  Non-coplanar Intensity-Modulated Radiation Therapy (IMRT) goes a step further
by orienting the gantry carrying the radiation beam and the patient couch in a
non-coplanar manner to accurately target the cancer region and better avoid
organs-at-risk. The use of a non-coplanar treatment trajectory significantly
enhances the degree of freedom and flexibility but increases drastically the
complexity of the optimization. In inverse planning optimization the dose
contribution for all potential beam directions is usually pre-calculates and
pre-loads into the Treatment Planning System (TPS). The size the dose matrix
becomes more critical when moving from coplanar IMRT to non-coplanar IMRT since
the number of beams increases drastically. A solution would be to calculate
"on-the-fly" the dose contribution to each new candidate beam during
optimization. This is only possible if a dose calculation engine is fast enough
to be used online during optimization iterations, which is not the case in
standard method. Therefore, in this work we propose an IMRT optimization scheme
using deep learning based dose engine to compute the dose matrix on-line. The
proposed deep learning approach will be combined into a
simulated-annealing-based optimization method for non-coplanar IMRT. Since the
dose engine will compute the dose contribution on-line during the optimization,
the final main optimization method requires to keep in memory a very
lightweight dose matrix. The proposed method was compared with clinical data
showing a good agreement considering dosimetry of the treatment plans. The main
advantage of the proposed method was the reduction of the memory storage from
9GB to 10MB during the optimization process.
