Automated gamma knife radiosurgery treatment planning with image registration, data-mining, and Nelder-Mead simplex optimization

2006 ◽  
Vol 33 (7Part1) ◽  
pp. 2532-2540 ◽  
Author(s):  
Kuan J. Lee ◽  
David C. Barber ◽  
Lee Walton
Keyword(s):  
Data Mining ◽  
Image Registration ◽  
Treatment Planning ◽  
Gamma Knife ◽  
Gamma Knife Radiosurgery ◽  
Simplex Optimization ◽  
Registration Data

scholarly journals Image Registration Strategy of T1-Weighted and FIESTA MRI Sequences in Trigeminal Neuralgia Gamma Knife Radiosurgery

2010 ◽  
Vol 88 (4) ◽  
pp. 239-245 ◽  
Author(s):  
Tony J.C. Wang ◽  
Ronald Brisman ◽  
Zheng Feng Lu ◽  
Xiang Li ◽  
Steven R. Isaacson ◽  
...  
Keyword(s):  
Image Registration ◽  
Trigeminal Neuralgia ◽  
Gamma Knife ◽  
Gamma Knife Radiosurgery ◽  
Mri Sequences

SU-E-T-542: Impact of Convolution Algorithm Based Treatment Planning in Gamma Knife Radiosurgery

2013 ◽  
Vol 40 (6Part19) ◽  
pp. 330-330
Author(s):  
gopishankar natanasabapathi ◽  
Raj Kishor Bisht
Keyword(s):  
Treatment Planning ◽  
Gamma Knife ◽  
Gamma Knife Radiosurgery ◽  
Convolution Algorithm

scholarly journals Integration of rotational angiography enables better dose planning in Gamma Knife radiosurgery for brain arteriovenous malformations

2018 ◽  
Vol 129 (Suppl1) ◽  
pp. 17-25 ◽  
Author(s):  
Hirotaka Hasegawa ◽  
Shunya Hanakita ◽  
Masahiro Shin ◽  
Mariko Kawashima ◽  
Taichi Kin ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Gamma Knife ◽  
Arteriovenous Malformations ◽  
Gamma Knife Radiosurgery ◽  
Volume Ratio ◽  
Rotational Angiography ◽  
Rank Tests ◽  
Signed Rank ◽  
Dose Planning ◽  
Before And After

OBJECTIVEIn Gamma Knife radiosurgery (GKS) for arteriovenous malformations (AVMs), CT angiography (CTA), MRI, and digital subtraction angiography (DSA) are generally used to define the nidus. Although the AVM angioarchitecture can be visualized with superior resolution using rotational angiography (RA), the efficacy of integrating RA into the GKS treatment planning process has not been elucidated.METHODSUsing data collected from 25 consecutive patients with AVMs who were treated with GKS at the authors’ institution, two neurosurgeons independently created treatment plans for each patient before and after RA integration. For all patients, MR angiography, contrasted T1 imaging, CTA, DSA, and RA were performed before treatment. The prescription isodose volume before (PIVB) and after (PIVA) RA integration was measured. For reference purposes, a reference target volume (RTV) for each nidus was determined by two other physicians independent of the planning surgeons, and the RTV covered by the PIV (RTVPIV) was established. The undertreated volume ratio (UVR), overtreated volume ratio (OVR), and Paddick’s conformal index (CI), which were calculated as RTVPIV/RTV, RTVPIV/PIV, and (RTVPIV)2/(RTV × PIV), respectively, were measured by each neurosurgeon before and after RA integration, and the surgeons’ values at each point were averaged. Wilcoxon signed-rank tests were used to compare the values obtained before and after RA integration. The percentage change from before to after RA integration was calculated for the average UVR (%ΔUVRave), OVR (%ΔOVRave), and CI (%ΔCIave) in each patient, as ([value after RA integration]/[value before RA integration] − 1) × 100. The relationships between prior histories and these percentage change values were examined using Wilcoxon signed-rank tests.RESULTSThe average values obtained by the two surgeons for the median UVR, OVR, and CI were 0.854, 0.445, and 0.367 before RA integration and 0.882, 0.478, and 0.463 after RA integration, respectively. All variables significantly improved after compared with before RA integration (UVR, p = 0.009; OVR, p < 0.001; CI, p < 0.001). Prior hemorrhage was significantly associated with larger %ΔOVRave (median 20.8% vs 7.2%; p = 0.023) and %ΔCIave (median 33.9% vs 13.8%; p = 0.014), but not %ΔUVRave (median 4.7% vs 4.0%; p = 0.449).CONCLUSIONSIntegrating RA into GKS treatment planning may permit better dose planning owing to clearer visualization of the nidus and, as such, may reduce undertreatment and waste irradiation. Further studies examining whether the observed RA-related improvement in dose planning also improves the radiosurgical outcome are needed.

scholarly journals Forward treatment planning techniques to reduce the normalization effect in Gamma Knife radiosurgery

2017 ◽  
Vol 18 (6) ◽  
pp. 114-122
Author(s):  
Hao-Wen Cheng ◽  
Wei-Lun Lo ◽  
Chun-Yuan Kuo ◽  
Yu-Kai Su ◽  
Jo-Ting Tsai ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Gamma Knife ◽  
Gamma Knife Radiosurgery ◽  
Planning Techniques ◽  
Normalization Effect

scholarly journals NIMG-57. ASSOCIATION OF RADIOMICS BASED TUMOR SHAPE IRREGULARITY MEASURES AND GAMMA KNIFE DOSE PLANNING INDICES IN VESTIBULAR SCHWANNOMAS

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii160-ii161
Author(s):  
Esra Sümer ◽  
Ece Tek ◽  
Meriç Şengöz ◽  
M Necmettiin Pamir ◽  
Alp Dinçer ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Gamma Knife ◽  
Gamma Knife Radiosurgery ◽  
Efficiency Index ◽  
Vestibular Schwannomas ◽  
Conformity Index ◽  
Shape Feature ◽  
Dose Planning ◽  
Shape Irregularity ◽  
Tumor Shape

Abstract Gamma knife radiosurgery (GKRS) delivers an unevenly distributed radiation dose to a tumor, with a sharp falloff outside the target. Although the dose inhomogeneity within a tumor is strongly influenced by its shape, routine GKRS dose planning does not account for it. We hypothesized that shape irregularity measures were correlated with treatment planning indices, and might provide insight during treatment planning. The aims of this study were to quantify the shape irregularity measures in vestibular schwannomas, estimate their correlations with core radiosurgical planning measures, and define the most predictive shape feature for dose effectiveness. METHODS: Four dose plan indices, which were the selectivity index (SI), gradient index (GI), efficiency index (EI), and Paddick’s conformity index (PCI) were estimated from the GKRS plans of 234 vestibular schwannomas. All dose plans were prepared using Gamma Plan 10.0 and above and all treatments were delivered using a perfexion/ICON platform. Three-dimensional (3D) tumor models were rendered using 3D Slicer Software from segmented T1-weighted MR images. Sixteen irregularity measures were calculated for each tumor using Radiomics in MATLAB. Spearman correlation coefficients (r) were computed to find associations of the dose plan indices with the irregularity descriptors. The most predictive shape feature for dose efficiency was identified using the least absolute shrinkage and selection operator (Lasso). RESULTS: The shape irregularity measures were negatively correlated with SI, EI, and PCI, and positively correlated with GI. Volumetric index of sphericity (VioS) had the highest correlations with SI (r = 0.63, p= 3.27E-23), GI (r= -0.58, p= 1.10E-19), EI (r = 0.69, p= 0.00), and PCI(r= 0.68, p = 6.73E-28), and Lasso feature selection identified VioS as the most important feature for predicting all dose plan indices. CONCLUSION: VioS provides a numerical quantification of tumor shape irregularity, and it is highly correlated with the GKRS dose planning indices. *indicates co-senior authors

scholarly journals A real-time optimal inverse planning for Gamma Knife radiosurgery by convex optimization: description of the system and first dosimetry data

2018 ◽  
Vol 129 (Suppl1) ◽  
pp. 111-117 ◽  
Author(s):  
Marc Levivier ◽  
Rafael E. Carrillo ◽  
Rémi Charrier ◽  
André Martin ◽  
Jean-Philippe Thiran
Keyword(s):  
Convex Optimization ◽  
Real Time ◽  
Treatment Planning ◽  
Gamma Knife ◽  
Gamma Knife Radiosurgery ◽  
Planning System ◽  
Optimal Plan ◽  
Inverse Planning ◽  
Planning Approach ◽  
Dosimetry Data

OBJECTIVEThe authors developed a new, real-time interactive inverse planning approach, based on a fully convex framework, to be used for Gamma Knife radiosurgery.METHODSThe convex framework is based on the precomputation of a dictionary composed of the individual dose distributions of all possible shots, considering all their possible locations, sizes, and shapes inside the target volume. The convex problem is solved to determine the plan, i.e., which shots and with which weights, that will actually be used, considering a sparsity constraint on the shots to fulfill the constraints while minimizing the beam-on time. The system is called IntuitivePlan and allows data to be transferred from generated dose plans into the Gamma Knife treatment planning software for further dosimetry evaluation.RESULTSThe system has been very efficiently implemented, and an optimal plan is usually obtained in less than 1 to 2 minutes, depending on the complexity of the problem, on a desktop computer or in only a few minutes on a high-end laptop. Dosimetry data from 5 cases, 2 meningiomas and 3 vestibular schwannomas, were generated with IntuitivePlan. Results of evaluation of the dosimetry characteristics are very satisfactory and adequate in terms of conformity, selectivity, gradient, protection of organs at risk, and treatment time.CONCLUSIONSThe possibility of using optimal, interactive real-time inverse planning in conjunction with the Leksell Gamma Knife opens new perspectives in radiosurgery, especially considering the potential use of the full capabilities of the latest generations of the Leksell Gamma Knife. This approach gives new users the possibility of using the system for easier and quicker access to good-quality plans with a shorter technical training period and opens avenues for new planning strategies for expert users. The use of a convex optimization approach allows an optimal plan to be provided in a very short processing time. This way, innovative graphical user interfaces can be developed, allowing the user to interact directly with the planning system to graphically define the desired dose map and to modify on-the-fly the dose map by moving, in a very user-friendly manner, the isodose surfaces of an initial plan. Further independent quantitative prospective evaluation comparing inverse planned and forward planned cases is warranted to validate this novel and promising treatment planning approach.

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