Image Reconstruction in Diffuse Optical Tomography 
Tutorial listMesh generation for Toast problems with GmshMesh generation  Tutorial 1: Creating a simple spherical meshMesh generation  Tutorial 2: Creating a sphere with internal structure Mesh generation  Tutorial 3: Creating an adaptive mesh Solving DOT forward problemsTutorial 1: Building a DOT forward solverTutorial 2: Extending to homogeneous parameters Tutorial 3: Generating frequencydomain data Tutorial 4: Generating timedomain data Tutorial 5: Nonuniqueness Tutorial 6: Constructing the Jacobian matrix

Matlab toolbox demosThe ToastMatlab toolbox contains a series of interactive demos that allow to explore specific topics of the forward and inverse problems in diffuse optical tomography. Once the toolbox is installed, type
demo toolbox toast
on the Matlab command prompt to open the list of demos, or access the page from the Matlab Help/TOAST Toolbox/TOAST Demos link.
More info about the demos is available here. Matlab online tutorialsBelow is a list of online tutorials shows typical use examples for the MatlabToast toolbox. Each tutorial contains a link to the Matlab script to recreate the example. The tutorials introduce the concept of the forward and inverse problems in optical tomography step by step. It is recommended to work through them in sequence, since later tutorials build upon the previous ones. Mesh generation for Toast problems with GmshMesh generation  Tutorial 1: Build a spherical meshCreate a spherical mesh with the Gmsh user interface, and import it into Toast. Mesh generation  Tutorial 2: Adding internal structure to the sphereCreate a sphere with multiple regions by adding internal surfaces. Mesh generation  Tutorial 3: Creating an adaptive meshCreate an adaptive mesh based on a nodal density function Solving DOT forward problemsTutorial 1: Building a DOT forward solverThis example shows a basic forward solver for a simple 2D homogeneous problem, generating steadystate boundary data. It explains how to create a mesh, set up homogeneous parameter vectors, define the source and detector profiles, and solve the linear FEM problem. Tutorial 2: Extending to inhomogeneous parametersThis example shows how to define inhomogeneous parameter distributions by mapping bitmap images into the mesh basis, and evaluates the data differences to the homogeneous solution. Tutorial 3: Generating frequencydomain dataThis example applies modifications to the script to allow for the calculation of complexvalued measurement data from modulated light sources. Tutorial 4: Generating timedomain dataThis example shows how to solve the timedependent diffusion equation by applying a finitedifference scheme to represent the time derivative. It shows the setup of the system matrices and the solver loop for the time steps. Tutorial 5: NonuniquenessSimultaneous reconstruction of the absorption and scattering parameters requires sufficient information in the measurement data to distinguish between the effects of the parameters. This example shows that steadystate intensity data do not allow to reconstruct for both parameter distributions. Tutorial 6: Constructing the Jacobian matrixThe Jacobian defines the derivative of the forward operator with respect to given parameter distributions. It is an important element of many reconstruction algorithms. This example shows how to calculate the Jacobian, and the interpretation of individual rows as photon measurement density functions. 