Previous chapters in this book have concentrated on the instrumentation and measurement techniques for dosimetry of the therapeutic beams. In this chapter, a variety of measurement and calculation techniques will be reviewed for characterizing the radiation dose from x-ray imaging systems used in radiation therapy (RT). X-ray imaging systems are now used extensively throughout a patient's treatment for all complex RTs, and in many cases for simple palliative RTs as well. Nearly, all patients will undergo a multislice computed tomography (CT) examination for localizing the target volume and nearby organs at risk. In addition, a variety of x-ray imaging systems are available to image the patient at the point of treatment, either immediately prior to beam delivery (e.g., Korreman et al., 2010; Moore et al., 2014) to ensure accurate patient alignment, or during beam delivery to monitor intrafraction motion 22.1 Introduction 22.2 Dose Measurement for CT 22.3 Dose Measurement for CBCT for Radiotherapy Applications 22.4 Measurement (and Calculation) of Dose for Planar kV Imaging 22.5 Dose Measurement for MV Portal Imaging 22.6 Dose Measurement for MVCT and MV-CBCT 22.7 Dosimeters for All Modalities 22.8 Dose Calculation Methods 22.9 Estimating Effective Dose and Risk 22.10 Combining Dose from RT and Imaging 22.11 Clinical Consequences and Benefits 22.12 Closing Remarks 9781482252217_C022.indd 561 21/07/17 5:53 PM * MV-CBCT was developed and commercialized by Siemens Medical Systems, but is no longer commercially available since Siemens stopped producing radiation therapy treatment machines.

No abstract

Previous studies have shown that there is a strong correlation between radiologists' diagnoses and their gaze when reading medical images. The extent to which gaze is attracted by content in a visual scene can be characterised as visual saliency. There is a potential for the use of visual saliency in computer-aided diagnosis in radiology. However, little is known about what methods are effective for diagnostic images, and how these methods could be adapted to address specific applications in diagnostic imaging. In this study, we investigate 20 state-of-the-art saliency models including 10 traditional models and 10 deep learning-based models in predicting radiologists' visual attention while reading 196 mammograms. We found that deep learning-based models represent the most effective type of methods for predicting radiologists' gaze in mammogram reading; and that the performance of these saliency models can be significantly improved by transfer learning. In particular, an enhanced model can be achieved by pre-training the model on a large-scale natural image saliency dataset and then finetuning it on the target medical image dataset. In addition, based on a systematic selection of backbone networks and network architectures, we proposed a parallel multi-stream encoded model which outperforms the state-of-the-art approaches for predicting saliency of mammograms.

230 Background: SCALOP, a randomized, multicenter, phase II trial, tested efficacy/toxicity of gemcitabine (Gem) versus capecitabine (Cap) based CRT following 4 cycles of induction chemotherapy. 114 patients from 28 centers were registered and 75 patients had CRT. The RT Quality Assurance included a test case. GTVs outlined by investigators were evaluated by the chief investigator (CI). We report analysis of the test case. Methods: The test case was LANPC of head of pancreas. The gold standard GTV (gsGTV) was outlined by the CI and trial radiologist. Protocol specified margins (GTV + 2cm sup-inf, 1.5 cm radial) were applied to generate the gold standard PTV (gsPTV). CT-DICOM dataset of the planning scan was sent to participating centers for tumor volume delineation by investigators and individualized feedback was provided by CI. CERR software was used to compare the investigator volumes (iGTV and iPTV) against the gold standard. Results: Volumes from 25 investigators were analyzed. The results are summarized below (Table). Slice-by-slice conformity indices and qualitative review showed that most variation occurred at the superior and inferior extremes of the tumor and a peri-tumoral node was frequently missed by investigators. Conclusions: SCALOP is the first trial in LANPC to test investigator-delineated volumes using conformity indices, and has demonstrated considerable variation in iGTVs. Trial workshops and real-time central review of tumor outlines are proposed. [Table: see text]

No abstract

No abstract

The purpose of this work was to assess the capability of radiomic features in distinguishing PET image regions with different uptake patterns. Furthermore, we assessed the stability of PET radiomic features with varying image reconstruction settings. An in-house phantom was designed and constructed, consisting of homogenous and heterogenous artificial phantom inserts. Four artificially constructed inserts were placed into a water filled phantom and filled with varying levels of radioactivity to simulate homogeneous and heterogeneous uptake patterns. The phantom was imaged for 80 min. PET images were reconstructed whilst varying reconstruction parameters. The parameters adjusted included, number of ordered subsets, number of iterations, use of time-of-flight and filter cut off. Regions of interest (ROI) were established by segmentation of the phantom inserts from the reconstructed images. In total seventy eight 3D radiomic features for each ROI with unique reconstructed parameters were extracted. The Friedman test was used to determine the statistical power of each radiomic feature in differentiating phantom inserts with different hetero/homogeneous configurations. The Coefficient of Variation (COV) of each feature, with respect to the reconstruction setting was used to determine feature stability. Forty three out of seventy eight radiomic features were found to be stable (COV ≤ 5%) against all reconstruction settings. To provide any utility, stable features are required to differentiate between regions with different hetro/homogeneity. Of the forty three stable features, fifteen (35%) features showed a statistically significant difference between the artificially constructed inserts. Such features included GLCM (Difference average, Difference entropy, Dissimilarity and Inverse difference), GLRL (Long run emphasis, Grey level non uniformity and Run percentage) and NGTDM (Complexity and Strength). The finding of this work suggests that radiomic features are capable of distinguishing between radioactive distribution patterns that demonstrate different levels of heterogeneity. Therefore, radiomic features could serve as an adjuvant diagnostic tool along with traditional imaging. However, the choice of the radiomic features needs to account for variability introduced when different reconstruction settings are used. Standardization of PET image reconstruction settings across sites performing radiomic analysis in multi-centre trials should be considered.

No abstract