Search

The past decade has seen the increasing integration of magnetic resonance (MR) imaging into radiation therapy (RT). This growth can be contributed to multiple factors, including hardware and software advances that have allowed the acquisition of high-resolution volumetric data of RT patients in their treatment position (also known as MR simulation) and the development of methods to image and quantify tissue function and response to therapy. More recently, the advent of MR-guided radiation therapy (MRgRT) - achieved through the integration of MR imaging systems and linear accelerators - has further accelerated this trend. As MR imaging in RT techniques and technologies, such as MRgRT, gain regulatory approval worldwide, these systems will begin to propagate beyond tertiary care academic medical centers and into more community-based health systems and hospitals, creating new opportunities to provide advanced treatment options to a broader patient population. Accompanying these opportunities are unique challenges related to their adaptation, adoption, and use including modification of hardware and software to meet the unique and distinct demands of MR imaging in RT, the need for standardization of imaging techniques and protocols, education of the broader RT community (particularly in regards to MR safety) as well as the need to continue and support research, and development in this space. In response to this, an ad hoc committee of the American Association of Physicists in Medicine (AAPM) was formed to identify the unmet needs, roadblocks, and opportunities within this space. The purpose of this document is to report on the major findings and recommendations identified. Importantly, the provided recommendations represent the consensus opinions of the committee's membership, which were submitted in the committee's report to the AAPM Board of Directors. In addition, AAPM ad hoc committee reports differ from AAPM task group reports in that ad hoc committee reports are neither reviewed nor ultimately approved by the committee's parent groups, including at the council and executive committee level. Thus, the recommendations given in this summary should not be construed as being endorsed by or official recommendations from the AAPM.

Purpose: We evaluate the performance of a deformable image registration (DIR) software package in registering abdominal magnetic resonance images (MRIs) and then develop a mechanical modeling method to mitigate detected DIR uncertainties. Materials and Methods: Three evaluation metrics, namely mean displacement to agreement (MDA), DICE similarity coefficient (DSC), and standard deviation of Jacobian determinants (STD‐JD), are used to assess the multi‐modality (MM), contour‐consistency (CC), and image‐intensity (II)‐based DIR algorithms in the MIM software package, as well as an in‐house developed, contour matching‐based finite element method (CM‐FEM). Furthermore, we develop a hybrid FEM registration technique to modify the displacement vector field of each MIM registration. The MIM and FEM registrations were evaluated on MRIs obtained from 10 abdominal cancer patients. One‐tailed Wilcoxon‐Mann‐Whitney (WMW) tests were conducted to compare the MIM registrations with their FEM modifications. Results: For the registrations performed with the MIM‐CC, MIM‐MM, MIM‐II, and CM‐FEM algorithms, their average MDAs are 0.62 ± 0.27, 2.39 ± 1.30, 3.07 ± 2.42, 1.04 ± 0.72 mm, and average DSCs are 0.94 ± 0.03, 0.80 ± 0.12, 0.77 ± 0.15, 0.90 ± 0.11, respectively. The p‐values of the WMW tests between the MIM registrations and their FEM modifications are less than 0.0084 for STD‐JDs and greater than 0.87 for MDA and DSC. Conclusions: Among the three MIM DIR algorithms, MIM‐CC shows the smallest errors in terms of MDA and DSC but exhibits significant Jacobian uncertainties in the interior regions of abdominal organs. The hybrid FEM technique effectively mitigates the Jacobian uncertainties in these regions. [ABSTRACT FROM AUTHOR]

Purpose: MRI‐guided adaptive radiation therapy (MRgART), particularly daily online adaptive replanning (OLAR) can substantially improve radiation therapy delivery, however, it can be labor‐intensive and time‐consuming. Currently, the decision to perform OLAR for a treatment fraction is determined subjectively. In this work, we develop a machine learning algorithm based on structural similarity index measure (SSIM) and change in entropy to quickly and objectively determine whether OLAR is necessary for a daily MRI set. Methods: A total of 109 daily MRI sets acquired on a 1.5T MR‐Linac during MRgART for 22 pancreatic cancer patients each treated with five fractions were retrospectively analyzed. For each daily MRI set, OLAR and reposition (No‐OLAR) plans were created and the superior plan with the daily fraction determined per clinical dose‐volume criteria. SSIM and entropy maps were extracted from each daily MRI set, with respect to its reference (e.g., dry‐run) MRI in the region enclosed by 50‐100% isodose surfaces. A total of six common features were extracted from SSIM maps. Pearson's rank correlation coefficient was utilized to rule out redundant SSIM features. A t‐test was used to determine significant SSIM features which were combined with the change in entropy to develop anensemble machine classifier with fivefold cross validation. The performance of the classifier was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve. Results: A machine learning classifier model using two SSIM features (mean and full width at half maximum) and change in entropy was determined to be able to significantly discriminate between No‐OLAR and OLAR groups. The obtained machine learning ensemble classifier can predict OLAR necessity with a cross validated AUC of 0.93. Misclassification was found primarily for No‐OLAR cases with dosimetric plan quality closely comparable to the corresponding OLAR plans, thus, are not a major practical concern. Conclusion: A machine learning classifier based on simple first‐order image features, that is, SSIM features and change in entropy, was developed to determine when OLAR is necessary for a daily MRI set with practical acceptable prediction accuracy. This classifier may be implemented in the MRgART process to automatically and objectively determine if OLAR is required following daily MRI. [ABSTRACT FROM AUTHOR]

Purpose: Flexibility in slice prescription is critical for precise motion monitoring during MR‐guided therapies. Adding more slices to improve spatial coverage during rapid 2D cine imaging often hampers temporal resolution. This work describes a framework to simultaneously acquire multiple arbitrarily oriented slices which share a common frequency encoding axis. This framework allows for higher frame rates for a given number of slices compared to conventional interleaved‐slice multi‐orientation cine imaging. Theory and Methods: A framework to calculate zeroth gradient moments to be played out between sequentially excited slices with multiple orientations is described here. Experiments were performed in phantom, and in vivo in the head/neck and abdomen of patients. Results: Images arbitrarily rotated relative to one another were successfully obtained in phantom and in vivo. Simultaneous multi‐orientation (SMO) images were also acquired with additional in‐plane acceleration to demonstrate the capability of this method to rapidly image objects moving with physiological motion. Conclusions: The technical feasibility of the generalized SMO imaging framework was tested in this study. It shows promise for continued development for motion monitoring during MR‐guided therapies. [ABSTRACT FROM AUTHOR]

Purpose: Magnetic Resonance (MR)‐guided online adaptive radiation therapy (MRgOART), enabled with MR‐Linac, has potential to revolutionize radiation therapy. MRgOART is a complex process. This work is to introduce a comprehensive end‐to‐end quality assurance (QA) workflow in routine clinic for MRgOART with a high‐magnetic‐field MR‐Linac. Materials and Method: The major components in MRgOART with a high‐magnetic field MR‐Linac (Unity, Elekta) include: (1) a patient record and verification (R&V) system (e.g., Mosaiq, Elekta), (2) a treatment session manager, (3) an offline treatment planning system (TPS), (4) an online adaptive TPS, (5) a 1.5T MRI scanner, (6) an 7MV Linac, (7) an MV imaging controller (MVIC), and (8) ArtQA: software for plan data consistency checking and secondary dose calculation. Our end‐to‐end QA workflow was designed to test the performance and connectivity of all these components by transferring, adapting and delivering a specifically designed five‐beam plan on a phantom. Beams 1–4 were designed to check Multi‐Leaves Collimator (MLC) position shift based on rigid image registration in TPS, while beam 5 was used to check daily radiation output based on image pixel factor of MV image of the field. The workflow is initiated in the R&V system and followed by acquiring and registering daily MRI of the phantom, checking isocenter shift, performing online adaptive replanning, checking plan integrity and secondary 3D dose calculation, delivering the plan while acquiring MV imaging using MVIC, acquiring real‐time images of the phantom, and checking the delivering parameters with ArtQA. Results: It takes 10 min to finish the entire end‐to‐end QA workflow. The workflow has detected communication problems, permitted resolution prior to setting up patients for MRgOART. Up to 0.9 mm discrepancies in isocenter shift based on the image registration were detected. ArtQA performed the secondary 3D dose calculation, verified the plan integrity as well as the MR‐MV isocenter alignment values in TPS. The MLC shapes of beam 1–4 in all adaptive plans were conformal to the target and agreed with MV images. The variation of daily output was within ±2.0%. Conclusions: The comprehensive end‐to‐end QA workflow can efficiently check the performance and communication between different components in MRgOART and has been successfully implemented for daily clinical practice. [ABSTRACT FROM AUTHOR]