Use of maximum intensity projections (MIP) for target volume generation in 4DCT scans for lung cancer

This study was to evaluate the interfractional and intrafractional setup errors and baseline shifts of golden fiducial markers in patients receiving postoperative radiotherapy (RT) using cone‐beam computed tomography (CBCT) in order to calculate PTV margins for patients with liver cancer. Twelve patients with liver tumors underwent postoperative RT. CBCT images were acquired before and after the treatment. Off‐line vertebral body match and fiducial marker match were used, respectively. The results of vertebral body match represented the setup errors of the patients, while the results of fiducial marker match represented the absolute position errors of the target volume. Baseline shifts of the target volume were calculated as the absolute target position errors minus setup errors. A total of 12 patients with 214 acquisitions of CBCTs were analyzed. Both Σ and σ of setup errors and baseline shifts in left–right (L/R), superior–inferior (S/I), and anterior–posterior(A/P) directions were calculated, including interfractional and intrafractional uncertainties. Planning target volume (PTV) margins were calculated according to margin=2.5Σ+0.7σ. Margins of 1.8 mm, 3.8 mm, and 1.4 mm in L/R, S/I, and A/P directions are needed to compensate intrafractional errors when daily online CBCT correction is used. When CBCT correction with no action level (NAL) protocol is used, PTV margin should be 2.6 mm, 5.9 mm, and 2.6 mm in L/R, S/I, and A/P directions. Margins of 5.5 mm, 14.6 mm, and 7.2 mm were needed to compensate the baseline shifts when electronic portal imaging devices (EPID) or CBCT with bone match is used for online correction of setup error.PACS number: 87.55.‐x

Section: Methodsmentioning

confidence: 99%

Inter‐ and intrafractional setup errors and baseline shifts of fiducial markers in patients with liver tumors receiving free‐breathing postoperative radiation analyzed by cone‐beam computed tomography

Zhang

Wang

et al. 2014

“…4D CT incorporates the patient's respiratory information into a stack of 3D images such that the sequential snapshot images at different respiratory phases can be reconstructed 4, 5. With the 4D images, the tumor excursion range with respiration can be obtained and a patient‐specific internal margin can be decided for contouring the internal tumor volume 1, 6, 7. Respiratory gating in radiation delivery provides effective motion control by disabling radiation beam when respiration magnitude exceeds certain threshold, which is beneficial for reducing the planned target size and sparing more healthy tissues 8, 9, 10…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the combined use of two different respiratory monitoring systems for 4D CT simulation and gated treatment

Liu

Lin

Fan

et al. 2018

PurposeTwo different respiratory monitoring systems (Varian's Real‐Time Position Management (RPM) System and Siemens’ ANZAI belt Respiratory Gating System) are compared in the context of respiratory signals and 4D CT images that are accordingly reconstructed. This study aims to evaluate the feasibility of combined use of RPM and ANZAI systems for 4DCT simulation and gated radiotherapy treatment, respectively.MethodsThe RPM infrared reflecting marker and the ANZAI belt pressure sensor were both placed on the patient's abdomen during 4DCT scans. The respiratory signal collected by the two systems was synchronized. Fifteen patients were enrolled for respiratory signal collection and analysis. The discrepancies between the RPM and ANZAI traces can be characterized by phase shift and shape distortion. To reveal the impact of the changes in respiratory signals on 4D images, two sets of 4D images based on the same patient's raw data were reconstructed using the RPM and ANZAI data for phase sorting, respectively. The volume of whole lung and the position of diaphragm apex were measured and compared for each respiratory phase.ResultsThe mean phase shift was measured as 0.2 ± 0.1 s averaged over 15 patients. The shape of the breathing trace was found to be in disagreement. For all the patients, the ANZAI trace had a steeper falloff in exhalation than RPM. The inhalation curve, however, was matched for nine patients, steeper in ANZAI for five patients and steeper in RPM for one patient. For 4D image comparison, the difference in whole‐lung volume was about −4% to +4% and the difference in diaphragm position was about −5 mm to +4 mm, compared in each individual phase and averaged over seven patients.ConclusionsCombined use of one system for 4D CT simulation and the other for gated treatment should be avoided as the resultant gating window would not fully match with each other due to the remarkable discrepancy in breathing traces acquired by the two different surrogate systems.

“…All patients received a four‐dimensional CT (4D CT) in which the internal tumor volume (ITV) was generated from the maximum intensity projection (MIP) (16) . Contouring and planning was performed on the average CT postprocessed from the 4D CT. Planning tumor volumes (PTV) were expanded from the ITV using a uniform 3 mm margin for central lesions, and a 6 mm uniform margin for peripheral lesions.…”

Section: Methodsmentioning

confidence: 99%

Development and evaluation of a clinical model for lung cancer patients using stereotactic body radiotherapy (SBRT) within a knowledge‐based algorithm for treatment planning

Snyder

Kim

Reding

et al. 2016

The purpose of this study was to describe the development of a clinical model for lung cancer patients treated with stereotactic body radiotherapy (SBRT) within a knowledge‐based algorithm for treatment planning, and to evaluate the model performance and applicability to different planning techniques, tumor locations, and beam arrangements. 105 SBRT plans for lung cancer patients previously treated at our institution were included in the development of the knowledge‐based model (KBM). The KBM was trained with a combination of IMRT, VMAT, and 3D CRT techniques. Model performance was validated with 25 cases, for both IMRT and VMAT. The full KBM encompassed lesions located centrally vs. peripherally (43:62), upper vs. lower (62:43), and anterior vs. posterior (60:45). Four separate sub‐KBMs were created based on tumor location. Results were compared with the full KBM to evaluate its robustness. Beam templates were used in conjunction with the optimizer to evaluate the model's ability to handle suboptimal beam placements. Dose differences to organs‐at‐risk (OAR) were evaluated between the plans generated by each KBM. Knowledge‐based plans (KBPs) were comparable to clinical plans with respect to target conformity and OAR doses. The KBPs resulted in a lower maximum spinal cord dose by 1.0±1.6Gy compared to clinical plans, p=0.007. Sub‐KBMs split according to tumor location did not produce significantly better DVH estimates compared to the full KBM. For central lesions, compared to the full KBM, the peripheral sub‐KBM resulted in lower dose to 0.035 cc and 5 cc of the esophagus, both by 0.4Gy±0.8Gy, p=0.025. For all lesions, compared to the full KBM, the posterior sub‐KBM resulted in higher dose to 0.035 cc, 0.35 cc, and 1.2 cc of the spinal cord by 0.2±0.4Gy, p=0.01. Plans using template beam arrangements met target and OAR criteria, with an increase noted in maximum heart dose (1.2±2.2Gy, p=0.01) and GI (0.2±0.4, p=0.01) for the nine‐field plans relative to KBPs planned with custom beam angles. A knowledge‐based model for lung SBRT consisting of multiple treatment modalities and lesion locations produced comparable plan quality to clinical plans. With proper training and validation, a robust KBM can be created that encompasses both IMRT and VMAT techniques, as well as different lesion locations.PACS number(s): 87.55de, 87.55kh, 87.53Ly