Evaluation of MRI-only based online adaptive radiotherapy of abdominal region on MR-linac.

PURPOSE: A hybrid magnetic resonance linear accelerator (MRL) can perform magnetic resonance imaging (MRI) with high soft-tissue contrast to be used for online adaptive radiotherapy (oART). To obtain electron densities needed for the oART dose calculation, a computed tomography (CT) is often deformably registered to MRI. Our aim was to evaluate an MRI-only based synthetic CT (sCT) generation as an alternative to the deformed CT (dCT)-based oART in the abdominal region. METHODS: The study data consisted of 57 patients who were treated on a 0.35 T MRL system mainly for abdominal tumors. Simulation MRI-CT pairs of 43 patients were used for training and validation of a prototype convolutional neural network sCT-generation algorithm, based on HighRes3DNet, for the abdominal region. For remaining test patients, sCT images were produced from simulation MRIs and daily MRIs. The dCT-based plans were re-calculated on sCT with identical calculation parameters. The sCT and dCT were compared in terms of geometric agreement and calculated dose. RESULTS: The mean and one standard deviation of the geometric agreement metrics over dCT-sCT-pairs were: mean error of 8 ± 10 HU, mean absolute error of 49 ± 10 HU, and Dice similarity coefficient of 55 ± 12%, 60 ± 5%, and 82 ± 15% for bone, fat, and lung tissues, respectively. The dose differences between the sCT and dCT-based dose for planning target volumes were 0.5 ± 0.9%, 0.6 ± 0.8%, and 0.5 ± 0.8% at D2% , D50% , and D98% in physical dose and 0.8 ± 1.4%, 0.8 ± 1.2%, and 0.6 ± 1.1% in biologically effective dose (BED). For organs-at-risk, the dose differences of all evaluated dose-volume histogram points were within [-4.5%, 7.8%] and [-1.1 Gy, 3.5 Gy] in both physical dose and BED. CONCLUSIONS: The geometric agreement metrics were within typically reported values and most average relative dose differences were within 1%. Thus, an MRI-only sCT-based approach is a promising alternative to the current clinical practice of the abdominal oART on MRL.

Characterizing geometric distortions of 3D sequences in clinical head MRI.

OBJECTIVE: Phantoms are often used to estimate the geometric accuracy in magnetic resonance imaging (MRI). However, the distortions may differ between anatomical and phantom images. This study aimed to investigate the applicability of a phantom-based and a test-subject-based method in evaluating geometric distortion present in clinical head-imaging sequences. MATERIALS AND METHODS: We imaged a 3D-printed phantom and test subjects with two MRI scanners using two clinical head-imaging 3D sequences with varying patient-table positions and receiver bandwidths. The geometric distortions were evaluated through nonrigid registrations: the displaced acquisitions were compared against the ideal isocenter positioning, and the varied bandwidth volumes against the volume with the highest bandwidth. The phantom acquisitions were also registered to a computed tomography scan. RESULTS: Geometric distortion magnitudes increased with larger table displacements and were in good agreement between the phantom and test-subject acquisitions. The effect of increased distortions with decreasing receiver bandwidth was more prominent for test-subject acquisitions. CONCLUSION: Presented results emphasize the sensitivity of the geometric accuracy to positioning and imaging parameters. Phantom limitations may become an issue with some sequence types, encouraging the use of anatomical images for evaluating the geometric accuracy.

Determination of acceptance criteria for geometric accuracy of magnetic resonance imaging scanners used in radiotherapy planning.

BACKGROUND AND PURPOSE: Magnetic resonance imaging is increasingly used in radiotherapy planning; yet, the performance of the utilized scanners is rarely regulated by any authority. The aim of this study was to determine the geometric accuracy of several magnetic resonance imaging scanners used for radiotherapy planning, and to establish acceptance criteria for such scanners. MATERIALS AND METHODS: The geometric accuracy of five different scanners was measured with three sequences using a commercial large-field-of-view phantom. The distortion magnitudes were determined in spherical volumes around the scanner isocenter and in cylindrical volumes along scanner z-axis. The repeatability of the measurements was determined on a single scanner with two quality assurance sequences with three single-setup and seven repeated-setup measurements. RESULTS: For all scanners and sequences except one, the mean and median distortion magnitude was <1 mm and <2 mm in spherical volumes with diameters of 400 mm and 500 mm, respectively. For all sequences maximum distortion was <2 mm in spherical volume with diameter of 300 mm. The mean standard deviation of marker-by-marker distortion magnitudes over repeated acquisitions was ≤0.6 mm with both tested sequences. CONCLUSIONS: All tested scanners were geometrically accurate for their current use in radiotherapy planning. The acceptance criteria of geometric accuracy for regulatory inspections of a supervising authority could be set according to these results.

Automating chest radiograph imaging quality control.

PURPOSE: To automate diagnostic chest radiograph imaging quality control (lung inclusion at all four edges, patient rotation, and correct inspiration) using convolutional neural network models. METHODS: The data comprised of 2589 postero-anterior chest radiographs imaged in a standing position, which were divided into train, validation, and test sets. We increased the number of images for the inclusion by cropping appropriate images, and for the inclusion and the rotation by flipping the images horizontally. The image histograms were equalized, and the images were resized to a 512 × 512 resolution. We trained six convolutional neural networks models to detect the image quality features using manual image annotations as training targets. Additionally, we studied the inter-observer variability of the image annotation. RESULTS: The convolutional neural networks' areas under the receiver operating characteristic curve were >0.88 for the inclusions, and >0.70 and >0.79 for the rotation and the inspiration, respectively. The inter-observer agreement between two human annotators for the assessed image-quality features were: 92%, 90%, 82%, and 88% for the inclusion at patient's left, patient's right, cranial, and caudal edges, and 78% and 89% for the rotation and inspiration, respectively. Higher inter-observer agreement was related to a smaller variance in the network confidence. CONCLUSIONS: The developed models provide automated tools for the quality control in a radiological department. Additionally, the convolutional neural networks could be used to obtain immediate feedback of the chest radiograph image quality, which could serve as an educational instrument.

Measuring geometric accuracy in magnetic resonance imaging with 3D-printed phantom and nonrigid image registration.

OBJECTIVE: We aimed to develop a vendor-neutral and interaction-free quality assurance protocol for measuring geometric accuracy of head and brain magnetic resonance (MR) images. We investigated the usability of nonrigid image registration in the analysis and looked for the optimal registration parameters. MATERIALS AND METHODS: We constructed a 3D-printed phantom and imaged it with 12 MR scanners using clinical sequences. We registered a geometric-ground-truth computed tomography (CT) acquisition to the MR images using an open-source nonrigid-registration-toolbox with varying parameters. We applied the transforms to a set of control points in the CT image and compared their locations to the corresponding visually verified reference points in the MR images. RESULTS: With optimized registration parameters, the mean difference (and standard deviation) of control point locations when compared to the reference method was (0.17 ± 0.02) mm for the 12 studied scanners. The maximum displacements varied from 0.50 to 1.35 mm or 0.89 to 2.30 mm, with vendors' distortion correction on or off, respectively. DISCUSSION: Using nonrigid CT-MR registration can provide a robust and relatively test-object-agnostic method for estimating the intra- and inter-scanner variations of the geometric distortions.