Perelman School of Medicine at the University of Pennsylvania

Section for Biomedical Image Analysis (SBIA)

Pre-Operative and post-Recurrence brain Tumor Registration


Pre-Operative and post-Recurrence brain Tumor Registration (PORTR) [TMI2014] is a software package designed for determining the optimal deformation between pre-operative and post-recurrence scans by finding the minimum of an energy function, which is based on the concept of symmetric registration.

Some typical applications of PORTR include,

  • Mapping post-recurrence scans to pre-operative scans;
  • Labeling entire brain regions of each scan.

PORTR is implemented as a command-line tool. It is semi-automatic and requires minimal user initializations. Users could use the visual interface called BrainTumorViewer to easily make initializations and a script for the execution. As a result, PORTR will output a label map of each scan, a mapping between two scans, etc.

About the algorithm

Nonrigid registration of the pre-operative an post-recurrence scans is very challenging due to large deformations, missing correspondences, and inconsistent intensity profiles between the scans. The large deformations and missing correspondences are due to the glioma in the pre-operative scans causing large mass effects as well as the resection cavities and tumor recurrence in the post-recurrence scans, which are acquired several months or years after surgery. The inconsistent intensity profiles result from tissue labeled as edema in the pre-operative scan transforming to healthy tissue in the post-recurrence scan (and vice versa). Thus, corresponding regions can have very different intensity profiles. Figure 1 shows a typical case with the anatomy around the tumor being confounded by resection cavity, tumor recurrences, and edema.

Figure 1: Example of the pre-operative scan and the corresponding post-recurrence scan. Pre-operative scan clearly shows the tumor in the T1-CE scan and edema in FLAIR scan. Edema is also clearly visible in the FLAIR scan of the post-recurrence scan . T1-CE of now shows resection cavity and tumor recurrence.

We solve this problem by the PORTR, introduced in [TMI2014]. PORTR determines the optimal deformation between two scans by finding the minimum of an energy function, which is based on the concept of symmetric registration. This energy function is not only comprised of image-based correspondences and smoothness constraints as customary for other registration methods, but also includes pathological information. The pathological information is inferred from the results of two segmenters that are targeted to each scan. Specifically, a new method is developed for segmenting post-recurrence scans, which generally consist of resection cavities after brain surgery and multiple tumor recurrences. For the pre-operative scans, GLISTR [TMI2012] is adapted to outline a single brain glioma which causes a large mass effect on healthy tissue. The resulting segmentations of both scans are a central component in the definition of the image and the shape-based correspondence terms within our symmetric registration framework. Determining the minimum within this framework is difficult as the function contains many local minima. PORTR deals with these difficulties by combining discrete and continuous optimizations. The discrete optimization method finds the optimal solution in a coarse solution space. The continuous optimization method locally improves this solution in a finer solution space.

PORTR requires minimal user input initializing the algorithm. Specifically, for the pre-operative scan, user provides one seed point and its approximate radius and one sample point for each tissue class. For post-recurrence scan, user provides seed points for abnormal masses and their corresponding radius and one sample point for each tissue class. Users could use the visual interface called BrainTumorViewer to easily mark each point. Another input to PORTR consists of the preprocessed patient scans and a list of probabilistic atlas priors representing a healthy population (we use eve currently). For preprocessing, we coregistered all modalities, corrected MR field inhomogeneity, skull stripping, and scaled intensities to fit [0, 255]. Then we affinely register the post-recurrence to the pre-operative scans. The output of PORTR consists of a deformation field between pre-operative and post-recurrence scans and a label map corresponding to each scan.

The current implementation of PORTR accepts four MR modalities: T1, T1-CE, T2, and FLAIR for each scan. It segments these images into 9 labels for pre-operative scans: cerebrospinal fluid (CSF), ventricles (VT), gray matter (GM), white matter (WM), vessel (VS), edema (ED), necrosis (NCR), enhancing tumor (TU), and background (BG). For post-recurrence scans, it uses 11 labels: cerebrospinal fluid (CSF), ventricles (VT), gray matter (GM), white matter (WM), vessel (VS), edema (ED), cavity region (CAN), enhanced cavity region (CAE), necrosis for tumor recurrence (RTN), enhanced tumor recurrence (RTE), and background (BG).

Figure 2: Registration results of follow-up onto baseline scans. In each row, we show T1-CE images of the pre-operative scan (baseline) in (a) and the post-recurrence scan (follow-up) in (b). Images (c)?(g) show the registered post-recurrence scans using DRAMMS, mDRAMMS, ANTS, mANTS, and PORTR, respectively. For baseline and registered scans, boundaries of segmented tumor (red) and ventricles (green) of baseline are overlaid.

In Figure 2, registration results from PORTR are displayed. the aligned follow-up of PORTR much better matches the baseline scan than those of other competing methods. The ventricles of the follow-up scans aligned by PORTR overlap well with the baseline across all examples.

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Software License

The PORTR software is freely available under a BSD-style open source license that is compatible with the Open Source Definition by The Open Source Initiative and contains no restrictions on use of the software. The full license text is included with the distribution package and available online.

Documentation

PORTR Software Manual: The software manual of PORTR in PDF.

System Requirements

Operating System: Linux, Windows (64 bit)

Memory Requirement: 12GB or more.

To Download please visit our PORTR NITRC page

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Installation

Build Dependencies

Before building PORTR, the following software libraries are required to be installed.

Package Version Description
CMake 2.8.4 or higher To compile and build PORTR. Use version 2.8.4 or higher.
ITK 3.14 or higher For ITK 3.x, ITK_USE_REVIEW option is required to set ON. For ITK 4.x, there’s no mandatory option. In Windows, build ITK using CMake with the Win64 (x64) solution platform of Visual Studio.

Note: To build in Windows, use CMake with the Win64 (x64) solution platform of Visual Studio. Our testing environment is Windows 7 64-bit and Visual Studio 2010.

Runtime Requirements

For the successful execution of PORTR, the following software packages have to be installed.

Dependency Version Description
FSL (FLIRT) 3.3.11 – 5.0.7 PORTR uses FLIRT for the affine alignment. For Linux and Mac OS, installation packages are provided. In Windows, it could be built from the patched source codes on the Cygwin environment. See the how-to guides
HOPSPACK 2.0.2 Used for the optimization of the tumor growth model parameters. The multithreaded version is used. Download and install the precompiled binary.
BrainTumorModeling_CoupledSolver 1.2.0 or higher Used for the tumor growth simulation. Note that this package depends on the PETSc library. In Windows, it could be built on the Cygwin environment.
CaPTk Current Used for making initializations of PORTR. It is an optional program and not required for executing PORTR.

 

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People

Advisor
Software Authors
  • Dongjin Kwon
    • Initiated the project.
    • Developed the software.

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Publications

  1. [TMI2014]    D. Kwon, M. Niethammer, H. Akbari, M. Bilello, C. Davatzikos, and K.M. Pohl, PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration, IEEE Trans. Med. Imaging 33(3): 651-667 (2014)
  1. [TMI2012]    A. Gooya, K.M. Pohl, M. Bilello, L. Cirillo, G. Biros, E.R. Melhem, C. Davatzikos, GLISTR: Glioma Image Segmentation and Registration, IEEE Trans. Med. Imaging 31(10): 1941-1954 (2012)

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Changelog

Version 1.0.0 (Nov 24, 2014)
  • First public release of the PORTR software.

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