The future of MRI in radiation therapy: Challenges and opportunities for the MR community

Abstract

Radiation therapy is a major component of cancer treatment pathways worldwide. The main aim of this treatment is to achieve tumor control through the delivery of ionizing radiation while preserving healthy tissues for minimal radiation toxicity. Because radiation therapy relies on accurate localization of the target and surrounding tissues, imaging plays a crucial role throughout the treatment chain. In the treatment planning phase, radiological images are essential for defining target volumes and organs-at-risk, as well as providing elemental composition (e.g., electron density) information for radiation dose calculations. At treatment, onboard imaging informs patient setup and could be used to guide radiation dose placement for sites affected by motion. Imaging is also an important tool for treatment response assessment and treatment plan adaptation. MRI, with its excellent soft tissue contrast and capacity to probe functional tissue properties, holds great untapped potential for transforming treatment paradigms in radiation therapy. The MR in Radiation Therapy ISMRM Study Group was established to provide a forum within the MR community to discuss the unmet needs and fuel opportunities for further advancement of MRI for radiation therapy applications. During the summer of 2021, the study group organized its first virtual workshop, attended by a diverse international group of clinicians, scientists, and clinical physicists, to explore our predictions for the future of MRI in radiation therapy for the next 25 years. This article reviews the main findings from the event and considers the opportunities and challenges of reaching our vision for the future in this expanding field.

Keywords: ISMRM workshop; MR; future; radiation therapy.

FIGURE 1

The role of imaging for radiation therapy (RT) in conventional, state‐of‐the‐art, and future workflows. The conventional workflow (top row) begins the pre‐treatment phase by scanning the patient in a computed tomography simulator (CT‐Sim), where the patient setup for treatment is simulated using the same flat‐top couch and positioning devices. MR scans are also acquired and registered to the CT images. Target volumes are delineated manually on MRI and dose distributions are simulated and optimized using the CT images. At treatment, patient setup on a Linac system is aided by onboard cone‐beam CT (CBCT) or planar X‐ray. The patient must return daily for repeated treatment fractions over the course of several weeks. The middle row illustrates a state‐of‐the‐art RT treatment chain. This MR‐only workflow replaces CT‐Sim with MR‐Sim, reducing the burden on hospitals and patients. Artificial intelligence (AI) assisted contouring increases the efficiency and reliability of delineation (2 DELINEATION). Treatment plans are calculated using synthetic CT generated from MR‐Sim images, eliminating CT‐MRI registration errors (3 DOSE CALCULATION). At treatment, hybrid MR‐Linac systems (6 HARDWARE) will facilitate the safe reduction of treatment margins via MRI‐informed adaptation to the daily anatomy and gated deliveries for moving targets (4 IMAGE GUIDANCE). Treatment sessions are more labor intensive than conventional treatments, but could lead to fewer patient visits overall (7 REDUCING PATIENT BURDEN). In the future (bottom row), an MR‐Linac‐only workflow without a pre‐treatment workup may be possible, where planning and treatment delivery is performed within minutes on the same system. Functional and structural MR imaging could inform AI‐driven algorithms to generate plans without input from clinicians. MR‐derived biomarkers (5 QUANTITATIVE MRI) hold the potential to establish new, contourless dose planning approaches, with information now available to inform the safe delivery of high‐dose boosts to targeted regions. Treatment plans could be delivered rapidly via real‐time MR‐guided tracking to continuously irradiate the target and safely (precisely) deliver dose distributions with steep spatial gradients. The presented workflow would greatly reduce patient and clinical burden (8 IMPLEMENTATION AND DISSEMINATION).

FIGURE 2

(A,B) Radiation treatment contours for a patient with brain cancer. Here, the gross tumor volume (GTV) (red) is contoured based on visible tumor tissue on MRI (A). The clinical target volume (CTV) (green) encompasses the GTV to account for subclinical spread not visible on imaging, based on anatomy and biological considerations. The planning target volume (PTV) (magenta) is designed to account for patient setup errors and beam inaccuracies, to ensure the prescribed dose is delivered to the CTV. The CT images (B) are not suitable for contouring here but are needed to provide elemental composition (e.g., eletron density) information for dose calculations. (C,D) Radiation treatment for a patient with rectal cancer. Again, the GTV is contoured based on MRI visibility (C). The MRI is registered to a CT image, which is used to calculate and optimize the planned dose distribution illustrated by the colorwash overlay (D). Note that the CTV‐PTV margin is large compared to the brain site treatment plan because of greater setup uncertainty and intrafraction motion.