MRI scan transforming into a CT image with AI neural connections

MRI-Only Prostate IMRT: Can AI Deliver Accurate Synthetic CT Images?

Reese Marlowe in Health & Wellness December 2025 • 4 min read.

"A deep learning U-net model shows promise in generating synthetic CT images from MRI for prostate cancer radiotherapy, potentially streamlining treatment planning and reducing radiation exposure."

Magnetic Resonance Imaging (MRI) has become a cornerstone in radiotherapy, offering superior soft tissue contrast compared to Computed Tomography (CT). This makes MRI ideal for delineating organs. Traditionally, MRI serves as a secondary tool, with images rigidly aligned to simulation CT scans for tumor and organ definition.

However, a fully MRI-guided radiotherapy workflow holds the potential to eliminate image registration uncertainties, reduce radiation exposure, and enhance patient comfort. The key lies in deriving CT-equivalent information, such as electron density distribution, directly from MR images for accurate radiation dose calculation. This data is commonly referred to as pseudo-CT or synthetic-CT (S-CT) images.

Researchers have been exploring various methods to generate S-CT images, including statistical modeling, multi-atlas deformable image registration, random forest-based approaches, and deep learning techniques. This article delves into a recent study investigating the feasibility of training a deep convolutional neural network (ConvNet) from scratch to generate S-CT images from conventional T2-weighted MR images, specifically for prostate cancer treatment planning.

U-Net: A Deep Learning Solution for Synthetic CT Generation

MRI scan transforming into a CT image with AI neural connections

The study, recently published, details the development and evaluation of a U-net based deep learning model for generating synthetic CT images from MRI data in prostate cancer patients. Here’s a breakdown of their approach:

Researchers used paired CT and T2-weighted MR images from 51 prostate cancer patients. They divided the data into a training set (36 patients) and a testing set (15 patients). The training data was augmented using artificial deformations to improve the model's robustness.

U-net Architecture: A 2-dimensional U-net, featuring 23 convolutional layers and 25.29 million trainable parameters, was employed. The U-net learns a nonlinear function to map MR image slices to corresponding S-CT slices.
Evaluation Metrics: The mean absolute error (MAE) of Hounsfield Units (HU) between true CT and S-CT images was used to assess HU estimation accuracy.
IMRT Planning: Intensity-modulated radiation therapy (IMRT) plans, prescribing a dose of 79.2Gy to the planning target volume (PTV), were created using the true CT images. These plans were then recalculated using the S-CT images.
Dosimetric Comparison: The dose matrices from the true CT and S-CT-based plans were compared using gamma index analysis and absolute point dose discrepancy measurements.

The U-net model was trained from scratch using a GP100-GPU over 58.67 hours. Generating a new S-CT volume image took only 3.84 to 7.65 seconds. The mean MAE within the body was 29.96 ± 4.87 HU. Gamma pass rates (1%/1mm and 2%/2mm) exceeded 98.03% and 99.36%, respectively. DVH parameter discrepancies were less than 0.87%, and the maximum point dose discrepancy within the PTV was less than 1.01%.

The Future of MRI-Only Radiotherapy

The study demonstrates the potential of deep learning, specifically the U-net architecture, to generate accurate S-CT images from conventional MR images in seconds. This opens doors for streamlined MRI-only radiotherapy workflows in prostate cancer treatment.

While the U-net model shows promising results, the researchers acknowledge areas for further improvement. These include incorporating more anatomical information into the training data, exploring advanced network architectures like 3D U-nets, and utilizing adversarial training techniques to generate more realistic images.

Despite these limitations, this research represents a significant step toward realizing the full potential of MRI-only radiotherapy, offering a faster, more accurate, and less invasive approach to prostate cancer treatment planning.

About this Article -

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Everything You Need To Know

What exactly is the U-net, and how does it relate to prostate cancer treatment?

The U-net is a deep learning model used to generate synthetic CT (S-CT) images from MRI data, specifically for prostate cancer treatment. It is a type of convolutional neural network (ConvNet) that excels at image segmentation and reconstruction tasks. In this context, the U-net learns to map MR image slices to corresponding S-CT slices. This is significant because it allows for the creation of CT-equivalent information from MRI images, which is crucial for accurate radiation dose calculation in Intensity-modulated radiation therapy (IMRT).

What are Synthetic CT (S-CT) images, and why are they important?

Synthetic CT (S-CT) images are CT-like images generated from MRI data. They are crucial because they provide information about electron density distribution, which is essential for accurate radiation dose calculation in radiotherapy. Traditionally, MRI is used alongside CT, but S-CT images allow for a fully MRI-guided radiotherapy workflow, potentially eliminating image registration uncertainties and reducing radiation exposure for patients. The process involves a deep learning model, like the U-net, which is trained to convert MRI data into S-CT images.

Why is MRI used, and how does the U-net help in its application?

MRI (Magnetic Resonance Imaging) offers superior soft tissue contrast compared to CT (Computed Tomography), making it ideal for delineating organs. However, MRI alone cannot provide the electron density information needed for radiation dose calculation. In prostate cancer radiotherapy, MRI is increasingly used for treatment planning. The U-net model enables the use of MRI by generating S-CT images which allows for accurate radiation dose calculation by the IMRT planning system. Thus, it streamlines the process and reduces radiation exposure to the patient.

How does the U-net fit into the process of Intensity-modulated radiation therapy (IMRT)?

Intensity-modulated radiation therapy (IMRT) is a sophisticated radiotherapy technique that uses CT scans to deliver precise radiation doses to the tumor while sparing surrounding healthy tissues. The U-net model plays a crucial role in IMRT by generating synthetic CT images from MRI data. This allows for creating IMRT plans directly from MRI scans, potentially eliminating the need for CT scans in the treatment planning process. These IMRT plans are then recalculated using the S-CT images to ensure that the radiation dose is delivered accurately.

Can you explain the complete process of using the U-net for prostate cancer treatment?

The process involves several steps. First, a U-net deep learning model is trained using paired CT and T2-weighted MR images. This training process involves using a training set of images and optimizing the model's ability to map MR images to S-CT images. Once trained, the U-net can generate S-CT images from new MRI data within seconds. These generated S-CT images are then used for IMRT planning and dose calculation. The results are then evaluated using metrics such as mean absolute error (MAE) and gamma pass rates to ensure accuracy and precision in radiation delivery. The model uses 23 convolutional layers and 25.29 million trainable parameters.