Comparing planning time, delivery time and plan quality for IMRT, RapidArc and tomotherapy

Oliver, Melody; Ansbacher, W.; Beckham, Wayne

doi:10.1120/jacmp.v10i4.3068

Cited by 87 publications

(79 citation statements)

References 36 publications

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“…5 and 6) have been implemented clinically, investigators have steadily reported plan and dosimetric comparisons for several tumor sites as compared to other modalities. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] In addition to dynamic leaf motion as in dynamic-MLC IMRT, RapidArc TM (Varian Medical Systems, Palo Alto, CA) utilizes gantry rotation as well as variations in gantry speed and dose rate. Many studies have advocated that the inherent complexities of this technique require similar, but additional, commissioning and quality assurance than that of conventional IMRT.…”

Section: Introductionmentioning

confidence: 99%

Is RapidArc more susceptible to delivery uncertainties than dynamic IMRT?

Betzel

Niu

et al. 2012

Medical Physics

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Purpose: Rotational IMRT has been adopted by many clinics for its promise to deliver treatments in a shorter amount of time than other conventional IMRT techniques. In this paper, the authors investigate whether RapidArc is more susceptible to delivery uncertainties than dynamic IMRT using fixed fields. Methods: Dosimetric effects of delivery uncertainties in dose rate, gantry angle, and MLC leaf positions were evaluated by incorporating these uncertainties into RapidArc and sliding window IMRT (SW IMRT) treatment plans for five head-and-neck and five prostate cases. Dose distributions and dose-volume histograms of original and modified plans were recalculated and compared using Gamma analysis and dose indices of planned treatment volumes (PTV) and organs at risk (OAR). Results of Gamma analyses using passing criteria ranging from 1%-1 mm up to 5%-3 mm were reported. Results: Systematic shifts in MLC leaf bank positions of SW-IMRT cases resulted in 2-4 times higher average percent differences than RapidArc cases. Uniformly distributed random variations of 2 mm for active MLC leaves had a negligible effect on all dose distributions. Sliding window cases were much more sensitive to systematic shifts in gantry angle. Dose rate variations during RapidArc must be much larger than typical machine tolerances to affect dose distributions significantly; dynamic IMRT is inherently not susceptible to such variations. Conclusions: RapidArc deliveries were found to be more tolerant to variations in gantry position and MLC leaf position than SW IMRT. This may be attributed to the fact that the average segmental field size or MLC leaf opening is much larger for RapidArc. Clinically acceptable treatments may be delivered successfully using RapidArc despite large fluctuations in dose rate and gantry position.

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Section: Introductionmentioning

confidence: 99%

Is RapidArc more susceptible to delivery uncertainties than dynamic IMRT?

Betzel

Niu

et al. 2012

Medical Physics

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“…According to the study conducted by Oliver et al [32] and Nicolini et al, [33] RapidArc plans are capable of producing better conformation in PTV than IMRT plans. RapidArc plans yielded better dosimetric indices because of inherent arc therapy nature of these plans, as is evident from this study.…”

Section: Discussionmentioning

confidence: 99%

A comparative study of RapidArc and intensity-modulated radiotherapy plan quality for cervical cancer treatment

Atiq

Iqbal

et al. 2018

Indian J Cancer

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BACKGROUND: RapidArc therapy, a complex form of intensity-modulated radiotherapy (IMRT), is now widely used to treat cancer patients. AIMS: This study aimed to investigate and compare the plan quality of IMRT and RapidArc techniques using various dosimetric indices to find the better treatment modality for treating patients with cervix cancer. MATERIALS AND METHODS: Thirteen cervical cancer patients treated with IMRT were selected for analysis and original plans were subsequently re-optimized using the RapidArc technique. Plans were generated such that dose of 5000 cGy was delivered in 25 equal fractions. Inverse planning was done by Eclipse (Varian Medical Systems, Palo Alto, CA) treatment planning system for 15 MV photon beams from computed tomographic data. Double arcs were used for RapidArc plans. Quality of treatment plans was evaluated by calculating conformity index (CI), homogeneity index (HI), gradient index (GI), coverage, and unified dosimetry index (UDI) for each plan. RESULTS AND CONCLUSION: RapidArc resulted in better planning target volume (PTV) coverage as is evident from its superior conformation number, coverage, CI, HI, GI, and UDI. Regarding organs at risk (OARs), RapidArc plans exhibit superior organ sparing as is evident from integral dose comparison. Difference between both techniques was determined by statistical analysis. For all cases under study, modest differences between IMRT and RapidArc treatment were observed. RapidArc-based treatment planning is safer with similar planning goals compared to the standard fixed IMRT technique. This study clearly demonstrated that favorable dose distribution in PTV and OARs was achieved using RapidArc technique, and hence, the risk of damage to normal tissues is reduced.

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“…As repeated forward dose calculation operations involving this huge DDC matrix are needed during an optimization process, the overall computation time of VMAT optimization is prolonged. At present, with the utilization of a multi-core computer or a computer cluster, it is possible to achieve a computation time of several minutes to tens of minutes 5,6 . However, the overall planning efficiency is still low in clinical practice, as a trial-and-error process is usually needed to manual tune some parameters repeatedly for the optimization until achieving a good plan.…”

Section: Introductionmentioning

confidence: 99%

“…At the same time, beam fluence map is modulated by a multi-leaf collimator (MLC) 1,2 to yield a carefully sculpted 3D dose distribution conformal to the cancer target [3][4][5][6] . Compared with conventional Intensity Modulated Radiation Therapy (IMRT), the treatment plan optimization problem for VMAT is much more complicated.…”

Section: Introductionmentioning

confidence: 99%

Multi-GPU implementation of a VMAT treatment plan optimization algorithm

et al. 2015

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Purpose: VMAT optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. Highperformance graphics processing units (GPUs) have been used to speed up the computations. However, GPU's relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation based VMAT algorithm, previously developed in our group, on a multi-GPU platform to solve the memory limitation problem. While the column-generation based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. We also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. Methods: The column-generation approach generates VMAT apertures sequentially by solving a pricing problem (PP) and a master problem (MP) iteratively. In our method, the sparse DDC matrix is first stored on CPU in coordinate list format (COO). On the GPU side, this matrix is split into four sub-matrices according to beam angles, which are stored on four GPUs in compressed sparse row (CSR) format. Computation of beamlet price, the first step in PP, is accomplished using multi-GPUs. A fast inter-GPU data transfer scheme is designed using peer-to-peer (P2P) access. The remaining steps of PP and MP problems are implemented on CPU or a single GPU due to their modest problem scale and computational loads. Barzilai and Borwein (BB) algorithm with subspace step scheme is adopted here to solve the MP problem. A head and neck (H&N) cancer case was used to validate our method. We also compare our multi-GPU implementation with three different single GPU implementation strategies: truncating DDC matrix (S1), repeatedly transferring DDC matrix between CPU and GPU (S2), and porting computations involving DDC matrix to CPU (S3), in terms of both plan quality and computational efficiency. Two 2 more H&N patient cases and three prostate cases were also used to demonstrate the advantages of our method. Results: Our multi-GPU implementation can finish the optimization process within ~1 minute for the H&N patient case. S1 leads to an inferior plan quality although its total time was 10 seconds shorter than the multi-GPU implementation due to the reduced matrix size. S2 and S3 yield the same plan quality as the multi-GPU implementation but take ~4 minutes and ~6 minutes, respectively. High computational efficiency was consistently achieved for the other 5 patient cases tested, with VMAT plans of clinically acceptable quality obtained within 23~46 seconds. Conversely, to obtain clinically comparable or acceptable plans for all these 6 VMAT cases that we have tested in t...

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Comparing planning time, delivery time and plan quality for IMRT, RapidArc and tomotherapy

Cited by 87 publications

References 36 publications

Is RapidArc more susceptible to delivery uncertainties than dynamic IMRT?

Is RapidArc more susceptible to delivery uncertainties than dynamic IMRT?

A comparative study of RapidArc and intensity-modulated radiotherapy plan quality for cervical cancer treatment

Multi-GPU implementation of a VMAT treatment plan optimization algorithm

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