Dr Emre Kopanoglu

Dr Emre Kopanoglu


School of Psychology

+44 (0)29 2251 0256
CUBRIC 1.016, School of Psychology, Park Place, Cardiff, CF10 3AT

Research summary

Magnetic Resonance Imaging is a powerful imaging modality with high soft-tissue contrast, inherent safety due to the lack of ionizing radiation, and diagnostically sufficient signal-to-noise ratio. My research aims to improve diagnostic image quality as well as patient comfort and safety in MRI, and involves signal/image processing, computer modelling, and novel imaging hardware.

With many MRI scans lasting several minutes, patient motion is a severe problem. If uncorrected in real-time, many motion patterns change the imaged volume, and therefore make imaging data inconsistent and necessitate re-scanning the patient. On the one hand, at lower field strengths, real-time (prospective) motion correction techniques can adapt the imaging volume in real-time. However, lower field strengths mean lower signal-to-noise ratio and contrast-to-noise ratio, i.e. lower image quality. On the other hand, ultra-high field (UHF, >3T) MRI offers many benefits in terms of image quality and contrast. Unfortunately, UHF MRI suffers from undesired contrast variations across the image. While such variations can be compensated for using tailored radiofrequency pulses and multi-channel transmit (parallel-transmit) systems, designing such pulses takes from upwards of several seconds to a few minutes with many algorithms. Therefore, real-time motion correction has not been possible yet with such pulses. My current research focuses on designing parallel-transmit pulses in real-time.


  • 2012: PhD in Electrical and Electronics  Engineering. Bilkent University, Ankara, Turkey. Novel Techniques Regarding Specific Absorption Rate and Field of View  Reduction in Magnetic Resonance Imaging
  • 2006: BSc in Electrical and Electronics  Engineering. Bilkent University, Ankara, Turkey.


  • 2017 – present: Lecturer in Psychology. Cardiff University, Cardiff, UK.
  • 2015 – 2017: Senior Research Scientist. Aselsan Research Center, Ankara, Turkey.
  • 2012 – 2015: Post-Doctoral Associate. Radiology and Biomedical Imaging. Yale University, New Haven, CT, USA.
  • 2006 – 2012: Research and Teaching Assistant. Electrical and Electronics Engineering. Bilkent University, Ankara, Turkey.
  • 2006 – 2006: Undergraduate Teaching Assistant. Electrical and Electronics Engineering. Bilkent University, Ankara, Turkey.








Research topics and related papers

Pulse design


In Magnetic Resonance Imaging, a pulse is transmitted to the subject via transmitter coils to generate an echo and this process is called excitation. Excitation affects quality of the  acquired image, and in most cases, a spatially uniform excitation is desired so that any contrast variations in the image is solely due to tissue variations.

Higher magnetic field strengths offer higher signal-to-noise-ratio and contrast-to-noise-ratio, which can be  leveraged towards higher resolution images (albeit at the expense of scan time). However, as the field strength increases, excitation starts to become spatially non-uniform. Brain imaging at 3T shows mild central brightening. At 7T, the non-uniformity becomes more severe, leading to darkening around the temporal lobes and affecting diagnostic quality of the images.

To compensate these effects, special pulses that can improve the uniformity of the excitation are designed. However, such pulses are quite long, and therefore, impractical. Transmitting pulses separately from multiple transmit coils reduces the required pulse duration to achieve the same effect.

Transmitting a pulse to the body causes radiofrequency power deposition in the body as well, which in turn leads to heating. While transmitting individual pulses from multiple coils in parallel (parallel-transmit: pTx) helps address excitation non-uniformity, it also affects the spatial variation of the radiofrequency power deposition. To minimize tissue heating, strict safety limits are imposed during pulse design.

With MRI scans lasting for several minutes, patient motion is a looming reality. When patient motion does not alter the imaged volume and is tracked during the scan, effects on the image can be corrected via post-processing. However, when the volume is altered, data becomes inconsistent, necessitating a new scan.

It is possible and common  to reduce patient motion via the use of sedatives, especially with uncooperative patients including paediatric patients and patients with  Parkinson’s or dementia. However, sedatives are invasive, and may lead to adverse side effects. Furthermore, sedatives affect the outcomes in certain  cases such as in functional MRI.

It is therefore desirable to compensate for effects of motion by adapting the imaging volume during the  scan. At lower field strengths (<3T), a single transmit coil is used, and it leads to a uniform excitation. In this case, real-time motion correction requires updating the gradient fields and the carrier frequency, which can be done in real-time. For 3T brain imaging, updating the gradient fields and the carrier frequency will still retain the same imaging volume. However, 3T brain images show mild central brightening, the location of which will be slightly affected by patient motion, leading to minor artefacts in the final image. At 7T, however, we have more intense contrast variations, and multiple transmit coils that have individually tailored pulses.
My research involves determining the effect of patient motion on excitation fidelity when custom pulses are used with parallel-transmit coils at 7T. I am interested in how much excitation quality changes, if safety is impaired, and how we can compensate such changes. For this purpose, I employ

  • signal processing techniques to design parallel-transmit pulses that yield an improved excitation uniformity while satisfying safety constraints
  • simulation software to evaluate what the fields generated inside a patient’s body look like and how these fields change with motion
  • experiments to evaluate real-life performance of pulses and confirm simulated findings.

Compressive  sensing


While images stored as PNG and BMP  files are lossless, many of us use the JPEG format to store our images in our daily lives, as the latter yields smaller files with little perceptible loss in image quality.This is because most images are “sparse” in a transformation domain. Sparser images require less coefficients/samples to represent the information, and therefore, are smaller in size.

MRI images are also compressible due to sparsity (in a transformation domain). But sparsity has a more important  implication for MRI. If we can represent our images with less samples, we can avoid acquiring the unnecessary samples in the first place (undersampling),  leading to shorter scans.

Compressive Sensing (CS) is a signal  processing technique that allows reconstructing images from fewer measurements  without losing too much in terms of image quality, and is one of the approaches used in MRI to reconstruct high-quality images from undersampled data. However, CS reconstruction is computationally more expensive, and therefore, take longer to reconstruct the images. Therefore, much research effort has been on  developing faster reconstruction algorithms for CS-MRI.

In many applications, multiple images of the same anatomy are acquired with different contrasts. This is because different contrasts provide complementary information about tissue structure. To acquire images with different contrasts, the parameters of an imaging protocol are adjusted accordingly.

While different contrast images have significantly different properties, the underlying anatomy is the same. In other words, tissue boundaries in these images are at the same locations. This similarity can be exploited in multi-contrast MRI by jointly reconstructing the images.

Because joint reconstruction uses more data for the reconstruction of each image than available for individual reconstruction due to data sharing across contrasts, higher quality images can be acquired. However, such data sharing may lead to leakage of unique features across contrasts. Thus, it is important for a method to prevent such leakage artefacts; and do this automatically, without the need  for the user to intervene in reconstruction.
My research involves developing a reconstruction algorithm for joint reconstruction of multi-contrast images while ensuring that no features leak across images. I am also interested in investigating the balance between contrasts in terms of the  amount of data. Acquiring some contrasts necessitate longer scans due to the specific combination of imaging parameters while other contrasts can be acquired much faster. In such cases, it might be beneficial to highly accelerate slower protocols, and acquire more data in that time using the faster sequences to increase the total amount of acquired data and improve  overall image quality across contrasts.

Research group