In a review of the literature, this author discusses the viability of artificial intelligence (AI), parallel imaging, compressed sensing and simultaneous multi-slice excitation for improving the scan times and use of magnetic resonance imaging (MRI) to facilitate CyberKnife treatment.
CyberKnife radiosurgery treatment has proven its efficacy in the nearly 30 years since the prototype of the device first appeared at Stanford University in 1994. The first fully robotic radiotherapy device, CyberKnife delivers highly precise radiation therapy targeting tumors from different angles while minimizing radiation exposure to other tissues in the body.
CyberKnife has demonstrated an unusually high success rate in treating cancerous and non-cancerous tumors. For example, one study found that CyberKnife was a safe and efficient option for treating elderly patients with brain metastases.1 Another analysis demonstrated that intermediate-risk and low-risk prostate cancer patients had respective five-year disease-free survival rates of 97.1 percent and 97.3 percent after treatment with CyberKnife.2
The key to the treatment’s success is CyberKnife’s ability to deliver accurate, clear, and timely images. CyberKnife processing platforms require high-resolution 3D isovoxel imaging for measuring the distance to the tumor and how to approach it. Magnetic resonance imaging (MRI) and computed tomography (CT) scans are required for patients undergoing CyberKnife treatment. Accordingly, it is essential to manage MRI scan times without sacrificing image quality due to patient movement and spatial resolution loss.
While CyberKnife and MRI technology have revolutionized the way diseases are diagnosed and treated, they also come with a fair share of challenges for MRI technologists. Challenges include operating complex technology and ensuring patient comfort and safety during MRI scans. For example, MRI machines are noisy, and patients must lie still for extended periods of time, which can be uncomfortable and even claustrophobic for some patients. MRI technologists must work closely with patients to ensure that they are comfortable and relaxed during the scan. With regard to patient safety, MRI machines use strong magnetic fields that can cause metal objects to move or heat up, which can be dangerous for patients with certain medical devices or implants. It is important for MRI technologists to identify potential hazards and take appropriate measures to ensure patient safety. Indeed, MRI technologists play a crucial role in the effective and safe use of these technologies.
Advantages of CyberKnife Over Other Radiation Treatments
CyberKnife has a number of advantages over traditional radiation treatments. While other radiation treatments may damage the surrounding normal tissues, the CyberKnife enables precise targeting of the radiation dose from different angles of the tumor and splitting of the dose for targeted treatment. For many patients with prostate cancer, the performance of CyberKnife treatment allows the number of treatments, or fractions, to vary along with the radiation dose the patient receives during a given treatment.3
In addition, each fraction includes a higher radiation dose than is used with traditional radiation, but fewer fractions are necessary. For instance, CyberKnife treatment typically includes four or five fractions versus the 30 to 40 fractions that traditional radiotherapy entails. The CyberKnife treatment takes place over a one-to-two-week course while standard radiation therapy takes between eight and 10 weeks to complete.3
As I noted earlier, patients must have CT and MRI exams prior to CyberKnife treatment. CyberKnife requires three-dimensional iso images of T2-weighted and T1-weighted post-contrast images, meaning that the X, Y, and Z axis of the images acquired should have the same value. Both MRI and CT images are fused on the CyberKnife software by the radiotherapist or physicist to measure the depth of the lesion to be treated without damaging the surrounding normal cells and structure.
The time required for the necessary MRI scans ranges from 20 to 30 minutes due to the coverage area and high-resolution requirements. From the technologist’s perspective, the challenge in performing these scans is to cut the time down to roughly 10 minutes to improve patient comfort without compromising image quality.
A Closer Look at Alternative Technologies to Improve MRI Scan Times
While MRI has become a powerful imaging tool, its chief drawback, historically, has been the speed, or lack thereof, with which scans are performed. The acquisition process needs to be calibrated to the patient, and the scan must capture all the necessary data in order to generate quality images for interpretation.
There are options available to reduce the time needed for the imaging exams that the CyberKnife platform requires. For example, artificial intelligence (AI) for accelerated MRI “has seen tremendous progress” in recent years, and has considerable potential for improving MRI scan times further in the future.4
(Editor's note: For related content, see "Study Shows Benefits of AI for Prostate Cancer Detection on Multiparametric MRI" and "FDA Clears RadNet's Updated AI Software for Prostate MRI.")
As researchers pointed out in a 2020 review, promising approaches include deep learning methods for reconstructing undersampled MRI data and generating high-resolution from low-resolution data.4 The authors indicated that parallel imaging and compressed sensing are major developments contributing to faster MRI scan times.4
They noted that generalized auto calibrating partial parallel acquisition (GRAPPA) and sensitivity encoding (SENSE) are the most common forms of parallel imaging.4 The GRAPPA technique is based on k-space, which can be used to shorten the scan time, while the SENSE technique can garner the same result by using multiple receiver coils in parallel scan time in Fourier imaging. These techniques can also help realize a reduction in susceptibility artifacts.
Despite these advantages, these parallel imaging techniques can contribute to compromised spatial resolution, and image quality can be affected by even minor patient movements.
Compressed sensing MRI can produce faster scans and better resolution simultaneously. For instance, one review of the technology outlines how compressed sensing accelerates MRI acquisition by acquiring fewer data through undersampling of k-space.5 Noting that the limited research thus far assessing the effects of compressed sensing on MRI has been largely positive, the review authors from the Albert Einstein School of Medicine said compressed sensing “ … has the potential to mitigate the time-intensiveness of MRI.”5
Compressed sensing is founded on the premise of reconstructing an image from an incompletely filled k-space. It is complementary to parallel imaging and requires sparsity in a transform domain and random undersampling. Unlike parallel imaging, compressed sensing collects no complementary information.
In MRI, compressed sensing relies on the Fourier coefficients, or k-space samples, to make accurate reconstructions from a small subset of k-space. There are a number of different compressed sensing techniques that technologists can use. For example, k-t space FOCal underdetermined system solver (k-t FOCUSS) locates a low-resolution estimate of a sparse signal before this solution is pruned to a sparse signal representation. This technique relies on the previous iteration solution to implement the pruning process by scaling the current solution. It is worth noting that a reasonable low-resolution estimate is a key requirement when using the k-t FOCUSS technique as this is necessary to provide the extra constraint required.
Another method, simultaneous multi-slice (SMS) excitation, reportedly offers significant reduction in image acquisition time with little signal-to-noise penalty, combining complex radiofrequency (RF) pulses with parallel imaging coil arrays to acquire multiple sections along the z-axis simultaneously.6 Modern coil arrays typically include just a few coil elements in the z-direction, which equates to less than optimal coil sensitivity differences along the z-axis. This means the simultaneously excited slices should be placed at least 25 millimeters to 30 millimeters apart.
In traditional MRI, a magnetic field gradient is used to selectively excite one slice of tissue at a time, with a subsequent echo signal being received from that slice. This process is then repeated for each slice that needs to be imaged, leading to a longer imaging time. However, with SMS excitation, multiple slices are excited simultaneously using parallel RF pulses, which allow for faster imaging times as multiple slices can be imaged in parallel. This means SMS can improve the signal-to-noise ratio (SNR) of the images since the total excitation energy is distributed among the multiple slices.
Magnetic resonance imaging is commonly used in CyberKnife treatment for several purposes.
• Targeting tumors. The MRI images provide detailed information about the tumor and surrounding tissues, which facilitates accurate targeting of the tumor with the CyberKnife.
• Treatment planning. The MRI images are used to create a 3D map of the tumor and surrounding tissues, which can be beneficial with planning of the CyberKnife treatment.
• Monitoring. During the CyberKnife treatment, one can utilize MRI images to monitor the tumor response to the radiation and adjust the treatment as needed.
Overall, the use of MRI images in CyberKnife treatments can help to improve treatment accuracy but suitable acceleration techniques could be utilized to obtain high-quality images in a shorter scan time.
Ultimately, CyberKnife has the potential to be a powerful treatment tool for a number of cancers. Still, much remains to be learned about the most optimal use of this technology. Continued assessment of complementing MRI techniques and protocols may contribute to improved understanding on the most optimal use of this technology.
Mr. Joseph is a clinical specialist with more than 15 years of experience in the field of MRI. He is currently a radiology manager and MRI clinical specialist based in Ireland.
1. Acker G, Hashemi SM, Fuellhase J, et al. Efficacy and safety of CyberKnife radiosurgery in elderly patients with brain metastases: a retrospective clinical evaluation. Radiat Oncol. 2020;15(1):225.
2. Meier RM, Bloch DA, Cotrutz C, et al. Mulicenter trial of stereotactic body radiation therapy for low- and intermediate-risk prostate cancer: survival and toxicity endpoints. Int J Radiat Oncol Biol Phys. 2018;102(2):296-303.
3. Misher C, Millar LB. Cyberknife for prostate cancer. OncoLink. Available at: https://www.oncolink.org/cancers/prostate/treatments/cyberknife-for-prostate-cancer . Reviewed March 31, 2023. Accessed May 3, 2023.
4. Johnson PM, Recht MP, Knoll F. Improving the speed of MRI with artificial intelligence. Semin Musculoskelet Radiol. 2020;24(1):12-20.
5. Jaspan ON, Fleysher R, Lipton ML. Compressed sensing MRI: a review of the clinical literature. Br J Radiol. 2015;88(1056):20150487. doi. 10.1259/bjr.20150487. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4984938/#:~:text=Compressed%20sensing%20(CS)%20is%20a,the%20time%2Dintensiveness%20of%20MRI. Published October 14, 2015. Accessed May 3, 2023.
6. Geethanath S, Reddy R, Konar AS, et al. Compressed sensing MRI: a review. Crit Rev Biomed Eng. 2013;41(3):183-204.