Improving the Efficiency of Single-Isocenter Multiple Metastases Stereotactic Radiosurgery Treatment.

Purpose: Multiple brain metastases can be treated efficiently with stereotactic radiosurgery (SRS) using a single-isocenter dynamic conformal arc (SIDCA) technique. Currently, plans are manually optimized, which may lead to unnecessary table angles and arcs being used. This study aimed to evaluate an automatic 4π optimization SIDCA algorithm for treatment efficiency and plan quality. Methods and Materials: Automatic 4π-optimized SIDCA plans were created and compared with the manually optimized clinical plans for 54 patients who underwent single-fraction SRS for 2 to 10 metastases. The number of table angles and number of arcs were compared with a paired t test using a Bonferroni-corrected significance level of P < .05/4 = .0125. The reduction in treatment time was estimated from the difference in the number of table angles and arcs. Plan quality was assessed through the volume-averaged inverse Paddick Conformity Index (CI) and Gradient Index (GI) and the volume of normal brain surrounding each metastasis receiving 12 Gy (local V12 Gy). For a 5-patient subset, the automatic plans were manually adjusted further. CI and GI were assessed for noninferiority using a 1-sided t test with the noninferiority limit equal to the 95% interobserver reproducibility limit from a separate planning study (corrected significance level P < .05/[4 - 1] = .017). Results: The automatic plans significantly improved treatment efficiency with a mean reduction in the number of table angles and arcs of -0.5 ± 0.1 and -1.3 ± 0.2, respectively (±SE; both P < .001). Estimated treatment time saving was -2.7 ± 0.5 minutes, 14% of the total treatment time. The volume-averaged CI and GI were noninferior to the clinical plans (both P < .001), although there was a small systematic shift in CI of 0.07 ± 0.01. The resulting difference in local V12 Gy, 0.25 ± 0.04 cm3, was not clinically significant. Minor manual adjustment of the automatic plans removed these slight differences while preserving the improved treatment efficiency. Conclusions: Automatic 4π optimization can generate SIDCA SRS plans with improved treatment efficiency and noninferior plan quality.

Evaluating a radiotherapy deep learning synthetic CT algorithm for PET-MR attenuation correction in the pelvis.

BACKGROUND: Positron emission tomography-magnetic resonance (PET-MR) attenuation correction is challenging because the MR signal does not represent tissue density and conventional MR sequences cannot image bone. A novel zero echo time (ZTE) MR sequence has been previously developed which generates signal from cortical bone with images acquired in 65 s. This has been combined with a deep learning model to generate a synthetic computed tomography (sCT) for MR-only radiotherapy. This study aimed to evaluate this algorithm for PET-MR attenuation correction in the pelvis. METHODS: Ten patients being treated with ano-rectal radiotherapy received a [Formula: see text]F-FDG-PET-MR in the radiotherapy position. Attenuation maps were generated from ZTE-based sCT (sCTAC) and the standard vendor-supplied MRAC. The radiotherapy planning CT scan was rigidly registered and cropped to generate a gold standard attenuation map (CTAC). PET images were reconstructed using each attenuation map and compared for standard uptake value (SUV) measurement, automatic thresholded gross tumour volume (GTV) delineation and GTV metabolic parameter measurement. The last was assessed for clinical equivalence to CTAC using two one-sided paired t tests with a significance level corrected for multiple testing of [Formula: see text]. Equivalence margins of [Formula: see text] were used. RESULTS: Mean whole-image SUV differences were -0.02% (sCTAC) compared to -3.0% (MRAC), with larger differences in the bone regions (-0.5% to -16.3%). There was no difference in thresholded GTVs, with Dice similarity coefficients [Formula: see text]. However, there were larger differences in GTV metabolic parameters. Mean differences to CTAC in [Formula: see text] were [Formula: see text] (± standard error, sCTAC) and [Formula: see text] (MRAC), and [Formula: see text] (sCTAC) and [Formula: see text] (MRAC) in [Formula: see text]. The sCTAC was statistically equivalent to CTAC within a [Formula: see text] equivalence margin for [Formula: see text] and [Formula: see text] ([Formula: see text] and [Formula: see text]), whereas the MRAC was not ([Formula: see text] and [Formula: see text]). CONCLUSION: Attenuation correction using this radiotherapy ZTE-based sCT algorithm was substantially more accurate than current MRAC methods with only a 40 s increase in MR acquisition time. This did not impact tumour delineation but did significantly improve the accuracy of whole-image and tumour SUV measurements, which were clinically equivalent to CTAC. This suggests PET images reconstructed with sCTAC would enable accurate quantitative PET images to be acquired on a PET-MR scanner.

Impact of attenuation correction of radiotherapy hardware for positron emission tomography-magnetic resonance in ano-rectal radiotherapy patients.

BACKGROUND: Positron Emission Tomography-Magnetic Resonance (PET-MR) scanners could improve ano-rectal radiotherapy planning through improved Gross Tumour Volume (GTV) delineation and enabling dose painting strategies using metabolic measurements. This requires accurate quantitative PET images acquired in the radiotherapy treatment position. PURPOSE: This study aimed to evaluate the impact on GTV delineation and metabolic parameter measurement of using novel Attenuation Correction (AC) maps that included the radiotherapy flat couch, coil bridge and anterior coil to see if they were necessary. METHODS: Seventeen ano-rectal radiotherapy patients received a 18 F $\mathrm{^{18}F}$ -FluoroDeoxyGlucose PET-MR scan in the radiotherapy position. PET images were reconstructed without ( CTAC std $\mathrm{CTAC_{std}}$ ) and with ( CTAC cba $\mathrm{CTAC_{cba}}$ ) the radiotherapy hardware included. Both AC maps used the same Computed Tomography image for patient AC. Semi-manual and threshold GTVs were delineated on both PET images, the volumes compared and the Dice coefficient calculated. Metabolic parameters: Standardized Uptake Values SUV max $\mathrm{SUV_{max}}$ , SUV mean $\mathrm{SUV_{mean}}$ and Total Lesion Glycolysis (TLG) were compared using paired t-tests with a Bonferroni corrected significance level of p = 0.05 / 8 = 0.006 $p = 0.05/8 = 0.006$ . RESULTS: Differences in semi-manual GTV volumes between CTAC cba $\mathrm{CTAC_{cba}}$ and CTAC std $\mathrm{CTAC_{std}}$ were approaching statistical significance (difference - 15.9 % ± 1.6 % $-15.9\%\pm 1.6\%$ , p = 0.007 $p = 0.007$ ), with larger differences in low FDG-avid tumours ( SUV mean < 8.5 g mL - 1 $\mathrm{SUV_{mean}} < 8.5\;\mathrm{g\: mL^{-1}}$ ). The CTAC cba $\mathrm{CTAC_{cba}}$ and CTAC std $\mathrm{CTAC_{std}}$ GTVs were concordant with Dice coefficients 0.89 ± 0.01 $0.89 \pm 0.01$ (manual) and 0.98 ± 0.00 $0.98 \pm 0.00$ (threshold). Metabolic parameters were significantly different, with SUV max $\mathrm{SUV_{max}}$ , SUV mean $\mathrm{SUV_{mean}}$ and TLG differences of - 11.5 % ± 0.3 % $-11.5\%\ \pm 0.3\%$ ( p < 0.001 $p < 0.001$ ), - 11.6 % ± 0.3 % $-11.6\% \pm 0.3\%$ ( p < 0.001 $p < 0.001$ ) and - 13.7 % ± 0.6 % $-13.7\%\ \pm 0.6\%$ ( p = 0.003 $p = 0.003$ ) respectively. The TLG difference resulted in 1/8 rectal cancer patients changing prognosis group, based on literature TLG cut-offs, when using CTAC cba $\mathrm{CTAC_{cba}}$ rather than CTAC std $\mathrm{CTAC_{std}}$ . CONCLUSIONS: This study suggests that using AC maps with the radiotherapy hardware included is feasible for patient imaging. The impact on tumour delineation was mixed and needs to be evaluated in larger cohorts. However using AC of the radiotherapy hardware is important for situations where accurate metabolic measurements are required, such as dose painting and treatment prognostication.

Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network.

Objective. In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. An important step required to eliminate CT scan from the workflow is to generate the information provided by CT via an MR image. In this work, we aim to demonstrate a method to generate accurate synthetic CT (sCT) from an MR image to suit the radiation therapy (RT) treatment planning workflow. We show the feasibility of the method and make way for a broader clinical evaluation.Approach. We present a machine learning method for sCT generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. The misestimation of bone density in the radiation path could lead to unintended dose delivery to the target volume and results in suboptimal treatment outcome. We propose a loss function that favors a spatially sparse bone region in the image. We harness the ability of the multi-task network to produce correlated outputs as a framework to enable localization of region of interest (RoI) via segmentation, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task.Main results. We have included 54 brain patient images in this study and tested the sCT images against reference CT on a subset of 20 cases. A pilot dose evaluation was performed on 9 of the 20 test cases to demonstrate the viability of the generated sCT in RT planning. The average quantitative metrics produced by the proposed method over the test set were-(a) mean absolute error (MAE) of 70 ± 8.6 HU; (b) peak signal-to-noise ratio (PSNR) of 29.4 ± 2.8 dB; structural similarity metric (SSIM) of 0.95 ± 0.02; and (d) Dice coefficient of the body region of 0.984 ± 0.Significance. We demonstrate that the proposed method generates sCT images that resemble visual characteristics of a real CT image and has a quantitative accuracy that suits RT dose planning application. We compare the dose calculation from the proposed sCT and the real CT in a radiation therapy treatment planning setup and show that sCT based planning falls within 0.5% target dose error. The method presented here with an initial dose evaluation makes an encouraging precursor to a broader clinical evaluation of sCT based RT planning on different anatomical regions.

Comprehensive dose evaluation of a Deep Learning based synthetic Computed Tomography algorithm for pelvic Magnetic Resonance-only radiotherapy.

BACKGROUND AND PURPOSE: Magnetic Resonance (MR)-only radiotherapy enables the use of MR without the uncertainty of MR-Computed Tomography (CT) registration. This requires a synthetic CT (sCT) for dose calculations, which can be facilitated by a novel Zero Echo Time (ZTE) sequence where bones are visible and images are acquired in 65 seconds. This study evaluated the dose calculation accuracy for pelvic sites of a ZTE-based Deep Learning sCT algorithm developed by GE Healthcare. MATERIALS AND METHODS: ZTE and CT images were acquired in 56 pelvic radiotherapy patients in the radiotherapy position. A 2D U-net convolutional neural network was trained using pairs of deformably registered CT and ZTE images from 36 patients. In the remaining 20 patients the dosimetric accuracy of the sCT was assessed using cylindrical dummy Planning Target Volumes (PTVs) positioned at four different central axial locations, as well as the clinical treatment plans (for prostate (n = 10), rectum (n = 4) and anus (n = 6) cancers). The sCT was rigidly and deformably registered, the plan recalculated and the doses compared using mean differences and gamma analysis. RESULTS: Mean dose differences to the PTV D98% were ≤ 0.5% for all dummy PTVs and clinical plans (rigid registration). Mean gamma pass rates at 1%/1 mm were 98.0 ± 0.4% (rigid) and 100.0 ± 0.0% (deformable), 96.5 ± 0.8% and 99.8 ± 0.1%, and 95.4 ± 0.6% and 99.4 ± 0.4% for the clinical prostate, rectum and anus plans respectively. CONCLUSIONS: A ZTE-based sCT algorithm with high dose accuracy throughout the pelvis has been developed. This suggests the algorithm is sufficiently accurate for MR-only radiotherapy for all pelvic sites.

Developing quality assurance tests for simultaneous Positron Emission Tomography - Magnetic Resonance imaging for radiotherapy planning.

Background and purpose Simultaneous Positron Emission Tomography - Magnetic Resonance (PET-MR) imaging can potentially improve radiotherapy by enabling more accurate tumour delineation and dose painting. The use of PET-MR imaging for radiotherapy planning requires a comprehensive Quality Assurance (QA) programme to be developed. This study aimed to develop the QA tests required and assess their repeatability and stability. Materials and methods QA tests were developed for: MR image quality, MR geometric accuracy, electromechanical accuracy, PET-MR alignment accuracy, Diffusion Weighted (DW)-MR Apparent Diffusion Coefficient (ADC) accuracy and PET Standard Uptake Value (SUV) accuracy. Each test used a dedicated phantom and was analysed automatically or semi-automatically, with in-house software. Repeatability was evaluated by three same-day measurements with independent phantom positions. Stability was assessed through 12 monthly measurements. Results The repeatability Standard Deviations (SDs) of distortion for the MR geometric accuracy test were ⩽ 0.7 mm . The repeatability SDs in ADC difference from reference were ⩽ 3 % for the DW-MR accuracy test. The PET SUV difference from reference repeatability SD was 0.3 % . The stability SDs agreed within 0.6 mm , 1 percentage point and 1.4 percentage points of the repeatability SDs for the geometric, ADC and SUV accuracy tests respectively. There were no monthly trends apparent. These results were representative of the other tests. Conclusions QA Tests for radiotherapy planning PET-MR have been developed. The tests appeared repeatable and stable over a 12-month period. The developed QA tests could form the basis of a QA programme that enables high-quality, robust PET-MR imaging for radiotherapy planning.

Cone beam computed tomography for dose calculation quality assurance for magnetic resonance-only radiotherapy.

BACKGROUND AND PURPOSE: Magnetic Resonance (MR)-only prostate radiotherapy using synthetic Computed Tomography (sCT) algorithms with high dose accuracy has been clinically implemented. MR images can suffer from geometric distortions so Quality Assurance (QA) using an independent, geometrically accurate, image could be required. The first-fraction Cone Beam CT (CBCT) has demonstrated potential but has not been evaluated in a clinical MR-only pathway. This study evaluated the clinical use of CBCT for dose accuracy QA of MR-only radiotherapy. MATERIALS AND METHODS: A total of 49 patients treated with MR-only prostate radiotherapy were divided into two cohorts. Cohort 1 (20 patients) received a back-up CT, whilst Cohort 2 (29 patients) did not. All patients were planned using the sCT and received daily CBCT imaging with MR-CBCT soft-tissue matching. Each CBCT was calibrated using a patient-specific stepwise Hounsfield Units-to-mass density curve. The treatment plan was recalculated on the first-fraction CBCT using the clinically applied soft-tissue match and the doses compared. For Cohort 1 the sCT was rigidly registered to the back-up CT, the plan recalculated and doses compared. RESULTS: Mean sCT-CBCT dose difference across both cohorts was - 0.6 ± 0.1 % (standard error of the mean, range - 2.3 % , 2.3 % ), with 47/49 patients within [ - 2 % , 1 % ]. The sCT-CBCT dose difference was systematically lower than the sCT-CT by - 0.7 ± 0.6 % ( ± 95 % limits of agreement). The mean sCT-CBCT gamma pass rate ( 2 % / 2 mm ) was 96.1 ± 0.4 % ( 85.4 % , 99.7 % ). CONCLUSIONS: CBCT-based dose accuracy QA for MR-only radiotherapy appears clinically feasible. There was a small systematic sCT-CBCT dose difference implying asymmetric tolerances of [ - 2 % , 1 % ] would be appropriate.

Evaluating the image quality of combined positron emission tomography-magnetic resonance images acquired in the pelvic radiotherapy position.

Positron emission tomography-magnetic resonance (PET-MR) scanners could improve radiotherapy planning through combining PET and MR functional imaging. This depends on acquiring high quality and quantitatively accurate images in the radiotherapy position. This study evaluated PET-MR image quality using a flat couch and coil bridge for pelvic radiotherapy. MR and PET image quality phantoms were imaged in three setups: phantom on the PET-MR couch with anterior coil on top (diagnostic), phantom on a flat couch with coil on top (couch), and phantom on the flat couch with coil on a coil bridge (radiotherapy). PET images were also acquired in each setup without the anterior coil. PET attenuation correction of the flat couch and coil bridge were generated using kilovoltage computed tomography (CT) images and of the anterior coil using megavoltage CT images. MR image quality was substantially affected, with MR signal to noise ratio (SNR) relative to the diagnostic setup of 89% ± 2% (mean ± standard error of the mean, couch) and 54% ± 1% (radiotherapy), likely due to the increased distance between the patient and receive coils. The reduction impacted the low-contrast detectability score: 23 ± 1 (diagnostic), 19.7 ± 0.3 (couch) and 15 ± 1 (radiotherapy). All other MR metrics agreed within one standard error. PET quantitative accuracy was also affected, with measured activity with anterior coil being different to diagnostic without anterior coil by -16.7% ± 0.2% (couch) and -17.7 ± 0.1% (radiotherapy), without attenuation correction modification. Including the couch and coil bridge attenuation correction reduced this difference to -7.5% ± 0.1%, and including the anterior coil reduced this to -2.7% ± 0.1%. This was better than the diagnostic setup with anterior coil (difference -8.3% ± 0.2%). This translated into greater PET SNR performance for the fully corrected radiotherapy setup compared to diagnostic with coil. However contrast recovery was unchanged by the modified attenuation correction, with the diagnostic setup remaining ∼2% better. Quantitative PET in the radiotherapy setup is possible if appropriate attenuation correction is used. Pelvic radiotherapy PET-MR imaging protocols will need to consider the impact on PET-MR image quality.

Audit feasibility for geometric distortion in magnetic resonance imaging for radiotherapy.

BACKGROUND AND PURPOSE: Magnetic Resonance Imaging (MRI) is increasingly being used in radiotherapy (RT). However, geometric distortions are a known challenge of using MRI in RT. The aim of this study was to demonstrate feasibility of a national audit of MRI geometric distortions. This was achieved by assessing large field of view (FOV) MRI distortions on a number of scanners used clinically for RT. MATERIALS AND METHODS: MRI scans of a large FOV MRI geometric distortion phantom were acquired on 11 MRI scanners that are used clinically for RT in the UK. The mean and maximum distortions and variance between scanners were reported at different distances from the isocentre. RESULTS: For a small FOV representing a brain (100-150 mm from isocentre) all distortions were < 2 mm except for the maximum distortion of one scanner. For a large FOV representing a head and neck/pelvis (200-250 mm from isocentre) mean distortions were < 2 mm except for one scanner, maximum distortions were > 10 mm in some cases. The variance between scanners was low and was found to increase with distance from isocentre. CONCLUSIONS: This study demonstrated feasibility of the technique to be repeated in a country wide geometric distortion audit of all MRI scanners used clinically for RT. Recommendations were made for performing such an audit and how to derive acceptable limits of distortion in such an audit.

The accuracy of Magnetic Resonance - Cone Beam Computed Tomography soft-tissue matching for prostate radiotherapy.

BACKGROUND AND PURPOSE: Magnetic Resonance (MR)-Only radiotherapy requires a method for matching image with on-treatment Cone Beam Computed Tomography (CBCT). This study aimed to investigate the accuracy of MR-CBCT soft-tissue matching for prostate MR-only radiotherapy. MATERIALS AND METHODS: Three patient cohorts were used, with all patients receiving MR and CT scans. For the first cohort (10 patients) the first fraction CBCT was automatically rigidly registered to the CT and MR scans and the MR-CT registration predicted using the MR-CBCT and CT-CBCT registrations. This was compared to the automatic MR-CT registration. For the second and third cohorts (five patients each) the first fraction CBCT was independently matched to the CT and MR by four radiographers, the MR-CBCT and CT-CBCT matches compared and the inter-observer variability assessed. The second cohort used a CT-based structure set and the third a MR-based structure set with the MR relabelled as a 'CT'. RESULTS: The mean difference between predicted and actual MR-CT registrations was Δ R All = - 0.1 ± 0.2 mm (s.e.m.). Radiographer MR-CBCT registrations were not significantly different to CT-CBCT, with mean differences in soft-tissue match ⩽ 0.2 mm and all except one difference ⩽ 3.3 mm . This was less than the MR-CBCT inter-observer limits of agreement [ 3.5 , 2.4 , 0.9 ] mm (vertical, longitudinal, lateral), which were similar ( ⩽ 0.5 mm ) to CT-CBCT. CONCLUSIONS: MR-CBCT soft-tissue matching is not significantly different to CT-CBCT. Relabelling the MR as a 'CT' does not appear to change the automatic registration. This suggests that MR-CBCT soft-tissue matching is feasible and accurate.

Systematic Review of Synthetic Computed Tomography Generation Methodologies for Use in Magnetic Resonance Imaging-Only Radiation Therapy.

Magnetic resonance imaging (MRI) offers superior soft-tissue contrast as compared with computed tomography (CT), which is conventionally used for radiation therapy treatment planning (RTP) and patient positioning verification, resulting in improved target definition. The 2 modalities are co-registered for RTP; however, this introduces a systematic error. Implementing an MRI-only radiation therapy workflow would be advantageous because this error would be eliminated, the patient pathway simplified, and patient dose reduced. Unlike CT, in MRI there is no direct relationship between signal intensity and electron density; however, various methodologies for MRI-only RTP have been reported. A systematic review of these methods was undertaken. The PRISMA guidelines were followed. Embase and Medline databases were searched (1996 to March, 2017) for studies that generated synthetic CT scans (sCT)s for MRI-only radiation therapy. Sixty-one articles met the inclusion criteria. This review showed that MRI-only RTP techniques could be grouped into 3 categories: (1) bulk density override; (2) atlas-based; and (3) voxel-based techniques, which all produce an sCT scan from MR images. Bulk density override techniques either used a single homogeneous or multiple tissue override. The former produced large dosimetric errors (>2%) in some cases and the latter frequently required manual bone contouring. Atlas-based techniques used both single and multiple atlases and included methods incorporating pattern recognition techniques. Clinically acceptable sCTs were reported, but atypical anatomy led to erroneous results in some cases. Voxel-based techniques included methods using routine and specialized MRI sequences, namely ultra-short echo time imaging. High-quality sCTs were produced; however, use of multiple sequences led to long scanning times increasing the chances of patient movement. Using nonroutine sequences would currently be problematic in most radiation therapy centers. Atlas-based and voxel-based techniques were found to be the most clinically useful methods, with some studies reporting dosimetric differences of <1% between planning on the sCT and CT and <1-mm deviations when using sCTs for positional verification.

Investigating the generalisation of an atlas-based synthetic-CT algorithm to another centre and MR scanner for prostate MR-only radiotherapy.

There is increasing interest in MR-only radiotherapy planning since it provides superb soft-tissue contrast without the registration uncertainties inherent in a CT-MR registration. However, MR images cannot readily provide the electron density information necessary for radiotherapy dose calculation. An algorithm which generates synthetic CTs for dose calculations from MR images of the prostate using an atlas of 3 T MR images has been previously reported by two of the authors. This paper aimed to evaluate this algorithm using MR data acquired at a different field strength and a different centre to the algorithm atlas. Twenty-one prostate patients received planning 1.5 T MR and CT scans with routine immobilisation devices on a flat-top couch set-up using external lasers. The MR receive coils were supported by a coil bridge. Synthetic CTs were generated from the planning MR images with ([Formula: see text]) and without (sCT) a one voxel body contour expansion included in the algorithm. This was to test whether this expansion was required for 1.5 T images. Both synthetic CTs were rigidly registered to the planning CT (pCT). A 6 MV volumetric modulated arc therapy plan was created on the pCT and recalculated on the sCT and [Formula: see text]. The synthetic CTs' dose distributions were compared to the dose distribution calculated on the pCT. The percentage dose difference at isocentre without the body contour expansion (sCT-pCT) was [Formula: see text] and with ([Formula: see text]-pCT) was [Formula: see text] (mean ± one standard deviation). The [Formula: see text] result was within one standard deviation of zero and agreed with the result reported previously using 3 T MR data. The sCT dose difference only agreed within two standard deviations. The mean ± one standard deviation gamma pass rate was [Formula: see text] for the sCT and [Formula: see text] for the [Formula: see text] (with [Formula: see text] global dose difference and [Formula: see text] distance to agreement gamma criteria). The one voxel body contour expansion improves the synthetic CT accuracy for MR images acquired at 1.5 T but requires the MR voxel size to be similar to the atlas MR voxel size. This study suggests that the atlas-based algorithm can be generalised to MR data acquired using a different field strength at a different centre.