Dosimetric and geometric evaluation of an open low-field magnetic resonance simulator for radiotherapy treatment planning of brain tumours

A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy

The aim of this systematic review is to determine whether Deep Learning (DL) algorithms can provide a clinically feasible alternative to classic algorithms for synthetic Computer Tomography (sCT). The following categories are presented in this study: ∙ MR-based treatment planning and synthetic CT generation techniques. ∙ Generation of synthetic CT images based on Cone Beam CT images. ∙ Low-dose CT to High-dose CT generation. ∙ Attenuation correction for PET images. To perform appropriate database searches, we reviewed journal articles published between January 2018 and June 2023. Current methodology, study strategies, and results with relevant clinical applications were analyzed as we outlined the state-of-the-art of deep learning based approaches to inter-modality and intra-modality image synthesis. This was accomplished by contrasting the provided methodologies with traditional research approaches. The key contributions of each category were highlighted, specific challenges were identified, and accomplishments were summarized. As a final step, the statistics of all the cited works from various aspects were analyzed, which revealed that DL-based sCTs have achieved considerable popularity, while also showing the potential of this technology. In order to assess the clinical readiness of the presented methods, we examined the current status of DL-based sCT generation.

Improving the clinical workflow of a MR-Linac by dosimetric evaluation of synthetic CT

Adaptive radiotherapy performed on the daily magnetic resonance imaging (MRI) is an option to improve the treatment quality. In the adapt-to-shape workflow of 1.5-T MR-Linac, the contours of structures are adjusted on the basis of patient daily MRI, and the adapted plan is recalculated on the MRI-based synthetic computed tomography (syCT) generated by bulk density assignment. Because dosimetric accuracy of this strategy is a priority and requires evaluation, this study aims to explore the usefulness of adding an assessment of dosimetric errors associated with recalculation on syCT to the clinical workflow. Sixty-one patients, with various tumor sites, treated using a 1.5-T MR-Linac were included in this study. In Monaco V5.4, the target and organs at risk (OARs) were contoured, and a reference CT plan that contains information about the outlined contours, their average electron density (ED), and the priority of ED assignment was generated. To evaluate the dosimetric error of syCT caused by the inherent approximation within bulk density assignment, the reference CT plan was recalculated on the syCT obtained from the reference CT by forcing all contoured structures to their mean ED defined on the reference plan. The dose–volume histogram (DVH) and dose distribution of the CT and syCT plan were compared. The causes of dosimetric discrepancies were investigated, and the reference plan was reworked to minimize errors if needed. For 54 patients, gamma analysis of the dose distribution on syCT and CT show a median pass rate of 99.7% and 98.5% with the criteria of 3%/3 mm and 2%/2 mm, respectively. DVH difference of targets and OARs remained less than 1.5% or 1 Gy. For the remaining patients, factors (i.e., inappropriate ED assignments) influenced the dosimetric agreement of the syCT vs. CT reference DVH by up to 21%. The causes of the errors were promptly identified, and the DVH dosimetry was realigned except for two lung treatments for which a significant discrepancy remained. The recalculation on the syCT obtained from the planning CT is a powerful tool to assess and decrease the minimal error committed during the adaptive plan on the MRI-based syCT.

Multi parametric magnetic resonance imaging for radiation treatment planning

In recent years, multi-parametric magnetic resonance imaging (MpMRI) has played a major role in radiation therapy treatment planning. The superior soft tissue contrast, functional or physiological imaging capabilities and the flexibility of site-specific image sequence development has placed MpMRI at the forefront. In this article, the present status of MpMRI for external beam radiation therapy planning is reviewed. Common MpMRI sequences, preprocessing and QA strategies are briefly discussed, and various image registration techniques and strategies are addressed. Image segmentation methods including automatic segmentation and deep learning techniques for organs at risk and target delineation are reviewed. Due to the advancement in MRI guided online adaptive radiotherapy, treatment planning considerations addressing MRI only planning are also discussed. This article is protected by copyright. All rights reserved.

Generating pseudo-computerized tomography (P-CT) scan images from magnetic resonance imaging (MRI) images using machine learning algorithms based on fuzzy theory for radiotherapy treatment planning

PURPOSE

The substitution of computerized tomography (CT) with magnetic resonance imaging (MRI) has been investigated for external radiotherapy treatment planning. The present study aims to use pseudo-CT (P-CT) images created by MRI images to calculate the dose distribution for facilitating the treatment planning process.

METHODS

In this work, following image segmentation with a fuzzy clustering algorithm, an adaptive neuro-fuzzy algorithm was utilized to design the Hounsfield unit (HU) conversion model based on the features vector of MRI images. The model was generated on the set of extracted features from the gray-level co-occurrence matrices and the gray-level run-length matrices for 14 arbitrarily selected patients with brain malady. The performance of the algorithm was investigated on blind datasets through dose-volume histogram and isodose curve evaluations, using the RayPlan treatment planning system (TPS), along with the gamma analysis and statistical indices.

RESULTS

In the proposed approach, the mean absolute error within the range of 45.4 HU was found among the test data. Also, the relative dose difference between the planning target volume region of the CT and the P-CT was 0.5%, and the best gamma pass rate for 3%/3 mm was 97.2%.

CONCLUSION

The proposed method provides a satisfactory average error rate for the generation of P-CT data in the different parts of the brain region from a collection of MRI series. Also, dosimetric parameters evaluation shows good agreement between reference CT and related P-CT images.

Gold Nanoparticle DNA Damage by Photon Beam in a Magnetic Field: A Monte Carlo Study

Ever since the emergence of magnetic resonance (MR)-guided radiotherapy, it is important to investigate the impact of the magnetic field on the dose enhancement in deoxyribonucleic acid (DNA), when gold nanoparticles are used as radiosensitizers during radiotherapy. Gold nanoparticle-enhanced radiotherapy is known to enhance the dose deposition in the DNA, resulting in a double-strand break. In this study, the effects of the magnetic field on the dose enhancement factor (DER) for varying gold nanoparticle sizes, photon beam energies and magnetic field strengths and orientations were investigated using Geant4-DNA Monte Carlo simulations. Using a Monte Carlo model including a single gold nanoparticle with a photon beam source and DNA molecule on the left and right, it is demonstrated that as the gold nanoparticle size increased, the DER increased. However, as the photon beam energy decreased, an increase in the DER was detected. When a magnetic field was added to the simulation model, the DER was found to increase by 2.5-5% as different field strengths (0-2 T) and orientations (x-, y- and z-axis) were used for a 100 nm gold nanoparticle using a 50 keV photon beam. The DNA damage reflected by the DER increased slightly with the presence of the magnetic field. However, variations in the magnetic field strength and orientation did not change the DER significantly.

Synthetic dual-energy CT for MRI-only based proton therapy treatment planning using label-GAN

MRI-only treatment planning is highly desirable in the current proton radiation therapy workflow due to its appealing advantages such as bypassing MR-CT co-registration, avoiding x-ray CT exposure dose and reduced medical cost. However, MRI alone cannot provide stopping power ratio (SPR) information for dose calculations. Given that dual energy CT (DECT) can estimate SPR with higher accuracy than conventional single energy CT, we propose a deep learning-based method in this study to generate synthetic DECT (sDECT) from MRI to calculate SPR. Since the contrast difference between high-energy and low-energy CT (LECT) is important, and in order to accurately model this difference, we propose a novel label generative adversarial network-based model which can not only discriminate the realism of sDECT but also differentiate high-energy CT (HECT) and LECT from DECT. A cohort of 57 head-and-neck cancer patients with DECT and MRI pairs were used to validate the performance of the proposed framework. The results of sDECT and its derived SPR maps were compared with clinical DECT and the corresponding SPR, respectively. The mean absolute error for synthetic LECT and HECT were 79.98 ± 18.11 HU and 80.15 ± 16.27 HU, respectively. The corresponding SPR maps generated from sDECT showed a normalized mean absolute error as 5.22% ± 1.23%. By comparing with the traditional Cycle GANs, our proposed method significantly improves the accuracy of sDECT. The results indicate that on our dataset, the sDECT image form MRI is close to planning DECT, and thus shows promising potential for generating SPR maps for proton therapy.

A review on medical imaging synthesis using deep learning and its clinical applications

This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion.

Estimation and evaluation of pseudo-CT images using linear regression models and texture feature extraction from MRI images in the brain region to design external radiotherapy planning

Aim

The aim of this study is to construct and evaluate Pseudo-CT images (P-CTs) for electron density calculation to facilitate external radiotherapy treatment planning.

Background

Despite numerous benefits, computed tomography (CT) scan does not provide accurate information on soft tissue contrast, which often makes it difficult to precisely differentiate target tissues from the organs at risk and determine the tumor volume. Therefore, MRI imaging can reduce the variability of results when registering with a CT scan.

Materials and methods

In this research, a fuzzy clustering algorithm was used to segment images into different tissues, also linear regression methods were used to design the regression model based on the feature extraction method and the brightness intensity values. The results of the proposed algorithm for dose-volume histogram (DVH), Isodose curves, and gamma analysis were investigated using the RayPlan treatment planning system, and VeriSoft software. Furthermore, various statistical indices such as Mean Absolute Error (MAE), Mean Error (ME), and Structural Similarity Index (SSIM) were calculated.

Results

The MAE of a range of 45-55 was found from the proposed methods. The relative difference error between the PTV region of the CT and the Pseudo-CT was 0.5, and the best gamma rate was 95.4% based on the polar coordinate feature and proposed polynomial regression model.

Conclusion

The proposed method could support the generation of P-CT data for different parts of the brain region from a collection of MRI series with an acceptable average error rate by different evaluation criteria.

Sensitivity analysis of different quality assurance methods for magnetic resonance imaging in radiotherapy

BACKGROUND AND PURPOSE

There are currently no standard quality assurance (QA) methods for magnetic resonance imaging (MRI) in radiotherapy (RT). This work was aimed at evaluating the ability of two QA protocols to detect common events that affect quality of MR images under RT settings.

MATERIALS AND METHODS

The American College of Radiology (ACR) MRI QA phantom was repeatedly scanned using a flexible coil and action limits for key image quality parameters were derived. Using an exploratory survey, issues that reduce MR image quality were identified. The most commonly occurring events were introduced as provocations to produce MR images with degraded quality. From these images, detection sensitivities of the ACR MRI QA protocol and a commercial geometric accuracy phantom were determined.

RESULTS

Machine-specific action limits for key image quality parameters set at mean ± 3 σ were comparable with the ACR acceptable values. For the geometric accuracy phantom, provocations from uncorrected gradient nonlinearity effects and a piece of metal in the bore of the scanner resulted in worst distortions of 22.2 mm and 3.4 mm, respectively. The ACR phantom was sensitive to uncorrected signal variations, electric interference and a piece of metal in the bore of the scanner but could not adequately detect individual coil element failures.

CONCLUSIONS

The ACR MRI QA phantom combined with the large field-of-view commercial geometric accuracy phantom were generally sensitive in identifying some common MR image quality issues. The two protocols when combined may provide a tool to monitor the performance of MRI systems in the radiotherapy environment.

[Radiotherapy treatment planning of prostate cancer using magnetic resonance imaging]

PURPOSE

Magnetic resonance imaging (MRI) plays an increasing role in radiotherapy dose planning. Indeed, MRI offers superior soft tissue contrast compared to computerized tomography (CT) and therefore could provide a better delineation of target volumes and organs at risk than CT for radiotherapy. Furthermore, an MRI-only radiotherapy workflow would suppress registration errors inherent to the registration of MRI with CT. However, the estimation of the electronic density of tissues using MRI images is still a challenging issue. The purpose of this work was to design and evaluate a pseudo-CT generation method for prostate cancer treatments.

MATERIALS AND METHODS

A pseudo-CT was generated for ten prostate cancer patients using an elastic deformation based method. For each patient, dose delivered to the patient was calculated using both the planning CT and the pseudo-CT. Dose differences between CT and pseudo-CT were investigated.

RESULTS

Mean dose relative difference in the planning target volume is 0.9% on average and ranges from 0.1% to 1.7%. In organs at risks, this value is 1.8%, 0.8%, 0.8% and 1% on average in the rectum, the right and left femoral heads, and the bladder respectively.

CONCLUSION

The dose calculated using the pseudo-CT is very close to the dose calculated using the CT for both organs at risk and PTV. These results confirm that pseudo-CT images generated using the proposed method could be used to calculate radiotherapy treatment doses on MRI images.

The Role of Standard and Advanced Imaging for the Management of Brain Malignancies From a Radiation Oncology Standpoint

Radiation therapy (RT) plays a critical role in the overall management of many central nervous system (CNS) tumors. Advances in RT treatment planning, with techniques such as intensity modulated radiation therapy, volumetric modulated arc therapy, and stereotactic radiosurgery, now allow the delivery of highly conformal dose with great precision. These techniques rely on high-resolution 3-dimensional anatomical imaging modalities such as computed tomography or magnetic resonance imaging (MRI) scans to accurately and reliably define CNS targets and normal tissue avoidance structures. The integration of cross-sectional imaging into radiation oncology has directly translated into improvements in the therapeutic window of RT, and the union between radiation oncology and imaging is only expected to grow stronger. In addition, advanced imaging modalities including diffusion, perfusion, and spectroscopic MRIs as well as positron emission tomography (PET) scans with novel tracers are being utilized to provide additional insight into tumor biology and behavior beyond anatomy. Together, these standard and advanced imaging modalities hold significant potential to improve future RT delivery and response assessment. In this review, we will discuss the current utilization of standard/advanced imaging for CNS tumors from a radiation oncology perspective as well as the implications of novel MRI and PET modalities currently under investigation.

Magnetic resonance imaging in radiotherapy treatment target volumes definition for brain tumours: a systematic review and meta-analysis

Purpose

The aim of this study is to establish clinical evidence regarding the use of magnetic resonance imaging (MRI) in target volume definition for radiotherapy treatment planning of brain tumours.

Methods

Primary studies were systematically retrieved from six electronic databases and other sources. Studies included were only those that quantitatively compared computed tomography (CT) and MRI in target volume definition for radiotherapy of brain tumours. Study characteristics and quality were assessed and the data were extracted from eligible studies. Effect estimates for each study was computed as mean percentage difference based on individual patient data where available. The included studies were then combined in meta-analysis using Review Manager (RevMan) software version 5.0.

Result

Five studies with a total number of 72 patients were included in this review. The quality of the studies was rated strong. The percentages mean differences of the studies were 7·47, 11·36, 30·70, 41·69 and −24·6% using CT as the baseline. The result of statistical analysis showed small-to-moderate heterogeneity; τ2=36·8; χ2=6·23; df=4 (p=0·18); I2=36%. The overall effect estimate was −1·85 [95% confidence interval (CI); −7·24, 10·94], Z=0·40 (p=0·069>0·5).

Conclusion

Brain tumour volumes measured using MRI-based method for radiotherapy treatment planning were larger compared with CT defined volumes but the difference lacks statistical significance.

MRI-based treatment planning for brain stereotactic radiosurgery: Dosimetric validation of a learning-based pseudo-CT generation method

Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast without ionizing radiation compared with computed tomography (CT). However, it requires the generation of pseudo CT from MRI images for patient setup and dose calculation. Our machine-learning-based method to generate pseudo CT images has been shown to provide pseudo CT images with excellent image quality, while its dose calculation accuracy remains an open question. In this study, we aim to investigate the accuracy of dose calculation in brain frameless stereotactic radiosurgery (SRS) using pseudo CT images which are generated from MRI images using the machine learning-based method developed by our group. We retrospectively investigated a total of 19 treatment plans from 14 patients, each of whom has CT simulation and MRI images acquired during pretreatment. The dose distributions of the same treatment plans were calculated on original CT simulation images as ground truth, as well as on pseudo CT images generated from MRI images. Clinically-relevant DVH metrics and gamma analysis were extracted from both ground truth and pseudo CT results for comparison and evaluation. The side-by-side comparisons on image quality and dose distributions demonstrated very good agreement of image contrast and calculated dose between pseudo CT and original CT. The average differences in Dose-volume histogram (DVH) metrics for Planning target volume (PTVs) were less than 0.6%, and no differences in those for organs at risk at a significance level of 0.05. The average pass rate of gamma analysis was 99%. These quantitative results strongly indicate that the pseudo CT images created from MRI images using our proposed machine learning method are accurate enough to replace current CT simulation images for dose calculation in brain SRS treatment. This study also demonstrates the great potential for MRI to completely replace CT scans in the process of simulation and treatment planning.

Quantifying the Effect of 3T Magnetic Resonance Imaging Residual System Distortions and Patient-Induced Susceptibility Distortions on Radiation Therapy Treatment Planning for Prostate Cancer

PURPOSE

To investigate the effect of magnetic resonance system- and patient-induced susceptibility distortions from a 3T scanner on dose distributions for prostate cancers.

METHODS AND MATERIALS

Combined displacement fields from the residual system and patient-induced susceptibility distortions were used to distort 17 prostate patient CT images. VMAT dose plans were initially optimized on distorted CT images and the plan parameters transferred to the original patient CT images to calculate a new dose distribution.

RESULTS

Maximum residual mean distortions of 3.19 mm at a radial distance of 25 cm and maximum mean patient-induced susceptibility shifts of 5.8 mm were found using the lowest bandwidth of 122 Hz per pixel. There was a dose difference of <0.5% between distorted and undistorted treatment plans. The 90% confidence intervals of the mean difference between the dCT and CT treatment plans were all within an equivalence interval of (-0.5, 0.5) for all investigated plan quality measures.

CONCLUSIONS

Patient-induced susceptibility distortions at high field strengths in closed bore magnetic resonance scanners are larger than residual system distortions after using vendor-supplied 3-dimensional correction for the delineated regions studied. However, errors in dose due to disturbed patient outline and shifts caused by patient-induced susceptibility effects are below 0.5%.

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.

A patch-based pseudo-CT approach for MRI-only radiotherapy in the pelvis

PURPOSE

In radiotherapy based only on magnetic resonance imaging (MRI), knowledge about tissue electron densities must be derived from the MRI. This can be achieved by converting the MRI scan to the so-called pseudo-computed tomography (pCT). An obstacle is that the voxel intensities in conventional MRI scans are not uniquely related to electron density. The authors previously demonstrated that a patch-based method could produce accurate pCTs of the brain using conventional T1-weighted MRI scans. The method was driven mainly by local patch similarities and relied on simple affine registrations between an atlas database of the co-registered MRI/CT scan pairs and the MRI scan to be converted. In this study, the authors investigate the applicability of the patch-based approach in the pelvis. This region is challenging for a method based on local similarities due to the greater inter-patient variation. The authors benchmark the method against a baseline pCT strategy where all voxels inside the body contour are assigned a water-equivalent bulk density. Furthermore, the authors implement a parallelized approximate patch search strategy to speed up the pCT generation time to a more clinically relevant level.

METHODS

The data consisted of CT and T1-weighted MRI scans of 10 prostate patients. pCTs were generated using an approximate patch search algorithm in a leave-one-out fashion and compared with the CT using frequently described metrics such as the voxel-wise mean absolute error (MAEvox) and the deviation in water-equivalent path lengths. Furthermore, the dosimetric accuracy was tested for a volumetric modulated arc therapy plan using dose-volume histogram (DVH) point deviations and γ-index analysis.

RESULTS

The patch-based approach had an average MAEvox of 54 HU; median deviations of less than 0.4% in relevant DVH points and a γ-index pass rate of 0.97 using a 1%/1 mm criterion. The patch-based approach showed a significantly better performance than the baseline water pCT in almost all metrics. The approximate patch search strategy was 70x faster than a brute-force search, with an average prediction time of 20.8 min.

CONCLUSIONS

The authors showed that a patch-based method based on affine registrations and T1-weighted MRI could generate accurate pCTs of the pelvis. The main source of differences between pCT and CT was positional changes of air pockets and body outline.

A review of substitute CT generation for MRI-only radiation therapy

Radiotherapy based on magnetic resonance imaging as the sole modality (MRI-only RT) is an area of growing scientific interest due to the increasing use of MRI for both target and normal tissue delineation and the development of MR based delivery systems. One major issue in MRI-only RT is the assignment of electron densities (ED) to MRI scans for dose calculation and a similar need for attenuation correction can be found for hybrid PET/MR systems. The ED assigned MRI scan is here named a substitute CT (sCT). In this review, we report on a collection of typical performance values for a number of main approaches encountered in the literature for sCT generation as compared to CT. A literature search in the Scopus database resulted in 254 papers which were included in this investigation. A final number of 50 contributions which fulfilled all inclusion criteria were categorized according to applied method, MRI sequence/contrast involved, number of subjects included and anatomical site investigated. The latter included brain, torso, prostate and phantoms. The contributions geometric and/or dosimetric performance metrics were also noted. The majority of studies are carried out on the brain for 5–10 patients with PET/MR applications in mind using a voxel based method. T1 weighted images are most commonly applied. The overall dosimetric agreement is in the order of 0.3–2.5%. A strict gamma criterion of 1% and 1mm has a range of passing rates from 68 to 94% while less strict criteria show pass rates > 98%. The mean absolute error (MAE) is between 80 and 200 HU for the brain and around 40 HU for the prostate. The Dice score for bone is between 0.5 and 0.95. The specificity and sensitivity is reported in the upper 80s% for both quantities and correctly classified voxels average around 84%. The review shows that a variety of promising approaches exist that seem clinical acceptable even with standard clinical MRI sequences. A consistent reference frame for method benchmarking is probably necessary to move the field further towards a widespread clinical implementation.

Technical Note: Characterization and correction of gradient nonlinearity induced distortion on a 1.0 T open bore MR-SIM

PURPOSE

Distortions in magnetic resonance imaging (MRI) compromise spatial fidelity, potentially impacting delineation and dose calculation. We characterized 2D and 3D large field of view (FOV), sequence-independent distortion at various positions in a 1.0 T high-field open MR simulator (MR-SIM) to implement correction maps for MRI treatment planning.

METHODS

A 36 × 43 × 2 cm(3) phantom with 255 known landmarks (∼1 mm(3)) was scanned using 1.0 T high-field open MR-SIM at isocenter in the transverse, sagittal, and coronal axes, and a 465 × 350 × 168 mm(3) 3D phantom was scanned by stepping in the superior-inferior direction in three overlapping positions to achieve a total 465 × 350 × 400 mm(3) sampled FOV yielding >13 800 landmarks (3D Gradient-Echo, TE/TR/α = 5.54 ms/30 ms/28°, voxel size = 1 × 1 × 2 mm(3)). A binary template (reference) was generated from a phantom schematic. An automated program converted MR images to binary via masking, thresholding, and testing for connectivity to identify landmarks. Distortion maps were generated by centroid mapping. Images were corrected via warping with inverse distortion maps, and temporal stability was assessed.

RESULTS

Over the sampled FOV, non-negligible residual gradient distortions existed as close as 9.5 cm from isocenter, with a maximum distortion of 7.4 mm as close as 23 cm from isocenter. Over six months, average gradient distortions were -0.07 ± 1.10 mm and 0.10 ± 1.10 mm in the x and y directions for the transverse plane, 0.03 ± 0.64 and -0.09 ± 0.70 mm in the sagittal plane, and 0.4 ± 1.16 and 0.04 ± 0.40 mm in the coronal plane. After implementing 3D correction maps, distortions were reduced to <1 pixel width (1 mm) for all voxels up to 25 cm from magnet isocenter.

CONCLUSIONS

Inherent distortion due to gradient nonlinearity was found to be non-negligible even with vendor corrections applied, and further corrections are required to obtain 1 mm accuracy for large FOVs. Statistical analysis of temporal stability shows that sequence independent distortion maps are consistent within six months of characterization.

Assessing the Dosimetric Accuracy of Magnetic Resonance-Generated Synthetic CT Images for Focal Brain VMAT Radiation Therapy

PURPOSE

The purpose of this study was to assess the dosimetric accuracy of synthetic CT (MRCT) volumes generated from magnetic resonance imaging (MRI) data for focal brain radiation therapy.

METHODS AND MATERIALS

A study was conducted in 12 patients with gliomas who underwent both MR and CT imaging as part of their simulation for external beam treatment planning. MRCT volumes were generated from MR images. Patients' clinical treatment planning directives were used to create 12 individual volumetric modulated arc therapy (VMAT) plans, which were then optimized 10 times on each of their respective CT and MRCT-derived electron density maps. Dose metrics derived from optimization criteria, as well as monitor units and gamma analyses, were evaluated to quantify differences between the imaging modalities.

RESULTS

Mean differences between planning target volume (PTV) doses on MRCT and CT plans across all patients were 0.0% (range: -0.1 to 0.2%) for D(95%); 0.0% (-0.7 to 0.6%) for D(5%); and -0.2% (-1.0 to 0.2%) for D(max). MRCT plans showed no significant changes in monitor units (-0.4%) compared to CT plans. Organs at risk (OARs) had average D(max) differences of 0.0 Gy (-2.2 to 1.9 Gy) over 85 structures across all 12 patients, with no significant differences when calculated doses approached planning constraints.

CONCLUSIONS

Focal brain VMAT plans optimized on MRCT images show excellent dosimetric agreement with standard CT-optimized plans. PTVs show equivalent coverage, and OARs do not show any overdose. These results indicate that MRI-derived synthetic CT volumes can be used to support treatment planning of most patients treated for intracranial lesions.

A criterion for the reliable use of MRI-only radiotherapy

BACKGROUND

MRI-only radiotherapy will eliminate the systematic registration errors introduced when transferring MRI information to the CT. However, challenges concerning the missing information on electron density, necessary for dose calculation and patient setup on bony anatomy are introduced. This study presents a possible statistical approach to evaluate, if deviations based on MRI-only radiotherapy as compared to the CT based radiotherapy are acceptable.

METHODS

18 head-and-neck, 21 prostate, 10 vesica and 8 pelvic patients were included in the study. Data from each patient contained a CT and a T2-weighted MRI scan, a structure set and a clinically approved CT based treatment plan, which was re-calculated with identical parameters on the density corrected MRI scans. A statistical analysis including a 95% confidence interval was performed in clinically relevant DVH points.

RESULTS

The mean differences in the investigated DVH points were in the order of 1.5% for the PTV and up to 4.2% for organs at risk. In addition, a proposed criterion of 2% dose difference in the PTV coverage for 95% of the patients was fulfilled for all diagnostic groups for a bulk segmented MRI in the DVH points, D(median) and D2%, while only head-and-neck and prostate further fulfilled the criterion in D98%.

CONCLUSION

Here, we suggested a method for establishing a reliable use of MRI-only radiotherapy. A population-based study comparing CT based dose calculations with those obtained on a suggested segmentation of MRI should be initiated and acceptable deviations in clinically relevant DVH points should be established. Such a population-based approach could form a part of the clinical commissioning of MRI-only radiotherapy.

Assessment and characterization of the total geometric uncertainty in Gamma Knife radiosurgery using polymer gels

PURPOSE

This work proposes and implements an experimental methodology, based on polymer gels, for assessing the total geometric uncertainty and characterizing its contributors in Gamma Knife (GK) radiosurgery.

METHODS

A treatment plan consisting of 26, 4-mm GK single shot dose distributions, covering an extended region of the Leksell stereotactic space, was prepared and delivered to a polymer gel filled polymethyl methacrylate (PMMA) head phantom (16 cm diameter) used to accurately reproduce every link in the GK treatment chain. The center of each shot served as a "control point" in the assessment of the GK total geometric uncertainty, which depends on (a) the spatial dose delivery uncertainty of the PERFEXION GK unit used in this work, (b) the spatial distortions inherent in MR images commonly used for target delineation, and (c) the geometric uncertainty contributor associated with the image registration procedure performed by the Leksell GammaPlan (LGP) treatment planning system (TPS), in the case that registration is directly based on the apparent fiducial locations depicted in each MR image by the N-shaped rods on the Leksell localization box. The irradiated phantom was MR imaged at 1.5 T employing a T2-weighted pulse sequence. Four image series were acquired by alternating the frequency encoding axis and reversing the read gradient polarity, thus allowing the characterization of the MR-related spatial distortions.

RESULTS

MR spatial distortions stemming from main field (B0) inhomogeneity as well as from susceptibility and chemical shift phenomena (also known as sequence dependent distortions) were found to be of the order of 0.5 mm, while those owing to gradient nonlinearities (also known as sequence independent distortions) were found to increase with distance from the MR scanner isocenter extending up to 0.47 mm at an Euclidean distance of 69.6 mm. Regarding the LGP image registration procedure, the corresponding average contribution to the total geometric uncertainty ranged from 0.34 to 0.80 mm. The average total geometric uncertainty, which also includes the GK spatial dose delivery uncertainty, was found equal to (0.88 ± 0.16), (0.88 ± 0.26), (1.02 ± 0.09), and (1.15 ± 0.24) mm for the MR image series acquired with the read gradient polarity (direction) set toward right, left, posterior, and anterior, respectively.

CONCLUSIONS

The implemented methodology seems capable of assessing the total geometric uncertainty, as well as of characterizing its contributors, ascribed to the entire GK treatment delivery (i.e., from MR imaging to GK dose delivery) for an extended region of the Leksell stereotactic space. Results obtained indicate that the selection of both the frequency encoding axis and the read gradient polarity during MRI acquisition may affect the magnitude as well as the spatial components of the total geometric uncertainty.

Voxel-wise uncertainty in CT substitute derived from MRI

PURPOSE

In an earlier work, we demonstrated that substitutes for CT images can be derived from MR images using ultrashort echo time (UTE) sequences, conventional T2 weighted sequences, and Gaussian mixture regression (GMR). In this study, we extend this work by analyzing the uncertainties associated with the GMR model and the information contributions from the individual imaging sequences.

METHODS

An analytical expression for the voxel-wise conditional expected absolute deviation (EAD) in substitute CT (s-CT) images was derived. The expression depends only on MR images and can thus be calculated along with each s-CT image. The uncertainty measure was evaluated by comparing the EAD to the true mean absolute prediction deviation (MAPD) between the s-CT and CT images for 14 patients. Further, the influence of the different MR images included in the GMR model on the generated s-CTs was investigated by removing one or more images and evaluating the MAPD for a spectrum of predicted radiological densities.

RESULTS

The largest EAD was predicted at air-soft tissue and bone-soft tissue interfaces. The EAD agreed with the MAPD in both these regions and in regions with lower EADs, such as the brain. Two of the MR images included in the GMR model were found to be mutually redundant for the purpose of s-CT generation.

CONCLUSIONS

The presented uncertainty estimation method accurately predicts the voxel-wise MAPD in s-CT images. Also, the non-UTE sequence previously used in the model was found to be redundant.

Comparison of bulk electron density and voxel-based electron density treatment planning

The use of magnetic resonance imaging (MRI) alone for radiation planning is limited by the lack of electron density for dose calculations. The purpose of this work is to evaluate the dosimetric accuracy of using bulk electron density as a substitute for computed tomography (CT)‐derived electron density in intensity‐modulated radiation therapy (IMRT) treatment planning of head and neck (HN) cancers. Ten clinically‐approved, CT‐based IMRT treatment plans of HN cancer were used for this study. Three dose distributions were calculated and compared for each treatment plan. The first calculation used CT‐derived density and was assumed to be the most accurate. The second calculation used a homogeneous patient density of 1 g/cm3. For the third dose calculation, bone and air cavities were contoured and assigned a uniform density of 1.5 g/cm3 and 0 g/cm3, respectively. The remaining tissues were assigned a density of 1 g/cm3. The use of homogeneous anatomy resulted in up to 4%–5% deviations in dose distribution as compared to CT‐derived electron density calculations. Assigning bulk density to bone and air cavities significantly improved the accuracy of the dose calculations. All parameters used to describe planning target volume coverage were within 2% of calculations based on CT‐derived density. For organs at risk, most of the parameters were within 2%, with the few exceptions located in low‐dose regions. The data presented here show that if bone and air cavities are overridden with the proper density, it is feasible to use a bulk electron density approach for accurate dose calculation in IMRT treatment planning of HN cancers. This may overcome the problem of the lack of electron density information should MRI‐only simulation be performed.

PACS number: 87.55.D‐

Clinical evaluation of 3D/3D MRI-CBCT automatching on brain tumors for online patient setup verification - A step towards MRI-based treatment planning

BACKGROUND

Magnetic Resonance Imaging (MRI) is often used in modern day radiotherapy (RT) due to superior soft tissue contrast. However, treatment planning based solely on MRI is restricted due to e.g. the limitations of conducting online patient setup verification using MRI as reference. In this study 3D/3D MRI-Cone Beam CT (CBCT) automatching for online patient setup verification was investigated.

MATERIAL AND METHODS

Initially, a multi-modality phantom was constructed and used for a quantitative comparison of CT-CBCT and MRI-CBCT automatching. Following the phantom experiment three patients undergoing postoperative radiotherapy for malignant brain tumors received a weekly CBCT. In total 18 scans was matched with both CT and MRI as reference. The CBCT scans were acquired using a Clinac iX 2300 linear accelerator (Varian Medical Systems) with an On-Board Imager (OBI).

RESULTS

For the phantom experiment CT-CBCT and MRI-CBCT automatching resulted in similar results. A significant difference was observed only in the longitudinal direction where MRI-CBCT resulted in the best match (mean and standard deviations of 1.85±2.68 mm for CT and -0.05±2.55 mm for MRI). For the clinical experiment the absolute difference in couch shift coordinates acquired from MRI-CBCT and CT-CBCT automatching, were ≤2 mm in the vertical direction and ≤3 mm in the longitudinal and lateral directions. For yaw rotation differences up to 3.3 degrees were observed. Mean values and standard deviations were 0.8±0.6 mm, 1.5±1.2 mm and 1.2±1.2 mm for the vertical, longitudinal and lateral directions, respectively and 1.95±1.12 degrees for the rotation (n=17).

CONCLUSION

It is feasible to use MRI as reference when conducting 3D/3D CBCT automatching for online patient setup verification. For both the phantom and clinical experiment MRI-CBCT performed similar to CT-CBCT automatching and significantly better in the longitudinal direction for the phantom experiment.

Treatment planning using MRI data: an analysis of the dose calculation accuracy for different treatment regions

BACKGROUND

Because of superior soft tissue contrast, the use of magnetic resonance imaging (MRI) as a complement to computed tomography (CT) in the target definition procedure for radiotherapy is increasing. To keep the workflow simple and cost effective and to reduce patient dose, it is natural to strive for a treatment planning procedure based entirely on MRI. In the present study, we investigate the dose calculation accuracy for different treatment regions when using bulk density assignments on MRI data and compare it to treatment planning that uses CT data.

METHODS

MR and CT data were collected retrospectively for 40 patients with prostate, lung, head and neck, or brain cancers. Comparisons were made between calculations on CT data with and without inhomogeneity corrections and on MRI or CT data with bulk density assignments. The bulk densities were assigned using manual segmentation of tissue, bone, lung, and air cavities.

RESULTS

The deviations between calculations on CT data with inhomogeneity correction and on bulk density assigned MR data were small. The maximum difference in the number of monitor units required to reach the prescribed dose was 1.6%. This result also includes effects of possible geometrical distortions.

CONCLUSIONS

The dose calculation accuracy at the investigated treatment sites is not significantly compromised when using MRI data when adequate bulk density assignments are made. With respect to treatment planning, MRI can replace CT in all steps of the treatment workflow, reducing the radiation exposure to the patient, removing any systematic registration errors that may occur when combining MR and CT, and decreasing time and cost for the extra CT investigation.

A simulation of MRI based dose calculations on the basis of radiotherapy planning CT images

BACKGROUND

The advantage of MRI-based radiotherapy planning is the superior soft tissue differentiation. However, for accurate patient dose calculations, a conversion of the MR images into Hounsfield CT maps is necessary. The aim of the present study was to investigate the dose accuracy that can be achieved with segmented MR-images derived from the planning CT images by assigning fixed densities to different classes of tissues.

METHODS

Treatment plans for ten prostate cancer patients were selected. A collapsed cone algorithm was used to calculate patient dose distributions. The dose calculations were based on four different image sets: (1) the original CT-series (DD(DP)), (2) a simulated MR series with all tissue set to a homogenous water equivalent material of density 1.02 g/cm(3) (DD(W)), (3) a simulated MR series with soft tissue set to a water equivalent material with density 1.02 g/cm(3) and the bone set to a density of 1.3 g/cm(3) (DD(W+B1.3)), and (4) a simulated MR series identical to (3) but with a bone density equal to 2.1 g/cm(3) (DD(W+B2.1)). The dose distributions were compared by analysing dose difference histograms as well as through a visual display of spatial dose deviations.

RESULTS

The population based minimum, mean and maximum dose difference between the DD(DP) and DD(W) in the target volume was -2.8, -1.0 and 0.6%, respectively. Corresponding differences between DD(DP) and DD(W+B1.3) were -1.6, 0.2 and 1.5%, respectively, and between DD(DP) and DD(W+B2.1) -4.3, 4.2 and 9.7%, respectively. For the rectum, the differences between CT(DP) and the other image sets were in the range of -19.5 to 8.8%. For the bladder, the differences were in the range of -9.6 to 7.0%.

CONCLUSIONS

A systematic study using segmented MR images was undertaken. To achieve an acceptable accuracy in the CTV dose, the MR images should be segmented into bone and water equivalent tissue. Still, significant dose deviation for the organs at risk may be present. As tissue segmentation in real MR images is introduced, segmentation errors and errors that stem from geometrical non-linearities may further reduce the accuracy.