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Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. The rich sources of prior information in IGRT are incorporated into a hidden Markov random field model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk. The voxel labels are estimated using iterated conditional modes. The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom. The mean voxel-wise misclassification rate was 6.2%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

An external field prior for the hidden Potts model with application to cone-beam computed tomography

In images with low contrast-to-noise ratio (CNR), the information gain from the observed pixel values can be insufficient to distinguish foreground objects. A Bayesian approach to this problem is to incorporate prior information about the objects into a statistical model. A method for representing spatial prior information as an external field in a hidden Potts model is introduced. This prior distribution over the latent pixel labels is a mixture of Gaussian fields, centred on the positions of the objects at a previous point in time. It is particularly applicable in longitudinal imaging studies, where the manual segmentation of one image can be used as a prior for automatic segmentation of subsequent images. The method is demonstrated by application to cone-beam computed tomography (CT), an imaging modality that exhibits distortions in pixel values due to X-ray scatter. The external field prior results in a substantial improvement in segmentation accuracy, reducing the mean pixel misclassification rate for an electron density phantom from 87% to 6%. The method is also applied to radiotherapy patient data, demonstrating how to derive the external field prior in a clinical context. External field prior improves image segmentation accuracy.Manual segmentation of one image is used as a prior for subsequent images.Applicable to longitudinal imaging, such as image-guided radiation therapy.

Achieving success in clinically based research: the importance of mentoring

Within the professions of radiation therapy and medical imaging, clinician led research activity is becoming more prevalent. However, more is needed. A key component of continuing to develop professional groups who are both research active and producing high quality clinical research, is research mentoring. The authors of this paper share a common interest in enhancing research capacity through research mentoring within the health workforce, and came together to run a workshop on this issue at the 11th Annual Scientific Meeting of Medical Imaging and Radiation Therapy (ASMMIRT 2016) conference in Brisbane. Theory, clinical insights and issues regarding research mentoring were raised in the workshop as were the benefits of having dedicated research positions embedded within the health workforce to help provide support and build capacity. Key elements from this workshop are shared within this article, with the objective to encourage clinicians and clinical researchers to invest the time and effort into seeking and providing good quality research mentoring. A single service example is used to demonstrate how this can lead to enhanced research engagement and productivity.

Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific subregions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

A feature alignment score for online cone‐beam CT‐based image‐guided radiotherapy for prostate cancer

Purpose To develop a method for scoring online cone‐beam CT (CBCT)‐to‐planning CT image feature alignment to inform prostate image‐guided radiotherapy (IGRT) decision‐making. The feasibility of incorporating volume variation metric thresholds predictive of delivering planned dose into weighted functions, was investigated. Methods Radiation therapists and radiation oncologists participated in workshops where they reviewed prostate CBCT‐IGRT case examples and completed a paper‐based survey of image feature matching practices. For 36 prostate cancer patients, one daily CBCT was retrospectively contoured then registered with their plan to simulate delivered dose if (a) no online setup corrections and (b) online image alignment and setup corrections, were performed. Survey results were used to select variables for inclusion in classification and regression tree (CART) and boosted regression trees (BRT) modeling of volume variation metric thresholds predictive of delivering planned dose to the prostate, proximal seminal vesicles (PSV), bladder, and rectum. Weighted functions incorporating the CART and BRT results were used to calculate a score of individual tumor and organ at risk image feature alignment (FASTV_OAR). Scaled and weighted FASTV_OAR were then used to calculate a score of overall treatment compliance (FASglobal) for a given CBCT‐planning CT registration. The FASTV_OAR were assessed for sensitivity, specificity, and predictive power. FASglobal thresholds indicative of high, medium, or low overall treatment plan compliance were determined using coefficients from multiple linear regression analysis. Results Thirty‐two participants completed the prostate CBCT‐IGRT survey. While responses demonstrated consensus of practice for preferential ranking of planning CT and CBCT match features in the presence of deformation and rotation, variation existed in the specified thresholds for observed volume differences requiring patient repositioning or repeat bladder and bowel preparation. The CART and BRT modeling indicated that for a given registration, a Dice similarity coefficient >0.80 and >0.60 for the prostate and PSV, respectively, and a maximum Hausdorff distance <8.0 mm for both structures were predictive of delivered dose ± 5% of planned dose. A normalized volume difference <1.0 and a CBCT anterior rectum wall >1.0 mm anterior to the planning CT anterior rectum wall were predictive of delivered dose >5% of planned rectum dose. A normalized volume difference <0.88, and a CBCT bladder wall >13.5 mm inferior and >5.0 mm posterior to the planning CT bladder were predictive of delivered dose >5% of planned bladder dose. A FASTV_OAR >0 is indicative of delivery of planned dose. For calculated FASTV_OAR for the prostate, PSV, bladder, and rectum using test data, sensitivity was 0.56, 0.75, 0.89, and 1.00, respectively; specificity 0.90, 0.94, 0.59, and 1.00, respectively; positive predictive power 0.90, 0.86, 0.53, and 1.00, respectively; and negative predictive power 0.56, 0.89, 0.91, and 1.00, respectively. Thresholds for the calculated FASglobal of were low <60, medium 60–80, and high >80, with a 27% misclassification rate for the test data. Conclusions A FASglobal incorporating nested FASTV_OAR and volume variation metric thresholds predictive of treatment plan compliance was developed, offering an alternative to pretreatment dose calculations to assess treatment delivery accuracy.

An image‐guided radiotherapy decision support framework incorporating a Bayesian network and visualization tool

Purpose To describe a Bayesian network (BN) and complementary visualization tool that aim to support decision‐making during online cone‐beam computed tomography (CBCT)‐based image‐guided radiotherapy (IGRT) for prostate cancer patients. Methods The BN was created to represent relationships between observed prostate, proximal seminal vesicle (PSV), bladder and rectum volume variations, an image feature alignment score (FASTV_OAR), delivered dose, and treatment plan compliance (TPC). Variables influencing tumor volume (TV) targeting accuracy such as intrafraction motion, and contouring and couch shift errors were also represented. A score of overall TPC (FASglobal) and factors such as image quality were used to inform the BN output node providing advice about proceeding with treatment. The BN was quantified using conditional probabilities generated from published studies, FASTV_OAR/global modeling, and a survey of IGRT decision‐making practices. A new IGRT visualization tool (IGRTREV), in the form of Mollweide projection plots, was developed to provide a global summary of residual errors after online CBCT‐planning CT registration. Sensitivity and scenario analyses were undertaken to evaluate the performance of the BN and the relative influence of the network variables on TPC and the decision to proceed with treatment. The IGRTREV plots were evaluated in conjunction with the BN scenario testing, using additional test data generated from retrospective CBCT‐planning CT soft‐tissue registrations for 13/36 patients whose data were used in the FASTV_OAR/global modeling. Results Modeling of the TV targeting errors resulted in a very low probability of corrected distances between the CBCT and planning CT prostate or PSV volumes being within their thresholds. Strength of influence evaluation with and without the BN TV targeting error nodes indicated that rectum‐ and bladder‐related network variables had the highest relative importance. When the TV targeting error nodes were excluded from the BN, TPC was sensitive to observed PSV and rectum variations while the decision to treat was sensitive to observed prostate and PSV variations. When root nodes were set so the PSV and rectum variations exceeded thresholds, the probability of low TPC increased to 40%. Prostate and PSV variations exceeding thresholds increased the likelihood of repositioning or repeating patient preparation to 43%. Scenario testing using the test data from 13 patients, demonstrated two cases where the BN provided increased high TPC probabilities, despite some of the prostate and PSV volume variation metrics not being within tolerance. The IGRTREV tool was effective in highlighting and quantifying where TV and OAR variations occurred, supporting the BN recommendation to reposition the patient or repeat their bladder and bowel preparation. In another case, the IGRTREV tool was also effective in highlighting where PSV volume variation significantly exceeded tolerance when the BN had indicated to proceed with treatment. Conclusions This study has demonstrated that both the BN and IGRTREV plots are effective tools for inclusion in a decision support system for online CBCT‐based IGRT for prostate cancer patients. Alternate approaches to modeling TV targeting errors need to be explored as well as extension of the BN to support offline IGRT decisions related to adaptive radiotherapy.

Modification of a modulated arc total body irradiation technique: Implementation and first clinical experience for paediatric patients

To implement the modulated arc total body irradiation (MATBI) technique within the existing infrastructure of a radiation oncology department. The technique needed to treat paediatric patients of all ages, some of whom would require general anaesthesia (GA).

Deep inspiration breath hold in breast cancer: Development and analysis of a patient experience questionnaire

Evidence that Deep Inspiration Breath Hold (DIBH) can reduce cardiac dose during left‐sided breast radiation therapy (RT) has led to widespread uptake of this technology. There is a paucity of published information documenting the impact of this technique on the patients treatment experience. The aim of this study was to develop a tool to assess the patients experience with the introduction of DIBH using the Elekta® Active Breathing Coordinator (ABC) in a single institution.

Automated replication of cone beam CT‐guided treatments in the Pinnacle3 treatment planning system for adaptive radiotherapy

Time‐consuming manual methods have been required to register cone‐beam computed tomography (CBCT) images with plans in the Pinnacle3 treatment planning system in order to replicate delivered treatments for adaptive radiotherapy. These methods rely on fiducial marker (FM) placement during CBCT acquisition or the image mid‐point to localise the image isocentre. A quality assurance study was conducted to validate an automated CBCT‐plan registration method utilising the Digital Imaging and Communications in Medicine (DICOM) Structure Set (RS) and Spatial Registration (RE) files created during online image‐guided radiotherapy (IGRT).