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# Radiotherapy Optimization 101 <a name="RadOpt101"></a>
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<img src="./images/discretization.png" align="right" alt="Radiotherapy 101" width="50%" height="40%">
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<img src="./images/discretization_.png" align="right" alt="Radiotherapy 101" width="50%" height="40%">
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The key variables in treatment planning optimization are the parameters of the **radiation beams** (e.g., beams' shape, angle, and intensity). However, the quality of a treatment is primarily measured by the **radiation dose** delivered to the patient’s body. We can connect the beam parameters to the radiation dose using a straightforward linear relationship. First, we divide the patient’s body into small three-dimensional units called **voxels** and each radiation beam into small two-dimensional sections called **beamlets**. By calculating how much radiation each beamlet (j) delivers to each voxel (i), and representing this with a value **aij**, we create what is known as the **dose deposition matrix (A)**. This matrix links the intensities of the beamlets (x) to the total radiation dose delivered (d) using the equation: **d=Ax**. A general radiotherapy optimization problem can be formulated as:
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# Data <a name="Data"></a>
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<img src="./images/PortPy_Data_Curation.png" align="right" width="50%" height="40%">
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<img src="./images/PortPy_Data_Curation.png" align="right" width="70%" height="60%">
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PortPy equips researchers with a robust benchmark patient dataset, sourced from the FDA-approved Eclipse commercial treatment planning system through its API. This dataset embodies all necessary elements for optimizing various machine configurations such as beam angles, aperture shapes, and leaf movements. It includes
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