The 50 most-cited articles in Medical Physics, 2017

Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is…

PURPOSE: Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiotherapy also simplifies clinical workflow and avoids uncertainties in aligning MR with CT. Methods, however, are needed to derive CT-equivalent representations, often known as synthetic CT (sCT), from patient MR images for dose calculation and DRR-based patient positioning. Synthetic CT estimation is also important for PET attenuation correction in hybrid PET-MR systems. We propose in this work a novel deep convolutional neural network (DCNN) method for sCT generation and evaluate its performance on a set of brain tumor patient images. METHODS: The proposed method builds upon recent developments of deep learning and convolutional neural networks in the computer vision literature. The proposed DCNN model has 27 convolutional layers interleaved with pooling and unpooling layers and 35 million free parameters, which can be trained to learn a…

PURPOSE: Due to the potential risk of inducing cancer, radiation exposure by X-ray CT devices should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts typically occur due to photon starvation, beam hardening, and other causes, all of which decrease the reliability of the diagnosis. Thus, a high-quality reconstruction method from low-dose X-ray CT data has become a major research topic in the CT community. Conventional model-based de-noising approaches are, however, computationally very expensive, and image-domain de-noising approaches cannot readily remove CT-specific noise patterns. To tackle these problems, we want to develop a new low-dose X-ray CT algorithm based on a deep-learning approach. METHOD: We propose an algorithm which uses a deep convolutional neural network (CNN) which is applied to the wavelet transform coefficients of low-dose CT images. More specifically, using a directional wavelet transform to extract the directional component of artifacts and exploit the intra- and inter- band correlations, our deep network can effectively…

Purpose Many radiomics features were originally developed for non‐medical imaging applications and therefore original assumptions may need to be reexamined. In this study, we investigated the impact of slice thickness and pixel spacing (or pixel size) on radiomics features extracted from Computed Tomography (CT) phantom images acquired with different scanners as well as different acquisition and reconstruction parameters. The dependence of CT texture features on gray‐level discretization was also evaluated. Methods and materials A texture phantom composed of 10 different cartridges of different materials was scanned on eight different CT scanners from three different manufacturers. The images were reconstructed for various slice thicknesses. For each slice thickness, the reconstruction Field Of View (FOV) was varied to render pixel sizes ranging from 0.39 to 0.98 mm. A fixed spherical region of interest (ROI) was contoured on the images of the shredded rubber cartridge and the 3D printed, 20% fill, acrylonitrile butadiene styrene plastic cartridge (ABS20) for all phantom imaging…

BACKGROUND: Deep learning methods for radiomics/computer-aided diagnosis (CADx) are often prohibited by small datasets, long computation time, and the need for extensive image preprocessing. AIMS: We aim to develop a breast CADx methodology that addresses the aforementioned issues by exploiting the efficiency of pre-trained convolutional neural networks (CNNs) and using pre-existing handcrafted CADx features. MATERIALS & METHODS: We present a methodology that extracts and pools low- to mid-level features using a pretrained CNN and fuses them with handcrafted radiomic features computed using conventional CADx methods. Our methodology is tested on three different clinical imaging modalities (dynamic contrast enhanced-MRI [690 cases], full-field digital mammography [245 cases], and ultrasound [1125 cases]). RESULTS: From ROC analysis, our fusion-based method demonstrates, on all three imaging modalities, statistically significant improvements in terms of AUC as compared to previous breast cancer CADx methods in the task of distinguishing between malignant and benign lesions. (DCE-MRI…

Purpose We report on the development of the open-source cross-platform radiation treatment planning toolkit matRad and its comparison against validated treatment planning systems. The toolkit enables three-dimensional intensity-modulated radiation therapy treatment planning for photons, scanned protons and scanned carbon ions. Methods matRad is entirely written in Matlab and is freely available online. It re-implements well-established algorithms employing a modular and sequential software design to model the entire treatment planning workflow. It comprises core functionalities to import DICOM data, to calculate and optimize dose as well as a graphical user interface for visualization. matRad dose calculation algorithms (for carbon ions this also includes the computation of the relative biological effect) are compared against dose calculation results originating from clinically approved treatment planning systems. Results We observe three-dimensional γ-analysis pass rates ≥ 99.67% for all three radiation modalities utilizing a distance to agreement of 2 mm and a dose difference…

PURPOSE: Using common datasets, to estimate and compare the diagnostic performance of image-based denoising techniques or iterative reconstruction algorithms for the task of detecting hepatic metastases. METHODS: Datasets from contrast-enhanced CT scans of the liver were provided to participants in an NIH-, AAPM- and Mayo Clinic-sponsored Low Dose CT Grand Challenge. Training data included full-dose and quarter-dose scans of the ACR CT accreditation phantom and 10 patient examinations; both images and projections were provided in the training data. Projection data were supplied in a vendor-neutral standardized format (DICOM-CT-PD). Twenty quarter-dose patient datasets were provided to each participant for testing the performance of their technique. Images were provided to sites intending to perform denoising in the image domain. Fully preprocessed projection data and statistical noise maps were provided to sites intending to perform iterative reconstruction. Upon return of the denoised or iteratively reconstructed quarter-dose images, randomized, blinded evaluation of the cases was…

Studies involving Monte Carlo simulations are common in both diagnostic and therapy medical physics research, as well as other fields of basic and applied science. As with all experimental studies, the conditions and parameters used for Monte Carlo simulations impact their scope, validity, limitations, and generalizability. Unfortunately, many published peer-reviewed articles involving Monte Carlo simulations do not provide the level of detail needed for the reader to be able to properly assess the quality of the simulations. The American Association of Physicists in Medicine Task Group #268 developed guidelines to improve reporting of Monte Carlo studies in medical physics research. By following these guidelines, manuscripts submitted for peer-review will include a level of relevant detail that will increase the transparency, the ability to reproduce results, and the overall scientific value of these studies. The guidelines include a checklist of the items that should be included in the Methods, Results, and Discussion sections of manuscripts submitted for peer-review. These…

The introduction of advanced techniques and technology in radiotherapy has greatly improved our ability to deliver highly conformal tumor doses while minimizing the dose to adjacent organs at risk. Despite these tremendous improvements, there remains a general concern about doses to normal tissues that are not the target of the radiation treatment; any "nontarget" radiation should be minimized as it offers no therapeutic benefit. As patients live longer after treatment, there is increased opportunity for late effects including second cancers and cardiac toxicity to manifest. Complicating the management of these issues, there are unique challenges with measuring, calculating, reducing, and reporting nontarget doses that many medical physicists may have limited experience with. Treatment planning systems become dramatically inaccurate outside the treatment field, necessitating a measurement or some other means of assessing the dose. However, measurements are challenging because outside the treatment field, the radiation energy spectrum, dose rate, and general shape of the dose…

PURPOSE: Delineation of the clinical target volume (CTV) and organs at risk (OARs) is very important for radiotherapy but is time-consuming and prone to inter-observer variation. Here, we proposed a novel deep dilated convolutional neural network (DDCNN)-based method for fast and consistent auto-segmentation of these structures. METHODS: Our DDCNN method was an end-to-end architecture enabling fast training and testing. Specifically, it employed a novel multiple-scale convolutional architecture to extract multiple-scale context features in the early layers, which contain the original information on fine texture and boundaries and which are very useful for accurate auto-segmentation. In addition, it enlarged the receptive fields of dilated convolutions at the end of networks to capture complementary context features. Then, it replaced the fully connected layers with fully convolutional layers to achieve pixel-wise segmentation. We used data from 278 patients with rectal cancer for evaluation. The CTV and OARs were delineated and validated by senior radiation oncologists in the…

PURPOSE: Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow. METHODS: In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography…

PURPOSE: Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms. METHODS: In this work, we describe and present the results of the Head and Neck Auto-Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands. RESULTS: This paper presents the quantitative results of this challenge using multiple established error metrics and a well-defined ranking system. The strengths and weaknesses of the different auto-segmentation approaches are analyzed…

PURPOSE: To investigate deep reinforcement learning (DRL) based on historical treatment plans for developing automated radiation adaptation protocols for nonsmall cell lung cancer (NSCLC) patients that aim to maximize tumor local control at reduced rates of radiation pneumonitis grade 2 (RP2). METHODS: In a retrospective population of 114 NSCLC patients who received radiotherapy, a three-component neural networks framework was developed for deep reinforcement learning (DRL) of dose fractionation adaptation. Large-scale patient characteristics included clinical, genetic, and imaging radiomics features in addition to tumor and lung dosimetric variables. First, a generative adversarial network (GAN) was employed to learn patient population characteristics necessary for DRL training from a relatively limited sample size. Second, a radiotherapy artificial environment (RAE) was reconstructed by a deep neural network (DNN) utilizing both original and synthetic data (by GAN) to estimate the transition probabilities for adaptation of personalized radiotherapy patients' treatment courses.…

Purpose The purpose of this work was to establish an empirical model of the ion recombination in the Advanced Markus ionization chamber for measurements in high dose rate/dose‐per‐pulse electron beams. In addition, we compared the observed ion recombination to calculations using the standard Boag two‐voltage‐analysis method, the more general theoretical Boag models, and the semiempirical general equation presented by Burns and McEwen. Methods Two independent methods were used to investigate the ion recombination: (a) Varying the grid tension of the linear accelerator (linac) gun (controls the linac output) and measuring the relative effect the grid tension has on the chamber response at different source‐to‐surface distances (SSD). (b) Performing simultaneous dose measurements and comparing the dose–response, in beams with varying dose rate/dose‐per‐pulse, with the chamber together with dose rate/dose‐per‐pulse independent Gafchromic™ EBT3 film. Three individual Advanced Markus chambers were used for the measurements with both methods. All measurements were performed in electron…

PURPOSE: To demonstrate the feasibility of spectral imaging using photon-counting detector (PCD) x-ray computed tomography (CT) for simultaneous material decomposition of three contrast agents in vivo in a large animal model. METHODS: This Institutional Animal Care and Use Committee-approved study used a canine model. Bismuth subsalicylate was administered orally 24-72 h before imaging. PCD CT was performed during intravenous administration of 40-60 ml gadoterate meglumine; 3.5 min later, iopamidol 370 was injected intravenously. Renal PCD CT images were acquired every 2 s for 5-6 min to capture the wash-in and wash-out kinetics of the contrast agents. Least mean squares linear material decomposition was used to calculate the concentrations of contrast agents in the aorta, renal cortex, renal medulla and renal pelvis. RESULTS: Using reference vials with known concentrations of materials, we computed molar concentrations of the various contrast agents during each phase of CT scanning. Material concentration maps allowed simultaneous quantification of both arterial and delayed renal…

PURPOSE: The Oriatron eRT6 is an experimental high dose-per-pulse linear accelerator (linac) which was designed to deliver an electron beam with variable dose-rates, ranging from a few Gy/min up to hundreds of Gy/s. It was built to study the radiobiological effects of high dose-per-pulse/dose-rate electron beam irradiation, in the context of preclinical and cognitive studies. In this work, we report on the commissioning and beam monitoring of the Oriatron eRT6 prototype linac. MATERIALS AND METHODS: The beam was characterized in different steps. The output stability was studied by performing repeated measurements over a period of 20 months. The relative output variations caused by changing beam parameters, such as the temporal electron pulse width, the pulse repetition frequency and the pulse amplitude were also analyzed. Finally, depth dose curves and field sizes were measured for two different beam settings, resulting in one beam with a conventional radiotherapy dose-rate and one with a much higher dose-rate. Measurements were performed with Gafchromic EBT3 films and with a PTW…

PURPOSE: The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application. APPROACH: A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the…

The efficacy of stereotactic body radiotherapy (SBRT) has been well demonstrated. However, it presents unique challenges for accurate planning and delivery especially in the lungs and upper abdomen where respiratory motion can be significantly confounding accurate targeting and avoidance of normal tissues. In this paper, we review the current literature on SBRT for lung and upper abdominal tumors with particular emphasis on addressing respiratory motion and its affects. We provide recommendations on strategies to manage motion for different, patient-specific situations. Some of the recommendations will potentially be adopted to guide clinical trial protocols.

PURPOSE: Wireless capsule endoscopy (WCE) enables physicians to examine the digestive tract without any surgical operations, at the cost of a large volume of images to be analyzed. In the computer-aided diagnosis of WCE images, the main challenge arises from the difficulty of robust characterization of images. This study aims to provide discriminative description of WCE images and assist physicians to recognize polyp images automatically. METHODS: We propose a novel deep feature learning method, named stacked sparse autoencoder with image manifold constraint (SSAEIM), to recognize polyps in the WCE images. Our SSAEIM differs from the traditional sparse autoencoder (SAE) by introducing an image manifold constraint, which is constructed by a nearest neighbor graph and represents intrinsic structures of images. The image manifold constraint enforces that images within the same category share similar learned features and images in different categories should be kept far away. Thus, the learned features preserve large intervariances and small intravariances among images. RESULTS: The…

PURPOSE: We propose a single network trained by pixel-to-label deep learning to address the general issue of automatic multiple organ segmentation in three-dimensional (3D) computed tomography (CT) images. Our method can be described as a voxel-wise multiple-class classification scheme for automatically assigning labels to each pixel/voxel in a 2D/3D CT image. METHODS: We simplify the segmentation algorithms of anatomical structures (including multiple organs) in a CT image (generally in 3D) to a majority voting scheme over the semantic segmentation of multiple 2D slices drawn from different viewpoints with redundancy. The proposed method inherits the spirit of fully convolutional networks (FCNs) that consist of "convolution" and "deconvolution" layers for 2D semantic image segmentation, and expands the core structure with 3D-2D-3D transformations to adapt to 3D CT image segmentation. All parameters in the proposed network are trained pixel-to-label from a small number of CT cases with human annotations as the ground truth. The proposed network naturally fulfills the requirements of…

PURPOSE: To reconstruct MR images from subsampled data, we propose a fast reconstruction method using the multilayer perceptron (MLP) algorithm. METHODS AND MATERIALS: We applied MLP to reduce aliasing artifacts generated by subsampling in k-space. The MLP is learned from training data to map aliased input images into desired alias-free images. The input of the MLP is all voxels in the aliased lines of multichannel real and imaginary images from the subsampled k-space data, and the desired output is all voxels in the corresponding alias-free line of the root-sum-of-squares of multichannel images from fully sampled k-space data. Aliasing artifacts in an image reconstructed from subsampled data were reduced by line-by-line processing of the learned MLP architecture. RESULTS: Reconstructed images from the proposed method are better than those from compared methods in terms of normalized root-mean-square error. The proposed method can be applied to image reconstruction for any k-space subsampling patterns in a phase encoding direction. Moreover, to further reduce the reconstruction…

PURPOSE: It is very important for calculation of clinical indices and diagnosis to detect thyroid nodules from ultrasound images. However, this task is a challenge mainly due to heterogeneous thyroid nodules with distinct components are similar to background in ultrasound images. In this study, we employ cascade deep convolutional neural networks (CNNs) to develop and evaluate a fully automatic detection of thyroid nodules from 2D ultrasound images. METHODS: Our cascade CNNs are a type of hybrid model, consisting of two different CNNs and a new splitting method. Specifically, it employs a deep CNN to learn the segmentation probability maps from the ground true data. Then, all the segmentation probability maps are split into different connected regions by the splitting method. Finally, another deep CNN is used to automatically detect the thyroid nodules from ultrasound thyroid images. RESULTS: Experiment results illustrate the cascade CNNs are very effective in detection of thyroid nodules. Specially, the value of area under the curve of receiver operating characteristic is 98.51%.…

Wireless capsule endoscopy (WCE) is a powerful tool for medical screening and diagnosis, where a small capsule is swallowed and moved by means of natural peristalsis and gravity through the human gastrointestinal (GI) tract. The camera-integrated capsule allows for visualization of the small intestine, a region which was previously inaccessible to classical flexible endoscopy. As a diagnostic tool, it allows to localize the sources of bleedings in the middle part of the gastrointestinal tract and to identify diseases, such as inflammatory bowel disease (Crohn's disease), polyposis syndrome, and tumors. The screening and diagnostic efficacy of the WCE, especially in the stomach region, is hampered by a variety of technical challenges like the lack of active capsular position and orientation control. Therapeutic functionality is absent in most commercial capsules, due to constraints in capsular volume and energy storage. The possibility of using body-exogenous magnetic fields to guide, orient, power, and operate the capsule and its mechanisms has led to increasing research in…

PURPOSE: Imaging phantoms are widely used for testing and optimization of imaging devices without the need to expose humans to irradiation. However, commercially available phantoms are commonly manufactured in simple, generic forms and sizes and therefore do not resemble the clinical situation for many patients. METHODS: Using 3D printing techniques, we created a life-size phantom based on a clinical CT scan of the thorax from a patient with lung cancer. It was assembled from bony structures printed in gypsum, lung structures consisting of airways, blood vessels >1 mm, and outer lung surface, three lung tumors printed in nylon, and soft tissues represented by silicone (poured into a 3D-printed mold). RESULTS: Kilovoltage x-ray and CT images of the phantom closely resemble those of the real patient in terms of size, shapes, and structures. Surface comparison using 3D models obtained from the phantom and the 3D models used for printing showed mean differences <1 mm for all structures. Tensile tests of the materials used for the phantom show that the phantom is able to endure radiation…

PURPOSE: Dual-energy CT (DECT) promises improvements in estimating stopping power ratios (SPRs) for proton therapy treatment planning. Although several comparable mathematical formalisms have been proposed in literature, the optimal techniques to characterize human tissue SPRs with DECT in a clinical environment are not fully established. The aim of this work is to compare the most robust DECT methods against conventional single-energy CT (SECT) in conditions reproducing a clinical environment, where CT artifacts and noise play a major role on the accuracy of these techniques. METHODS: Available DECT tissue characterization methods are investigated and their ability to predict SPRs is compared in three contexts: (a) a theoretical environment using the XCOM cross section database; (b) experimental data using a dual-source CT scanner on a calibration phantom; (c) simulations of a virtual humanoid phantom with the ImaSim software. The latter comparison accounts for uncertainties caused by CT artifacts and noise, but leaves aside other sources of uncertainties such as CT grid size and…

Purpose To assess image quality and to quantify the accuracy of relative electron densities (ρe) and effective atomic numbers (Zeff) for three dual‐energy computed tomography (DECT) scanners: a novel single‐source split‐filter (i.e., twin‐beam) and two dual‐source scanners. Methods Measurements were made with a second generation dual‐source scanner at 80/140Sn kVp, a third‐generation twin‐beam single‐source scanner at 120 kVp with gold (Au) and tin (Sn) filters, and a third‐generation dual‐source scanner at 90/150Sn kVp. Three phantoms with tissue inserts were scanned and used for calibration and validation of parameterized methods to extract ρe and Zeff, whereas iodine and calcium inserts were used to quantify Contrast‐to‐Noise‐Ratio (CNR). Spatial resolution in tomographic images was also tested. Results The third‐generation scanners have an image resolution of 6.2, ~0.5 lp/cm higher than the second generation scanner. The twin‐beam scanner has low imaging contrast for iodine materials due to its limited spectral separation. The parameterization methods resulted in calibrations…

PURPOSE: The aim of this paper is to provide access to a database consisting of the raw radio-frequency ultrasonic echoes acquired from malignant and benign breast lesions. The database is freely available for study and signal analysis. ACQUISITION AND VALIDATION METHODS: The ultrasonic radio-frequency echoes were recorded from breast focal lesions of patients of the Institute of Oncology in Warsaw. The data were collected between 11/2013 and 10/2015. Patients were examined by a radiologist with 18 yr' experience in the ultrasonic examination of breast lesions. The set of data includes scans from 52 malignant and 48 benign breast lesions recorded in a group of 78 women. For each lesion, two individual orthogonal scans from the pathological region were acquired with the Ultrasonix SonixTouch Research ultrasound scanner using the L14-5/38 linear array transducer. All malignant lesions were histologically assessed by core needle biopsy. In the case of benign lesions, part of them was histologically assessed and another part was observed over a 2-year period. DATA FORMAT AND USAGE…

With the recent clinical implementation of real-time MRI-guided x-ray beam therapy (MRXT), attention is turning to the concept of combining real-time MRI guidance with proton beam therapy; MRI-guided proton beam therapy (MRPT). MRI guidance for proton beam therapy is expected to offer a compelling improvement to the current treatment workflow which is warranted arguably more than for x-ray beam therapy. This argument is born out of the fact that proton therapy toxicity outcomes are similar to that of the most advanced IMRT treatments, despite being a fundamentally superior particle for cancer treatment. In this Future of Medical Physics article, we describe the various software and hardware aspects of potential MRPT systems and the corresponding treatment workflow. Significant software developments, particularly focused around adaptive MRI-based planning will be required. The magnetic interaction between the MRI and the proton beamline components will be a key area of focus. For example, the modeling and potential redesign of a magnetically compatible gantry to allow for beam…

PURPOSE: The advancement of medical image processing techniques, such as image registration, can effectively help improve the accuracy and efficiency of brain tumor surgeries. However, it is often challenging to validate these techniques with real clinical data due to the rarity of such publicly available repositories. ACQUISITION AND VALIDATION METHODS: Pre-operative magnetic resonance images (MRI), and intra-operative ultrasound (US) scans were acquired from 23 patients with low-grade gliomas who underwent surgeries at St. Olavs University Hospital between 2011 and 2016. Each patient was scanned by Gadolinium-enhanced T1w and T2-FLAIR MRI protocols to reveal the anatomy and pathology, and series of B-mode ultrasound images were obtained before, during, and after tumor resection to track the surgical progress and tissue deformation. Retrospectively, corresponding anatomical landmarks were identified across US images of different surgical stages, and between MRI and US, and can be used to validate image registration algorithms. Quality of landmark identification was assessed with…

Purpose To evaluate the feasibility of using an objective computer‐aided system to assess bladder cancer stage in CT Urography ( CTU ). Materials and methods A dataset consisting of 84 bladder cancer lesions from 76 CTU cases was used to develop the computerized system for bladder cancer staging based on machine learning approaches. The cases were grouped into two classes based on pathological stage ≥ T2 or below T2, which is the decision threshold for neoadjuvant chemotherapy treatment clinically. There were 43 cancers below stage T2 and 41 cancers at stage T2 or above. All 84 lesions were automatically segmented using our previously developed auto‐initialized cascaded level sets ( AI ‐ CALS ) method. Morphological and texture features were extracted. The features were divided into subspaces of morphological features only, texture features only, and a combined set of both morphological and texture features. The dataset was split into Set 1 and Set 2 for two‐fold cross‐validation. Stepwise feature selection was used to select the most effective features. A linear discriminant…

PURPOSE: Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 yr of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically nested neural networks that could be employed for brain tumor segmentation of MRI images. METHODS: Two preprocessing techniques were applied to MRI images. The N4ITK method was employed for correction of bias field distortion. A novel landmark-based intensity normalization method was developed so that tissue types have a similar intensity scale in images of different subjects for the same MRI protocol. The holistically nested neural networks (HNN), which extend from the convolutional…

Low-dose X-ray computed tomography (LDCT) imaging is highly recommended for use in the clinic because of growing concerns over excessive radiation exposure. However, the CT images reconstructed by the conventional filtered back-projection (FBP) method from low-dose acquisitions may be severely degraded with noise and streak artifacts due to excessive X-ray quantum noise, or with view-aliasing artifacts due to insufficient angular sampling. In 2005, the nonlocal means (NLM) algorithm was introduced as a non-iterative edge-preserving filter to denoise natural images corrupted by additive Gaussian noise, and showed superior performance. It has since been adapted and applied to many other image types and various inverse problems. This paper specifically reviews the applications of the NLM algorithm in LDCT image processing and reconstruction, and explicitly demonstrates its improving effects on the reconstructed CT image quality from low-dose acquisitions. The effectiveness of these applications on LDCT and their relative performance are described in detail.

PURPOSE: It is estimated that 7% of women in the western world will develop palpable breast cysts in their lifetime. Even though cysts have been correlated with risk of developing breast cancer, many of them are benign and do not require follow-up. We develop a method to discriminate benign solitary cysts from malignant masses in digital mammography. We think a system like this can have merit in the clinic as a decision aid or complementary to specialized modalities. METHODS: We employ a deep convolutional neural network (CNN) to classify cyst and mass patches. Deep CNNs have been shown to be powerful classifiers, but need a large amount of training data for which medical problems are often difficult to come by. The key contribution of this paper is that we show good performance can be obtained on a small dataset by pretraining the network on a large dataset of a related task. We subsequently investigate the following: (a) when a mammographic exam is performed, two different views of the same breast are recorded. We investigate the merit of combining the output of the classifier…

Purpose Exploiting the x‐ray measurements obtained in different energy bins, spectral computed tomography (CT) has the ability to recover the 3‐D description of a patient in a material basis. This may be achieved solving two subproblems, namely the material decomposition and the tomographic reconstruction problems. In this work, we address the material decomposition of spectral x‐ray projection images, which is a nonlinear ill‐posed problem. Methods Our main contribution is to introduce a material‐dependent spatial regularization in the projection domain. The decomposition problem is solved iteratively using a Gauss–Newton algorithm that can benefit from fast linear solvers. A Matlab implementation is available online. The proposed regularized weighted least squares Gauss–Newton algorithm (RWLS‐GN) is validated on numerical simulations of a thorax phantom made of up to five materials (soft tissue, bone, lung, adipose tissue, and gadolinium), which is scanned with a 120 kV source and imaged by a 4‐bin photon counting detector. To evaluate the method performance of our algorithm,…

Purpose Hyperpolarized 129Xe magnetic resonance imaging (MRI) using Dixon-based decomposition enables single-breath imaging of 129Xe in the airspaces, interstitial barrier tissues, and red blood cells (RBCs). However, methods to quantitatively visualize information from these images of pulmonary gas transfer are lacking. Here, we introduce a novel method to transform these data into quantitative maps of pulmonary ventilation, and 129Xe gas transfer to barrier and RBC compartments. Methods A total of 13 healthy subjects and 12 idiopathic pulmonary fibrosis (IPF) subjects underwent thoracic 1H MRI and hyperpolarized 129Xe MRI with one-point Dixon decomposition to obtain images of 129Xe in airspaces, barrier and red blood cells (RBCs). 129Xe images were processed into quantitative binning maps of all three compartments using thresholds based on the mean and standard deviations of distributions derived from the healthy reference cohort. Binning maps were analyzed to derive quantitative measures of ventilation, barrier uptake, and RBC transfer. This method was also used to illustrate…

PURPOSE: The aim of this study was to evaluate the performance of very high-energy electron beams (VHEE) in comparison to clinically derived treatment plans generated with volumetric modulated arc therapy (VMAT) and proton pencil beam scanning (PPBS) technology. We developed a custom optimization script that could be applied automatically across modalities to eliminate operator bias during IMRT optimization. METHODS: Four clinical cases were selected (prostate cancer, lung cancer, pediatric brain tumor, and head and neck cancer (HNC)). The VHEE beams were calculated in the EGSnrc/DOSXYZnrc Monte Carlo code for 100 and 200 MeV beams. Treatment plans with VHEE, VMAT, and PPBS were optimized in a research version of RayStation using an in-house developed script to minimize operator bias between the different techniques. RESULTS: The in-house developed script generated similar or superior plans to the clinically used plans. In the comparisons between the modalities, the integral dose was lowest for the PPBS-generated plans in all cases. For the prostate case, the 200 MeV VHEE plan…

Microwave imaging (MI) technology has come a long way to introduce a noninvasive, inexpensive, fast, convenient, and safe screening tool for clinical breast monitoring. However, there is a niche between the existing understanding of MI by engineers versus clinicians. Our manuscript targets that niche and highlights the state of the art in MI technology compared to the existing breast cancer detection modalities (mammography, ultrasound, molecular imaging, and magnetic resonance). The significance of our review article is in consolidation of up-to-date breast clinician views with the practical needs and engineering challenges of a novel breast screening modality. We summarize breast tissue abnormalities and highlight the benefits as well as potential drawbacks of the MI as a cancer detection methodology. Our goal is to present an article that MI researchers as well as practitioners in the field can use to assess the viability of the MI technology as a competing or complementary modality to the existing means of breast cancer screening.

Purpose Dynamic contrast‐enhanced (DCE) MRI can be used to measure blood‐brain barrier (BBB) leakage. In neurodegenerative disorders such as small vessel disease and dementia, the leakage can be very subtle and the corresponding signal can be rather noisy. For these reasons, an optimized DCE‐MRI measurement and study design is required. To this end, a new measure indicative of the spatial extent of leakage is introduced and the effects of scan time and sample size are explored. Methods Dual‐time resolution DCE‐MRI was performed in 16 patients with early Alzheimer's disease (AD) and 17 healthy controls. The leakage rate (Ki) and volume fraction of detectable leaking tissue (vL) to quantify the spatial extent of BBB leakage were calculated in cortical gray matter and white matter using noise‐corrected histogram analysis of leakage maps. Computer simulations utilizing realistic Ki histograms, mimicking the strong effect of noise and variation in Ki values, were performed to understand the influence of scan time on the estimated leakage. Results The mean Ki was very low (order of 10−4…

Purpose To use EGSnrc Monte Carlo simulations for magnetic field dosimetry to determine optimal measurement orientations, calculate beam quality conversion factors for 32 cylindrical and three parallel‐plate (PP) ion chambers, evaluate the beam quality and angular dependence of these factors, and examine the magnetic field effects on %dd(10)x and TPR . Methods Beam quality conversion factors, k , and magnetic field conversion factors, kB = k /kQ, are calculated as a function of chamber rotation for six cylindrical ionization chamber in either a 60Co beam with a 0.35 T magnetic field or a 7 MV beam with a 1.5 T field, both magnetic fields are perpendicular to the photon beam. The chambers’ sensitive air volumes are varied by either using the entire geometric volume or excluding the air volume associated with the first 1 mm away from the stem. The kB and k factors are evaluated using four clinical photon spectra. The variation in %dd(10)x and TPR as a function of magnetic field for six photon spectra are studied using DOSXYZnrc. Results When the magnetic field is perpendicular to the…

PURPOSE: We propose a robust treatment planning model that simultaneously considers proton range and patient setup uncertainties and reduces high linear energy transfer (LET) exposure in organs at risk (OARs) to minimize the relative biological effectiveness (RBE) dose in OARs for intensity-modulated proton therapy (IMPT). Our method could potentially reduce the unwanted damage to OARs. METHODS: We retrospectively generated plans for 10 patients including two prostate, four head and neck, and four lung cancer patients. The "worst-case robust optimization" model was applied. One additional term as a "biological surrogate (BS)" of OARs due to the high LET-related biological effects was added in the objective function. The biological surrogate was defined as the sum of the physical dose and extra biological effects caused by the dose-averaged LET. We generated nine uncertainty scenarios that considered proton range and patient setup uncertainty. Corresponding to each uncertainty scenario, LET was obtained by a fast LET calculation method developed in-house and based on Monte Carlo…

PURPOSE: This study introduces a new hybrid ZTE/Dixon MR-based attenuation correction (MRAC) method including bone density estimation for PET/MRI and quantifies the effects of bone attenuation on metastatic lesion uptake in the pelvis. METHODS: Six patients with pelvic lesions were scanned using fluorodeoxyglucose (18F-FDG) in an integrated time-of-flight (TOF) PET/MRI system. For PET attenuation correction, MR imaging consisted of two-point Dixon and zero echo-time (ZTE) pulse sequences. A continuous-value fat and water pseudoCT was generated from a two-point Dixon MRI. Bone was segmented from the ZTE images and converted to Hounsfield units (HU) using a continuous two-segment piecewise linear model based on ZTE MRI intensity. The HU values were converted to linear attenuation coefficients (LAC) using a bilinear model. The bone voxels of the Dixon-based pseudoCT were replaced by the ZTE-derived bone to produce the hybrid ZTE/Dixon pseudoCT. The three different AC maps (Dixon, hybrid ZTE/Dixon, CTAC) were used to reconstruct PET images using a TOF-ordered subset expectation…

Purpose The purpose of this study was to acquire beam data for an MR ‐linac, with and without a 1.5 T magnetic field, by using a variety of commercially available detectors to assess their relative response in the magnetic field. The impact of the magnetic field on the measured dose distribution was also assessed. Methods An MR ‐safe 3D scanning water phantom was used to measure output factors, depth dose curves, and off‐axis profiles for various depths and for field sizes between 2 × 2 cm 2 and 22 × 22 cm 2 for an Elekta MR ‐linac beam with the orthogonal 1.5 T magnetic field on or off. An on‐board MV portal imaging system was used to ensure that the reproducibility of the detector position, both with and without the magnetic field, was within 0.1 mm. The detectors used included ionization chambers with large, medium, and small sensitive volumes; a diamond detector; a shielded diode; and an unshielded diode. Results The offset of the effective point of measurement of the ionization chambers was found to be reduced by at least half for each chamber in the direction parallel with the…

PURPOSE: Precise delineation of organs at risk is a crucial task in radiotherapy treatment planning for delivering high doses to the tumor while sparing healthy tissues. In recent years, automated segmentation methods have shown an increasingly high performance for the delineation of various anatomical structures. However, this task remains challenging for organs like the esophagus, which have a versatile shape and poor contrast to neighboring tissues. For human experts, segmenting the esophagus from CT images is a time-consuming and error-prone process. To tackle these issues, we propose a random walker approach driven by a 3D fully convolutional neural network (CNN) to automatically segment the esophagus from CT images. METHODS: First, a soft probability map is generated by the CNN. Then, an active contour model (ACM) is fitted to the CNN soft probability map to get a first estimation of the esophagus location. The outputs of the CNN and ACM are then used in conjunction with a probability model based on CT Hounsfield (HU) values to drive the random walker. Training and evaluation…

PURPOSE: We investigated whether metabolic, textural, and morphological tumoral indices evaluated on baseline PET-CT were predictive of early metabolic response on interim PET-CT in a cohort of patients with bulky Hodgkin and non-Hodgkin malignant lymphomas. METHODS: This retrospective study included 57 patients referred for initial PET-CT examination. In-house dedicated software was used to delineate tumor contours using a fixed 30% threshold of SUV max and then to compute tumoral metabolic parameters (SUV max, mean, peak, standard deviation, skewness and kurtosis, metabolic tumoral volume (MTV), total lesion glycolysis, and area under the curve of the cumulative histogram), textural parameters (Moran's and Geary's indices, energy, entropy, contrast, correlation derived from the gray-level co-occurrence matrix, area under the curve of the power spectral density, auto-correlation distance, and granularity), and shape parameters (surface, asphericity, convexity, surfacic extension, and 2D and 3D fractal dimensions). Early metabolic response was assessed on interim PET-CT using the…

PURPOSE: To investigate the performance of various algorithms for deformable image registration (DIR) to propagate regions of interest (ROIs) using multiple commercial platforms. METHODS AND MATERIALS: Thirteen institutions participated in the study with six commercial platforms: RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM (Cleveland, OH, USA), VelocityAI and Smart Adapt (Varian Medical Systems, Palo Alto, CA, USA), Mirada XD (Mirada Medical Ltd, Oxford, UK), and ABAS (Elekta AB, Stockholm, Sweden). The DIR algorithms were tested on synthetic images generated with the ImSimQA package (Oncology Systems Limited, Shrewsbury, UK) by applying two specific Deformation Vector Fields (DVF) to real patient data-sets. Head-and-neck (HN), thorax, and pelvis sites were included. The accuracy of the algorithms was assessed by comparing the DIR-mapped ROIs from each center with those of reference, using the Dice Similarity Coefficient (DSC) and Mean Distance to Conformity (MDC) metrics. Statistical inference on validation results was carried out in order to identify the prognostic…

PURPOSE: The purpose of this work is to evaluate the performance of dual-energy CT (DECT) for determining proton stopping power ratios (SPRs) in an experimental environment and to demonstrate its potential advantages over conventional single-energy CT (SECT) in clinical conditions. METHODS: Water equivalent range (WER) measurements of 12 tissue-equivalent plastic materials and 12 fresh animal tissue samples are performed in a 195 MeV broad proton beam using the dose extinction method. SECT and DECT scans of the samples are performed with a dual-source CT scanner (Siemens SOMATOM Definition Flash). The methods of Schneider et al. (1996), Bourque et al. (2014), and Lalonde et al. (2017) are used to predict proton SPR on SECT and DECT images. From predicted SPR values, the WER of the proton beam through the sample is predicted for SECT and DECT using Monte Carlo simulations and compared to the measured WER. RESULTS: For homogeneous tissue-equivalent plastic materials, results with DECT are consistent with experimental measurements and show a systematic reduction of SPR uncertainty…

PURPOSE: Despite improvements in optimization and automation algorithms, the quality of radiation treatment plans still varies dramatically. A tool that allows a priori estimation of the best possible sparing (Feasibility DVH, or FDVH) of an organ at risk (OAR) in high-energy photon planning may help reduce plan quality variability by deriving patient-specific OAR goals prior to optimization. Such a tool may be useful for (a) meaningfully evaluating patient-specific plan quality and (b) supplying best theoretically achievable DVH goals, thus pushing the solution toward automatic Pareto optimality. This work introduces such a tool and validates it for clinical Head and Neck (HN) datasets. METHODS: To compute FDVH, first the targets are assigned uniform prescription doses, with no reference to any particular beam arrangement. A benchmark 3D dose built outside the targets is estimated using a series of energy-specific dose spread calculations reflecting observed properties of radiation distribution in media. For the patient, the calculation is performed on the heterogeneous dataset,…

Purpose Nonsmall cell lung cancer (NSCLC) patient radiation therapy (RT) is planned without consideration of spatial heterogeneity in lung function or tumor response. We assessed the dosimetric and clinical feasibility of functional lung avoidance and response-adaptive escalation (FLARE) RT to reduce dose to [99mTc]MAA-SPECT/CT perfused lung while redistributing an escalated boost dose within [18F]FDG-PET/CT-defined biological target volumes (BTV). Methods Eight stage IIB-IIIB NSCLC patients underwent FDG-PET/CT and MAA-SPECT/CT treatment planning scans. Perfused lung objectives were derived from scatter/collimator/attenuation-corrected MAA-SPECT uptake relative to ITV-subtracted lung to maintain < 20 Gy mean lung dose (MLD). Prescriptions included 60 Gy to the planning target volume (PTV) and concomitant boost of 74 Gy mean to biological target volumes (BTV = GTV + PET gradient segmentation) scaled to each BTV voxel by relative FDG-PET SUV. Dose-painting-by-numbers prescriptions were integrated into commercial treatment planning systems via uptake threshold discretization. Dose…

Purpose Stereotactic body radiation therapy (SBRT) for pancreatic cancer requires a skillful approach to deliver ablative doses to the tumor while limiting dose to the highly sensitive duodenum, stomach, and small bowel. Here, we develop knowledge‐based artificial neural network dose models (ANN‐DMs) to predict dose distributions that would be approved by experienced physicians. Methods Arc‐based SBRT treatment plans for 43 pancreatic cancer patients were planned, delivering 30–33 Gy in five fractions. Treatments were overseen by one of two physicians with individual treatment approaches, with variations in prescribed dose, target volume delineation, and primary organs at risk. Using dose distributions calculated by a commercial treatment planning system (TPS), physician‐approved treatment plans were used to train ANN‐DMs that could predict physician‐approved dose distributions based on a set of geometric parameters (vary from voxel to voxel) and plan parameters (constant across all voxels for a given patient). Patient datasets were randomly allocated, with two‐thirds used for…

Purpose The main objective of this study is to propose a simple formulation (which we called DEEDZ) for deriving effective atomic numbers (Zeff) via electron density (ρe) calibration from dual-energy (DE) CT data. We carried out numerical analysis of this DEEDZ method for a large variety of materials with known elemental compositions and mass densities using an available photon cross sections database. The new conversion approach was also applied to previously published experimental DECT data to validate its practical feasibility. Methods We performed numerical analysis of the DEEDZ conversion method for tissue surrogates that have the same chemical compositions and mass densities as a commercial tissue-characterization phantom in order to determine the parameters necessary for the ρe and Zeff calibrations in the DEEDZ conversion. These parameters were then applied to the human-body-equivalent tissues of ICRU Report 46 as objects of interest with unknown ρe and Zeff. The attenuation coefficients of these materials were calculated using the XCOM photon cross sections database. We…