17 research outputs found

    Evaluation of diffusion weighted imaging for tumor delineation in head-and-neck radiotherapy by comparison with automatically segmented 18F-fluorodeoxyglucose positron emission tomography

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    Background and purpose: Diffusion weighted (DW) MRI may facilitate target volume delineation for head-and-neck (HN) radiation treatment planning. In this study we assessed the use of a dedicated, geometrically accurate, DW-MRI sequence for target volume delineation. The delineations were compared with semi-automatic segmentations on 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) images and evaluated for interobserver variation. Methods and materials: Fifteen HN cancer patients underwent both DW-MRI and FDG-PET for RT treatment planning. Target delineation on DW-MRI was performed by three observers, while for PET a semi-automatic segmentation was performed using a Gaussian mixture model. For interobserver variation and intermodality variation, volumes, overlap metrics and Hausdorff distances were calculated from the delineations. Results: The median volumes delineated by the three observers on DW-MRI were 10.8, 10.5 and 9.0 cm3 respectively, and was larger than the median PET volume (8.0 cm3). The median conformity index of DW-MRI for interobserver variation was 0.73 (range 0.38–0.80). Compared to PET, the delineations on DW-MRI by the three observers showed a median dice similarity coefficient of 0.71, 0.69 and 0.72 respectively. The mean Hausdorff distance was small with median (range) distances between PET and DW-MRI of 2.3 (1.5–6.8), 2.5 (1.6–6.9) and 2.0 (1.35–7.6) mm respectively. Over all patients, the median 95th percentile distances were 6.0 (3.0–13.4), 6.6 (4.0–24.0) and 5.3 (3.4–26.0) mm. Conclusion: Using a dedicated DW-MRI sequence, target volumes could be defined with good interobserver agreement and a good overlap with PET. Target volume delineation using DW-MRI is promising in head-and-neck radiotherapy, combined with other modalities, it can lead to more precise target volume delineation. Keywords: Radiotherapy, Head and neck, Target volume delineation, Diffusion MRI, PE

    Automatically tracking brain metastases after stereotactic radiosurgery

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    Background and purpose: Patients with brain metastases (BMs) are surviving longer and returning for multiple courses of stereotactic radiosurgery. BMs are monitored after radiation with follow-up magnetic resonance (MR) imaging every 2–3 months. This study investigated whether it is possible to automatically track BMs on longitudinal imaging and quantify the tumor response after radiotherapy. Methods: The METRO process (MEtastasis Tracking with Repeated Observations was developed to automatically process patient data and track BMs. A longitudinal intrapatient registration method for T1 MR post-Gd was conceived and validated on 20 patients. Detections and volumetric measurements of BMs were obtained from a deep learning model. BM tracking was validated on 32 separate patients by comparing results with manual measurements of BM response and radiologists’ assessments of new BMs. Linear regression and residual analysis were used to assess accuracy in determining tumor response and size change. Results: A total of 123 irradiated BMs and 38 new BMs were successfully tracked. 66 irradiated BMs were visible on follow-up imaging 3–9 months after radiotherapy. Comparing their longest diameter changes measured manually vs. METRO, the Pearson correlation coefficient was 0.88 (p < 0.001); the mean residual error was −8 ± 17%. The mean registration error was 1.5 ± 0.2 mm. Conclusions: Automatic, longitudinal tracking of BMs using deep learning methods is feasible. In particular, the software system METRO fulfills a need to automatically track and quantify volumetric changes of BMs prior to, and in response to, radiation therapy

    Illustrated instructions for mechanical quality assurance of a medical linear accelerator

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    PurposeThe purpose of this study was to develop and test a set of illustrated instructions for effective training for mechanical quality assurance (QA) of medical linear accelerators (linac).MethodsIllustrated instructions were created for mechanical QA and underwent several steps of review, testing, and refinement. Eleven testers with no recent QA experience were then recruited from our radiotherapy department (one student, two computational scientists, and eight dosimetrists). This group was selected because they have experience of radiation therapy but no preconceived ideas about how to do QA. The following parameters were progressively decalibrated on a Varian C-series linac: Group A&nbsp;=&nbsp;gantry angle, ceiling laser position, X1 jaw position, couch longitudinal position, physical graticule position (five testers); Group B&nbsp;=&nbsp;Group A&nbsp;+&nbsp;wall laser position, couch lateral and vertical position, collimator angle (three testers); Group C&nbsp;=&nbsp;Group B&nbsp;+&nbsp;couch angle, wall laser angle, and optical distance indicator (three testers). Testers were taught how to use the linac and then used the instructions to try to identify these errors. An experienced physicist observed each session, giving support on machine operation as necessary.ResultsTesters were able to follow the instructions. They determined gantry, collimator, and couch angle errors within 0.4°, 0.3°, and 0.9° of the actual changed values, respectively. Laser positions were determined within 1&nbsp;mm and jaw positions within 2&nbsp;mm. Couch position errors were determined within 2&nbsp;mm and 3&nbsp;mm for lateral/longitudinal and vertical errors, respectively. Accessory-positioning errors were determined within 1&nbsp;mm. Optical distance indicator errors were determined within 2&nbsp;mm when comparing with distance sticks and 6&nbsp;mm when using blocks, indicating that distance sticks should be the preferred approach for inexperienced staff.ConclusionsInexperienced users were able to follow these instructions and catch errors within the criteria suggested by AAPM TG-142 for linacs used for intensity-modulated radiation therapy. These instructions are, therefore, suitable for QA training

    p-values for the comparison of δ<sub>3D-4D</sub> between adenocarcinoma and squamous cell carcinoma using Mann-Whitney U-test.

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    <p>p-values for the comparison of δ<sub>3D-4D</sub> between adenocarcinoma and squamous cell carcinoma using Mann-Whitney U-test.</p

    The mean difference (δ<sub>3D-4D</sub>) between 3D and 4D PET images in texture features.

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    <p>The ranges of δ<sub>3D-4D</sub> and the p-values for Wilcoson signed-rank test are also shown. MCC = maximal correlation coefficient. LRLG = Long run low gray-level emphasis</p><p>The mean difference (δ<sub>3D-4D</sub>) between 3D and 4D PET images in texture features.</p

    Distribution of the difference between 3D and 4D PET (δ<sub>3D-4D</sub>) in the texture features across 34 lesions.

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    <p>The top vertical line of a boxplot represents 75<sup>th</sup>—95<sup>th</sup> percentiles of the data. The bottom vertical line is the 5<sup>th</sup>—25<sup>th</sup> percentiles. Interquartile range (IQR) of the data is indicated by the width of the boxplot. Asterisks indicate the maximum and minimum differences. Median and mean differences are indicated by bar and square inside the box plots, respectively. MCC = Maximal correlation coefficient. LRLG = Long run low gray-level emphasis. The first boxplot represents the comparisons of 3D and 3D PET textures (δ<sub>3D-3D</sub>). δ<sub>3D-3D</sub> is therefore zero by definition as shown in the first “boxplot” for each texture.</p

    Spearman correlation coefficient of Amplitude:ATV (mm<sup>−2</sup>) and δ<sub>3D-4D</sub> and its p-value.

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    <p>MCC = Maximal correlation coefficient. LRLG = Long run low gray-level emphasis.</p><p>Spearman correlation coefficient of Amplitude:ATV (mm<sup>−2</sup>) and δ<sub>3D-4D</sub> and its p-value.</p
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