software was developed in MATLAB to assess the HU statistics and the trend along the longitudinal axis.

Results

The FCT dataset showed a minimal variation (central slice ± 3 HU) in HU values along the long axis. A similar trend was also observed between the studied clinical protocols acquired on FCT. Variation among multiple linac CBCTs was insignificant. In the case of the water insert, a maximum HU variation of -7.23 ± 68.67 was observed for Linac 1 towards the inferior end of the phantom. All five linacs appeared to have a similar trend in terms of HU variation from the proximal to the distal end of the phantom, with a few outliers for Linac 5. Among three imaging modalities, the maximum variation was observed in gamma knife CBCTs, whereas FCT showed no appreciable deviation from the central value. In terms of dosimetric comparison, the mean dose in CT and Linac CBCT scans differed by <0.5 Gy, whereas at least a 1 Gy difference was observed between CT and gamma knife CBCT.

Conclusion

This study shows a minimal variation with FCT between single, volume-based, and multislice methods, and hence the current approach of determining the CT-electron density curve based on a single-slice method would be sufficient for producing a HU calibrations curve for treatment planning. However, CBCTs acquired on linac, and in particular, gamma knife systems, show noticeable variations along the long axis, which is likely to affect the dose calculations performed on CBCTs. It is highly recommended to assess the Hounsfield values on multiple slices before using the HU curve for dose calculations.

Comparison of the Medical Uses and Cellular Effects of High and Low Linear Energy Transfer Radiation

Exposure to ionizing radiation can occur during medical treatments, from naturally occurring sources in the environment, or as the result of a nuclear accident or thermonuclear war. The severity of cellular damage from ionizing radiation exposure is dependent upon a number of factors including the absorbed radiation dose of the exposure (energy absorbed per unit mass of the exposure), dose rate, area and volume of tissue exposed, type of radiation (e.g., X-rays, high-energy gamma rays, protons, or neutrons) and linear energy transfer. While the dose, the dose rate, and dose distribution in tissue are aspects of a radiation exposure that can be varied experimentally or in medical treatments, the LET and eV are inherent characteristics of the type of radiation. High-LET radiation deposits a higher concentration of energy in a shorter distance when traversing tissue compared with low-LET radiation. The different biological effects of high and low LET with similar energies have been documented in vivo in animal models and in cultured cells. High-LET results in intense macromolecular damage and more cell death. Findings indicate that while both low- and high-LET radiation activate non-homologous end-joining DNA repair activity, efficient repair of high-LET radiation requires the homologous recombination repair pathway. Low- and high-LET radiation activate p53 transcription factor activity in most cells, but high LET activates NF-kB transcription factor at lower radiation doses than low-LET radiation. Here we review the development, uses, and current understanding of the cellular effects of low- and high-LET radiation exposure.

Computed Tomography and Magnetic Resonance Imaging Overlay in the Spine for Surgical Planning: A Technical Report

BACKGROUND

Computed tomography (CT) and magnetic resonance imaging (MRI) studies are used separately for surgical planning of spine surgery. Advanced techniques exist for creating CT-MR fusion images, but at this time these techniques are not easily accessible for large-scale use.

TECHNIQUE

We propose a simple graphical technique for CT-MR image overlay, for use in the surgical planning of spinal decompression and guidance of intraoperative resection. The proposed technique involves overlaying a single cross-section from anatomically comparable MRI and CT studies on any software with basic image editing functions.

RESULTS

We demonstrate CT-MR fusion images of 8 patients of the senior author in which the technique was used. We found that it can also be referenced intraoperatively for navigation.

CONCLUSIONS

Compared to other techniques, our proposed method can be easily implemented by clinicians to create simple CT-MRI fusion images that can be useful for preoperative planning and intraoperative navigation.

Automated development of the contrast-detail curve based on statistical low-contrast detectability in CT images

Purpose

We have developed a software to automatically find the contrast–detail (C–D) curve based on the statistical low‐contrast detectability (LCD) in images of computed tomography (CT) phantoms at multiple cell sizes and to generate minimum detectable contrast (MDC) characteristics.

Methods

A simple graphical user interface was developed to set the initial parameters needed to create multiple grid region of interest of various cell sizes with a 2‐pixel increment. For each cell in the grid, the average CT number was calculated to obtain the standard deviation (SD). Detectability was then calculated by multiplying the SD of the mean CT numbers by 3.29. This process was automatically repeated as many times as the cell size was set at initialization. Based on the obtained LCD, the C–D curve was obtained and the target size at an MDC of 0.6% (i.e., 6‐HU difference) was determined. We subsequently investigated the consistency of the target sizes for a 0.6% MDC at four locations within the homogeneous image. We applied the software to images with six noise levels, images of two modules of the American College of Radiology CT phantom, images of four different phantoms, and images of four different CT scanners. We compared the target sizes at a 0.6% MDC based on the statistical LCD and the results from a human observer.

Results

The developed system was able to measure C–D curves from different phantoms and scanners. We found that the C–D curves follow a power‐law fit. We found that higher noise levels resulted in a higher MDC for a target of the same size. The low‐contrast module image had a slightly higher MDC than the distance module image. The minimum size of an object detected by visual observation was slightly larger than the size using statistical LCD.

Conclusions

The statistical LCD measurement method can generate a C–D curve automatically, quickly, and objectively.

Exploring the impact of metabolic imaging in head and neck cancer treatment

Background

Target volume delineation is performed with anatomical imaging for head and neck cancer. Molecular imaging allows the recognition of specific tumor regions. Its inclusion in the pathway could lead to changes in delineation and resultant treatment plans.

Methods

PRISMA methodology was adhered to when selecting the articles for analysis and only full articles were quality assessed.

Results

Seventeen articles were included. Gross tumor volume (GTV) primary, GTV nodal, and other target volumes were evaluated. Positron emission tomography/computerized tomography (PET/CT) produced smaller primary GTVs, although not with diffusion‐weighted imaging‐magnetic resonance imaging (DWI‐MRI) or PET/MRI. The impact of these image modalities on GTV nodal did not display any consistency. Additionally, there was considerable heterogeneity in metrics comparing delineations. Four studies included appraised the dosimetric impact of the changes in target volume delineation.

Conclusion

Quantifying the impact of molecular imaging is difficult, due to heterogeneity in reporting metrics in molecular imaging modalities and a paucity of detail regarding delineation method and guideline adherence.

Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy

Objective.Next generation online and real-time adaptive radiotherapy workflows require precise particle transport simulations in sub-second times, which is unfeasible with current analytical pencil beam algorithms (PBA) or Monte Carlo (MC) methods. We present a deep learning based millisecond speed dose calculation algorithm (DoTA) accurately predicting the dose deposited by mono-energetic proton pencil beams for arbitrary energies and patient geometries.Approach.Given the forward-scattering nature of protons, we frame 3D particle transport as modeling a sequence of 2D geometries in the beam's eye view. DoTA combines convolutional neural networks extracting spatial features (e.g. tissue and density contrasts) with a transformer self-attention backbone that routes information between the sequence of geometry slices and a vector representing the beam's energy, and is trained to predict low noise MC simulations of proton beamlets using 80 000 different head and neck, lung, and prostate geometries.Main results.Predicting beamlet doses in 5 ± 4.9 ms with a very high gamma pass rate of 99.37 ± 1.17% (1%, 3 mm) compared to the ground truth MC calculations, DoTA significantly improves upon analytical pencil beam algorithms both in precision and speed. Offering MC accuracy 100 times faster than PBAs for pencil beams, our model calculates full treatment plan doses in 10-15 s depending on the number of beamlets (800-2200 in our plans), achieving a 99.70 ± 0.14% (2%, 2 mm) gamma pass rate across 9 test patients.Significance.Outperforming all previous analytical pencil beam and deep learning based approaches, DoTA represents a new state of the art in data-driven dose calculation and can directly compete with the speed of even commercial GPU MC approaches. Providing the sub-second speed required for adaptive treatments, straightforward implementations could offer similar benefits to other steps of the radiotherapy workflow or other modalities such as helium or carbon treatments.

Absorbed dose distribution in human eye simulated by FOTELP-VOX code and verified by volumetric modulated arc therapy treatment plan

This paper illustrates the potential of the FOTELP-VOX code, a modification of the general-purpose FOTELP code, combining Monte Carlo techniques to simulate particle transportation from an external source through the internal organs, resulting in a 3-D absorbed dose distribution. The study shows the comparison of results obtained by FOTELP software and the volumetric modulated arc therapy technique. This planning technique with two full arcs was applied, and the plan was created to destroy the diseased tissue in the eye tumor bed and avoid damage to surrounding healthy tissue, for one patient. The dose coverage, homogeneity index, conformity index of the target, and the dose volumes of critical structures were calculated. Good agreement of the results for absorbed dose in the human eye was obtained using these two techniques.

Variability of redundant scan coverages along the Z-axis and dose implications for common computed tomography examinations

BACKGROUND

Scan length optimization is a method of optimization which ensures that, imaging is performed to cover just the area of interest without unnecessarily exposing structures that would not add value to answer a given clinical question.

PURPOSE

This study assessed the variability and degree of redundant scan coverages along the z-axis of CT examinations of common indications and the associated radiation dose implications in CT facilities in Ghana for optimization measures to be recommended.

METHODS

On reconstructed acquired CT images, the study measured extra distances covered above and below anatomical targets for common indications with calibrated calipers across 25 CT facilities. The National Cancer Institute Dosimetry System for CT (NCICT) (Monte Carlo-based-software) was used to simulate the scanning situations and organ dose implications for scans with and without the inclusion of the redundant scan areas.

RESULTS

A total of 1,640 patients' CT data sets were used in this study. The results demonstrated that CT imaging utilized varying scan lengths (16.45±21.0-45.99±4.3 cm), and 70.6% of the scans exceeded their pre-defined anatomic boundaries by a mean range of 2.86±1.07-5.81±1.66 cm, thereby resulting in extra patient radiation dose. Hence, scanning without the redundant coverages could generate a dose length product (DLP) reduction of 17.5%, 18.8%, 15.5% and 9.0% without degrading image quality for brain lesion, lung lesion, pulmonary embolism and abdominopelvic lesion CT imaging, respectively, whilst ensuring organ dose reduction of0.8%-79.1%.

CONCLUSION

The study strongly recommends that radiographers should avoid the inclusion of redundant areas in CT examinations to reduce organ doses.

The role of feature-based radiomics for predicting response and radiation injury after stereotactic radiation therapy for brain metastases: A critical review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO)

Introduction

differential diagnosis of tumor recurrence and radiation injury after stereotactic radiotherapy (SRT) is challenging. The advances in imaging techniques and feature-based radiomics could aid to discriminate radionecrosis from progression.

Methods

we performed a systematic review of current literature, key references were obtained from a PubMed query. Data extraction was performed by 3 researchers and disagreements were resolved with a discussion among the authors.

Results

we identified 15 retrospective series, one prospective trial, one critical review and one editorial paper. Radiomics involves a wide range of imaging features referred to necrotic regions, rate of contrast-enhancing area or the measure of edema surrounding the metastases. Features were mainly defined through a multistep extraction/reduction/selection process and a final validation and comparison.

Conclusions

feature-based radiomics has an optimal potential to accurately predict response and radionecrosis after SRT of BM and facilitate differential diagnosis. Further validation studies are eagerly awaited to confirm radiomics reliability.

Iterative Metal Artifact Reduction (iMAR) of the Non-adhesive Liquid Embolic Agent Onyx in Computed Tomography : An Experimental Study

Background

A drawback of Onyx, one of the most used embolic agents for endovascular embolization of intracranial arteriovenous malformations (AVM), is the generation of imaging artifacts (IA) in computed tomography (CT). Since these artifacts can represent an obstacle for the detection of periprocedural bleeding, this study investigated the effect of artifact reduction by an iterative metal artifact reduction (iMAR) software in CT in a brain phantom.

Methods

Two different in vitro models with two-dimensional tube and three-dimensional AVM-like configuration were filled with Onyx 18. The models were inserted into a brain imaging phantom and images with (n = 5) and without (n = 10) an experimental hemorrhage adjacent were acquired. Afterwards, the iMAR algorithm was applied for artifact reduction. The IAs of the original and the post-processed images were graded quantitatively and qualitatively. Moreover, qualitative definition of the experimental hemorrhage was investigated.

Results

Comparing the IAs of the original and the post-processed CT images, quantitative and qualitative analysis showed a lower degree of IAs in the post-processed images, i.e. quantitative analysis: 2D tube model: 23.92 ± 8.02 Hounsfield units (HU; no iMAR; mean ± standard deviation) vs. 5.93 ± 0.43 HU (with iMAR; p < 0.001); qualitative analysis: 3D AVM model: 4.93 ± 0.18 vs. 3.40 ± 0.48 (p < 0.001). Furthermore, definition of the experimental hemorrhage was better in the post-processed images of both in vitro models (2D tube model: p = 0.004; 3D AVM model: p = 0.002).

Conclusion

The iMAR algorithm can significantly reduce the IAs evoked by Onyx 18 in CT. Applying iMAR could thus improve the accuracy of postprocedural CT imaging after embolization with Onyx in clinical practice.

Planning target volume density impact on treatment planning for lung stereotactic body radiation therapy

BACKGROUND

To evaluate the impact of the planning target volume (PTV) density on treatment planning for lung Stereotactic Body Radiation Therapy (SBRT).

MATERIAL AND METHODS

The PTV coverage was analyzed in two groups of 40 lung SBRT patients. One group had PTV density <0.5 g/cm³, while the other group had PTV density >0.5 g/cm³. The treatments were planned in Pinnacle 9.10, using the collapsed cone convolution (CCC) algorithm. The prescribed dose was 60 Gy to the PTV in 4-8 fractions. Respecting constraint for the PTV coverage (D98% > 95%), we compared changes in the isodose line prescription, the number of monitor units (MU), maximum dose (D_max), irradiated volume covered with 30 Gy (V_30Gy), and the optimization planning volume (OPV).

RESULTS

For the same median values of the PTV coverage (98.3%), the differences are presented with median values between lower and higher density than 0.5 g/cm³. The isodose line prescription was 83 vs. 90% (p < 0.0001), the MUs were 2294 vs. 1655 MU (p < 0.0001), D_max was 74.26 vs. 68.09 Gy (p < 0.0001), V_30Gy was 117.03 vs. 104.81 cc (p = 0.04), and OPV was 28.48 vs. 39.35 cc (p < 0.001). By overriding the ITV density to 0.8 g/cm³, the isodose line prescription decreases. The D_max and MUs decrease by 7%, V_30Gy by 34%, and OPV by 70%.

CONCLUSION

To obtain similar PTV coverage for PTV which is <0.5 g/cm³, a larger margin irradiating a large OPV was used. More MUs and a higher maximum dose were delivered. For the PTV density of ≤0.36 g/cm^3, overriding is recommended to reduce the dose and irradiated volume.

FDG-PET/CT and MR imaging for target volume delineation in rectal cancer radiotherapy treatment planning: a systematic review

Aim:

The aim of this systematic review was to synthesise and summarise evidence surrounding the clinical use of fluoro-2-deoxy-d-glucose positron emission tomography/computed tomography (FDG-PET/CT) and magnetic resonance imaging (MRI) for target volume delineation (TVD) in rectal cancer radiotherapy planning.

Methods:

PubMed, EMBASE, Cochrane library, CINAHL, Web of Science and Scopus databases and other sources were systematically queried using keywords and relevant synonyms. Eligible full-text studies were assessed for methodological quality using the QUADAS-2 tool.

Results:

Eight of the 1448 studies identified met the inclusion criteria. Findings showed that MRI significantly delineate larger tumour volumes (TVs) than FDG-PET/CT while diffusion-weighted magnetic resonance imaging (DW-MRI) defined smaller gross tumour volumes (GTVs) compared to T2 weighted-Magnetic Resonance Image. CT-based GTVs were found to be larger compared to FDG-PET/CT. FDG-PET/CT also identified new lesions in 15–17% patients and TVs extending outside the routinely used clinical standard CT TV in 29–83% patients. Between observers, delineated volumes were similar and consistent between MRI sequences, whereas interobserver agreement was significantly improved with FDG-PET/CT than CT.

Conclusion:

FDG-PET/CT and DW-MRI appear to delineate smaller rectal TVs and show improved interobserver variability. Overall, this study provides valuable insights into the amount of attention in the research literature that has been paid to imaging for TVD in rectal cancer.

The impact of software-based metal artifact reduction on the liquid embolic agent Onyx in cone-beam CT: a systematic in vitro and in vivo study

Background

Onyx is frequently used for endovascular embolization of intracranial arteriovenous malformations (AVMs) and dural arteriovenous fistulas (dAVFs). One drawback of using Onyx is the generation of artifacts in cone-beam CT (CBCT). These artifacts can represent an obstacle for the detection of periprocedural hemorrhage or planning of subsequent radiosurgery. This study investigates the effect of artifact reduction by the syngo DynaCT SMART Metal Artifact Reduction (MAR) software.

Methods

A standardized in vitro tube model (n=10) was filled with Onyx 18 and CBCT image acquisition was conducted in a brain imaging phantom. Furthermore, post-interventional CBCT images of 20 patients with AVM (n=13) or dAVF (n=7), each treated with Onyx, were investigated. The MAR software was applied for artifact reduction. Artifacts of the original and the post-processed images were analyzed quantitatively (standard deviation in a region of interest on the layer providing the most artifacts) and qualitatively. For the patient images, the effect of the MAR software on brain parenchyma on artifact-free images was further investigated.

Results

Quantitative and qualitative analyses of both datasets demonstrated a lower degree of artifacts in the post-processed images (eg, patient images: 38.30±22.03 density units (no MAR; mean SD±SD) vs 19.83±12.31 density units (with MAR; p<0.001). The MAR software had no influence on the brain parenchyma in artifact-free images.

Conclusion

The MAR software significantly reduced the artifacts evoked by Onyx in CBCT without affecting the visualization of brain parenchyma on artifact-free images. Applying this software could thus improve the quality of periprocedural CBCT images after embolization with Onyx.

Evaluating 3D-printed Bolus Compared to Conventional Bolus Types Used in External Beam Radiation Therapy

BACKGROUND

When treating superficial tumors with external beam radiation therapy, bolus is often used. Bolus increases surface dose, reduces dose to underlying tissue, and improves dose homogeneity.

INTRODUCTION

The conventional bolus types used clinically in practice have some disadvantages. The use of Three-Dimensional (3D) printing has the potential to create more effective boluses. CT data is used for dosimetric calculations for these treatments and often to manufacture the customized 3D-printed bolus.

PURPOSE

The aim of this review is to evaluate the published studies that have compared 3D-printed bolus against conventional bolus types.

METHODS AND RESULTS

A systematic search of several databases and a further appraisal for relevance and eligibility resulted in the 14 articles used in this review. The 14 articles were analyzed based on their comparison of 3D-printed bolus and at least one conventional bolus type.

CONCLUSION

The findings of this review indicated that 3D-printed bolus has a number of advantages. Compared to conventional bolus types, 3D-printed bolus was found to have equivalent or improved dosimetric measures, positional accuracy, fit, and uniformity. 3D-printed bolus was also found to benefit workflow efficiency through both time and cost effectiveness. However, factors such as patient comfort and staff perspectives need to be further explored to support the use of 3Dprinted bolus in routine practice.

Glue, Onyx, Squid or PHIL? Liquid Embolic Agents for the Embolization of Cerebral Arteriovenous Malformations and Dural Arteriovenous Fistulas

Background

Endovascular embolization is an effective treatment option for cerebral arteriovenous malformations (AVMs) and dural arteriovenous fistulas (DAVFs). A variety of liquid embolic agents have been and are currently used for embolization of AVMs and DAVFs. Knowledge of the special properties of the agent which is used is crucial for an effective and safe embolization procedure.

Material and Methods

This article describes the properties and indications of the liquid embolic agents which are currently available: cyanoacrylates (also called glues), and the copolymers Onyx, Squid and PHIL, as well as their respective subtypes.

Results

Cyanoacrylates were the predominantly used agents in the 1980s and 1990s. They are currently still used in specific situations, for example for the occlusion of macro-shunts, for the pressure cooker technique or in cases in which microcatheters are used that are not compatible with dimethyl-sulfoxide. The first broadly used copolymer-based embolic agent Onyx benefits from a large amount of available experience and data, which demonstrated its safety and efficacy in the treatment of cerebral vascular malformations, while its drawbacks include temporary loss of visibility during longer injections and artifacts in cross-sectional imaging. The more recently introduced agents Squid and PHIL aim to overcome these shortcomings and to improve the success rate of endovascular embolization. Novelties of these newer agents with potential advantages include extra-low viscosity versions, more stable visibility, and a lower degree of imaging artifacts.

Conclusion

All the available liquid embolic agents feature specific potential advantages and disadvantages over each other. The choice of the most appropriate embolic agent must be made based on the specific material characteristics of the agent, related to the specific anatomical characteristics of the target pathology.

Deep learning for segmentation in radiation therapy planning: a review

Segmentation of organs and structures, as either targets or organs-at-risk, has a significant influence on the success of radiation therapy. Manual segmentation is a tedious and time-consuming task for clinicians, and inter-observer variability can affect the outcomes of radiation therapy. The recent hype over deep neural networks has added many powerful auto-segmentation methods as variations of convolutional neural networks (CNN). This paper presents a descriptive review of the literature on deep learning techniques for segmentation in radiation therapy planning. The most common CNN architecture across the four clinical sub sites considered was U-net, with the majority of deep learning segmentation articles focussed on head and neck normal tissue structures. The most common data sets were CT images from an inhouse source, along with some public data sets. N-fold cross-validation was commonly employed; however, not all work separated training, test and validation data sets. This area of research is expanding rapidly. To facilitate comparisons of proposed methods and benchmarking, consistent use of appropriate metrics and independent validation should be carefully considered.

Imaging Artifacts of Nonadhesive Liquid Embolic Agents in Conventional and Cone-beam CT in a Novel in Vitro AVM Model

Background

A major drawback of liquid embolic agents (LEAs) is the generation of imaging artifacts (IA), which may represent a crucial obstacle for the detection of periprocedural hemorrhage or subsequent radiosurgery of cerebral arteriovenous malformations (AVMs). This study aimed to compare the IAs of Onyx, Squid and PHIL in a novel three-dimensional in vitro AVM model in conventional computed tomography (CT) and cone-beam CT (CBCT).

Methods

Tubes with different diameters were configured in a container resembling an AVM with an artificial nidus at its center. Subsequently, the AVM models were filled with Onyx 18, Squid 18, PHIL 25% or saline and inserted into an imaging phantom (n = 10/LEA). Afterwards CT and CBCT scans were acquired. The degree of IAs was graded quantitatively (Hounsfield units in a defined region of interest) and qualitatively (feasibility of defining the nidus)—Onyx vs. Squid vs. PHIL vs. saline, respectively.

Results

Quantitative density evaluation demonstrated more artifacts for Onyx compared to Squid and PHIL, e.g. 48.15 ± 14.32 HU for Onyx vs. 7.56 ± 1.34 HU for PHIL in CT (p < 0.001) and 41.88 ± 7.22 density units (DU) for Squid vs. 35.22 ± 5.84 DU for PHIL in CBCT (p = 0.044). Qualitative analysis showed less artifacts for PHIL compared to Onyx and Squid in both imaging modalities while there was no difference between Onyx and Squid regarding the definition of the nidus (p > 0.999).

Conclusion

In this novel three-dimensional in vitro AVM model, IAs were higher for the EVOH/tantalum-based LEAs Onyx and Squid compared to iodine-based PHIL. Onyx induced the highest degree of IAs with only minor differences to Squid.

Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning

Radiotherapy plays an important role in controlling the local recurrence of esophageal cancer after radical surgery. Segmentation of the clinical target volume is a key step in radiotherapy treatment planning, but it is time-consuming and operator-dependent. This paper introduces a deep dilated convolutional U-network to achieve fast and accurate clinical target volume auto-segmentation of esophageal cancer after radical surgery. The deep dilated convolutional U-network, which integrates the advantages of dilated convolution and the U-network, is an end-to-end architecture that enables rapid training and testing. A dilated convolution module for extracting multiscale context features containing the original information on fine texture and boundaries is integrated into the U-network architecture to avoid information loss due to down-sampling and improve the segmentation accuracy. In addition, batch normalization is added to the deep dilated convolutional U-network for fast and stable convergence. In the present study, the training and validation loss tended to be stable after 40 training epochs. This deep dilated convolutional U-network model was able to segment the clinical target volume with an overall mean Dice similarity coefficient of 86.7% and a respective 95% Hausdorff distance of 37.4 mm, indicating reasonable volume overlap of the auto-segmented and manual contours. The mean Cohen kappa coefficient was 0.863, indicating that the deep dilated convolutional U-network was robust. Comparisons with the U-network and attention U-network showed that the overall performance of the deep dilated convolutional U-network was best for the Dice similarity coefficient, 95% Hausdorff distance, and Cohen kappa coefficient. The test time for segmentation of the clinical target volume was approximately 25 seconds per patient. This deep dilated convolutional U-network could be applied in the clinical setting to save time in delineation and improve the consistency of contouring.

Imaging Artifacts of Liquid Embolic Agents on Conventional CT in an Experimental in Vitro Model

BACKGROUND AND PURPOSE

Endovascular embolization using liquid embolic agents is a safe and effective treatment option for AVMs and dural arteriovenous fistulas. The aim of this study was to assess the degree of artifact inducement by the most frequently used liquid embolic agents in conventional CT in an experimental in vitro model.

MATERIALS AND METHODS

Dimethyl-sulfoxide-compatible tubes were filled with the following liquid embolic agents (n = 10, respectively): Onyx 18, all variants of Squid, PHIL 25%, PHIL LV, and n-BCA mixed with iodized oil. After inserting the tubes into a CT imaging phantom, we acquired images. Artifacts were graded quantitatively by the use of Hounsfield units in a donut-shaped ROI using a customized software application that was specifically designed for this study and were graded qualitatively using a 5-point scale.

RESULTS

Quantitative and qualitative analyses revealed the most artifacts for Onyx 18 and the least artifacts for n-BCA, PHIL 25%, and PHIL LV. Squid caused more artifacts compared with PHIL, both for the low-viscosity and for the extra-low-viscosity versions (eg, quantitative analysis, Squid 18: mean ± SD, 30.3 ± 9.7 HU versus PHIL 25%: mean ± SD, 10.6 ± 0.8 HU; P < .001). Differences between the standard and low-density variants of Squid were observed only quantitatively for Squid 12. There were no statistical differences between the different concentrations of Squid and PHIL.

CONCLUSIONS

In this systematic in vitro analysis investigating the most commonly used liquid embolic agents, relevant differences in CT imaging artifacts could be demonstrated. Ethylene-vinyl alcohol-based liquid embolic agents induced more artifacts compared with liquid embolic agents that use iodine as a radiopaque component.

Mechanisms for Tuning Engineered Nanomaterials to Enhance Radiation Therapy of Cancer

Engineered nanomaterials that produce reactive oxygen species on exposure to X- and gamma-rays used in radiation therapy offer promise of novel cancer treatment strategies. Similar to photodynamic therapy but suitable for large and deep tumors, this new approach where nanomaterials acting as sensitizing agents are combined with clinical radiation can be effective at well-tolerated low radiation doses. Suitably engineered nanomaterials can enhance cancer radiotherapy by increasing the tumor selectivity and decreasing side effects. Additionally, the nanomaterial platform offers therapeutically valuable functionalities, including molecular targeting, drug/gene delivery, and adaptive responses to trigger drug release. The potential of such nanomaterials to be combined with radiotherapy is widely recognized. In order for further breakthroughs to be made, and to facilitate clinical translation, the applicable principles and fundamentals should be articulated. This review focuses on mechanisms underpinning rational nanomaterial design to enhance radiation therapy, the understanding of which will enable novel ways to optimize its therapeutic efficacy. A roadmap for designing nanomaterials with optimized anticancer performance is also shown and the potential clinical significance and future translation are discussed.

Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods

Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (H&N), which requires a precise spatial description of the target volumes and organs at risk (OARs) to deliver a highly conformal radiation dose to the tumor cells while sparing the healthy tissues. For this purpose, target volumes and OARs have to be delineated and segmented from medical images. As manual delineation is a tedious and time-consuming task subjected to intra/interobserver variability, computerized auto-segmentation has been developed as an alternative. The field of medical imaging and RT planning has experienced an increased interest in the past decade, with new emerging trends that shifted the field of H&N OAR auto-segmentation from atlas-based to deep learning-based approaches. In this review, we systematically analyzed 78 relevant publications on auto-segmentation of OARs in the H&N region from 2008 to date, and provided critical discussions and recommendations from various perspectives: image modality - both computed tomography and magnetic resonance image modalities are being exploited, but the potential of the latter should be explored more in the future; OAR - the spinal cord, brainstem, and major salivary glands are the most studied OARs, but additional experiments should be conducted for several less studied soft tissue structures; image database - several image databases with the corresponding ground truth are currently available for methodology evaluation, but should be augmented with data from multiple observers and multiple institutions; methodology - current methods have shifted from atlas-based to deep learning auto-segmentation, which is expected to become even more sophisticated; ground truth - delineation guidelines should be followed and participation of multiple experts from multiple institutions is recommended; performance metrics - the Dice coefficient as the standard volumetric overlap metrics should be accompanied with at least one distance metrics, and combined with clinical acceptability scores and risk assessments; segmentation performance - the best performing methods achieve clinically acceptable auto-segmentation for several OARs, however, the dosimetric impact should be also studied to provide clinically relevant endpoints for RT planning.

Simultaneous Metabolic and Perfusion Imaging Using Hyperpolarized 13C MRI Can Evaluate Early and Dose-Dependent Response to Radiation Therapy in a Prostate Cancer Mouse Model

PURPOSE

To investigate use of a novel imaging approach, hyperpolarized (HP) ¹³C magnetic resonance imaging (MRI) for simultaneous metabolism and perfusion assessment, to evaluate early and dose-dependent response to radiation therapy (RT) in a prostate cancer mouse model.

METHODS AND MATERIALS

Transgenic Adenocarcinoma of Mouse Prostate (TRAMP) mice (n = 18) underwent single-fraction RT (4-14 Gy steep dose across the tumor) and were imaged serially at pre-RT baseline and 1, 4, and 7 days after RT using HP ¹³C MRI with combined [1-¹³C]pyruvate (metabolic active agent) and [¹³C]urea (perfusion agent), coupled with conventional multiparametric ¹H MRI including T2-weighted, dynamic contrast-enhanced, and diffusion-weighted imaging. Tumor tissues were collected 4 and 7 days after RT for biological correlative studies.

RESULTS

We found a significant decrease in HP pyruvate-to-lactate conversion in tumors responding to RT, with concomitant significant increases in HP pyruvate-to-alanine conversion and HP urea signal; the opposite changes were observed in tumors resistant to RT. Moreover, HP lactate change was dependent on radiation dose; tumor regions treated with higher radiation doses (10-14 Gy) exhibited a greater decrease in HP lactate signal than low-dose regions (4-7 Gy) as early as 1 day post-RT, consistent with lactate dehydrogenase enzyme activity and expression data. We also found that HP [¹³C]urea MRI provided assessments of tumor perfusion similar to those provided by ¹H dynamic contrast-enhanced MRI in this animal model. However, apparent diffusion coefficien , a conventional ¹H MRI functional biomarker, did not exhibit statistically significant changes within 7 days after RT.

CONCLUSION

These results demonstrate the ability of HP ¹³C MRI to monitor radiation-induced physiologic changes in a timely and dose-dependent manner, providing the basic science premise for further clinical investigation and translation.

Woven Natural Fibre Reinforced Composite Materials for Medical Imaging

Repeatable patient positioning is key to minimising the burden on planning radiotherapy treatment. There are very few materials commercially available which are suitable for use in all common imaging and treatment modalities such as magnetic resonance imaging (MRI), X-Ray computed tomography (CT) and radiotherapy. In this article, we present several such materials based on woven natural fibres embedded in a range of different resin materials which are suitable for such applications. By investigating a range of resins and natural fibre materials in combination and evaluating their performance in terms of MRI and X-Ray imaging, we show that a woven cotton material impregnated with a two-part epoxy resin provides a 15% improvement in passage of X-Rays and has no impact on the MRI signal (unlike the 40% MRI signal attenuation from carbon fibre), whilst also retaining a flexural modulus up to 71% of that of carbon fibre. These results demonstrate that natural fibre composites produced using such materials provide desirable properties for use in patient support and positioning devices for multi-modal imaging, without the need to significantly compromise on the strength of the material.

A minimal modeling framework of radiation and immune system synergy to assist radiotherapy planning

Recent evidence indicates the ability of radiotherapy to induce local and systemic tumor-specific immune responses as a result of immunogenic cell death. However, fractionation regimes routinely used in clinical practice typically ignore the synergy between radiation and the immune system, and instead attempt to completely eradicate tumors by the direct lethal effect of radiation on cancer cells. This paradigm is expected to change in the near future due to the potential benefits of considering radiation-induced antitumor immunity during treatment planning. Towards this goal, we propose a minimal modeling framework based on key aspects of the tumor-immune system interplay to simulate the effects of radiation on tumors and the immunological consequences of radiotherapy. The impacts of tumor-associated vasculature and intratumoral oxygen-mediated heterogeneity on treatment outcomes are ininvestigated. The model provides estimates of the minimum radiation doses required for tumor eradication given a certain number of treatment fractions. Moreover, estimates of treatment duration for disease control given predetermined fractional radiation doses can be also obtained. Although theoretical in nature, this study motivates the development and establishment of immune-based decision-support tools in radiotherapy planning.

In Vivo Measurement of Brain Tissue Response After Irradiation: Comparison of T2 Relaxation, Apparent Diffusion Coefficient, and Electrical Conductivity

Radiation therapy (RT) has been widely used as a powerful treatment tool to address cancerous tissues because of its ability to control cell growth. Its ionizing radiation damages the DNA of cancerous tissues, leading to cell death. Medical imaging, however, still has limitations regarding the reliability of its assessment of tissue response and in predicting the treatment effect because of its inability to provide contrast information on the gradual, minute tissue changes after RT. A recently developed magnetic resonance (MR)-based conductivity imaging method may provide direct, highly sensitive information on this tissue response because its contrast mechanism is based on the concentration and mobility of ions in intracellular and extracellular spaces. In this feasibility study, we applied T2-weighted, diffusion-weighted, and electrical conductivity imaging to mouse brain, thus, using the MR imaging to map the tissue response after radiation exposure. To evaluate the degree of response, we measured the T2 relaxation, apparent diffusion coefficient (ADC), and electrical conductivity of brain tissues before and after irradiation. The conductivity images, which showed significantly higher sensitivity than other MR imaging methods, indicated that the contrast is distinguishable in different ways at different areas of the brain. Future studies will focus on verifying these results and the long-term evaluation of conductivity changes using various irradiation methods for clinical applications.

MRI-Based Proton Treatment Planning for Base of Skull Tumors

Purpose

To introduce a novel, deep-learning method to generate synthetic computed tomography (SCT) scans for proton treatment planning and evaluate its efficacy.

Materials and Methods

50 Patients with base of skull tumors were divided into 2 nonoverlapping training and study cohorts. Computed tomography and magnetic resonance imaging pairs for patients in the training cohort were used for training our novel 3-dimensional generative adversarial network (cycleGAN) algorithm. Upon completion of the training phase, SCT scans for patients in the study cohort were predicted based on their magnetic resonance images only. The SCT scans obtained were compared against the corresponding original planning computed tomography scans as the ground truth, and mean absolute errors (in Hounsfield units [HU]) and normalized cross-correlations were calculated. Proton plans of 45 Gy in 25 fractions with 2 beams per plan were generated for the patients based on their planning computed tomographies and recalculated on SCT scans. Dose-volume histogram endpoints were compared. A γ-index analysis along 3 cardinal planes intercepting at the isocenter was performed. Proton distal range along each beam was calculated.

Results

Image quality metrics show agreement between the generated SCT scans and the ground truth with mean absolute error values ranging from 38.65 to 65.12 HU and an average of 54.55 ± 6.81 HU and a normalized cross-correlation average of 0.96 ± 0.01. The dosimetric evaluation showed no statistically significant differences (p > 0.05) within planning target volumes for dose-volume histogram endpoints and other metrics studied, with the exception of the dose covering 95% of the target volume, with a relative difference of 0.47%. The γ-index analysis showed an average passing rate of 98% with a 10% threshold and 2% and 2-mm criteria. Proton ranges of 48 of 50 beams (96%) in this study were within clinical tolerance adopted by 4 institutions.

Conclusions

This study shows our method is capable of generating SCT scans with acceptable image quality, dose distribution agreement, and proton distal range compared with the ground truth. Our results set a promising approach for magnetic resonance imaging-based proton treatment planning.

MRI safety risks in the obese: The case of the disposable lighter stored in the pannus

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Obese patients are subject to higher MRI risks than lower weight patients.

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Obese patients typically require additional setup and acquisition times for MRI.

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Unanticipated safety threats may arise in obese patients despite vigilant screening.

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Threats to MRI safety may impact other clinical procedures.

Obese patients constitute 40% of the adult population. MRIs of obese patients are typically challenging because of the effects of a large field of view on image quality and the increased risk of thermal burns from contact with the bore. In this case report, the impacts of obesity on MRI procedures and safety are introduced. Then a case is presented of a 30-year old female cervical cancer patient who received an MRI simulation to verify the placement of a titanium tandem and colpostats for brachytherapy. A large magnetic susceptibility artifact was detected near the right pelvis during the MRI scout indicating the presence of ferrous material. The source of the artifact turned out to be a disposable lighter that was stored inside the patient's pannus. The finding highlights an unanticipated risk to MRI safety and image quality associated with large body habitus.

Nanotheranostics, a future remedy of neurological disorders

INTRODUCTION

Effective therapy of various neurological disorders is hindered on account of the failure of various therapeutics crossing blood-brain-barrier (BBB). Nanotheranostics has emerged as a cutting-edge unconventional theranostic nanomedicine, capable of realizing accurate diagnosis together with effective and targeted delivery of therapeutics across BBB to the unhealthy regions of the brain for potential clinical success.

AREAS COVERED

We have tried to review the current status of nanotheranostic based approaches followed to manage neurological disorders. The focus has been majorly laid on to explore various theranostic nanoparticles and their application potential towards image-guided neurotherapies. Additionally, the usefulness of exceptional diagnostic, imaging techniques including magnetic resonance imaging and fluorescence imaging are being discussed by highlighting their promising opportunities in the detection, diagnosis, and treatment of the neurological disorders.

EXPERT OPINION

Inimitable diagnostic and therapeutic potential of nanotheranostics have accomplished the aim of personalized therapies by governing the therapeutic efficacy of the system along with facilitating patient pre-selection grounded on non-invasive imaging, thereby predicting the responses of patients to nanomedicine treatments. While these accomplishments are encouraging, they are still the minority and demands for a continuous effort to improve sensitivity and precision in screening/diagnosis along with improving therapeutic efficacy in various neural disorders.

FDG-PET/CT in the management of lymphomas: current status and future directions

FDG-PET/CT is the current state-of-the-art imaging in lymphoma and plays a central role in treatment decisions. At diagnosis, accurate staging is crucial for appropriate therapy selection: FDG-PET/CT can identify areas of lymphoma missed by CT alone and avoid under-treatment of patients with advanced disease stage who would have been misclassified as having limited stage disease by CT. Particularly in Hodgkin lymphoma, positive interim FDG-PET/CT scans are adversely prognostic for clinical outcomes and can inform PET-adapted treatment strategies, but such data are less consistent in diffuse large B-cell lymphoma. The use of quantitative FDG-PET/CT metrics using metabolic tumour volume, possibly in combination with other biomarkers, may better define prognostic subgroups and thus facilitate better treatment selection. After chemotherapy, FDG-PET/CT response is predictive of outcome and may identify a subgroup who benefit from consolidative radiotherapy. Novel therapies, in particular immunotherapies, exhibit different response patterns than conventional chemotherapy, which has led to modified response criteria that take into account the risk of transient pseudo-progression. In relapsed lymphoma, FDG-PET/CT after second-line therapy and prior to high-dose therapy is also strongly associated with outcome and may be used to guide intensity of salvage therapy in relapsed Hodgkin lymphoma. Currently, FDG-PET/CT has no role in the routine follow-up after complete metabolic response to therapy, but it remains a powerful tool for excluding relapse if patients develop clinical features suggestive of disease relapse. In conclusion, FDG-PET/CT plays major roles in the various phases of management of lymphoma and constitutes a step towards the pursuit of personalized treatment.

Assessment of image quality of a radiotherapy-specific hardware solution for PET/MRI in head and neck cancer patients

BACKGROUND AND PURPOSE

Functional PET/MRI has great potential to improve radiotherapy planning (RTP). However, data integration requires imaging with radiotherapy-specific patient positioning. Here, we investigated the feasibility and image quality of radiotherapy-customized PET/MRI in head-and-neck cancer (HNC) patients using a dedicated hardware setup.

MATERIAL AND METHODS

Ten HNC patients were examined with simultaneous PET/MRI before treatment, with radiotherapy and diagnostic scan setup, respectively. We tested feasibility of radiotherapy-specific patient positioning and compared the image quality between both setups by pairwise image analysis of 18F-FDG-PET, T1/T2-weighted and diffusion-weighted MRI. For image quality assessment, similarity measures including average symmetric surface distance (ASSD) of PET and MR-based tumor contours, MR signal-to-noise ratio (SNR) and mean apparent diffusion coefficient (ADC) value were used.

RESULTS

PET/MRI in radiotherapy position was feasible - all patients were successfully examined. ASSD (median/range) of PET and MR contours was 0.6 (0.4-1.2) and 0.9 (0.5-1.3) mm, respectively. For T2-weighted MRI, a reduced SNR of -26.2% (-39.0--11.7) was observed with radiotherapy setup. No significant difference in mean ADC was found.

CONCLUSIONS

Simultaneous PET/MRI in HNC patients using radiotherapy positioning aids is clinically feasible. Though SNR was reduced, the image quality obtained with a radiotherapy setup meets RTP requirements and the data can thus be used for personalized RTP.

MR-CBCT image-guided system for radiotherapy of orthotopic rat prostate tumors

Multi-modality image-guided radiotherapy is the standard of care in contemporary cancer management; however, it is not common in preclinical settings due to both hardware and software limitations. Soft tissue lesions, such as orthotopic prostate tumors, are difficult to identify using cone beam computed tomography (CBCT) imaging alone. In this study, we characterized a research magnetic resonance (MR) scanner for preclinical studies and created a protocol for combined MR-CBCT image-guided small animal radiotherapy. Two in-house dual-modality, MR and CBCT compatible, phantoms were designed and manufactured using 3D printing technology. The phantoms were used for quality assurance tests and to facilitate end-to-end testing for combined preclinical MR and CBCT based treatment planning. MR and CBCT images of the phantoms were acquired utilizing a Varian 4.7 T scanner and XRad-225Cx irradiator, respectively. The geometry distortion was assessed by comparing MR images to phantom blueprints and CBCT. The corrected MR scans were co-registered with CBCT and subsequently used for treatment planning. The fidelity of 3D printed phantoms compared to the blueprint design yielded favorable agreement as verified with the CBCT measurements. The geometric distortion, which varied between -5% and 11% throughout the scanning volume, was substantially reduced to within 0.4% after correction. The distortion free MR images were co-registered with the corresponding CBCT images and imported into a commercial treatment planning software SmART Plan. The planning target volume (PTV) was on average 19% smaller when contoured on the corrected MR-CBCT images relative to raw images without distortion correction. An MR-CBCT based preclinical workflow was successfully designed and implemented for small animal radiotherapy. Combined MR-CBCT image-guided radiotherapy for preclinical research potentially delivers enhanced relevance to human radiotherapy for various disease sites. This novel protocol is wide-ranging and not limited to the orthotopic prostate tumor study presented in the study.