Voxel-based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI

Purpose: The aim of this study was to develop and assess the performance of supervised machine learning technique to classify magnetic resonance imaging (MRI) voxels as cancerous or noncancerous using noncontrast multiparametric MRI (mp-MRI), comprised of T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and advanced diffusion tensor imaging (DTI) parameters. Materials and methods: In this work, 191 radiomic features were extracted from mp-MRI from prostate cancer patients. A comprehensive set of support vector machine (SVM) models for T2WI and mp-MRI (T2WI + DWI, T2WI + DTI, and T2WI + DWI + DTI) were developed based on novel Bayesian parameters optimization method and validated using leave-one-patient-out approach to eliminate any possible overfitting. The diagnostic performance of each model was evaluated using the area under the receiver operating characteristic curve (AUROC). The average sensitivity, specificity, and accuracy of the models were evaluated using the test data set and the corresponding binary maps generated. Finally, the SVM plus sigmoid function of the models with the highest performance were used to produce cancer probability maps. Results: The T2WI + DWI + DTI models using the optimal feature subset achieved the best performance in prostate cancer detection, with the average AUROC, sensitivity, specificity, and accuracy of 0.93 ± 0.03, 0.85 ± 0.05, 0.82 ± 0.07, and 0.83 ± 0.04, respectively. The average diagnostic performance of T2WI + DTI models was slightly higher than T2WI + DWI models (+3.52%) using the optimal radiomic features. Conclusions: Combination of noncontrast mp-MRI (T2WI, DWI, and DTI) features with the framework of a supervised classification technique and Bayesian optimization method are able to differentiate cancer from noncancer voxels with high accuracy and without administration of contrast agent. The addition of cancer probability maps provides additional functionality for image interpretation, lesion heterogeneity evaluation, and treatment management.</p

Comparison of Synthetic Computed Tomography Generation Methods, Incorporating Male and Female Anatomical Differences, for Magnetic Resonance Imaging-Only Definitive Pelvic Radiotherapy

Purpose: There are several means of synthetic computed tomography (sCT) generation for magnetic resonance imaging (MRI)-only planning; however, much of the research omits large pelvic treatment regions and female anatomical specific methods. This research aimed to apply four of the most popular methods of sCT creation to facilitate MRI-only radiotherapy treatment planning for male and female anorectal and gynecological neoplasms. sCT methods were validated against conventional computed tomography (CT), with regard to Hounsfield unit (HU) estimation and plan dosimetry. Methods and Materials: Paired MRI and CT scans of 40 patients were used for sCT generation and validation. Bulk density assignment, tissue class density assignment, hybrid atlas, and deep learning sCT generation methods were applied to all 40 patients. Dosimetric accuracy was assessed by dose difference at reference point, dose volume histogram (DVH) parameters, and 3D gamma dose comparison. HU estimation was assessed by mean error and mean absolute error in HU value between each sCT and CT. Results: The median percentage dose difference between the CT and sCT was &lt;1.0% for all sCT methods. The deep learning method resulted in the lowest median percentage dose difference to CT at −0.03% (IQR 0.13, −0.31) and bulk density assignment resulted in the greatest difference at −0.73% (IQR −0.10, −1.01). The mean 3D gamma dose agreement at 3%/2 mm among all sCT methods was 99.8%. The highest agreement at 1%/1 mm was 97.3% for the deep learning method and the lowest was 93.6% for the bulk density method. Deep learning and hybrid atlas techniques gave the lowest difference to CT in mean error and mean absolute error in HU estimation. Conclusions: All methods of sCT generation used in this study resulted in similarly high dosimetric agreement for MRI-only planning of male and female cancer pelvic regions. The choice of the sCT generation technique can be guided by department resources available and image guidance considerations, with minimal impact on dosimetric accuracy.</p

Fabric Image Representation Encoding Networks for Large-scale 3D Medical Image Analysis

Deep neural networks are parameterised by weights that encode feature representations, whose performance is dictated through generalisation by using large-scale feature-rich datasets. The lack of large-scale labelled 3D medical imaging datasets restrict constructing such generalised networks. In this work, a novel 3D segmentation network, Fabric Image Representation Networks (FIRENet), is proposed to extract and encode generalisable feature representations from multiple medical image datasets in a large-scale manner. FIRENet learns image specific feature representations by way of 3D fabric network architecture that contains exponential number of sub-architectures to handle various protocols and coverage of anatomical regions and structures. The fabric network uses Atrous Spatial Pyramid Pooling (ASPP) extended to 3D to extract local and image-level features at a fine selection of scales. The fabric is constructed with weighted edges allowing the learnt features to dynamically adapt to the training data at an architecture level. Conditional padding modules, which are integrated into the network to reinsert voxels discarded by feature pooling, allow the network to inherently process different-size images at their original resolutions. FIRENet was trained for feature learning via automated semantic segmentation of pelvic structures and obtained a state-of-the-art median DSC score of 0.867. FIRENet was also simultaneously trained on MR (Magnatic Resonance) images acquired from 3D examinations of musculoskeletal elements in the (hip, knee, shoulder) joints and a public OAI knee dataset to perform automated segmentation of bone across anatomy. Transfer learning was used to show that the features learnt through the pelvic segmentation helped achieve improved mean DSC scores of 0.962, 0.963, 0.945 and 0.986 for automated segmentation of bone across datasets.Comment: 12 pages, 10 figure

A review of segmentation and deformable registration methods applied to adaptive cervical cancer radiation therapy treatment planning

Objective: Manual contouring and registration for radiotherapy treatment planning and online adaptation for cervical cancer radiation therapy in computed tomography (CT) and magnetic resonance images (MRI) are often necessary. However manual intervention is time consuming and may suffer from inter or intra-rater variability. In recent years a number of computer-guided automatic or semi-automatic segmentation and registration methods have been proposed. Segmentation and registration in CT and MRI for this purpose is a challenging task due to soft tissue deformation, inter-patient shape and appearance variation and anatomical changes over the course of treatment. The objective of this work is to provide a state-of-the-art review of computer-aided methods developed for adaptive treatment planning and radiation therapy planning for cervical cancer radiation therapy. Methods: Segmentation and registration methods published with the goal of cervical cancer treatment planning and adaptation have been identified from the literature (PubMed and Google Scholar). A comprehensive description of each method is provided. Similarities and differences of these methods are highlighted and the strengths and weaknesses of these methods are discussed. A discussion about choice of an appropriate method for a given modality is provided. Results: In the reviewed papers a Dice similarity coefficient of around 0.85 along with mean absolute surface distance of 2-4. mm for the clinically treated volume were reported for transfer of contours from planning day to the treatment day. Conclusions: Most segmentation and non-rigid registration methods have been primarily designed for adaptive re-planning for the transfer of contours from planning day to the treatment day. The use of shape priors significantly improved segmentation and registration accuracy compared to other models

Validation of virtual water phantom software for pre-treatment verification of single-isocenter multiple-target stereotactic radiosurgery

Objectiu múltiple; SRS; Fantasma virtualObjetivo múltiple; SRS; Fantasma virtualMultiple‐target; SRS; Virtual phantomThe aim of this study was to benchmark the accuracy of the VIrtual Phantom Epid dose Reconstruction (VIPER) software for pre-treatment dosimetric verification of multiple-target stereotactic radiosurgery (SRS). VIPER is an EPID-based method to reconstruct a 3D dose distribution in a virtual phantom from in-air portal images. Validation of the VIPER dose calculation was assessed using several MLC-defined fields for a 6 MV photon beam. Central axis percent depth doses (PDDs) and output factors were measured with an ionization chamber in a water tank, while dose planes at a depth of 10 cm in a solid flat phantom were acquired with radiochromic films. The accuracy of VIPER for multiple-target SRS plan verification was benchmarked against Monte Carlo simulations. Eighteen multiple-target SRS plans designed with the Eclipse treatment planning system were mapped to a cylindrical water phantom. For each plan, the 3D dose distribution reconstructed by VIPER within the phantom was compared with the Monte Carlo simulation, using a 3D gamma analysis. Dose differences (VIPER vs. measurements) generally within 2% were found for the MLC-defined fields, while film dosimetry revealed gamma passing rates (GPRs) ≥95% for a 3%/1 mm criteria. For the 18 multiple-target SRS plans, average 3D GPRs greater than 93% and 98% for the 3%/2 mm and 5%/2 mm criteria, respectively. Our results validate the use of VIPER as a dosimetric verification tool for pre-treatment QA of single-isocenter multiple-target SRS plans. The method requires no setup time on the linac and results in an accurate 3D characterization of the delivered dose

Gender and Videogames: The political valency of Lara Croft

The Face: Is Lara a feminist icon or a sexist fantasy? Toby Gard: Neither and a bit of both. Lara was designed to be a tough, self-reliant, intelligent woman. She confounds all the sexist cliches apart from the fact that she’s got an unbelievable figure. Strong, independent women are the perfect fantasy girls—the untouchable is always the most desirable (Interview with Lara’s creator Toby Gard in The Face magazine, June 1997)

Development of a Geographic Information System for Natural Resources and Recreational Facilities

unpublishednot peer reviewedOpe

Intensity-based dual model method for generation of synthetic CT images from standard T2-weighted MR images - Generalized technique for four different MR scanners

Background and purpose: Recent studies have shown that it is possible to conduct entire radiotherapy treatment planning (RTP) workflow using only MR images. This study aims to develop a generalized intensity-based method to generate synthetic CT (sCT) images from standard T2-weighted (T2(W)) MR images of the pelvis. Materials and methods: This study developed a generalized dual model HU conversion method to convert standard T2(W) MR image intensity values to synthetic HU values, separately inside and outside of atlas-segmented bone volume contour. The method was developed and evaluated with 20 and 35 prostate cancer patients, respectively. MR images with scanning sequences in clinical use were acquired with four different MR scanners of three vendors. Results: For the generated synthetic CT (sCT) images of the 35 prostate patients, the mean (and maximal) HU differences in soft and bony tissue volumes were 16 +/- 6 HUs (34 HUs) and -46 +/- 56 HUs (181 HUs), respectively, against the true CT images. The average of the PTV mean dose difference in sCTs compared to those in true CTs was -0.6 +/- 0.4% (-1.3%). Conclusions: The study provides a generalized method for sCT creation from standard T2(W) images of the pelvis. The method produced clinically acceptable dose calculation results for all the included scanners and MR sequences. (c) 2017 Elsevier B.V. All rights reserved.Peer reviewe

Commissioning and quality assurance for VMAT delivery systems: An efficient time-resolved system using real-time EPID imaging.

PURPOSE: An ideal commissioning and quality assurance (QA) program for Volumetric Modulated Arc Therapy (VMAT) delivery systems should assess the performance of each individual dynamic component as a function of gantry angle. Procedures within such a program should also be time-efficient, independent of the delivery system and be sensitive to all types of errors. The purpose of this work is to develop a system for automated time-resolved commissioning and QA of VMAT control systems which meets these criteria. METHODS: The procedures developed within this work rely solely on images obtained, using an electronic portal imaging device (EPID) without the presence of a phantom. During the delivery of specially designed VMAT test plans, EPID frames were acquired at 9.5 Hz, using a frame grabber. The set of test plans was developed to individually assess the performance of the dose delivery and multileaf collimator (MLC) control systems under varying levels of delivery complexities. An in-house software tool was developed to automatically extract features from the EPID images and evaluate the following characteristics as a function of gantry angle: dose delivery accuracy, dose rate constancy, beam profile constancy, gantry speed constancy, dynamic MLC positioning accuracy, MLC speed and acceleration constancy, and synchronization between gantry angle, MLC positioning and dose rate. Machine log files were also acquired during each delivery and subsequently compared to information extracted from EPID image frames. RESULTS: The largest difference between measured and planned dose at any gantry angle was 0.8% which correlated with rapid changes in dose rate and gantry speed. For all other test plans, the dose delivered was within 0.25% of the planned dose for all gantry angles. Profile constancy was not found to vary with gantry angle for tests where gantry speed and dose rate were constant, however, for tests with varying dose rate and gantry speed, segments with lower dose rate and higher gantry speed exhibited less profile stability. MLC positional accuracy was not observed to be dependent on the degree of interdigitation. MLC speed was measured for each individual leaf and slower leaf speeds were shown to be compensated for by lower dose rates. The test procedures were found to be sensitive to 1 mm systematic MLC errors, 1 mm random MLC errors, 0.4 mm MLC gap errors and synchronization errors between the MLC, dose rate and gantry angle controls systems of 1°. In general, parameters measured by both EPID and log files agreed with the plan, however, a greater average departure from the plan was evidenced by the EPID measurements. CONCLUSION: QA test plans and analysis methods have been developed to assess the performance of each dynamic component of VMAT deliveries individually and as a function of gantry angle. This methodology relies solely on time-resolved EPID imaging without the presence of a phantom and has been shown to be sensitive to a range of delivery errors. The procedures developed in this work are both comprehensive and time-efficient and can be used for streamlined commissioning and QA of VMAT delivery systems