A Deep-Learning-based Multi-segment VMAT Plan Generation Algorithm from Patient Anatomy for Prostate Simultaneous Integrated Boost (SIB) Cases

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Introduction: Several studies have realized fluence-map-prediction-based DL IMRT planning algorithms. However, DL-based VMAT planning remains unsolved. A main difficult in DL-based VMAT planning is how to generate leaf sequences from the predicted radiation intensity maps. Leaf sequences are required for a large number of control points and meet physical restrictions of MLC. A previous study1 reported a DL algorithm to generate 64-beam IMRT plans to approximate VMAT plans with certain dose distributions as input. As a step forward, another study2 reported a DL algorithm to generate one-arc VMAT plans from patient anatomy. This study generated MLC leaf sequence from thresholded predicted intensity maps for one-arc VMAT plans. Based on this study, we developed an algorithm to convert DL-predicted intensity maps to multi-segment VMAT plans to improve the performance of one-arc plans.

Methods: Our deep learning model utilizes a series of 2D projections of a patient’s dose prediction and contour structures to generate a multi-arc 360º dynamic MLC sequence in a VMAT plan. The backbone of this model is a novel U-net implementation which has a 4-resolution-step analysis path and a 4-resolution-step synthesis path. In the pretrained DL model, a total of 130 patients were involved, with 120 patients in the training and 11 patients in testing groups, respectively. These patients were prescribed with 70Gy/58.8Gy to the primary/boost PTVs in 28 fractions in a simulated integrated boost (SIB) regime. In this study, 7-8 arcs with the same collimator angle are used to simulate the predicted intensity maps. The predicted intensity maps are separated into 7-8 segments along the collimator angle. Hence, the arcs could separately simulate predicted intensity maps with independent weight factors. This separation also potentially allows MLC leaves to simulate more dose gradient in the predicted intensity mapsResults: After dose normalization (PTV70 V70Gy=95%), all 11 multi-segment test plans met institutional clinic guidelines of dose distribution outside PTV. Bladder (V70Gy=5.3±3.3cc, V40Gy=16.1±8.6%) and rectum (V70Gy=4.5±2.3cc, V40Gy=33.4±8.1%) results in multi-segment plans were comparable with the commercial TPS plan results. 3D max dose results in AVP-DSP plans(D1cc=112.6±1.9%) were higher than the commercial TPS plans results(D1cc=106.7±0.8%). On average, AVP-DSP used 600 seconds for a plan generation in contrast to the current clinical practice (>20 minutes).

Conclusion: Results suggest that multi-segment plans can generate a prostate VMAT plan with clinically-acceptable dosimetric quality. the proposed multi-segment plan generation algorithm has the capability to achieve higher modulation and lower maximum dose. With its high efficiency, multi-segment may hold great potentials of real-time planning application after further validation.





Zhu, Qingyuan (2021). A Deep-Learning-based Multi-segment VMAT Plan Generation Algorithm from Patient Anatomy for Prostate Simultaneous Integrated Boost (SIB) Cases. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/23831.


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