Continuous real time 3D motion reproduction using dynamic MRI and precomputed 4DCT deformation fields

Real-time image-guided treatment of mobile tumors in proton therapy by a library of treatment plans: a simulation study

PURPOSE

To improve target coverage and reduce the dose in the surrounding organs-at-risks (OARs), we developed an image-guided treatment method based on a precomputed library of treatment plans controlled and delivered in real-time.

METHODS

A library of treatment plans is constructed by optimizing a plan for each breathing phase of a four dimensional computed tomography (4DCT). Treatments are delivered by simulation on a continuous sequence of synthetic computed tomographies (CTs) generated from real magnetic resonance imaging (MRI) sequences. During treatment, the plans for which the tumor are at a close distance to the current tumor position are selected to deliver their spots. The study is conducted on five liver cases.

RESULTS

We tested our approach under imperfect knowledge of the tumor positions with a 2 mm distance error. On average, compared to a 4D robustly optimized treatment plan, our approach led to a dose homogeneity increase of 5% (defined as 1 - D 5 - D 95 prescription $1-\frac{D_5-D_{95}}{\text{prescription}}$ ) in the target and a mean liver dose decrease of 23%. The treatment time was roughly increased by a factor of 2 but remained below 4 min on average.

CONCLUSIONS

Our image-guided treatment framework outperforms state-of-the-art 4D-robust plans for all patients in this study on both target coverage and OARs sparing, with an acceptable increase in treatment time under the current accuracy of the tumor tracking technology.

Locally tuned deformation fields combination for 2D cine-MRI-based driving of 3D motion models

PURPOSE

To target mobile tumors in radiotherapy with the recent MR-Linac hardware solutions, research is being conducted to drive a 3D motion model with 2D cine-MRI to reproduce the breathing motion in 4D. This work presents a method to combine several deformation fields using local measures to better drive 3D motion models.

METHODS

The method uses weight maps, each representing the proximity with a specific area of interest. The breathing state is evaluated on cine-MRI frames in these areas and a different deformation field is estimated for each using a 2D to 3D motion model. The different deformation fields are multiplied by their respective weight maps and combined to form the final field to apply to a reference image. A global motion model is adjusted locally on the selected areas and creates a 3DCT for each cine-MRI frame.

RESULTS

The 13 patients on which it was tested showed on average an improvement of the accuracy of our model of 0.71 mm for areas selected to drive the model and 0.5 mm for other areas compared to our previous method without local adjustment. The additional computation time for each region was around 40 ms on a modern laptop.

CONCLUSION

The method improves the accuracy of the2D-based driving of 3D motion models. It can be used on top of existing methods relying on deformation fields. It does add some computation time but, depending on the area to deform and the number of regions of interests, offers the potential of online use.