Modern imaging is central to the management of most diseases and is fundamental throughout the radiation therapy workflow—from simulation and target delineation to treatment planning, verification, and post-treatment assessment using both conventional imaging and radiomics. Advances in imaging continue to enable greater precision, reduced treatment margins, and more accurate radiation dose delivery.

These developments have supported the evolution of highly targeted treatments such as stereotactic body radiotherapy (SBRT), Gamma Knife radiosurgery, particle therapy (including proton and carbon-ion therapy), and emerging applications such as FLASH radiotherapy.

The AMIRT Journal

The launch of Academia Medical Imaging and Radiation Therapy (AMIRT) reflects the rapid advancement of imaging, image-guided therapies, and radiation therapy planning and delivery. AMIRT is intended to provide a platform for high-quality, peer-reviewed research that integrates imaging science with therapeutic applications to improve patient care. As an international, peer-reviewed, open access journal, it aims to serve a broad community involved in both imaging and radiation therapy.

The journal is organized into key thematic areas:

  • Diagnostic imaging and imaging modalities
  • Radiation therapy planning and delivery
  • Imaging and radiation physics with emphasis on quality assurance
  • Interventional and image-guided procedures
  • Nuclear medicine and molecular imaging
  • Artificial intelligence and radiomics in clinical practice

Historical Foundation

The origins of these advances can be traced to early developments in medical imaging. Röntgen's discovery of X-rays in 1895 opened up a new frontier in medicine by enabling visualization of internal body structures without surgery. It soon became evident that X-rays could also be used to treat human diseases. However, widespread clinical application was not possible until the development of the modern X-ray tube by American physicist William D. Coolidge in 1913, which provided a stable, reliable, and long-lasting radiation source.

With the development of computed tomography (CT) by Hounsfield and McCormack in the early 1970s, the field of medical imaging expanded dramatically. Magnetic resonance imaging (MRI) was developed during the 1970s, with the first human images produced in 1977. MRI revolutionized soft-tissue imaging by providing superior tissue characterization and has become a cornerstone of modern medicine.

Image-Guided Radiotherapy (IGRT)

Radiation therapy has historically relied on radiographic imaging for tumor localization, often with limited accuracy. Traditionally, treatment positioning was verified weekly using port films, a practice that required substantial improvement. The development of image-guided radiotherapy (IGRT), using implanted fiducial markers detectable with X-ray imaging, marked a major advancement and has since become mainstream, fully integrating imaging into radiation delivery.

Surface-guided radiotherapy (SGRT), based on infrared- or laser-based surface mapping, is now available; however, it has inherent limitations because surface motion does not always correlate with internal anatomy. Cherenkov light imaging (CLI) is entering clinical practice and may further enhance treatment accuracy by providing real-time visualization of the radiation delivery field.

AI and Radiomics Integration

Artificial intelligence (AI) for computer-aided diagnosis, image interpretation, and molecular imaging is a rapidly expanding field that demands timely dissemination of scientific knowledge. Additional emerging areas include hybrid systems such as PET/CT, PET/MRI, MR-linac, and PET-linac platforms, which tightly integrate imaging modalities with radiation therapy delivery.

Artificial intelligence and radiomics-based image analysis are expected to further transform clinical practice. Although AI is not yet fully integrated into radiation therapy workflows, it is anticipated to play an important role across all aspects of treatment, including planning, delivery, adaptation, and quality assurance (QA). AI has the potential to represent a major milestone in QA processes by reducing analysis time, improving consistency, and ultimately enhancing treatment outcomes.

Adaptive Radiation Therapy

Real-time and near-real-time imaging now support adaptive radiation therapy (ART), transforming conventional treatment paradigms and helping to minimize toxicity by reducing target miss and allowing margin reduction to spare normal tissues. Two primary approaches to ART have emerged: X-ray-based methods, such as cone-beam CT, and MRI-based methods, such as cine MRI. Early clinical data demonstrate significant reductions in toxicity and improved patient outcomes.

Particle Therapy Imaging Needs

Despite these advances, there remains a paucity of dedicated imaging solutions for particle-beam therapy. Significant unmet imaging needs have been identified, and rapid scientific advances are being reported, including cone-beam CT, CT on rails, proton radiography, self-activated positron emission tomography, prompt Gamma imaging, acoustic imaging, and other technologies on the horizon.

Citation: Das IJ. Medical imaging at the core of modern radiation therapy. Academia Medical Imaging and Radiation Therapy 2026;1. https://doi.org/10.20935/MedImaging8241

References

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About the Author

Dr. Indra J. Das is Editor-in-Chief of Academia Medical Imaging and Radiation Therapy and an internationally acclaimed medical physicist with expertise in radiation dosimetry, dose calculation, small-field dosimetry, treatment planning, MR-linac systems, and proton-beam therapy. He is Professor at Northwestern University Feinberg School of Medicine and has held senior leadership roles at NYU Langone Medical Center, Indiana University School of Medicine, University of Pennsylvania, Fox Chase Cancer Center, and University of Massachusetts Medical Center.

Dr. Das is a Fellow of multiple professional societies (FIPEM, FAAPM, FACMP, FACR, FASTRO) and recipient of the AAPM Edith Quimby Lifetime Achievement Award, AAPM Farrington Daniel Award, and the Dr. Ramaiah Naidu Memorial Lifetime Achievement Award. With nearly five decades of experience, he has published over 500 abstracts, 300 peer-reviewed papers, and 36 books or book chapters.