Index

Deep Learning–Driven Lorentzian Fitting for 31P Spectrum

To isolate different peaks in the phosphorus spectrum, several preprocessing steps are usually performed, and the final information about the different metabolites is extracted by multiple Lorentzian line fitting of the spectrum [1]; this least-squares fitting is prone to noise and also depends on preprocessing steps [2]. This study will investigate the use of the Lorentzian distribution generator as a known operator in a fully connected network to fit the phosphorus spectrum.

The thesis will include the following points:

Number of Lorentzian distributions required to fit the phosphorus spectrum
Comparison of the least square fit and the deep Lorentzain fit
Correlation of deep lorentzain fit peaks with tumor types

References:

Meyerspeer M, Boesch C, Cameron D, et al. 31 P magnetic resonance spectroscopy in skeletal muscle: Experts’ consensus recommendations. NMR Biomed. Published online February 10, 2020. doi:10.1002/nbm.4246
Rajput, J.R., et al.: Physics-informed conditional autoencoder approach for robust metabolic CEST MRI at 7T. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. LNCS, vol. 14227. Springer, Cham (2023)

Unsupervised detection

Evaluate computer vision and detection methods.

Pathology detection in medical images

This work will investigate applying computer vision detection techniques in medical images

Required: strong skills in

proogramming python
deep learning , training methods, pattern recognition, loss functions
medical imaging background – X-rays, CT scan images, DICOM processing
communication, scientific writing

Low Field MR Image Denoising

Automated calibration of the scan trajectory in dedicated breast CT with circle-spiral trajectory

Design of an improved automatic exposure control algorithm in mammography using breast shape and tissue information

Influence of Demographic Parameters in Radar-Based Blood Pressure Estimation

Continuous non-invasive blood pressure (BP) monitoring is a critical advancement in healthcare,
allowing for the early detection and management of hypertension one of the leading risk factors
for cardiovascular diseases [1, 2, 3]. Traditional cuff-based methods, though widely used, provide
only intermittent readings and fail to capture BP fluctuations throughout the day. To address
this limitation, machine learning models leveraging radar-based skin displacement signals have
been proposed for continuous BP estimation. However, the influence of demographic factors
such as age, gender, height, and weight on prediction accuracy remains insufficiently explored.
Existing studies primarily focus on machine learning models trained without explicit demographic
considerations. While some methods integrate features such as Pulse Transit Time
(PTT) from Photoplethysmography (PPG) sensors [4, 5, 6, 7], fewer approaches investigate the
role of demographic characteristics in radar-based BP monitoring [8]. This research aims to systematically
analyze the impact of demographic features on BP prediction using a Transformer-based
deep learning model.
The current methodology relies on pretraining a model on a large-scale arterial blood pressure
dataset (PulseDB [9]) and fine-tuning it with radar-based BP measurements [3] from human
participants. An ablation study is conducted to assess the contribution of individual demographic
features to model performance. Evaluation metrics include the Mean Absolute Error
(MAE) and Standard Deviation (STD), as well as compliance with established medical standards
such as those from the Association for the Advancement of Medical Instrumentation
(AAMI) [10] and the British Hypertension Society (BHS) [11].

The main objectives of this thesis include:

Analyze the impact of demographic characteristics on BP prediction accuracy.
Compare the performance of two distinct model architectures: a feed-forward neural
network and a transformer network.

To achieve these objectives, the following proposed steps will be undertaken:

Analyze and Compare the distribution of the demographic features in both datasets
retrain both model architectures on a large-scale arterial blood pressure dataset
Finetune both model architectures on the radar dataset
Evaluate and assess the model’s performance against set criteria.

References
[1] X. Xing, Z. Ma, M. Zhang, Y. Zhou, W. Dong, and M. Song, “An Unobtrusive and
Calibration-free Blood Pressure Estimation Method using Photoplethysmography and Biometrics,”
vol. 9, no. 1, p. 8611.
[2] D. Barvik, M. Cerny, M. Penhaker, and N. Noury, “Noninvasive Continuous Blood Pressure
Estimation From Pulse Transit Time: A Review of the Calibration Models,” vol. 15,
pp. 138–151.
[3] N. Vysotskaya, C. Will, L. Servadei, N. Maul, C. Mandl, M. Nau, J. Harnisch, and A. Maier,
“Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility
Study,” vol. 23, no. 8, p. 4111.
[4] S. González, W.-T. Hsieh, and T. P.-C. Chen, “A benchmark for machine-learning based
non-invasive blood pressure estimation using photoplethysmogram,” vol. 10, no. 1, p. 149.
[5] R. Mukkamala, J.-O. Hahn, O. T. Inan, L. K. Mestha, C.-S. Kim, H. Töreyin, and S. Kyal,
“Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice,”
vol. 62, no. 8, pp. 1879–1901.
[6] S. Maqsood, S. Xu, M. Springer, and R. Mohawesh, “A Benchmark Study of Machine
Learning for Analysis of Signal Feature Extraction Techniques for Blood Pressure Estimation
Using Photoplethysmography (PPG),” vol. 9, pp. 138817–138833.
[7] C. El-Hajj and P. Kyriacou, “A review of machine learning techniques in photoplethysmography
for the non-invasive cuff-less measurement of blood pressure,” vol. 58, p. 101870.
[8] N. Vysotskaya, N. Maul, A. Fusco, S. Hazra, J. Harnisch, T. Arias-Vergara, and A. Maier,
“Transforming Cardiovascular Health: A Transformer-Based Approach to Continuous,
Non-Invasive Blood Pressure Estimation via Radar Sensing,” in ICASSP 2024 – 2024
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
pp. 2041–2045.
[9] W. Wang, P. Mohseni, K. L. Kilgore, and L. Najafizadeh, “PulseDB: A large, cleaned
dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation
methods,” vol. 4, p. 1090854.
[10] G. S. Stergiou, B. Alpert, S. Mieke, R. Asmar, N. Atkins, S. Eckert, G. Frick, B. Friedman,
T. Graßl, T. Ichikawa, J. P. Ioannidis, P. Lacy, R. McManus, A. Murray, M. Myers,
P. Palatini, G. Parati, D. Quinn, J. Sarkis, A. Shennan, T. Usuda, J. Wang, C. O. Wu, and
E. O’Brien, “A Universal Standard for the Validation of Blood Pressure Measuring Devices:
Association for the Advancement of Medical Instrumentation/European Society of Hypertension/
International Organization for Standardization (AAMI/ESH/ISO) Collaboration
Statement,” vol. 71, no. 3, pp. 368–374.
[11] E. O’Brien, “Blood pressure measuring devices: Recommendations of the European Society
of Hypertension,” vol. 322, no. 7285, pp. 531–536.