The long way to dose reduction in myocardial perfusion imaging

Prone Myocardial Perfusion Imaging and Breast Attenuation: A Phantom Study

Background:

Soft tissue attenuation artifacts are the most common cause of misinterpretation in myocardial perfusion Imaging (MPI). Few studies assessing the value of prone imaging in women have been published. Breast attenuation artifacts can be present in up to 40% of the MPI studies in women.

Objectives:

This study aimed at evaluating the potential impact of prone MPI on breast attenuation, with a critical analysis of activity optimization and breast size influence.

Methods:

MPI of an Anthropomorphic Torso Phantom with silicone breast prostheses and equivalent adipose tissue was compared to a standard MPI database.

Results:

A medical qualitative and semiquantitative analysis demonstrated higher uptake in the LV anterior segments in the prone position for all injected activities. An artificial myocardium lesion was diagnosable in the right segment in all images, which shows that prone positioning would not mask a true lesion and it assists the cardiologist with a more accurate analysis. These results showed that it is possible to optimize the activity to be injected by up to 55.6% when using combined supine-prone images.

Conclusion:

Prone position has a high impact on the interpretation of MPI in female patients since it reduces the breast attenuation artifacts, and optimizes the radiation protection of the patient and all staff involved in the procedure, making it more cost-effective.

Personalized Models for Injected Activity Levels in SPECT Myocardial Perfusion Imaging

We propose a patient-specific ("personalized") approach for tailoring the injected activities to individual patients in order to achieve dose reduction in SPECT-myocardial perfusion imaging (MPI). First, we develop a strategy to determine the minimum dose levels required for each patient in a large set of clinical acquisitions (857 subjects) such that the reconstructed images are sufficiently similar to that obtained at conventional clinical dose. We then apply machine learning models to predict the required dose levels on an individual basis based on a set of patient attributes which include body measurements and various clinical variables. We demonstrate the personalized dose models for two commonly used reconstruction methods in clinical SPECT-MPI: 1) conventional filtered backprojection (FBP) with post-filtering and 2) ordered-subsets expectation-maximization (OS-EM) with corrections for attenuation, scatter and resolution, and evaluate their performance in perfusion-defect detection by using the clinical Quantitative Perfusion SPECT software package. The results indicate that the achieved dose reduction can vary greatly among individuals from their conventional clinical dose and that the personalized dose models can achieve further reduction on average compared with a global (non-patient specific) dose reduction approach. In particular, the average personalized dose level can be reduced to 58% and 54% of the full clinical dose, respectively, for FBP and OS-EM reconstruction, while without deteriorating the accuracy in perfusion-defect detection. Furthermore, with the average personalized dose further reduced to only 16% of full dose, OS-EM can still achieve a detection accuracy level comparable to that of FBP with full dose.