Magnetic Resonance Angiography (MRA) has become an essential MR contrast for imaging and evaluation of vascular anatomy and related diseases. MRA acquisitions are typically ordered for vascular interventions, whereas in typical scenarios, MRA sequences can be absent in the patient scans. This motivates the need for a technique that generates inexistent MRA from existing MR multi-contrast, which could be a valuable tool in retrospective subject evaluations and imaging studies. In this paper, we present a generative adversarial network (GAN) based technique to generate MRA from T1-weighted and T2-weighted MRI images, for the first time to our knowledge. To better model the representation of vessels which the MRA inherently highlights, we design a loss term dedicated to a faithful reproduction of vascularities. To that end, we incorporate steerable filter responses of the generated and reference images inside a Huber function loss term. Extending the wellestablished generator-discriminator architecture based on the recent PatchGAN model with the addition of steerable filter loss, the proposed steerable GAN (sGAN) method is evaluated on the large public database IXI. Experimental results show that the sGAN outperforms the baseline GAN method in terms of an overlap score with similar PSNR values, while it leads to improved visual perceptual quality.
Purpose
The purpose of this paper is to investigate the relative influences of technical and functional quality levels of service quality and patient satisfaction. In this context, the healthcare service quality and the factors affecting customer satisfaction were evaluated using the grey relational analysis (GRA) method.
Design/methodology/approach
This is a survey-based study which involves 15 patients in a dialysis center, so the GRA is applied to clarify the uncertainty on service quality level with a limited number of patients without any statistical distribution. In order to reveal whether service quality and customer satisfaction are two different structures, a GRA model is built with ten different quality factors.
Findings
Results show that each quality factor has a different effect on the quality of service. Another important finding is that service quality and customer satisfaction are different structures for customers.
Practical implications
The results enable healthcare managers to understand the importance of patient care and the importance of service quality if they want to facilitate their use of their expectations in related factors.
Originality/value
The study is the first in terms of the application of GRA models in a private health institution operating in Turkey. Successful implementation of the GRA method allows a reasonable decision to be made with a limited number of data at hand. It is considered that the method can be used successfully in other health institutions in the Turkish Health System.
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