IMRT QA devices have differences in their ability to accurately detect dosimetrically acceptable and unacceptable plans. Field-by-field analysis with a MapCheck device and use of the MapCheck with a MapPhan phantom while delivering at planned rotational gantry angles resulted in a significantly poorer ability to accurately sort acceptable and unacceptable plans compared with the other techniques examined. Patient-specific IMRT QA techniques in general should be thoroughly evaluated for their ability to correctly differentiate acceptable and unacceptable plans. Additionally, optimal agreement thresholds should be identified and used as common clinical thresholds typically worked very poorly to identify unacceptable plans.

We investigated the sensitivity of the gamma index to two factors: the spatial resolution and the noise level in the measured dose distribution. We also examined how the choice of reference distribution and analysis software affect the sensitivity of gamma analysis to these two factors for quality assurance (QA) of intensity‐modulated radiation therapy (IMRT) treatment plans. For ten clinical IMRT plans, the dose delivered to a transverse dose plane was measured with EDR2 radiographic film. To evaluate the effects of spatial resolution, each irradiated film was digitized using three different resolutions (71, 142, and 285 dpi). To evaluate the effects of image noise, 1% and 2% local Gaussian noise was added to the film images. Gamma analysis was performed using 2%/2 mm and 3%/3 mm acceptance criteria and two commercial software packages, OmniPro I'mRT and DoseLab Pro. Dose comparisons were performed with the treatment planning system (TPS)‐calculated dose as the reference, and then repeated with the film as the reference to evaluate how the choice of reference distribution affects the results of dose comparisons. When the TPS‐calculated dose was designated as the reference distribution, the percentage of pixels with passing gamma values increased with both increasing resolution and noise. For 3%/3 mm acceptance criteria, increasing the film image resolution by a factor of two and by a factor of four caused a median increase of 0.9% and 2.6%, respectively, in the percentage of pixels passing. Increasing the noise level in the film image resulted in a median increase in percentage of pixels passing of 5.5% for 1% added local Gaussian noise and 5.8% for 2% added noise. In contrast, when the film was designated as the reference distribution, the percentage of pixels passing decreased with increased film noise, while increased resolution had no significant effect on passing rates. Furthermore, the sensitivity of gamma analysis to noise and resolution differed between OmniPro I'mRT and DoseLab Pro, with DoseLab Pro being less sensitive to the effects of noise and resolution. Noise and high scanning resolution can artificially increase the percentage of pixels with passing gamma values in IMRT QA. Thus, these factors, if not properly taken into account, can potentially affect the results of IMRT QA by causing a plan that should be classified as failing to be falsely classified as passing. In designing IMRT QA protocols, it is important to be aware that gamma analysis is sensitive to these parameters.PACS number: 87.55.Qr, 87.55.km, 87.56.Fc

Purpose To develop and demonstrate the efficacy of a novel head‐and‐neck multimodality image registration technique using deep‐learning‐based cross‐modality synthesis. Methods and Materials Twenty‐five head‐and‐neck patients received magnetic resonance (MR) and computed tomography (CT) (CTaligned) scans on the same day with the same immobilization. Fivefold cross validation was used with all of the MR‐CT pairs to train a neural network to generate synthetic CTs from MR images. Twenty‐four of 25 patients also had a separate CT without immobilization (CTnon‐aligned) and were used for testing. CTnon‐aligned's were deformed to the synthetic CT, and compared to CTnon‐aligned registered to MR. The same registrations were performed from MR to CTnon‐aligned and from synthetic CT to CTnon‐aligned. All registrations used B‐splines for modeling the deformation, and mutual information for the objective. Results were evaluated using the 95% Hausdorff distance among spinal cord contours, landmark error, inverse consistency, and Jacobian determinant of the estimated deformation fields. Results When large initial rigid misalignment is present, registering CT to MRI‐derived synthetic CT aligns the cord better than a direct registration. The average landmark error decreased from 9.8 ± 3.1 mm in MR→CTnon‐aligned to 6.0 ± 2.1 mm in CTsynth→CTnon‐aligned deformable registrations. In the CT to MR direction, the landmark error decreased from 10.0 ± 4.3 mm in CTnon‐aligned→MR deformable registrations to 6.6 ± 2.0 mm in CTnon‐aligned→CTsynth deformable registrations. The Jacobian determinant had an average value of 0.98. The proposed method also demonstrated improved inverse consistency over the direct method. Conclusions We showed that using a deep learning‐derived synthetic CT in lieu of an MR for MR→CT and CT→MR deformable registration offers superior results to direct multimodal registration.

The k-space self-gated 4D-MRI technique provides a robust method for accurately imaging phase-based target motion and geometry. Compared to 4D-CT, the current 4D-MRI technique demonstrates superior spatiotemporal resolution, and robust resistance to motion artifacts caused by fast target motion and irregular breathing patterns. The technique can be used extensively in abdominal targeting, motion gating, and toward implementing MRI-based adaptive radiotherapy.

The purpose was to report clinical experience of a video‐guided spirometry system in applying deep inhalation breath‐hold (DIBH) radiotherapy for left‐sided breast cancer, and to study the systematic and random uncertainties, intra‐ and interfraction motion and impact on cardiac dose associated with DIBH. The data from 28 left‐sided breast cancer patients treated with spirometer‐guided DIBH radiation were studied. Dosimetric comparisons between free‐breathing (FB) and DIBH plans were performed. The distance between the heart and chest wall measured on the digitally reconstructed radiographs (DRR) and MV portal images, dDRR(DIBH) and dport(DIBH), respectively, was compared as a measure of DIBH setup uncertainty. The difference (Δd) between dDRR(DIBH) and dport(DIBH) was defined as the systematic uncertainty. The standard deviation of Δd for each patient was defined as the random uncertainty. MV cine images during radiation were acquired. Affine registrations of the cine images acquired during one fraction and multiple fractions were performed to study the intra‐ and interfraction motion of the chest wall. The median chest wall motion was used as the metric for intra‐ and interfraction analysis. Breast motions in superior–inferior (SI) direction and “AP” (defined on the DRR or MV portal image as the direction perpendicular to the SI direction) are reported. Systematic and random uncertainties of 3.8 mm and 2 mm, respectively, were found for this spirometer‐guided DIBH treatment. MV cine analysis showed that intrafraction chest wall motions during DIBH were 0.3 mm in “AP” and 0.6 mm in SI. The interfraction chest wall motions were 3.6 mm in “AP” and 3.4 mm in SI. Utilization of DIBH with this spirometry system led to a statistically significant reduction of cardiac dose relative to FB treatment. The DIBH using video‐guided spirometry provided reproducible cardiac sparing with minimal intra‐ and interfraction chest wall motion, and thus is a valuable adjunct to modern breast treatment techniques.PACS number: 87.55.kh, 87.55.ne, 87.55.tg