For many types of cancer, early detection and treatment improves mortality. In the case of prostate cancer (PCa), however, the morbidity/mortality of definitive therapy for non-aggressive PCa may exceed the morbidity/mortality associated with the natural course of the disease. Therefore, it is paramount to focus on detection of the more aggressive PCa cases and implement a cost-effective screening and treatment program.

In the past few years, multiparametric MRI (mp-MRI) has become the standard of care for diagnosis of aggressive PCa. Although mp-MRI guided biopsy preferentially detects more aggressive PCa relative to traditional systematic biopsy, several studies have demonstrated that mp-MRI does miss aggressive PCa. Moreover, with about 60% false positive detections, mp-MRI causes many biopsies which are in retrospect unnecessary. Together with the high cost of MRI, the geographic variability in the availability of MRI systems, and the poor reproducibility of the results in low-volume clinical centers, the current situation underlines a strong clinical need for a more cost-effective, accurate, and accessible alternative to detect aggressive PCa.

This project brings together basic scientists, clinicians and industry ultrasound engineers to develop multiparametric-ultrasound (mp-US) as such an alternative technique. The main component of mp-US will be microbubble-based contrast enhanced ultrasound imaging (CEUS), based on novel sub-harmonic imaging techniques as well as contrast ultrasound dispersion imaging (CUDI). In addition, tissue viscoelasticity will be assessed by ultrasound elastography. Altogether, the extracted ultrasound parameters will provide a powerful set of the key imaging markers of PCa. These parameters will be optimally combined in a multiparametric fashion by machine learning in order to achieve accurate PCa diagnosis. While 2D ultrasound has inherent limitations in accuracy and clinical workflow, 3D ultrasound is nowadays emerging as a valuable technology for PCa diagnostics.

Our ultimate goal is to develop 3D mp-US techniques that can selectively identify aggressive PCa and prove that the accuracy of mp-US is non-inferior to mp-MRI. Thus, at the end of this project our academic industrial partnership will deliver an accurate, 3D mp-US system ready for clinical deployment. As our mp-US technology is based on ultrasound imaging, it will be cost effective for clinicians as well as for patients and, therefore, perfect for use in low resource neighborhoods or even underdeveloped countries.

With this general aim, this specific PhD position will focus on further developments of the CUDI technology and the design of a machine-learning framework combining complementary ultrasound parameters that reflect those changes in the microvascular architecture and the mechanical properties of tissue which are related to cancer.

This project is financed by the National Institute of Health (NIH) in the USA. The position is available within the BM/d research lab, part of the Signal Processing Systems (SPS) group (Electrical Engineering department, TU/e), and it involves tight collaborations with the University Medical Center in Amsterdam (location AMC), Thomas Jefferson University (USA), and 3 key industrial partners, namely, GE, Eigen, and Angiogenesis Analytics.

Biomedical diagnostic (BM/d) research lab at TU/e

The BM/d lab is devoted to model-based quantitative analysis of medical images and bio-signals, with the goal of improving patient care and management. The lab, which counts over 30 PhD students both technical and clinical, has a long tradition in ultrasound imaging and 15-year experience in prostate cancer diagnostics. This has led to the development of CUDI and the foundation of the startup company Angiogenesis Analytics.

We are seeking a highly motivated master graduate with a strong background and interest in the analysis and interpretation of ultrasound images, meet the following requirements:

• Master degree in Electrical Engineering, Applied Physics, or Biomedical Engineering;
• Background in medical image processing and analysis;
• Background in machine learning;
• Background in ultrasound physics and imaging;
• Excellent education track record;
• Good analytical skills;
• Affinity for working in an interdisciplinary and highly international environment;
• Proficiency in English.

• A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
• A full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months.
• To develop your teaching skills, you will spend 10% of your employment on teaching tasks.
• To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students (PROOF program).
• A gross monthly salary and benefits (such as a pension scheme, pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• Should you come from abroad and comply with certain conditions, you can make use of the so-called '30% facility', which permits you not to pay tax on 30% of your salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.