The goals of this study were (1) to characterize the optical artefacts affecting measurement accuracy in a volumetric liquid scintillation detector, and (2) to develop methods to correct for these artefacts. The optical artefacts addressed were photon scattering, refraction, camera perspective, vignetting, lens distortion, the lens point spread function, stray radiation, and noise in the camera. These artefacts were evaluated by theoretical and experimental means, and specific correction strategies were developed for each artefact. The effectiveness of the correction methods was evaluated by comparing raw and corrected images of the scintillation light from proton pencil beams against validated Monte Carlo calculations. Blurring due to the lens and refraction at the scintillator tank-air interface were found to have the largest effect on the measured light distribution, and lens aberrations and vignetting were important primarily at the image edges. Photon scatter in the scintillator was not found to be a significant source of artefacts. The correction methods effectively mitigated the artefacts, increasing the average gamma analysis pass rate from 66% to 98% for gamma criteria of 2% dose difference and 2 mm distance to agreement. We conclude that optical artefacts cause clinically meaningful errors in the measured light distribution, and we have demonstrated effective strategies for correcting these optical artefacts.

The QA tool assists users to detect potential delineation errors. QA tool integration into clinical procedures may reduce the frequency of inaccurate OAR delineation, and potentially improve safety and quality of radiation treatment planning.

The purpose of this study is to investigate the feasibility of using internal respiratory (IR) surrogates to sort four-dimensional (4D) magnetic resonance (MR) images. The 4D MR images were constructed by acquiring fast 2D cine MR images sequentially, with each slice scanned for more than one breathing cycle. The 4D volume was then sorted retrospectively using the IR signal. In this study, we propose to use multiple low-frequency components in the Fourier space as well as the anterior body boundary as potential IR surrogates. From these potential IR surrogates, we used a clustering algorithm to identify those that best represented the respiratory pattern to derive the IR signal. A study with healthy volunteers was performed to assess the feasibility of the proposed IR signal. We compared this proposed IR signal with the respiratory signal obtained using respiratory bellows. Overall, 99% of the IR signals matched the bellows signals. The average difference between the end inspiration times in the IR signal and bellows signal was 0.18 s in this cohort of matching signals. For the acquired images corresponding to the other 1% of non-matching signal pairs, the respiratory motion shown in the images was coherent with the respiratory phases determined by the IR signal, but not the bellows signal. This suggested that the IR signal determined by the proposed method could potentially correct the faulty bellows signal. The sorted 4D images showed minimal mismatched artefacts and potential clinical applicability. The proposed IR signal therefore provides a feasible alternative to effectively sort MR images in 4D.

Purpose: To develop and implement a new approach for correcting the intensity inhomogeneity in magnetic resonance imaging (MRI) data. Materials and Methods:The algorithm is based on the assumption that intensity inhomogeneity in MR data is multiplicative and smoothly varying. Using a statistically stable method, the algorithm first calculates the partial derivative of the inhomogeneity gradient across the data. The algorithm then solves for the gradient field and fits it to a parametric surface. It was tested on both simulated and real human and animal MRI data. Results:The algorithm is shown to restore the homogeneity in all images that were tested. On real human brain images the algorithm demonstrated superior or comparable performance relative to some of the commonly used intensity inhomogeneity correction methods such as SPM, BrainSuite, and N3. Conclusion:The proposed algorithm provides an alternative method for correcting the intensity inhomogeneity in MR images. It is shown to be fast and its performance is superior or comparable to algorithms described in the published literature. Due to its generality, this algorithm is applicable to MR images of both humans and animals.

Purpose: To determine if radiation treatment plans created based on autosegmented (AS) regionsof-interest (ROI)s are clinically equivalent to plans created based on manually segmented ROIs, where equivalence is evaluated using probabilistic dosimetric metrics and probabilistic biological endpoints for prostate IMRT. Method and materials: Manually drawn contours and autosegmented ROIs were created for 167 CT image sets acquired from 19 prostate patients. Autosegmentation was performed utilizing Pinnacle's Smart Probabilistic Image Contouring Engine. For each CT set, 78 Gy/39 fraction 7-beam IMRT treatment plans with 1 cm CTV-to-PTV margins were created for each of the three contour scenarios; P MD using manually delineated (MD) ROIs, P AS using autosegmented ROIs, and P AM using autosegmented organ-at-risks (OAR)s and the manually drawn target. For each plan, 1000 virtual treatment simulations with different systematic errors for each simulation and a different random error for each fraction were performed. The statistical probability of achieving dose-volume metrics (coverage probability (CP)), expectation values for normal tissue complication probability (NTCP), and tumor control probability (TCP) metrics for all possible cross-evaluation pairs of ROI types and planning scenarios were reported. In evaluation scenarios, the root mean square loss (RMSL) and maximum absolute loss (MAL) of coverage probability of dose-volume objectives, E[TCP], and E [NTCP] were compared with respect to the base plan created and evaluated with manually drawn contours. Results: Femoral head dose objectives were satisfied in all situations, as well as the maximum dose objectives for all ROIs. Bladder metrics were within the clinical coverage tolerances except D 35Gy for the autosegmented plan evaluated with the manual contours. Dosimetric indices for CTV and rectum could be highly compromised when the definition of the ROIs switched from manually delineated to autosegmented. Seventy-two percent of CT image sets satisfied the worst-case CP thresholds for all dosimetric objectives in all scenarios, the percentage dropped to 50% if biological indices were taken into account. Among evaluation scenarios, (MD,P AM ) bore the highest resemblance to (MD,P MD ) where 99% and 88% of cases met all CP thresholds for bladder and rectum, respectively. Conclusions: When including daily setup variations in prostate IMRT, the dose-volume metric CP, and biological indices of ROIs were approximately equivalent for the plans created based on manually drawn targets and autosegmented OARs in 88% of cases. The accuracy of autosegmented prostates and rectums are impediment to attain statistically equivalent plans created based on manually drawn ROIs.