nirfast

Diffuse optical tomography (Spot) is a volumetric optical imaging method that depends on demonstrating light vehicle in tissue utilizing the dissemination estimation, which is for the most part relevant in disperse overwhelmed frameworks. The ghastly proportion of the diffuse vehicle of close infrared light through delicate tissue can give the capacity to picture utilitarian tissue data like hemoglobin oxygenation and water division, which can be valuable as a noninvasive method for recognizing cancer.1–3 This technique has additionally been demonstrated fruitful by the utilization of radiance tests utilizing, for instance, fluorescence markers to permit quantitative sub-atomic imaging of useful exogenous reporters.4,5 Light displaying should be possible analytically,6 giving high exactness and computational speed, however just on straightforward and predominantly homogeneous calculations. Mathematical methodologies permit answers for be registered for more unpredictable calculations, yet require more computational time just as a discrete portrayal (volume network) of the domain.7,8 Because of the by and large poor spatial goal of Spot, the overarching pattern in the field is toward consolidating it with other imaging modalities and fusing high-goal tissue primary data in the picture recuperation calculation. Remarkable instances of this incorporate processed tomography (CT) or attractive reverberation imaging (X-ray)- directed Speck, and these procedures give the possibility to expanded accuracy.9–12 Albeit the subtleties of limited component based techniques for demonstrating light vehicle in tissue are all around canvassed in literature,13–21 the computational bundles accessible for such displaying have as of recently included very restricted lattice creation instruments or no cross section creation devices by any stretch of the imagination. In this work, an incorporated and openly accessible programming bundle is illustrated and tried, which permits clients to go right from import of standard advanced imaging and interchanges in medication (DICOM) pictures (and other related organizations) to division and coinciding, and through to light recreation and property recuperation. Picture directed Spot is extremely subject to the capacity to effectively deliver excellent three-dimensional (3-D) volume networks from clinical pictures, and the interaction of cross section creation is an essentially overlooked however complex issue, which is straightforwardly tackled as a rule by programming like this.

The software tool developed at Dartmouth College and University of Birmingham, United Kingdom, called Nirfast, is a finite-element-based package for modeling near-infrared light transport in tissue for medical applications. It is open source, free, and cross platform as developed under MATLAB (Mathworks Inc.), which also allows user-friendly understanding and modifications. Applications of Nirfast are diverse, including optical modeling for small animal imaging, breast imaging, brain imaging, and light dose verification in photodynamic therapy of the pancreas.

Accurate diffusion modeling in optical tomography requires a 3-D geometry since the photon scattering is in all directions. Since the core finite element method (FEM) code of Nirfast is based on MATLAB, this has in the past hindered its ability to allow for easy coupling to highly complex 3-D meshing tools. One issue stems from the inability of MATLAB to efficiently visualize large 3-D meshes, while another issue is the necessity for custom image processing tools when dealing with an assortment of different medical image types and formats. Using a visualization toolkit/insight segmentation and registration toolkit-based platform, which itself is an open-source application, for segmentation and meshing has helped to address these issues by providing a seamless coupling within Nirfast. Providing the tools and workflow needed to create an FEM mesh from a variety of different types of medical images and seamlessly using this mesh for light transport modeling are essential to making DOT accessible and useful.

The current version of Nirfast includes full-featured segmentation and mesh creation tools for quickly and easily creating high-quality 3-D finite-element meshes from medical images. The segmentation tools have been developed in collaboration with Kitware Inc. (Clifton Park, NY). Creating suitable volumetric meshes of complex tissue geometries is a particular challenge for multimodal DOT, due to the variety of contrast characteristics present in different imaging modalities and tissue types/models. Manual manipulation of the segmentation and mesh creation process often requires an overwhelming time investment, and high mesh element quality is notoriously difficult to ensure. It is also very important to retain both the outer and inner region surfaces (internal boundaries) in a mesh to allow the application of prior knowledge for both the forward and inverse models. Segmentation is rarely fully automated because some manual manipulation or input is standard for many complex problems, but by providing a customized collection of semiautomated routines, it is possible to substantially reduce the amount of manual touch-up required. There are various mesh creation tools available either commercially or freely, but each has its own limitations in application to optical tomography. For example, MeshLab is an open-source tool for creating unstructured 3-D triangular meshes, but has no semiautomated segmentation routines and is also lacking some workflow features such as the ability to undo the last action. Mimics is a commonly used commercial package designed for medical image processing, but mesh creation requires a great deal of manual input, and it has difficulty with multiple-region problems.Netgen is a freely available 3-D tetrahedral mesh generator, but has limitations with multiple-region problems.Some other mesh creation tools include DistMesh,iso2mesh,and quality mesh generation,but these are not linked to segmentation tools per se. There is no freely available tool that incorporates all of the workflow elements needed for segmentation and mesh creation in optical tomography in a seamless manner. The new tools in Nirfast help to address these issues, and in this study, their capabilities are tested and quantified in a series of cases which are representative of key application areas.

Materials and Methods

The segmentation and mesh creation tools in Nirfast allow for a variety of different inputs, including standard DICOM formats for medical images, general image formats (stacks of bmp, jpg, png, etc.), and structured geometry formats (vtk, mha, etc.). It can be used for a variety of different medical imaging modalities, such as CT, MR, ultrasound, and microCT. Both automatic and manual means of segmenting these images have been provided, and mesh creation is fully automated with customizable parameters.

The capability of these tools is demonstrated on four different cases that are relevant to the modeling of light propagation in tissue and optical tomography: small animal imaging, breast imaging, brain imaging, and light dose modeling in photodynamic therapy of the pancreas. The small animal example used a stack of CT images of the front portion of a mouse, consisting of 30 axial slices of 256 by 256 pixels, with a slice thickness of 0.35 mm. The images were taken on a Phillips MR Achieva medical system, in the form of a DICOM stack. The breast example used a stack of T1-weighted MR images, consisting of 149 coronal slices of 360 by 360 pixels, with a slice thickness of 0.64 mm. The images were taken on a Phillips MR Achieva medical system, in the form of a DICOM stack. The brain example used a stack of T1-weighted MR images, consisting of 256 axial slices of 256 by 256 pixels, with a slice thickness of 1 mm. The images were taken on a Siemens Trio 3T scanner and are stored in .hdr and .img files. The pancreas example used a stack of arterial phase CT images, consisting of 90 axial slices of 512 by 512 pixels, with a slice thickness of 1 mm. The images were taken on a Seimens Sensation 64 CT system, in the form of a DICOM stack. In each case, the appropriate modules were used to maximize the quality of the resulting mesh and increase the speed of the entire process.

The general procedure for processing the images follows: First, the medical images are imported into the segmentation interface, shown in Fig. 1. Next, automatic segmentation modules are used to identify different tissue types and regions as accurately as possible. See Table 1 for the steps used in each case, as well as parameter values. The modules and their respective parameters are detailed in Table 2. Explanations of the major segmentation modules are described below.

Fig. 1

The interface for segmentation of tissue types in medical images is shown, with 3-D orthogonal views at right and histogram information at left.

Results

The time of each step in segmentation and meshing was recorded for all cases, and the results are shown in Table 1. A visualization of a mesh for each case is shown in Figs for illustration. Using the case of the pancreas, the time taken for segmenting and creating a mesh using the tools in Nirfast was compared with that of the commercial package Mimics, designed for medical image processing. As seen in , Nirfast shows drastic improvements in the speed of both segmentation and meshing. It is worth noting that postprocessing mesh improvements have not been applied to the Mimics mesh, other than tools available in Mimics.

Original MRI axial slice of the brain (a), segmentation of different tissue types (b), and the 3-D tetrahedral mesh for the brain (c), showing the regions as different colors: red is the skin, yellow is the cerebral spinal fluid, green is the skull, blue is the white matter, and orange is the gray matter.

Original CT slice of the pancreas and surrounding tissue (a), segmentation of different tissue types (b), and the 3-D tetrahedral mesh for the pancreas (c), showing the regions as different colors: green indicates the blood vessels and red is pancreas and surrounding tissue.

Original MRI slice of the breast (a), segmentation of different tissue types (b), and the 3-D tetrahedral mesh for the breast (c), showing the regions as different colors: red is the glandular tissue and green is other breast tissue.

Original CT slice of the mouse (a), segmentation of different tissue types (b), and the 3-D tetrahedral mesh for the mouse (c), showing the regions as different colors: red is general tissue, green indicates the brain, and yellow is the tumor.

Time comparison of segmentation and mesh creation from pancreas CT between Nirfast and the commercial package Mimics.

The resulting 3-D tetrahedral meshes from both programs were then analyzed to assess element quality. Also analyzed was a new mesh optimization feature in Nirfast. This is an optional procedure that searches the mesh for poor-quality elements and attempts to fix them to improve quality, as defined below. It can take a significant amount of time for large mesh sizes, but can vastly increase the quality. In this case, optimization took 15 min. The meshes were made to have a similar number of nodes: 224,049 for the Mimics mesh, 224,445 for the Nirfast mesh, and 224,989 for the optimized Nirfast mesh. The metric used for quality criterion is the sine of minimum dihedral angle of each tetrahedron. Values close to zero would indicate an almost flat element. Such elements can cause loss of numerical accuracy as well as make the stiffness matrix in the FEM formulation ill-conditioned. The optimal value of this quality would be sin(70.52  deg)=0.942 for an equilateral tetrahedron; however the upper bound is 1.0. Figure 7 shows the quality histograms using Mimics, Nirfast, and mesh optimization in Nirfast. The minimum quality for each case respectively was 0.06, 0.12, and 0.65, with average quality values of 0.71, 0.73, and 0.77.

Histograms comparing mesh element quality between the commercial package Mimics and the tools developed in Nirfast. Also shown is the quality histogram when using the mesh optimization feature in Nirfast. No subsequent postprocessing improvements were applied to the Mimics mesh (but are available in the 3-matic package).

Figure 8 shows the reconstruction results in the mouse head using the Mimics mesh and the Nirfast mesh, displaying fluorescence yield overlaid on the MR images. The recovered fluorescence yield for each region in both cases is reported in Table 4.

Reconstructed fluorescence yield overlaid on sagittal MR images of the mouse head, based on reconstructions on a mesh created in Mimics (a) and in Nirfast (b). The fluorescence tomographic reconstructions are based on the segmentation of tissue types and region-based reconstruction on the resulting tetrahedral meshes.

Table 4

Recovered fluorescence yield for each region in reconstruction using the mouse head. Results are reported on both the Mimics- and Nirfast-created meshes.

Discussion

New segmentation and mesh creation tools have been implemented in Nirfast, with the ability to work from the variety of medical images encountered in optical tomography. The efficacy of these tools has been compared with the commercial package Mimics in a case study. The minimum and average tetrahedron element quality values are better using Nirfast (especially when using mesh optimization). In particular, the minimum quality is 62% higher relative to the optimal value using Nirfast. Low-quality elements can produce erroneous numerical solutions by several orders of magnitude, or even prevent a solution from being computed, so this improvement in the minimum quality threshold is essential for DOT. There is a large difference in the amount of time spent, with Nirfast being far more efficient by approximately fivefold. In segmentation, this is partly affected by the efficiency of the automatic segmentation methods, and also by the availability of many advanced segmentation tools that are particularly useful for the typical contrast profiles seen in MR/CT. A good example is breast imaging using MR guidance, where low-frequency gradients are often seen in the images. In the past, this has often hindered the ability to segment these images, as grayscale values of the same tissue type will no longer be in the same range. These gradients can be easily removed using MR bias removal, thus greatly reducing the amount of manual touch-up needed after automatic segmentation. In meshing, the improved computational time is in part due to the fact that the new meshing tools are completely automatic and do not require any fixing after mesh creation. The metrics used for speed and quality in comparison with Mimics account for all pre- and post-processing done using tools available in Mimics to improve mesh quality, but not any external tools that may be used separate from Mimics. For example, 3-matic is also a tool marketed by Materialise, capable of postprocessing mesh quality improvement, which could significantly improve the quality of meshes produced by Mimics.

An advantage of the Nirfast package that is not evident from the time benchmarks is the ease of use in the workflow. Since the entire package has been designed around seamlessly segmenting, creating a mesh, modeling light transport, and then visualizing the result, it is much easier to use than a combination of packages that are not optimized for optical tomography. In reconstruction results, as seen in  the recovered fluorescence yield is very similar between Mimics and Nirfast. In fact, there is a small improvement in the tumor and tumor boundary to background tissue fluorescence yield contrast recovered. This indicates that the new tools do not adversely affect reconstruction, despite saving significant time during segmentation and mesh creation. Furthermore, the higher minimum element qualities ensure that numerical issues do not arise with generating forward data on a poor-quality mesh, which can often cause a reconstruction to fail entirely and terminate before converging upon a solution.

The tools have been presented with a focus on optical tomography and the types of medical images often encountered in image-guided optical tomography. However, these tools could certainly be used for other applications in which it is useful to have a 3-D tetrahedral mesh created from 2-D image slices, such as electrical impedance tomography. One of the advantages of the meshing tools presented is the fact that interior region surfaces are maintained in the mesh, as opposed to simply labeling interior elements based on region proximity. This is very important in FEM modeling for optical tomography, as having the boundary of a surface inaccurately represented can lead to poor quantification.

Conclusion

Tools have been created to allow for segmentation and 3-D tetrahedral mesh creation from a variety of medical images and systems used in optical tomography applications. These tools show promising computational time and element quality benchmarks. The ease and speed of segmentation and meshing is very useful in promoting the use of optical tomography, which has long suffered from long, difficult, and nonrobust meshing procedures. Furthermore, the available automatic segmentation modules provide essential tools for many different types of medical images, particularly in regard to artifacts often seen in MR images. The tools are provided as part of a complete package designed for modeling diffuse light transport in tissue, allowing for a seamless workflow that has never before been available.