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Quantitative analysis of MRI-guided radiotherapy treatment process time for tumor real-time gating efficiency.

ABSTRACT: PURPOSE:Magnetic Resonance-guided radiotherapy (MRgRT) systems allow continuous monitoring of therapy volumes during treatment delivery and personalized respiratory gating approaches. Treatment length may therefore be significantly affected by patient's compliance and breathing control. We quantitatively analyzed treatment process time efficiency (TE ) using data obtained from real-world patient treatment logs to optimize MRgRT delivery settings. METHODS:Data corresponding to the first 100 patients treated with a low T hybrid MRI-Linac system, both in free breathing (FB) and in breath hold inspiration (BHI) were collected. TE has been computed as the percentage difference of the actual single fraction's total treatment time and the predicted treatment process time, as computed by the TPS during plan optimization. Differences between the scheduled and actual treatment room occupancy time were also evaluated. Finally, possible correlations with planning, delivery and clinical parameters with TE were also investigated. RESULTS:Nine hundred and nineteen treatment fractions were evaluated. TE difference between BHI and FB patients' groups was statistically significant and the mean TE were 42.4%, and -0.5% respectively. No correlation was found with TE for BHI and FB groups. Planning, delivering and clinical parameters classified BHI and FB groups, but no correlation with TE was found. CONCLUSION:The use of BHI gating technique can increase the treatment process time significantly. BHI technique could be not always an adequate delivery technique to optimize the treatment process time. Further gating techniques should be considered to improve the use of MRgRT.

SUBMITTER: Placidi L

PROVIDER: S-EPMC7701108 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Quantitative analysis of MRI-guided radiotherapy treatment process time for tumor real-time gating efficiency.

Placidi Lorenzo L Cusumano Davide D Boldrini Luca L Votta Claudio C Pollutri Veronica V Antonelli Marco Valerio MV Chiloiro Giuditta G Romano Angela A De Luca Viola V Catucci Francesco F Indovina Luca L Valentini Vincenzo V

Journal of applied clinical medical physics 20201022 11

<h4>Purpose</h4>Magnetic Resonance-guided radiotherapy (MRgRT) systems allow continuous monitoring of therapy volumes during treatment delivery and personalized respiratory gating approaches. Treatment length may therefore be significantly affected by patient's compliance and breathing control. We quantitatively analyzed treatment process time efficiency (T<sub>E</sub> ) using data obtained from real-world patient treatment logs to optimize MRgRT delivery settings.<h4>Methods</h4>Data correspond ...[more]

PMID: 33089954

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Project description:PURPOSE:On-board magnetic resonance imaging (MRI) greatly enhances real-time target tracking capability during radiotherapy treatments. However, multislice and volumetric MRI techniques are frame rate limited and introduce unacceptable latency between the target moving out of position and the beam being turned off. We present a technique to estimate continuous volumetric tissue motion using motion models built from a repeated acquisition of a stack of MR slices. Applications including multislice target visualization and out-of-slice motion estimation during MRI-guided radiotherapy are demonstrated. METHODS:Eight healthy volunteer studies were performed using a 0.35 T MRI-guided radiotherapy system. Images were acquired at three frames per second in an interleaved fashion across ten adjacent sagittal slice positions covering 4.5 cm using a balanced steady-state-free precession sequence. A previously published five-dimensional (5D) linear motion model used for MRI-guided radiotherapy gating was extended to include multiple slices. This model utilizes an external respiratory bellows signal recorded during imaging to simultaneously estimate motion across all imaged slices. For comparison to an image-based approach, the manifold learning technique local linear embedding (LLE) was used to derive a respiratory surrogate for motion modeling. Manifolds for every slice were aligned during LLE in a group-wise fashion, enabling motion estimation outside the current imaged slice using a motion model, a process which we denote as mSGA. Additionally, a method is developed to evaluate out-of-slice motion estimates. The multislice motion model was evaluated in a single slice with each newly acquired image using a leave-one-out approach. Model-generated gating decision accuracy and beam-on positive predictive value (PPV) are reported along with the median and 95th percentile distance between model and ground truth target centroids. RESULTS:The average model gating decision accuracy and PPV across all volunteer studies was 93.7% and 92.8% using the 5D model, and 96.8% and 96.1% using the mSGA model, respectively. The median and 95th percentile distance between model and ground truth target centroids was 0.91 and 2.90 mm, respectively, using the 5D model and 0.58 and 1.49 mm using the mSGA model, averaged over all eight subjects. The mSGA motion model provided a statistically significant improvement across all evaluation metrics compared to the external surrogate-based 5D model. CONCLUSION:The proposed techniques for out-of-slice target motion estimation demonstrated accuracy likely sufficient for clinical use. Results indicate the mSGA model may provide higher accuracy, however, the external surrogate-based model allows for unbiased in vivo accuracy evaluation.

Project description:BackgroundSurface-guided radiation therapy (SGRT) employs a non-invasive real-time optical surface imaging (OSI) technique for patient surface motion monitoring during radiotherapy. The main purpose of this study is to verify the real-time tracking accuracy of SGRT for respiratory motion and provide a fitting method to detect the time delay of gating.MethodsA respiratory motion phantom was utilized to simulate respiratory motion using 17 cosine breathing pattern curves with various periods and amplitudes. The motion tracking of the phantom was performed by the Catalyst™ system. The tracking accuracy of the system (with period and amplitude variations) was evaluated by analyzing the adjusted coefficient of determination (A_R2) and root mean square error (RMSE). Furthermore, 13 actual respiratory curves, which were categorized into regular and irregular patterns, were selected and then simulated by the phantom. The Fourier transform was applied to the respiratory curves, and tracking accuracy was compared through the quantitative analyses of curve similarity using the Pearson correlation coefficient (PCC). In addition, the time delay of amplitude-based respiratory-gating radiotherapy based on the OSI system with various beam hold times was tested using film dosimetry for the Elekta Versa-HD and Varian Edge linacs. A dose convolution-fitting method was provided to accurately measure the beam-on and beam-off time delays.ResultsA_R2 and RMSE for the cosine curves were 0.9990-0.9996 and 0.110-0.241 mm for periods ranging from 1 s to 10 s and 0.9990-0.9994 and 0.059-0.175 mm for amplitudes ranging from 3 mm to 15 mm. The PCC for the actual respiratory curves ranged from 0.9955 to 0.9994, which was not significantly affected by breathing patterns. For gating radiotherapy, the average beam-on and beam-off time delays were 1664 ± 72 and 25 ± 30 ms for Versa-HD and 303 ± 45 and 34 ± 25 ms for Edge, respectively. The time delay was relatively stable as the beam hold time increased.ConclusionsThe OSI technique provides high accuracy for respiratory motion tracking. The proposed dose convolution-fitting method can accurately measure the time delay of respiratory-gating radiotherapy. When the OSI technique is used for respiratory-gating radiotherapy, the time delay for the beam-on is considerably longer than the beam-off.

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Quantitative analysis of MRI-guided radiotherapy treatment process time for tumor real-time gating efficiency.

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Quantitative analysis of MRI-guided radiotherapy treatment process time for tumor real-time gating efficiency.

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