The transformative effect of oxygen on conventional x-ray radiotherapy has long been known, with the presence of molecular oxygen boosting treatment efficacy multi-fold. This effect is present too in charged particle therapy, but the boosting potential decreases with increasing linear energy transfer of the incident radiation. With particle modalities such as proton therapy becoming common-place and

The suppression of streak artifacts in computed tomography with a limited-angle configuration is challenging. Conventional analytical algorithms, such as filtered backprojection (FBP), are not successful due to incomplete projection data. Moreover, model-based iterative total variation algorithms effectively reduce small streaks but do not work well at eliminating large streaks. In contrast, FBP mapping

One of the major concerns associated with high-performance gradients is peripheral nerve stimulation (PNS) of the subject during MRI exams. Since the installation, more than 680 volunteer subjects (patients and controls) have been scanned on a compact 3 T MRI system with high-performance gradients, capable of 80 mT m −1 gradient amplitude and 700 T m −1 s −1 slew rate simultaneously. Despite PNS concerns

Gynecologic cancers are often asymmetric, yet current Ir-192 brachytherapy techniques provide only limited radial modulation of the dose. The shielded solutions investigated here solve this by providing the ability to modulate between highly asymmetric and radially symmetric dose distributions at a given location. To find applicator designs that can modulate between full dose and less than 50% dose

To develop a Monte Carlo (MC)-based and robust ion beam therapy optimization system that separates the optimization algorithm from the relative biological effectiveness (RBE) modeling. Robustly optimized dose distributions were calculated and compared across three ion therapy beams (proton, helium, carbon). The effect of different averaging techniques in calculating RBE in mixed beams was also investigated

The novel coronavirus, SARS-CoV-2, that causes the COVID-19 disease currently has healthcare systems around the world dealing with unprecedented numbers of critically ill patients. One of the primary concerns associated with this illness is acute respiratory distress syndrome (ARDS) and the pneumonia that accompanies it. Historical literature dating back to the 1940s and earlier contains many reports

Thoracic tumours are increasingly considered indications for pencil beam scanned proton therapy (PBS-PT) treatments. Conservative robustness settings have been suggested due to potential range straggling effects caused by the lung micro-structure. Using proton radiography (PR) and a 4D porcine lung phantom, we experimentally assess range errors to be considered in robust treatment planning for thoracic

Artifacts caused by patient breathing and movement during PET data acquisition affect image quality. Respiratory gating is commonly used to gate the list-mode PET data into multiple bins over a respiratory cycle. Non-rigid registration of respiratory-gated PET images can reduce motion artifacts and preserve count statistics, but it is time consuming. In this work, we propose an unsupervised non-rigid

The purpose of this work is, firstly, to propose an optimized parametrization of the attenuation coefficient to describe human tissues in the context of projection-based material characterization with multi-energy CT. The approach is based on eigentissue decomposition (ETD). Secondly, to evaluate its benefits in terms of accuracy and precision of radiotherapy-related parameters against established

Carbon-ion beams are increasingly used in the clinical practice for external radiotherapy treatments of deep-seated tumors. At therapeutic energies, carbon ions yield significant secondary products, including neutrons, which may be of concern for the radiation protection of the patient and personnel. We simulated the neutron yield produced by proton and carbon-ion pencil beams impinging on a clinical

TOPAS-nBio was used to simulate, collision-to-collision, the complete trajectories of electrons in water generated during the explicit simulation of 64 Cu decay. S-values and direct damage to the DNA were calculated representing the cell (C) and the cell nucleus (N) with concentric spheres of 5 μ m and 4 μ m in radius, respectively. The considered ‘target’←‘source’ configurations, including the cell

The LNE-LNHB has developed a methodology to standardize electronic brachytherapy sources in terms of absorbed dose to water. It is based on the measurement of the air-kerma rate at a given distance from the source and the Monte Carlo calculation of a conversion factor. This factor converts the air-kerma in measurement conditions into absorbed dose to water at a 1 cm reference depth in a water phantom

In particle therapy, the x-ray based treatment planning converting photon attenuation values to relative stopping power ratio (RSP) introduces clinically relevant range uncertainties. Recently, novel imaging technologies using transmission ion beams have been investigated to directly assess the water equivalent thickness (WET) of tissue, showing improved accuracy in RSP reconstruction, while potentially

Modelling of the multi-leaf collimator (MLC) in treatment planning systems (TPS) is crucial for the dose calculation accuracy of intensity-modulated radiation therapy plans. However, no standardised methodology for their configuration exists to date. In this study we present a method that separates the effect of each dosimetric characteristic of the MLC, offering comprehensive equations for the determination

Accurate in vivo quantification of airway mucociliary transport (MCT) in animal models is important for understanding diseases such as cystic fibrosis, as well as for developing therapies. A non-invasive method of measuring MCT behaviour, based on tracking the position of micron sized particles using synchrotron x-ray imaging, has previously been described. In previous studies, the location (and path)

While many pre-defined computed tomographic (CT) measures have been utilized to characterize chronic obstructive pulmonary disease (COPD), it is still challenging to represent pathological alternations of multiple dimensions and highly spatial heterogeneity. Deep CNN transferred multiple instance learning (DCT-MIL) is proposed to identify COPD via CT images. After the lung is divided into eight sections

This paper presents a simulation framework for dynamic PET-MR. The main focus of this framework is to provide motion-resolved MR and PET data and ground truth motion information. This can be used in the optimisation and quantitative evaluation of image registration and in assessing the error propagation due to inaccuracies in motion estimation in complex motion-compensated reconstruction algorithms

The advent of technologies such as magnetic resonance imaging (MRI)-guided radiation therapy has led to the need for phantom materials that are capable of producing tissue-like contrast on both MRI and computed tomography (CT) imaging modalities. The purpose of this work is to develop a system of easily made and formed materials with adjustable T 1 and T 2 relaxation times, and x-ray attenuation properties

A convolutional neural network (CNN)-based tumor localization method with a single x-ray projection was previously developed by us. One finding is that the discrepancy in the discrepancy in the intensity between a digitally reconstructed radiograph (DRR) of a three-dimensional computed tomography (3D-CT) and the measured x-ray projection has an impact on the performance. To address this issue, a patient-dependent

Brain PET, which has led research in molecular imaging and diagnosis of brain cancer, epilepsy and neurodegenerative disorders, is being spotlighted again to promote earlier diagnosis of dementia with the advent of amyloid and tau tracers. To meet this demand, in this paper, we developed a brain-dedicated PET imaging device with a hemispherical detector arrangement, which provides comparable sensitivity

Myocardial perfusion (MP) PET imaging plays a key role in risk assessment and stratification of patients with coronary artery disease. In this work, we proposed a patch-based artificial neural network (ANN) fusion approach that integrates information from the ML and the post-smoothed ML reconstruction to improve MP PET imaging. The proposed method was applied to images reconstructed from different

Surgical reduction of pelvic dislocation is a challenging procedure with poor long-term prognosis if reduction does not accurately restore natural morphology. The procedure often requires long fluoroscopic exposure times and trial-and-error to achieve accurate reduction. We report a method to automatically compute the target pose of dislocated bones in preoperative CT and provide 3D guidance of reduction

Range and setup uncertainties in charged particle therapy may induce a discrepancy between the planned and the delivered dose. Countermeasures based on probabilistic (stochastic) optimization usually assume a Gaussian probability density to model the underlying range and setup error. While this standard assumption is generally taken for granted, this study explicitly investigates the dosimetric consequences

To implement a multi-field-optimization (MFO) technique for treating patients with high-Z implants in pencil beam scanning proton-therapy and generate treatment plans that avoids small implants. Two main issues were addressed: (i) the assessment of the optimal CT acquisition and segmentation technique to define the dimension of the implant and (ii) the distance of pencil beams from the implant (avoidance

The primary cone-beam computed tomography (CBCT) imaging beam scatters inside the patient and produces a contaminating photon fluence that is registered by the detector. Scattered photons cause artifacts in the image reconstruction, and are partially responsible for the inferior image quality compared to diagnostic fan-beam CT. In this work, a deep convolutional autoencoder (DCAE) and projection-based

Compton Cameras are electronically collimated photon imagers suitable for sub-MeV to few MeV gamma-ray detection. Such features are desirable to enable in vivo range verification in hadron therapy, through the detection of secondary Prompt Gammas. A major concern with this technique is the poor image quality obtained when the incoming gamma-ray energy is unknown. Compton Cameras with more than two

Automated multi-organ segmentation on abdominal CT images may replace or complement manual segmentation for clinical applications including image-guided radiation therapy. However, the accuracy of auto-segmentation is challenged by low image contrast, large spatial and inter-patient anatomical variations. In this study, we propose an end-to-end segmentation network, termed self-paced DenseNet, for

In the study, a local approach to setting reference tolerance values for the distance-to-agreement (DTA) component of the gamma index is proposed. The reference tolerance values are calculated in simulations, following a dose delivery model presented in a previous work. An analytical model for determining the quantiles of DTA distribution is also proposed and verified. It is shown that the distributions

List mode proton imaging relies on accurate reconstruction of the proton most likely path (MLP) through the patient. This typically requires two sets of position sensitive detector systems, one upstream (front) and one downstream (rear) of the patient. However, for a clinical implementation it can be preferable to omit the front trackers (single-sided proton imaging). For such a system, the MLP can

In computed tomography (CT)-imaging an optimal compromise between the radiation burden and the image quality for the imaging task is needed. Lower-dose CT is desirable, however, lowering the dose results in a lower signal-to-noise ratio and therefore in a reduced image quality. In this research, we aim to develop a tool to simulate lower-dose scans from an existing standard-dose scan. The main application

In proton-based radiotherapy, proton radiography could allow for direct measurement of the water-equivalent path length (WEPL) in tissue, which can then be used to determine relative stopping power (RSP). Additionally, proton radiographs allow for imaging in the beam’s-eye-view. In this work, a proton radiography technique using a flat-panel imager and a pencil-beam scanning (PBS) system is demonstrated

Skin lesion datasets consist predominantly of normal samples with only a small percentage of abnormal ones, giving rise to the class imbalance problem. Also, skin lesion images are largely similar in overall appearance owing to the low inter-class variability. In this paper, we propose a two-stage framework for automatic classification of skin lesion images using adversarial training and transfer learning

A practical wearable brain PET scanner capable of dynamic neuroimaging during free bodily movement will enable potential breakthrough basic neuroscience studies and help develop imaging-based neurological diagnoses and treatments. Weight, brain coverage, and sensitivity are three fundamental technical obstacles in the development of Fully Wearable High-Performance (FWHP) brain PET scanners. The purpose

Intensive computation time is required to simulate images of electronic portal imaging device (EPID) using Monte Carlo (MC) technique, limiting the development of applications associated with EPID, such as mega-voltage cone-beam computed tomography (MV-CBCT). In this study, a fast, accurate simulation strategy for MV-CBCT utilizing the FastEPID technique has been developed and validated. During FastEPID

Gold nanoparticles (GNPs) have been studied extensively as promising radiation dose enhancing agents. In the current study, the dose enhancement effect of GNPs for Ir-192 HDR brachytherapy is studied using Monte Carlo N-Particle code, version 6.2 (MCNP6.2) and compared with experimental results obtained using Burlin cavity theory formalism. The Ir-192 source is verified using TG-43 parameters and dose

In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Learning methods using iterative neural networks (NNs) and cascaded NNs have been reported to achieve state-of-the-art results with respect to various quantitative quality measures as PSNR, NRMSE and SSIM across different imaging

A dynamically compressible phantom of the human abdomen that simulates organ motion with breathing is being developed for possible testing of image-gated beam delivery in radiotherapy. The polyvinyl chloride plastisol (PVCP) phantom features a cavity that can contain a deformable normoxic polyacrylamide gel (nPAG) dosimeter that is intended for use with MRI to provide dosimetric data. The phantom has

Cone-beam x-ray luminescence computed tomography (CB-XLCT) has been proposed as a promising imaging tool, which enables three-dimensional imaging of the distribution of nanophosphors (NPs) in small animals. However, the reconstruction performance is usually unsatisfactory in terms of spatial resolution due to the ill-posedness of the CB-XLCT inverse problem. To alleviate this problem and to achieve

To investigate deep learning electrical properties tomography (EPT) for application on different simulated and in-vivo datasets, including pathologies for brain conductivity reconstructions, 3D patch-based convolutional neural networks were trained to predict conductivity maps from B 1 transceive phase data. To compare the performance of DL-EPT networks on different datasets, three datasets were used

Registration and fusion of magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) of the prostate can provide guidance for prostate brachytherapy. However, accurate registration remains a challenging task due to the lack of ground truth regarding voxel-level spatial correspondence, limited field of view, low contrast-to-noise ratio, and signal-to-noise ratio in TRUS. In this study, we proposed

Four-dimensional dose calculation (4D-DC) is crucial for predicting the dosimetric outcome in the presence of intra-fractional organ motion. Time-resolved dosimetry can provide significant insights into 4D pencil beam scanning dose accumulation and is therefore irreplaceable for benchmarking 4D-DC. In this study a novel approach of time-resolved dosimetry using five PinPoint ionization chambers (ICs)

Positron emission tomography (PET) is an ill-posed inverse problem and suffers high noise due to limited number of detected events. Prior information can be used to improve the quality of reconstructed PET images. Deep neural networks have also been applied to regularized image reconstruction. One method is to use a pretrained denoising neural network to represent the PET image and to perform a constrained

Tumor tracking during radiotherapy treatment can improve dose accuracy, conformity and sparing of healthy tissue. Many methods have been introduced to tackle this challenge utilizing multiple imaging modalities, including a template matching based approach using the megavoltage (MV) on-board portal imager demonstrated on 3D conformal treatments. However, the complexity of treatments is evolving with

Nanocarriers offer a promising approach to significantly improve therapeutic delivery to solid tumors as well as limit the side effects associated with anti-cancer agents. However, their relatively large size can negatively affect their ability to efficiently penetrate into more interior tumor regions, ultimately reducing therapeutic efficacy. Poor penetration of large agents such as nanocarriers is

In the evolving field of adaptive MR guided radiotherapy, the need for dedicated procedures for acceptance and quality assurance is increasing. Research has been devoted to MR compatible dosimeters and phantoms, but to date no end-to-end test has been presented that covers an MRgRT workflow. Such an end-to-end test should comprise each step of the workflow and include all associated uncertainties.

Recently, ICRP Task Group 103 developed new mesh-type reference computational phantoms (MRCPs) for the adult male and female by converting the current voxel-type reference computational phantoms (VRCPs) of ICRP Publication 110 into a high-quality/fidelity mesh format. Utilizing the great deformability/flexibility of the MRCPs compared with the VRCPs, in the present study, we established a body-size-dependent

This paper describes a realistic simulation of a Compton-camera (CC) based prompt-gamma (PG) imaging system for proton range verification for a range of clinical dose rates, and its comparison to PG measured data with a pre-clinical CC. We used a Monte Carlo plus Detector Effects (MCDE) model to simulate the production of prompt gamma-rays (PG) and their energy depositions in the CC. With Monte Carlo

We proposed a concept of whole gamma imaging (WGI) that utilizes all detectable gamma rays for imaging. An additional detector ring, which is used as the scatterer, is inserted in the field-of-view of a PET ring so that single gamma rays can be detected by the Compton imaging method. In particular, for the non-pure positron emitters which emit an additional gamma ray almost at the same time, triple

Motion is problematic during radiotherapy as it could lead to potential underdosage of the tumor, and/or overdosage in organs-at-risk. A solution is adaptive radiotherapy guided by magnetic resonance imaging (MRI). MRI allows for imaging of target volumes and organs-at-risk before and during treatment delivery with superb soft tissue contrast in any desired orientation, enabling motion management by

We calculate differential and integrated cross sections for the Compton interaction as well as mass attenuation ( ##IMG## [http://ej.iop.org/images/0031-9155/65/12/125010/pmbab8108ieqn1.gif] {$\mu^{\mathrm{C}}/\rho$} ), mass energy-transfer ( ##IMG## [http://ej.iop.org/images/0031-9155/65/12/125010/pmbab8108ieqn2.gif] {$\mu_{\mathrm{tr}}^{\mathrm{C}}/\rho$} ), and mass energy-absorption ( ##IMG## [http://ej

Radiochromic films are useful as dosimeters in high-precision radiotherapy owing to their high spatial resolution. However, when a particle beam is measured using a radiochromic film, the dose cannot be estimated accurately because the film darkness varies with variations in linear energy transfer (LET). This paper proposes a novel method for estimating the LET and the dose based on the film darkness

Low-dose x-ray computed tomography (LDCT) is desirable for reduced patient dose. This work develops new image reconstruction methods with deep learning (DL) regularization for LDCT. Our methods are based on the unrolling of a proximal forward–backward splitting (PFBS) framework with data-driven image regularization via deep neural networks. In contrast to PFBS-IR, which utilizes standard data fidelity

Magnetic resonance imaging (MRI) is gaining popularity in guiding radiation treatment for intrahepatic cancers due to its superior soft tissue contrast and potential of monitoring individual motion and liver function. This study investigates a deep learning-based method that generates synthetic CT volumes from T1-weighted MR Dixon images in support of MRI-based intrahepatic radiotherapy treatment planning

In heavy-ion therapy, the stopping position of primary ions in tumours needs to be monitored for effective treatment and to prevent overdose exposure to normal tissues. Positron-emitting ion beams, such as 11 C and 15 O, have been suggested for range verification in heavy-ion therapy using in-beam positron emission tomography (PET) imaging, which offers the capability of visualizing the ion stopping

We are building a high sensitivity preclinical PET/MRI insert using a highly multiplexed light sharing PET module. Each module incorporates four 19 × 19 arrays of 1 × 1 × 20 mm 3 LYSO crystals with dual-ended DOI encoding readout, requiring 32 readout channels for positioning and eight channels for timing. These constraints necessitate compact, robust electronics for digitization. We have characterized

We investigate the effects of an increase in the production of secondary electrons when a β − source commonly used in internal radionuclide therapy, 67 Cu, is radiolabelled to a super-paramagnetic iron oxide nanoparticle (SPION), with specific emphasis on the role of SPION cluster size and geometry. A positive relationship is found between the degree to which the nanoparticles are clustered and the

Description unavailable

We propose a novel BIRADS-SSDL network that integrates clinically-approved breast lesion characteristics (BIRADS features) into task-oriented semi-supervised deep learning (SSDL) for accurate diagnosis of ultrasound (US) images with a small training dataset. Breast US images are converted to BIRADS-oriented feature maps (BFMs) using a distance-transformation coupled with a Gaussian filter. Then, the

Description unavailable

We are developing magnetic nanoparticle (NP) methods to characterize inflammation and infection in vivo . Peritoneal infection in C57BL/6 mice was used as a biological model. An intraperitoneal NP injection was followed by measurement of magnetic nanoparticle spectroscopy of Brownian rotation (MSB) spectra taken over time. MSB measures the magnetization of NPs in a low frequency alternating magnetic