Accelerated Reliability Growth Test for Magnetic Resonance Imaging System Using Time-of-Flight Three-Dimensional Pulse Sequence

Abstract

A magnetic resonance imaging (MRI) system is a complex, high cost, and long-life product. It is a widely known fact that performing a system reliability test of a MRI system during the development phase is a challenging task. The major challenges include sample size, high test cost, and long test duration. This paper introduces a novel approach to perform a MRI system reliability test in a reasonably acceptable time with one sample size. Our approach is based on an accelerated reliability growth test, which consists of test cycle made of a very high-energy time-of-flight three-dimensional (TOF3D) pulse sequence representing an actual hospital usage scenario. First, we construct a nominal day usage scenario based on actual data collected from an MRI system used inside the hospital. Then, we calculate the life-time stress based on a usage scenario. Finally, we develop an accelerated reliability growth test cycle based on a TOF3D pulse sequence that exerts highest vibration energy on the gradient coil and MRI system. We use a vibration energy model to map the life-time stress and reduce the test duration from 537 to 55 days. We use a Crow AMSAA plot to demonstrate that system design reaches its useful life after crossing the infant mortality phase.

A highly accelerated stress reliability growth test method

“…Secondly, Equation (19) gave the relationship between 𝑌 𝑖 , 𝑇 𝑖 , and 𝜀 𝑖 . Then, by minimizing Equation (20), the estimation of parameters can be presented as Equation (21). Finally, using Equation ( 22), the values of parameters 𝑐 and 𝑑 can be respectively obtained as 𝑐 = −3.85, 𝑑 = 3500.…”

“…In Ruiz et al., [ 20 ] a Bayesian framework was proposed to analyze the ALT data in the reliability growth. In Anand et al., [ 21 ] based on the ARGT, an approach was introduced to perform the magnetic resonance imaging system reliability test. In Ruiz et al., [ 22 ] a statistical model was proposed to utilize degradation data for system reliability prediction in an accelerated reliability growth program.…”