• cardiac MRI
• medical imaging
• Optimization

In magnetic resonance imaging (MRI), optimization has often been seen as a reduction in examination times that are usually considerably longer compared to the other medical imaging modalities. In cardiac MRI, patient’s condition can negatively affect the image quality leading to the challenges in interpretation of the examination. In such cases optimization of the examination aimed to the difficulties arising from the patient’s condition may improve the image quality, support the workflow and in some cases even improve the patient’s comfort during the examination.

Cardiac magnetic resonance imaging is being used to diagnose and follow a variety of cardiovascular diseases, for example inflammation or ischemia of heart muscle and structural heart defects (Roifman et al. 2018, 133; Raman et al. 2022, 1–2; Longére et al. 2023, 1). In addition, cardiac MRI is also being used to assess the patient’s need for treatment with cardiac implantable device, such as a pacemaker (Raman et al. 2022, 3).

Despite being a versatile tool for diagnosing and following cardiovascular diseases, cardiac MRI has its limitations that are often tied to the patient’s condition and breath-holding capability. When successful, cardiac MRI gives valuable and precise information of the heart and surrounding blood vessels, but the image quality is often impaired with the patients suffering from arrhythmias or difficulty of holding their breath for extended periods of time.

The deterioration of image quality often comes down to the technical aspects of MRI imaging and its sensitivity to motion. While imaging the heart there is a mixture of different types of movement mainly from the breathing and beating heart. The motion from breathing is mainly being controlled by giving breathing instructions to the patient and gathering the imaging information while patient holds their breath. Similarly, the cardiac motion is managed by detecting the patterns in the patient’s ECG and timing the imaging acquisition according to this information. However, if the patient is unable to hold their breath repeatedly or suffers from arrhythmia, it can be quite challenging to time the imaging acquisition accurately to avoid motion and to achieve an adequate image quality. (Ferreira et al. 2013, 14.) Besides motion-related artefacts, patients with cardiac implantable devices pose another challenge to the image quality since the device alters the magnetic field locally near the heart often leading to severe impairment of the image quality (Vuorinen et al. 2023, 1230).

Advantages of the patient-by-patient optimization of the examination

By combining the multiprofessional workshops, descriptive literature review and radiographers’ thematic interview, the thesis revealed several ways to optimize the cardiac MRI examination considering the individual needs of the patient.

The thematic interview revealed the hesitance many of the radiographers felt while facing challenges with poor image quality when the only solution was to manually modify the imaging parameters on the go. According to the radiographers, the manual adaptation of the examination often prolonged examination times which affected workflow and, in some cases, created scheduling challenges throughout the day and reduced patient’s comfort during the examination.

With that being said, the aim of the patient-by-patient optimization is not to speed up the examination but to recognize the individual optimization needs of a patient and to offer readily available technical means for radiographers to implement during the examination process reducing the need of manual modification of the imaging parameters. The reduced need to the manual optimization also reduces the quality variations since the level of optimization and implementation of different technical optimization tools are not so strongly related to the radiographer’s individual skill level.

Implementing patient-by-patient optimization requires multiprofessional teamwork

The patient-by-patient optimization consists of three vital steps:

1. the early recognition of the optimization needs
2. pre-optimized scanning protocols
3. a structured approach for the most typical optimization scenarios

Early recognition of the optimization needs allows radiographers to choose the right approach to the optimization which can include choosing the right pre-optimized imaging protocol or having the time to consult the radiologist if needed without complicating the workflow.

The pre-optimized scanning protocols allow radiographers to use appropriate technical tools to support image quality. The structured approach ensures that these tools are used correctly and are supported by other optimization methods, such as a proper patient positioning in a case of the pacemaker or making sure that the breathing instructions are manageable for the patient.

To successfully implement the optimization process it is vital to engage not only the radiographers, but also medical physicist and radiologists. By implementing the optimization model in multiprofessional collaboration, every aspect of the examination process can be considered. This includes both technical implementation of the optimized tools in the imaging protocols and the development of procedures and guidelines that support the clinical workflow as well as continuous learning.

With early recognition, a structured approach and optimized scanning protocols the workflow can be enhanced to support radiographers, ensure sufficient image quality, and the most importantly, achieve the best possible outcome for each patient.

The article is based on a Finnish-language thesis available in the Theseus database: Strandman-Berg, Sanna (2025): Sydämen magneettitutkimuksen potilaskohtainen optimointi Meilahden sairaalan magneettiyksikössä.

References

Ferreira, P. F.; Gatehouse, P. D.; Mohiaddin, R. H. & Firmin, D. N. 2013. Cardiovascular magnetic resonance artefacts. Journal of Cardiovascular Magnetic Resonance. Vol. 15, No 2013, Article 41. Cited 14.12.2023. https://doi.org/10.1186/1532-429X-15-41.

Longère, B.; Abassebay, N.; Gkizas, C.; Hennicaux, J.; Simeone, A.; Rodriguez Musso, A.; Carpentier, P.; Coisne, A.; Pang, J.; Schmidt, M.; Toupin, S.; Montaigne, D. & Pontana, F. 2023. A new compressed sensing cine cardiac MRI sequence with free-breathing real-time acquisition and fully automated motion-correction: A comprehensive evaluation. Diagnostic and Interventional Imaging. Vol. 104, No 11, 538–546. Cited 16.9.2023. https://doi.org/10.1016/j.diii.2023.06.005.

Raman, S. V.; Markl, M.; Patel, A. R.; Bryant, J.; Allen, B. D.; Plein, S. & Seiberlich, N. 2022. 30-minute CMR for common clinical indications: a Society of Cardiovascular Magnetic Resonance white paper. Journal of Cardiovascular Magnetic Resonance. Vol. 24, No 1, Article 13. Cited 8.3.2024. https://doi.org/10.1186/s12968-022-00844-6.

Roifman, I.; Paterson, I.; Jimenez-Juan, L.; Friedrich, M. G.; Howarth, A. G.; Wintersperger, B. J.; Thavendiranathan, P.; White, J. A. & Connelly, K. A. 2018. The state of cardiovascular magnetic resonance imaging in Canada: Results from the CanSCMR pan-canadian survey. Canadian Journal of Cardiology Vol. 34. No, 3, 333–336. Cited 14.12.2023. https://doi.org/10.1016/j.cjca.2017.12.026.

Vuorinen, A. M.; Lehmonen, L.; Karvonen, J.; Holmström, M.; Kivistö, S. & Kaasalainen, T. 2023. Reducing cardiac implantable electronic device–induced artefacts in cardiac magnetic resonance imaging. European Radiology. Vol. 33. No 2, 1229–1242. Cited 16.9.2023. https://doi.org/10.1007/s00330-022-09059-w.

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