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Review
. 2023 Jan 27:13:1117874.
doi: 10.3389/fonc.2023.1117874. eCollection 2023.

MRI-LINAC: A transformative technology in radiation oncology

John Ng  1 , Fabiana Gregucci  1   2 , Ryan T Pennell  1 , Himanshu Nagar  1 , Encouse B Golden  1 , Jonathan P S Knisely  1 , Nicholas J Sanfilippo  1 , Silvia C Formenti  1
Affiliations
Review

MRI-LINAC: A transformative technology in radiation oncology

John Ng et al. Front Oncol. .

Abstract

Advances in radiotherapy technologies have enabled more precise target guidance, improved treatment verification, and greater control and versatility in radiation delivery. Amongst the recent novel technologies, Magnetic Resonance Imaging (MRI) guided radiotherapy (MRgRT) may hold the greatest potential to improve the therapeutic gains of image-guided delivery of radiation dose. The ability of the MRI linear accelerator (LINAC) to image tumors and organs with on-table MRI, to manage organ motion and dose delivery in real-time, and to adapt the radiotherapy plan on the day of treatment while the patient is on the table are major advances relative to current conventional radiation treatments. These advanced techniques demand efficient coordination and communication between members of the treatment team. MRgRT could fundamentally transform the radiotherapy delivery process within radiation oncology centers through the reorganization of the patient and treatment team workflow process. However, the MRgRT technology currently is limited by accessibility due to the cost of capital investment and the time and personnel allocation needed for each fractional treatment and the unclear clinical benefit compared to conventional radiotherapy platforms. As the technology evolves and becomes more widely available, we present the case that MRgRT has the potential to become a widely utilized treatment platform and transform the radiation oncology treatment process just as earlier disruptive radiation therapy technologies have done.

Keywords: MR-guided radiation therapy; MRI; external beam radiotherapy; image-guided radiation therapy; medical physics; radiation therapy technology.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
An example of clinical MRI-Linac images utilized for therapy guidance. The panel on the left shows a patient receiving prone breast irradiation with an MRI image in the axial plane. The same patient with a sagittal plane image. The red contour depicts the lumpectomy surgical cavity which serves as the clinical target volume.
Figure 2
Figure 2
An example of clinical MRI-Linac images utilized for inter-fractional management. Comparison of anatomy seen on the day of simulation (upper left panel) and days of treatment (panels labeled Fractions 1 to 5 respectively) for a pancreatic cancer patient treated at our institution. In each panel, the target volume, stomach, and bowel anatomy are contoured.
Figure 3
Figure 3
An example of clinical MRI-Linac images utilized for intra-fractional management. The panel on the left shows a patient receiving pancreatic radiotherapy while imaged with a deep inspiratory breathe hold. The panel of the right shows the same patient breathing freely. The red contour depicts the tracking contour and the yellow contour depicts the treatment envelope boundary.
Figure 4
Figure 4
A representative example of the care pathway implemented during a MRgRT treatment. Each blue box represents a distinct task that a clinical treatment team member must complete before triggering the next task to be done, demonstrated by the arrow diagram.
Figure 5
Figure 5
An example of real time imaging during a prostate MRgRT treatment. The blue contour depicts the prostate target tracking contour. The red contour depicts the gated treatment envelope boundary.
Figure 6
Figure 6
An example of a generated synthetic-CT (sCT) for prostate radiotherapy. The left panel shows an axial MR image acquired with a MRI-Linac during a MRI simulation. The right panel shows the corresponding sCT generated based on that MRI image by converting MR intensities into Hounsfield Units.
See this image and copyright information in PMC

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