MRI Simulation Research Articles

Simulated Single Fraction Stereotactic Body Radiation Therapy for the Treatment of Abdominal Oligometastatic Disease with Online Adaptive Magnetic Resonance Guidance: A Feasibility Study

Local ablative therapy improves overall survival in patients with limited oligometastatic disease. Single fraction SBRT is efficient and is safe in several body sites and can provide a high rate of local control, with 20Gy in 1 fraction commonly used in Europe. However, treating metastases in the abdomen is difficult due close proximity to mobile luminal organs and poor visualization on cone beam CT. The purpose of this study is to determine the feasibility of delivering MR-guided adaptive stereotactic body radiation therapy (SBRT) in a single fraction for abdominal metastases. Patients with abdominal metastases within 8mm from luminal organs treated with hypofractionation or SBRT using maximum inhale breath hold (MIBH) respiratory gating on an integrated MR-Linac or MR-cobalt radiotherapy system were retrospectively analyzed. Single fraction SBRT plans were created on the original MRI simulation with dose constraints from the AAPM Task Group 101 and Timmerman et al. Optimized PTVs (PTVopt) created by a Boolean subtraction of the 3-5mm expansion around luminal organs from the 3mm expansion around the GTV. Using a prescription of 24Gy in 1 fraction, goals included PTV V95% > 90%, PTVopt V95% > 90%, and PTVopt D99% > 20Gy with no OAR dose violations. The single fraction plans were tested on 5 fractional MRI scans. Dosimetric data was collected with the original plan (“predicted”) and after the plan was adapted using an isotoxic approach to the daily re-contoured luminal organ anatomy (“Re-optimized”). The Wilcoxon rank sum test was used to compare dosimetric data and the frequency of meeting dosimetric goals between the multi-fraction and single fraction baseline plans and between the predicted and re-optimized fractional plans. Ten patients with abdominal metastases (4 liver, 3 lymph node, 2 kidney, and 1 adrenal site) with GTV mean and range of 25.5 (1.64, 84.2) mL of various histologies were analyzed. The multi-fraction and single fraction plans mean PTV V95% and PTVopt V95% were 89.7% vs. 81.5% and 97.0% vs. 90.9% respectively (all p>0.05), while 2/10 of both the multi-fraction and single fraction plans had a PTVopt V95% < 90%, and no OAR dose constraints were violated. Comparing the predicted and re-optimized fractional plans, there was a statistically significant, 32.8% increase in the mean PTVopt D99% and 68% decrease in the mean frequency of OAR dose violations (Table 1). Single fraction SBRT for the treatment of abdominal metastases with online adaptive MR guidance is feasible and may be an efficient alternative to multi-fraction treatments. Online MR-guided adaptation is required to maintain adequate target coverage while respecting OAR dose constraints.Abstract 2406; TableTable 1: Isotoxic OptimizationMeanFrequencyPTV V95% (%)PTVopt V95% (%)PTVopt D99% (Gy)PTV V95% > 90%PTVopt V95% > 90%PTVopt D99% > 20GyOAR ViolationsPredicted81.187.917.40.240.680.240.68Re-optimized84.892.822.60.420.820.740p-value0.680.310.01*0.160.360.01*0.001* Open table in a new tab

Reduction of cardiac dose using respiratory-gated MR-linac plans for gastro-esophageal junction cancer

Treatment of locally advanced adenocarcinoma of the gastroesophageal junction (GEJ) with chemoradiation may be associated with high rates of symptomatic cardiac toxicity. Large margins are typically required to ensure coverage of GEJ tumors with free-breathing volumetric modulated arc therapy (VMAT) radiotherapy. The purpose of this study is to determine whether treatment with tighter margins enabled by maximum-inhalation breath hold (MIBH)-gated intensity modulated radiation therapy (IMRT) on an integrated MRI-linear accelerator system (MR-linac) can decrease radiation doses to the heart and cardiac substructures. Ten patients with locally advanced GEJ adenocarcinoma underwent both free breathing 4-dimensional computed tomography (4DCT) and MIBH MRI simulation scans. MR-linac IMRT plans were created with a 3 mm clinical target volume (CTV) to planning target volume (PTV) isotropic margin and 4DCT VMAT plans were created with a 11, 13, and 9 mm CTV to PTV anisotropic margins in the left-right, cranial-caudal, and anterior-posterior directions according to GEJ-specific PTV expansion recommendations by Voncken et al. Prescription dose to PTV was 50.4 Gy in 28 fractions. Dosimetry to the heart and cardiac substructures was compared with paired t test; p < 0.05 was considered significant. Mean PTV on the MR-linac IMRT plans was significantly smaller compared to the 4DCT VMAT plans (689 cm3vs 1275 cm3, p < 0.01). Mean dose to the heart and all cardiac substructures was significantly lower in the MR-linac IMRT plans compared to the 4DCT VMAT plans: heart 20.9 Gy vs 27.8 Gy, left atrium 29.6 Gy vs 39.4 Gy, right atrium 20.5 Gy vs 25.6 Gy, left ventricle 21.6 Gy vs 29.6 Gy, and right ventricle 18.7 Gy vs 25.2 Gy (all p values <0.05). MIBH-gated MR-linac IMRT treatment of locally advanced GEJ adenocarcinoma can significantly decrease doses to the heart and cardiac substructures and this may translate to reduced rates of cardiac toxicity.

A generative adversarial network-based (GAN-based) architecture for automatic fiducial marker detection in prostate MRI-only radiotherapy simulation images.

Clinical sites utilizing magnetic resonance imaging (MRI)-only simulation for prostate radiotherapy planning typically use fiducial markers for pretreatment patient positioning and alignment. Fiducial markers appear as small signal voids in MRI images and are often difficult to discern. Existing clinical methods for fiducial marker localization require multiple MRI sequences and/or manual interaction and specialized expertise. In this study, we develop a robust method for automatic fiducial marker detection in prostate MRI simulation images and quantify the pretreatment alignment accuracy using automatically detected fiducial markers in MRI. In this study, a deep learning-based algorithm was used to convert MRI images into labeled fiducial marker volumes. Seventy-seven prostate cancer patients who received marker implantation prior to MRI and CT simulation imaging were selected for this study. Multiple-Echo T1 -VIBE MRI images were acquired, and images were stratified (at the patient level) based on the presence of intraprostatic calcifications. Ground truth (GT) contours were defined by an expert on MRI using CT images. Training was done using the pix2pix generative adversarial network (GAN) image-to-image translation package and model testing was done using fivefold cross validation. For performance comparison, an experienced medical dosimetrist and a medical physicist each manually contoured fiducial markers in MRI images. The percent of correct detections and F1 classification scores are reported for markers detected using the automatic detection algorithm and human observers. The patient positioning errors were quantified by calculating the target registration errors (TREs) from fiducial marker driven rigid registration between MRI and CBCT images. Target registration errors were quantified for fiducial marker contours defined on MRI by the automatic detection algorithm and the two expert human observers. Ninety-six percent of implanted fiducial markers were correctly identified using the automatic detection algorithm. Two expert raters correctly identified 97% and 96% of fiducial markers, respectively. The F1 classification score was 0.68, 0.75, and 0.72 for the automatic detection algorithm and two human raters, respectively. The main source of false discoveries was intraprostatic calcifications. The mean TRE differences between alignments from automatic detection algorithm and human detected markers and GT were <1mm. We have developed a deep learning-based approach to automatically detect fiducial markers in MRI-only simulation images in a clinically representative patient cohort. The automatic detection algorithm-predicted markers can allow for patient setup with similar accuracy to independent human observers.

Technical Note: Extended field-of-view (FOV) MRI distortion determination through multi-positional phantom imaging.

Comprehensive characterization of geometric distortions for MRI simulators and MRI‐guided treatment delivery systems is typically performed with large phantoms that are costly and unwieldy to handle. Here we propose an easily implementable methodology for MR distortion determination of the entire imaging space of the scanner through the use of a compact commercially available distortion phantom. The MagphanRT phantom was scanned at several locations within a MR scanner. From each scan, an approximate location of the phantom was determined from a subset of the fiducial spheres. The fiducial displacements were determined, and a displacement field was fitted to the displacement data using the entire multi‐scan data set. An orthogonal polynomial expansion fitting function was used that had been augmented to include independent rigid‐body transformations for each scan. The rigid‐body portions of the displacement field were thereafter discarded, and the resultant fit then represented the distortion field. Multi‐positional scans of the phantom were used successfully to determine the distortion field with extended coverage. A single scan of the phantom covered 20 cm in its smallest dimension. By stitching together overlapping scans we extended the distortion measurements to 30 cm. No information about the absolute location or orientation of each scan was required. The method, termed the Multi‐Scan Expansion (MSE) method, can be easily applied for larger field‐of‐views (FOVs) by using a combination of larger phantom displacements and more scans. The implementation of the MSE method allows for distortion determination beyond the physical limitations of the phantom. The method is scalable to the user’s needs and does not require any specialized equipment. This approach could open up for easier determination of the distortion magnitude at distances further from the scanner’s isocenter. This is especially important in the newly proposed methodologies of MR‐only simulation in RT and in adaptive replanning in MR linac systems.

Development of a method for the Bloch image simulation of biological tissues

PurposeThe purpose of this study is to develop a method for the Bloch image simulation of biological tissues including various chemical components and T2* distribution. MethodsThe nuclear spins in the object material were modeled as a spectral intensity function Sr→ω defined by superposition of Lorentz functions with various central precession frequencies and the half width of 1/(πT2′), where 1/T2′ is a relaxation rate attributable to microscopic field inhomogeneity in a voxel. Four-dimensional numerical phantoms were created to simulate Sr→ω and used for MRI simulations of the phantoms containing water and fat protons. Single slice multiple (16) gradient-echo sequences (ΔTE = 2.2 and 1.384 ms) were used for experiments at 1.5 T and 3 T and MRI simulations to evaluate the validity of the approach. ResultsExperimentally measured image intensities of the multiple gradient-echo imaging sequences were well reproduced by the MRI simulations. The correlation coefficients between the experimentally measured image intensities and those numerically simulated were 0.9895 to 0.9992 for the 4-component phantom at 1.5 T and 0.9580 to 0.9996 for the 7-component phantom at 3 T. ConclusionT2* and chemical shift effects were successfully implemented in the MRI simulator (BlochSolver). Because this approach can be applied to other MRI simulators, the method developed in this study is useful for MRI simulation of biological tissues containing water and fat protons.

70: Implementation of Mri Simulation in Extremity Soft Tissue Sarcoma

70: Implementation of Mri Simulation in Extremity Soft Tissue Sarcoma

Comparison of gross tumor volumes of pulmonary metastasis defined by CT and MRI in 0.345 T MRI-guided radiotherapy.

Objective:To assess the difference in gross tumor volumes (GTVs) defined by CT (GTV-CT) and by low magnetic field strength (0.345 T) MRI (GTV-MRI) in patients simulated for MRI-guided radiotherapy forlung metastasis.Methods:28 patients (148 lesions) who underwent CT and MRI simulation with the tri-60Co MRI-guided radiotherapy system (MRIdian, ViewRay) were included in this study. GTV-CT and GTV-MRI were compared using the paired t-test. The equivalence of variance between GTV-CT and GTV-MRI of small lesions (GTV-CT <1 ml) and large ones (GTV-CT >= 1 ml) was evaluated using F-test. The correlation between GTV-CT and GTV-MRI was evaluated by the correlation coefficient.Results:GTV-MRI was 120% larger than GTV-CT (p < 0.001) for small lesions, whereas GTV-MRI was 40% larger than GTV-CT (p < 0.001) for large lesions. In small lesions, the variation in GTV-MRI was significantly larger than that of GTV-CT (p < 0.001). There was no significant difference in the variation of GTV-MRI and GTV-CT in large lesions (p = 0.121). The correlation coefficient for small lesions was 0.93, whereas that for large lesions was 0.99, with large lesions having better correlation.Conclusions:GTV-MRI was larger than GTV-CT and the correlation between GTV-MRI and GTV-CT was better in large lesions. If the tumor volume is 1 ml or larger, the lesion can be accurately monitored even with a low magnetic field strength MRI.Advances in knowledge:This study is the first clinical report to evaluate the tolerability of MRI images in 0.345 T MRI-guided radiotherapy for lung metastasis. GTV contoured by MRI was larger than GTV by CT, and this tendency was more pronounced in small tumors of less than 1 ml.

Practical computation of the diffusion MRI signal of realistic neurons based on Laplace eigenfunctions.

The complex transverse water proton magnetization subject to diffusion-encoding magnetic field gradient pulses in a heterogeneous medium such as brain tissue can be modeled by the Bloch-Torrey partial differential equation. The spatial integral of the solution of this equation in realistic geometry provides a gold-standard reference model for the diffusion MRI signal arising from different tissue micro-structures of interest. A closed form representation of this reference diffusion MRI signal called matrix formalism, which makes explicit the link between the Laplace eigenvalues and eigenfunctions of the biological cell and its diffusion MRI signal, was derived 20 years ago. In addition, once the Laplace eigendecomposition has been computed and saved, the diffusion MRI signal can be calculated for arbitrary diffusion-encoding sequences and b-values at negligible additional cost. Up to now, this representation, though mathematically elegant, has not been often used as a practical model of the diffusion MRI signal, due to the difficulties of calculating the Laplace eigendecomposition in complicated geometries. In this paper, we present a simulation framework that we have implemented inside the MATLAB-based diffusion MRI simulator SpinDoctor that efficiently computes the matrix formalism representation for realistic neurons using the finite element method. We show that the matrix formalism representation requires a few hundred eigenmodes to match the reference signal computed by solving the Bloch-Torrey equation when the cell geometry originates from realistic neurons. As expected, the number of eigenmodes required to match the reference signal increases with smaller diffusion time and higher b-values. We also convert the eigenvalues to a length scale and illustrate the link between the length scale and the oscillation frequency of the eigenmode in the cell geometry. We give the transformation that links the Laplace eigenfunctions to the eigenfunctions of the Bloch-Torrey operator and compute the Bloch-Torrey eigenfunctions and eigenvalues. This work is another step in bringing advanced mathematical tools and numerical method development to the simulation and modeling of diffusion MRI.

Diffusion MRI simulation of realistic neurons with SpinDoctor and the Neuron Module

The diffusion MRI signal arising from neurons can be numerically simulated by solving the Bloch-Torrey partial differential equation. In this paper we present the Neuron Module that we implemented within the Matlab-based diffusion MRI simulation toolbox SpinDoctor. SpinDoctor uses finite element discretization and adaptive time integration to solve the Bloch-Torrey partial differential equation for general diffusion-encoding sequences, at multiple b-values and in multiple diffusion directions. In order to facilitate the diffusion MRI simulation of realistic neurons by the research community, we constructed finite element meshes for a group of 36 pyramidal neurons and a group of 29 spindle neurons whose morphological descriptions were found in the publicly available neuron repository NeuroMorpho.Org. These finite elements meshes range from having 15,163 nodes to 622,553 nodes. We also broke the neurons into the soma and dendrite branches and created finite elements meshes for these cell components. Through the Neuron Module, these neuron and cell components finite element meshes can be seamlessly coupled with the functionalities of SpinDoctor to provide the diffusion MRI signal attributable to spins inside neurons. We make these meshes and the source code of the Neuron Module available to the public as an open-source package.To illustrate some potential uses of the Neuron Module, we show numerical examples of the simulated diffusion MRI signals in multiple diffusion directions from whole neurons as well as from the soma and dendrite branches, and include a comparison of the high b-value behavior between dendrite branches and whole neurons. In addition, we demonstrate that the neuron meshes can be used to perform Monte-Carlo diffusion MRI simulations as well. We show that at equivalent accuracy, if only one gradient direction needs to be simulated, SpinDoctor is faster than a GPU implementation of Monte-Carlo, but if many gradient directions need to be simulated, there is a break-even point when the GPU implementation of Monte-Carlo becomes faster than SpinDoctor. Furthermore, we numerically compute the eigenfunctions and the eigenvalues of the Bloch-Torrey and the Laplace operators on the neuron geometries using a finite elements discretization, in order to give guidance in the choice of the space and time discretization parameters for both finite elements and Monte-Carlo approaches. Finally, we perform a statistical study on the set of 65 neurons to test some candidate biomakers that can potentially indicate the soma size. This preliminary study exemplifies the possible research that can be conducted using the Neuron Module.

Enhanced N Cut and Watershed based Model for Brain MRI Segmentation

Segmentation of an image is most important and essential task in medical image processing, specifically while analyzing magnetic resonance (MR) image of brain clinically. during the clinical investigation of brain MRI images. Lot of research has been carried out for MRI segmentation but still it is challenging task. Hybrid approach which uses enhanced normalized cut and watershed transform to segment brain MRI images is developed in this paper. Watershed transform is used for the initial partitioning of the MRI, which creates primitive regions. In the next stage these primitive regions resembled for graph depiction and then the normalized cut method is used for segmenting an image. Variety of simulated and actual MR images are being segmented by using proposed algorithm to test its efficiency, in addition to it segmentation results are also compared with the other available techniques of brain MRI segmentation.

Comparison of gross target volumes based on four-dimensional CT, positron emission tomography-computed tomography, and magnetic resonance imaging in thoracic esophageal cancer.

PurposeThe application value of 18F‐FDG PET‐CT combined with MRI in the radiotherapy of esophageal carcinoma was discussed by comparing the differences in position, volume, and the length of GTVs delineated on the end‐expiration (EE) phase of 4DCT, 18F‐FDG PET‐CT, and T2W‐MRI.MethodsA total of 26 patients with thoracic esophageal cancer sequentially performed 3DCT, 4DCT, 18F‐FDG PET‐CT, and MRI simulation for thoracic localization. All images were fused with the 3DCT images by deformable registration. GTVCT and GTV50% were delineated on 3DCT and the EE phase of 4DCT images, respectively. The GTV based on PET‐CT images was determined by thresholds of SUV ≥ 2.5 and designated as GTVPET2.5. The images of T2‐weighted sequence and diffusion‐weighted sequence were referred as GTVMRI and GTVDWI, respectively. The length of the abnormality seen on the 4DCT, PET‐CT, and DWI was compared.ResultsGTVPET2.5 was significantly larger than GTV50% and GTVMRI (P = .000 and 0.008, respectively), and the volume of GTVMRI was similar to that of GTV50% (P = .439). Significant differences were observed between the CI of GTVMRI to GTV50% and GTVPET2.5 to GTV50% (P = .004). The CI of GTVMRI to GTVCT and GTVPET2.5 to GTVCT were statistically significant (P = .039). The CI of GTVMRI to GTVPET2.5 was significantly lower than that of GTVMRI to GTV50%, GTVMRI to GTVCT, GTVPET2.5 to GTV50%, and GTVPET2.5 to GTVCT (P = .000‐0.021). Tumor length measurements by endoscopy were similar to the tumor length as measured by PET and DWI scan (P > .05), and there was no significant difference between the longitudinal length of GTVPET2.5 and GTVDWI (P = .072).ConclusionThe volumes of GTVMRI and GTV50% were similar. However, GTVMRI has different volumes and poor spatial matching compared with GTVPET2.5.The MRI imaging could not include entire respiration. It may be a good choice to guide target delineation and construction of esophageal carcinoma by combining 4DCT with MRI imaging. Utilization of DWI in treatment planning for esophageal cancer may provide further information to assist with target delineation. Further studies are needed to determine if this technology will translate into meaningful differences in clinical outcome.

Safety and Functional Integrity of Continuous Glucose Monitoring Components After Simulated Radiologic Procedures.

Background:We investigated wearable components of the Dexcom G6 continuous glucose monitoring (CGM) System in simulated therapeutic and diagnostic radiologic procedures.Methods:G6 transmitters were loaded with simulated glucose data and attached to sensors. Sets of sensor/transmitter pairs were exposed to x-rays to simulate a radiotherapeutic procedure and to radiofrequency (RF) and magnetic fields to simulate diagnostic magnetic resonance imaging (MRI). The x-ray simulation provided a cumulative dose of 80 Gy. The MRI simulation used RF fields oscillating at 64 or 128 MHz and magnetic fields of 1.5 or 3 T. During the MRI simulation, displacement force, induced heating, and induced currents were measured. After the simulations, bench tests were used to assess data integrity on the transmitters and responsiveness of sensors to various concentrations of aqueous glucose.Results:Glucose concentrations reported by sensor/transmitter pairs after undergoing x-irradiation or a simulated MRI exam were similar to those from control (unexposed) devices. During the 3 T MRI simulation, the devices experienced a displacement force of 306 g, which was insufficient to dislodge the sensor/transmitter from the substrate, RF-induced heating of <2°C, and an induced current of <16 pA. Data stored on the transmitters prior to the MRI simulation remained intact.Conclusion:Wearable components of the G6 CGM System retain basic functionality and data integrity after exposure to simulated therapeutic and diagnostic radiologic procedures. The devices are unlikely to be affected by x-irradiation used in typical imaging studies. Simulated MRI procedures create displacement force, minimal heating, and current in sensor/transmitter pairs.

A novel fuzzy clustering algorithm by minimizing global and spatially constrained likelihood-based local entropies for noisy 3D brain MR image segmentation

In this paper, we propose a novel fuzzy clustering algorithm by minimizing global and spatially constrained likelihood-based local entropies (FCMGsLE) for segmenting noisy 3D brain magnetic resonance (MR) image volumes. For each voxel, in order to measure uncertainties that arise while identifying its class, two different entropies are defined. In particular, they measure the amount of uncertainties in terms of global entropy using fuzzifier weighted global membership function and spatially constrained likelihood-based local entropy using fuzzifier weighted local membership function. To mitigate the effect of noise and intensity inhomogeneity (IIH) or radio frequency (RF) inhomogeneity, the local membership function is induced by spatially constrained likelihood measure. These entropies are minimized through a fuzzy objective function to obtain the cluster prototypes and membership functions. The final membership function is obtained by integrating these global and local membership functions using weighted parameters. The algorithm is assessed both qualitatively and quantitatively on ten 3D volumes of simulated and clinical brain MR image data having high levels of noise and intensity inhomogeneity and a synthetic 3D image volume with Rician noise. The simulation results reveal that the proposed algorithm outperforms several state-of-the-art algorithms devised in recent past when evaluated in terms of segmentation accuracy, Dice similarity coefficient, partition coefficient, and partition entropy

Motion prediction enables simulated MR-imaging of freely moving model organisms.

Magnetic resonance tomography typically applies the Fourier transform to k-space signals repeatedly acquired from a frequency encoded spatial region of interest, therefore requiring a stationary object during scanning. Any movement of the object results in phase errors in the recorded signal, leading to deformed images, phantoms, and artifacts, since the encoded information does not originate from the intended region of the object. However, if the type and magnitude of movement is known instantaneously, the scanner or the reconstruction algorithm could be adjusted to compensate for the movement, directly allowing high quality imaging with non-stationary objects. This would be an enormous boon to studies that tie cell metabolomics to spontaneous organism behaviour, eliminating the stress otherwise necessitated by restraining measures such as anesthesia or clamping. In the present theoretical study, we use a phantom of the animal model C. elegans to examine the feasibility to automatically predict its movement and position, and to evaluate the impact of movement prediction, within a sufficiently long time horizon, on image reconstruction. For this purpose, we use automated image processing to annotate body parts in freely moving C. elegans, and predict their path of movement. We further introduce an MRI simulation platform based on bright field videos of the moving worm, combined with a stack of high resolution transmission electron microscope (TEM) slice images as virtual high resolution phantoms. A phantom provides an indication of the spatial distribution of signal-generating nuclei on a particular imaging slice. We show that adjustment of the scanning to the predicted movements strongly reduces distortions in the resulting image, opening the door for implementation in a high-resolution NMR scanner.

Feasibility Research of the New Fixation Device Compatible with Head and Neck Coil of MRI for Radiotherapy

MRI simulation images quality of head and neck coil scanning is better than that of radiotherapy surface coil, but currently the head and neck coil is not compatible with radiotherapy positioning devices. In this paper, a new fixation device is developed based on computer reverse engineering technology, which can be used in combination with head and neck coil. This article focuses on discussing the feasibility of the new device in radiotherapy. The obtained ACR phantom and Cat phantom 504 images were used to analyze MR and CT images quality assurance indicators. The dose attenuation of 6 MV photons was measured using the ionization chamber. The results showed each index met the clinical application requirements of intracranial tumor radiotherapy, thereby it can be used in intracranial tumor radiotherapy.

Real-time Magnetic Resonance-guided Liver Stereotactic Body Radiation Therapy: An Institutional Report Using a Magnetic Resonance-Linac System.

BackgroundStereotactic body radiation therapy (SBRT) is a proven and effective modality for treatment of hepatic primary and metastatic tumors. However, these lesions are challenging for planning and treatment execution due to natural anatomic changes associated with respiration. Magnetic resonance imaging (MRI) offers superior soft tissue contrast resolution and the ability for real-time image-guided treatment delivery and lesion tracking.ObjectiveTo evaluate the plan quality, treatment delivery, and tumor response of a set of liver SBRT cancer treatments delivered with magnetic resonance (MR)-guided radiotherapy on a MR-linear accelerator (MR-linac).MethodsTreatment data from 29 consecutive patients treated with SBRT were reviewed. All treatments were performed using a step and shoot technique to one or more liver lesions on an MR-linac platform. Patients received 45 to 50 Gy prescribed to at least 95% of the planning target volume (PTV) in five fractions except for two patients who received 27-30 Gy in three fractions. Computed tomography and MRI simulation were performed in the supine position prior to treatment in the free-breathing, end exhalation, and end inhalation breath-hold positions to determine patient tolerability and potential dosimetric advantages of each technique. Immobilization consisted of using anterior and posterior torso MRI receive coils embedded in a medium-sized vacuum cushion. Gating was performed using sagittal cine images acquired at 4 frames/second. Gating boundaries were defined in the three major axes to be 0.3 to 0.5 cm. An overlapping region of interest, defined as the percentage volume allowed outside the boundary for beam-on to occur, was set between 1 and 10%. The contoured target was assigned a 5-mm PTV expansion. Organs at risk constraints adopted by the American Association of Physicists in Medicine Task Group 101 were used during optimization.ResultsTwenty-nine patients, with a total of 34 lesions, successfully completed the prescribed treatment with minimal treatment breaks or delays. Twenty-one patients were treated at end-exhale, and six were treated at end-inhale. Two patients were treated using a free-breathing technique due to poor compliance with breath-hold instructions. The reported mean liver dose was 5.56 Gy (1.39 - 10.43; STD 2.85) and the reported mean liver volume receiving the prescribed threshold dose was 103.1 cm3 (2.9 - 236.6; STD 75.2). Follow-up imaging at one to 12 months post treatment confirmed either stable or decreased size of treated lesions in all but one patient. Toxicities were mild and included nausea/vomiting, abdominal pain and one case of bloody diarrhea. Four patients died due to complications from liver cirrhosis unrelated to radiation effect.ConclusionSBRT treatment using a gated technique on an MR-linac has been successfully demonstrated. Potential benefits of this modality include decreased liver dose leading to decreased toxicities. Further studies to identify the benefits and risks associated with MR-guided SBRT are necessary.

Portable simulation framework for diffusion MRI.

The numerical simulation of the diffusion MRI signal arising from complex tissue micro-structures is helpful for understanding and interpreting imaging data as well as for designing and optimizing MRI sequences. The discretization of the Bloch-Torrey equation by finite elements is a more recently developed approach for this purpose, in contrast to random walk simulations, which has a longer history. While finite element discretization is more difficult to implement than random walk simulations, the approach benefits from a long history of theoretical and numerical developments by the mathematical and engineering communities. In particular, software packages for the automated solutions of partial differential equations using finite element discretization, such as FEniCS, are undergoing active support and development. However, because diffusion MRI simulation is a relatively new application area, there is still a gap between the simulation needs of the MRI community and the available tools provided by finite element software packages. In this paper, we address two potential difficulties in using FEniCS for diffusion MRI simulation. First, we simplified software installation by the use of FEniCS containers that are completely portable across multiple platforms. Second, we provide a portable simulation framework based on Python and whose code is open source. This simulation framework can be seamlessly integrated with cloud computing resources such as Google Colaboratory notebooks working on a web browser or with Google Cloud Platform with MPI parallelization. We show examples illustrating the accuracy, the computational times, and parallel computing capabilities. The framework contributes to reproducible science and open-source software in computational diffusion MRI with the hope that it will help to speed up method developments and stimulate research collaborations.

A Team Approach to Commissioning Online MRI-Guided Adaptive Radiotherapy

Magnetic resonance imaging guided adaptive radiation therapy (MRgART) has led to a major paradigm shift resulting in personalized daily patient treatment. Implementing a online MRgART program requires commissioning the adaptive process. This process is performed primarily by medical physicists. In this work, we propose an efficient team approach to the commissioning process. This approach involves dividing different aspects of the verification process among all individuals involved in the adaptive workflow including: qualified medical physicists (QMP), clinical medical dosimetrists (CMD), radiation therapists (RT), and radiation oncologists (RO). Phantom- and volunteer-based commissioning approaches were performed. The phantom based approach is not discussed here. We have implemented a team approach to verifying the ART workflow using volunteers. The commissioning process was divided into the verification of: 1) the treatment planning system (TPS), and 2) the treatment delivery system (TDS). MRI simulations were performed by the RT and images were imported into the TPS by a CMD. Target structures were drawn by a RO. Organ at risk (OAR) structures and structures specific to the ART workflow were drawn by a CMD; including density override and optimization structures. Boolean operations were added to minimize contouring time on the machine. Clinically acceptable plans were then generated by the CMD and reviewed by the QMP; this included verifying contours, Boolean operations, correct electron density override assignment, and appropriate settings. The RO then reviewed the plan quality and approved for delivery. Patient specific QA was then performed by the QMP. The second part was verifying the TDS by simulating a patient treatment. For each volunteer; set-up, image acquisition and registration were performed by the RT and reviewed by the RO. A CMD edited OAR structures followed by RO review. The QMP applied density overrides and Boolean operations who then re-calculated dose on the daily images. The RO reviewed the plan and made a decision on whether to re-optimize. The QMP performed the optimization and a secondary check using a Monte Carlo based algorithm. Volunteers were imaged to commission the adaptive workflow over a week. Daily MR images were acquired and image segmentation was performed. The volunteer approach for commissioning took 2.5 times longer than the phantom based approach. However, the time for commissioning was halved using volunteers with a team based approach. Team members gained more confidence and were better prepared to start the MRgART program. Efficiency was increased when a team approach to commission MRgART program was adapted. This approach served as a proactive training for individuals involved in the adaptive workflow. Patients treated during the initial implementation phase of the adaptive program benefit from this approach due to the confidence and deep understanding of their radiation therapy team.

Low-Dose Radiation Spillage During Stereotactic Radiosurgery for Brain Metastases is Associated with a Decreased Incidence of New Supratentorial Brain Metastases.

Stereotactic radiosurgery (SRS) for brain metastases is increasingly used in lieu of whole brain radiation (WBRT), with reduced risk of cognitive side effects but at the expense of an increased risk of developing new lesions in non-treated parts of the brain. Here we investigated whether the low dose fall-off during SRS is associated with a lower incidence of brain metastases. We reviewed 130 patients treated with single or fractionated SRS for definitive or post-op brain metastases at our institution during 2016-2017. Patients with MRI findings of local recurrence or new brain metastases within 18 months of the first SRS were selected. WBRT before SRS was an exclusion. MRI or CT simulation for repeat treatment was fused with the initial SRS treatment plan. The new lesions and the corresponding contralateral anatomical site were outlined on the initial planning scan. The mean doses delivered to the site of these new lesions from the initial SRS treatment were tabulated. Comparison of mean doses to the new lesions and contralateral site were performed using t-test, and the difference in number of lesions at varying low dose levels was examined using Z-test for proportion. Thirty-six patients met the inclusion criteria. 24 patients (67%) had lung primary (2 squamous cell carcinoma and 19 adenocarcinoma), 6 breast (17%), 2 renal cell carcinoma (RCC), one each of esophagus, melanoma, cervix and unknown primary. 21 patients (58%) received chemotherapy during the time interval of 18 months, and 20 (56%) received immunotherapy. One hundred sixty-six new brain lesions were evaluated. Three of the 7 cerebellar lesions (43%) showed local recurrence. In patients with initial supratentorial metastasis, only 5 of total 159 lesions (3%) appeared when exposed to >6 Gy from prior SRS. 11 (6.9%), 30 (19%) and 49 (31%) lesions occurred in the >4 Gy, >2 Gy and >1 Gy dose levels, respectively. 14 lesions (9%) had no recurrences in their contralateral comparable site which received >6 Gy (p= 0.033) and 21 lesions had no recurrences in their contralateral comparable site if >4 Gy was received (13%) (p=0.063). Among the lesions that appeared after exposure to >6 Gy and >4 Gy dose, 2/5 (40%) and 5/11 (55%) lesions, respectively, were from RCC primaries. New metastases developed in sites that received less mean dose from prior treatments as compared to mean dose received in the corresponding contralateral hemisphere, where the patients did not experience recurrence. This was true for all dose levels, >1Gy through >6 Gy (all p values ≤0.02). New lesions appeared more often on the side that received lower dose of radiation compared to their contralateral site. Dose levels of >6 Gy or >4 Gy were associated with decreased incidence of new supratentorial brain metastases, less so for RCC primaries. Low dose bath to the non-target brain may be protective. Validation in larger dataset is warranted.

Quantum mechanical MRI simulations: Solving the matrix dimension problem.

We propose a solution to the matrix dimension problem in quantum mechanical simulations of MRI (magnetic resonance imaging) experiments on complex molecules. This problem is very old; it arises when Kronecker products of spin operators and spatial dynamics generators are taken-the resulting matrices are far too large for any current or future computer. However, spin and spatial operators individually have manageable dimensions, and we note here that the action by their Kronecker products on any vector may be computed without opening those products. This eliminates large matrices from the simulation process. MRI simulations for coupled spin systems of complex metabolites in three dimensions with diffusion, flow, chemical kinetics, and quantum mechanical treatment of spin relaxation are now possible. The methods described in this paper are implemented in versions 2.4 and later of the Spinach library.