Purpose: Recent years have seen growing interest in adaptive radiation therapy (RT), but the existing software tools are not ideal for research use, as they are either expensive and closed proprietary applications or free open-source packages with limited scope, extensibility, reliability, or user support. To address these limitations, we have developed SlicerRT, an open-source toolkit for RT research, providing fast computations and user-friendly interface for researchers. It is a medium for RT researchers to integrate their workflows in, assists clinical translation of experimental approaches, and enables comparative testing.

Methods: SlicerRT builds on the widely used medical image visualization and analysis platform, 3D Slicer (www.slicer.org). In addition to the tools that 3D Slicer offers for visualization, registration and segmentation, the toolkit provides functions specifically designed for RT research. Established development processes as well as testing and validation environment ensure software quality. Standardized software development mechanisms of 3D Slicer were applied for documentation, distribution, and user support. The design and architecture of the toolkit was outlined in [1]. SlicerRT has evolved to contain the most widely used features in the field (see www.slicerrt.org), which have been defined through consensus discussions with a large pool of RT researchers.

Results: Numerous modules have been developed for RT-specific features such as DICOM-RT data import and export, as well as dose analysis tools including dose volume histogram, dose accumulation, dose comparison, and isodose line and surface generation. The toolkit also includes contour analysis modules for handling various contour representations, contour morphology, and contour comparison. Advanced registration tools are provided by the included Plastimatch library [2]. The main new features developed in the last year are RT plan and RT image import and the subject hierarchy module, which arranges the loaded data to a structure familiar to clinicians, while providing advanced automation features through the underlying extendible mechanism. The toolkit is being used by several research groups to support and evaluate adaptive radiation therapy workflows. SlicerRT is available for download through the Extension Manager component of 3D Slicer 4.3 or later (see instructions on our website).

Future work: As SlicerRT has matured to contain most of the planned functions, our focus has shifted from feature development to usability and stability. The subject hierarchy module, and its features such as bulk deformation of studies embodies our efforts toward usability, while the integration of the subject hierarchy and the advanced contour handling mechanism into the 3D Slicer core points towards stability, also benefiting the whole 3D Slicer community. There is ongoing work to evaluate and potentially standardize the way the 2D contours are rasterized into 3D volumes, thus mitigating the great variability between the different solutions for this problem. Current funding ensures continuous development for several years, so SlicerRT is expected to develop further.

Acknowledgement: This work was in part funded by Cancer Care Ontario through Applied Cancer Research Unit and Research Chair in Cancer Imaging grants, and the Ontario Consortium for Adaptive Invention in Radiation Oncology (OCAIRO).


Improvements in SlicerRT, the radiation therapy research toolkit for 3D Slicer

In radiotherapy (RT) for tumor delineation and diagnostics, complementary information of multi-modal images is used. Using high ionizing radiation, the accuracy of registered volume data is crucial; therefore a reliable and robust evaluation method for registered images is needed in clinical practice. Multi-modal image registration aligns images from different modalities like computed tomography (CT) and magnetic resonance imaging (MRI) or cone beam computed tomography (CBCT) into one common frame of reference. The gold standard validation methods are visual inspection by radiation oncology experts and fiducial-based evaluation. However, visual inspection is a qualitative measure with a range of 2-6 mm inaccuracy, it is time consuming and prone to errors. The fiducial-based evaluation is an invasive method when fiducial markers are fixated to bone or implanted in organs. Therefore, in clinical practice a robust non-invasive automated method is needed to validate registration of multi-modal images. 
The aim of this study is to introduce and validate an automatic landmark-based accuracy measure for multi-modal image rigid registration using feature descriptors. A porcine dataset with fixed fiducial markers was used to compare our accuracy measure with the target registration error of fiducial markers.In addition, the robustness of our evaluation method was tested on multi-vendor database consisted of 10 brain and 20 lung cases comparing the automatic landmark accuracy measure based on feature descriptors with manual landmark based evaluation. 
An automatic, non-invasive method based on feature descriptors for accuracy evaluation of multi-modal rigid registration was introduced. The method can be used to provide accuracy information slice-by slice on CT, CBCT and CT, MR-T1, -T2 weighted, MR-T1 contrast enhanced (ce) multi-modal images.

Automatic accuracy measurement for multi-modal rigid registration using feature descriptors

In this work, we try to develop a fast converging method for
segmentation assisted deformable registration. The segmentation step
consists of a piece-wise constant Mumford-Shah energy model while reg-
istration is driven by the sum of squared distances of both initial images
and segmented mask with a diffusion regularization. In order to solve
this energy minimization problem, a second order Gauss-Newton opti-
mization method is used. For the numerical experiments we used CT
data sets from the EMPIRE10 challenge. In this preliminary study, we
show high accuracy of our algorithm.


Gauss-Newton Method for Segmentation assisted Deformable Registration

The goal of radiation treatment is to irradiate cancer cells (i.e., a target region) without destroying adjacent healthy tissue. Thus, it is advantageous to form the beam so that it best approximates the target, thereby reducing the amount of dose absorbed in critical regions outside the target area. While multi-leaf collimators are common in human clinical systems, small animal radiotherapy systems are typically limited to a set of fixed-size collimators. For these systems, dose painting can be used for conformal dose delivery, but is significantly slower than a multi-leaf collimator. As a compromise solution, a variable rectangular collimator has been developed for the Small Animal Radiation Research Platform (SARRP). This enables more efficient dose painting via the decomposition of a 2D target region into a minimum number of rectangles of variable size, which is the topic of this paper. The proposed method consists of several distinct steps and was implemented on the SARRP Treatment Planning System (TPS).

Dose Painting with a Variable Collimator for the Small Animal Radiation Research Platform (SARRP)

Patient motion during radiotherapy or intra-fractional motion, is one of the major sources of uncertainty in dose application. 2D/3D registration is an intensity based method used successfully to track tumor motion with the potential to reduce uncertainty. Despite promising results, validation remains a problem due to the lack of a gold standard. In this paper, we demonstrate the use of clinical datasets acquired with the VERO LINAC to validate intensity based 2D/3D registration. The patient considered in this study had a gold marker implanted to enable live tumor tracking. We verify our results against the tracked gold marker position. Our results show that it is possible to accurately track the tumor using only intensity based 2D/3D registration. The mean error of our registration is of 1.6 ± 0.3mm and as low as 1.1 ± 0.5mm when looking only at the cranial-caudal direction. The results pave the way for a possible clinical application of the method.

Validation of real-time intensity based 2D/3D registration for image guided radiotherapy

Automated segmentation is a frequently applied task in the course of medical imaging. Furthermore, it is a substantial component of image-guided radiotherapy. Atlas based segmentation is one of the most frequently used approach for automated segmentation. Especially for multi-atlas based segmentation, segmentation quality and speed largely depends on the underlying registration and atlas selection strategy. In this work an atlas selection strategy that is based on the correlation of inter-atlas similarities within a set of atlas images is presented. Segmentation quality is analyzed by calculating dice coefficients and 95% Hausdorff distances for the left and right parotid with respect to different numbers of atlases. Results are compared to other state of the art atlas selection strategies. It can be shown that the developed atlas selection technique performs slightly better than NMI-based selection if a low number of atlases is used.

A novel atlas-selection approach for multi-atlas based segmentation using the correlation of inter-atlas similarities

Glioblastoma (GBM) is the most common type of primary
brain tumor, which is characterized by an infiltrative growth pattern.
In current practice, radiotherapy planning is primarily based upon T2
FLAIR MRI despite its known lack of specificity in the detection of tu-
mor infiltration. While hyperintensity on T2 FLAIR is widely considered
to represent infiltrative tumor, it may also be caused by the presence
of vasogenic edema (VE), caused by a leakage of fluid into the brain
parenchyma. Distinguishing VE from infiltrative tumor could have im-
pact on improving radiotherapy planning. In this paper we study a data
set of 17 GBM patients treated with anti-angiogenic therapy for which
a fast decrease of T2 FLAIR hypersignal is observed, which indicates
the resolution of VE. We investigate if multimodal MRI acquisitions in-
cluding diffusion tensor imaging can distinguish between VE and tumor
infiltration prior to therapy. Using a random forest classifier, we show
that, in this study, morphological information based on the contrast en-
hanced T1 image explains up to 75% of the extent of VE. The information
from different imaging modalities did not significantly improve the clas-
sification. We then show that delineating the VE prior to therapy can
have substantial impact on radiotherapy target delineation, leading to
smaller treatment volumes and reducing potentially harmful radiation
dose to normal brain tissue.


Multimodal Analysis of Vasogenic Edema in Glioblastoma Patients for Radiotherapy Planning

Introduction. Computed tomography (CT) is the standard imaging modality for radiation therapy treatment planning (RTTP) because of its ability to provide information on electron density. However, magnetic resonance (MR) imaging provides superior soft tissue contrast, especially in small animal imaging, facilitating the precise selection of the target volume. This makes the technique interesting for irradiation of brain tumors. The aim of this study was to present an MR-only based workflow for RTTP on a small animal radiation research platform (SARRP) by investigating the potential of probabilistic classification of voxels using multiple MR sequences.
Methods. Six female Fisher rats were anesthetized using isoflurane and individually fixed on an in-house made multimodality bed before starting MR and CT acquisitions. MR measurements were performed on a 7-Tesla system using a rat brain volume coil. Four different MR sequences were acquired for each animal, including a T1-weighted (MDEFT) sequence, a T2-weighted (RARE) sequence, an ultra-short echo time sequence with 20 μs echo time (UTE1) and an ultra- short echo time sequence with 2 ms echo time (UTE2). UTE offers the opportunity to acquire images from proton-poor structures with very short transverse relaxation times, such as bone, by using a rapid readout of the fast decaying signal. Following MR, the animals were moved to the SARRP to start a cone-beam CT (CB-CT) by acquiring 720 projections over 360°. Cone-beam CT projection data were reconstructed by filtered back-projection to obtain the standard-CT for RTTP. Then the images were bias field corrected and manually co-registered to the CB-CT. After that, images were segmented in three tissue classes (air, soft tissue and bone) with k-means for the CB-CT and fuzzy c-means segmentation algorithm (FCM) for the MR images with multiple MR images as input. The membership probability can be between 0 and 1, with one indicating 100% probability and zero indicating 0% probability to belong to a specific tissue class. To obtain a pseudo-CT image, voxels were assigned to the tissue class having the highest membership probability. The dice coefficient was used to evaluate the correctness of the segmentation for soft-tissue and bone. The pseudo-CT images with the highest similarity index were used for further radiotherapy treatment planning (RTTP), in addition, to the standard UTE1-UTE2. The target of the RTTP that was selected in the primary cortex (M1) and three different beam arrangements were investigated to compare CB-CT and MR-based dose calculations. The dose plans were a single static beam of 3x3 mm, using a single arc (3x3 mm beam size, 120° arc, couch at 0°), and three non-coplanar arcs (3x3 mm beam size, 120° arc, couch at 0°, 45° and 90°). Dose distributions were calculated using the TPS of the SARRP and cumulative dose volume histograms (DVHs) of the target and normal brain tissue were obtained for the three dose plans.
Results The highest dice coefficient was obtained for the T1-UTE1-T2 combination, which was used for further RTTP. The contribution of bone to the total dice coefficient did not exceed 27%. However, bone accounts for only 2% of the image, therefore a misclassified bone pixel has a bigger effect in the dice coefficient than a misclassified soft tissue pixel. Using only 1 beam, both MR combinations underestimate the dose to be delivered to the target. When more complex beam configurations were used to irradiate the target, very small differences were observed between CB-CT and MR based dose calculations.

Pseudo-CT generation from multiple MR images for small animal irradiation

The contribution to VTK presented in this article is specialized on the extraction of contact surfaces (CS). This extraction can be regarded as the intersection Boolean-operation of only touching meshes. The vtkCoplanarSurfaceExtractor filter produces either polygonal or triangulated CSs by reconstructing the contact faces of co-planar triangles. Specified tolerances account for discrepancies in coplanarity of faces which might occur due to rounding effects.
This article is accompanied with the source code, input data, parameters and output data that were used for validating the VTK-filter.


Extracting Intersections of Coplanar Surfaces (Boolean-operation on touching meshes)

We present a generic interpolator for label images. The basic idea is to interpolate each label with an ordinary image interpolator, and return the label with the highest value. This is the idea used by the itk::LabelImageGaussianInterpolateImageFunction interpolator. Unfortunately, this class is currently limited to Gaussian interpolation. Using generic programming, our proposed interpolator extends this idea to any image interpolator. Combined with linear interpolation, this results in similar or better accuracy and much improved computation speeds on a test image.