17 research outputs found
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Comparison of Texture Features Derived from Static and Respiratory-Gated PET Images in Non-Small Cell Lung Cancer
Background: PET-based texture features have been used to quantify tumor heterogeneity due to their predictive power in treatment outcome. We investigated the sensitivity of texture features to tumor motion by comparing static (3D) and respiratory-gated (4D) PET imaging. Methods: Twenty-six patients (34 lesions) received 3D and 4D [18F]FDG-PET scans before the chemo-radiotherapy. The acquired 4D data were retrospectively binned into five breathing phases to create the 4D image sequence. Texture features, including Maximal correlation coefficient (MCC), Long run low gray (LRLG), Coarseness, Contrast, and Busyness, were computed within the physician-defined tumor volume. The relative difference (δ3D-4D) in each texture between the 3D- and 4D-PET imaging was calculated. Coefficient of variation (CV) was used to determine the variability in the textures between all 4D-PET phases. Correlations between tumor volume, motion amplitude, and δ3D-4D were also assessed. Results: 4D-PET increased LRLG ( = 1%â2%, p0.08) compared to 3D-PET. Nearly negligible variability was found between the 4D phase bins with CV<5% for MCC, LRLG, and Coarseness. For Contrast and Busyness, moderate variability was found with CV = 9% and 10%, respectively. No strong correlation was found between the tumor volume and δ3D-4D for the texture features. Motion amplitude had moderate impact on δ for MCC and Busyness and no impact for LRLG, Coarseness, and Contrast. Conclusions: Significant differences were found in MCC, LRLG, Coarseness, and Busyness between 3D and 4D PET imaging. The variability between phase bins for MCC, LRLG, and Coarseness was negligible, suggesting that similar quantification can be obtained from all phases. Texture features, blurred out by respiratory motion during 3D-PET acquisition, can be better resolved by 4D-PET imaging. 4D-PET textures may have better prognostic value as they are less susceptible to tumor motion
Evaluation of diffusion weighted imaging for tumor delineation in head-and-neck radiotherapy by comparison with automatically segmented 18F-fluorodeoxyglucose positron emission tomography
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 9.0âŻcm3 respectively, and was larger than the median PET volume (8.0âŻcm3). The median conformity index of DW-MRI for interobserver variation was 0.73 (range 0.38â0.80). 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 (1.5â6.8), 2.5 (1.6â6.9) and 2.0 (1.35â7.6) mm respectively. Over all patients, the median 95th percentile distances were 6.0 (3.0â13.4), 6.6 (4.0â24.0) and 5.3 (3.4â26.0)âŻmm. 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
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 2â3 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 radiologistsâ 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 3â9 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 â8 ¹ 17%. The mean registration error was 1.5 ¹ 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
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 = gantry angle, ceiling laser position, X1 jaw position, couch longitudinal position, physical graticule position (five testers); Group B = Group A + wall laser position, couch lateral and vertical position, collimator angle (three testers); Group C = Group B + 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 mm and jaw positions within 2 mm. Couch position errors were determined within 2 mm and 3 mm for lateral/longitudinal and vertical errors, respectively. Accessory-positioning errors were determined within 1 mm. Optical distance indicator errors were determined within 2 mm when comparing with distance sticks and 6 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
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Illustrated instructions for mechanical quality assurance of a medical linear accelerator
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 = gantry angle, ceiling laser position, X1 jaw position, couch longitudinal position, physical graticule position (five testers); Group B = Group A + wall laser position, couch lateral and vertical position, collimator angle (three testers); Group C = Group B + 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 mm and jaw positions within 2 mm. Couch position errors were determined within 2 mm and 3 mm for lateral/longitudinal and vertical errors, respectively. Accessory-positioning errors were determined within 1 mm. Optical distance indicator errors were determined within 2 mm when comparing with distance sticks and 6 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.
<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.
<p>The ranges of δ<sub>3D-4D</sub> and the p-values for Wilcoson signed-rank test are also shown. MCCâ=âmaximal correlation coefficient. LRLGâ=âLong 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
3D (top row) and 4D (bottom row) PET images overlaid onto the 3D CT.
<p>All images are displayed in the same intensity window with SUV between 1 and 15.</p
Distribution of the difference between 3D and 4D PET (δ<sub>3D-4D</sub>) in the texture features across 34 lesions.
<p>The top vertical line of a boxplot represents 75<sup>th</sup>â95<sup>th</sup> percentiles of the data. The bottom vertical line is the 5<sup>th</sup>â25<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. MCCâ=âMaximal correlation coefficient. LRLGâ=âLong 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 âboxplotâ for each texture.</p
Spearman correlation coefficient of Amplitude:ATV (mm<sup>â2</sup>) and δ<sub>3D-4D</sub> and its p-value.
<p>MCCâ=âMaximal correlation coefficient. LRLGâ=âLong run low gray-level emphasis.</p><p>Spearman correlation coefficient of Amplitude:ATV (mm<sup>â2</sup>) and δ<sub>3D-4D</sub> and its p-value.</p