Acquisition and Frequency Spectroscopic Evaluation of Broadband Clinical Ultrasound Raw Data for Liver Cirrhosis and Focal Pathologies Using Neural Networks for Tissue and Pathology Differentiation
Phase: NA
Conditions: Artificial Intelligence, Ultrasonography, Elasticity Imaging Techniques, Liver Diseases, Metastasis to Liver
Sponsor: Technische Universität Dresden

Study protocol for the acquisition and frequency spectroscopic evaluation of broadband clinical 
ultrasound raw data for tissue and pathology differentiation using neural networks 
Department of Medicine I, University Hospital Carl Gustav Carus at TU Dresden  
 
 
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Acquisition and frequency spectroscopic evaluation of broadband 
clinical ultrasound raw data for liver cirrhosis and focal pathologies 
using neural networks for tissue and pathology differentiation 
 
 
 
Protocol 
 
 
 
 
Clinical Project Management: 
Moritz Herzog MD 
 
Participating doctors: 
Maia Arsova MD, Nicole Kampfrath MD, Antje Urbig MD, Theresa Thieme, Prof. 
Jakob Kather, Prof. Jochen Hampe 
 
 
Department of Medicine I, University Hospital and Else Kröner Fresenius Center 
for Digital Health at the Faculty of Medicine Carl Gustav Carus at TU Dresden  
 
Fetscherstraße 74, 01307 Dresden  
 
 
 
 
 
 
 
Contact: 
Moritz Herzog, M.D. 
Moritz.herzog@ukdd.de 
 
 
 
Dresden, 01.03.2024 
 
 
 

Study protocol for the acquisition and frequency spectroscopic evaluation of broadband clinical 
ultrasound raw data for tissue and pathology differentiation using neural networks 
Department of Medicine I, University Hospital Carl Gustav Carus at TU Dresden  
 
 
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Table of contents 
 
TABLE OF CONTENTS ................................................................................................................ - 2 - 
1. 
SUMMARY ........................................................................................................................ - 3 - 
BACKGROUND .............................................................................................................................. - 3 - 
GOAL ......................................................................................................................................... - 3 - 
DESIGN ....................................................................................................................................... - 3 - 
2. 
OBJECTIVE OF THE STUDY .................................................................................................. - 3 - 
GENERAL OBJECTIVE ....................................................................................................................... - 4 - 
INITIAL MORE SPECIFIC HYPOTHESES: .................................................................................................. - 4 - 
3. 
STUDY DESIGN .................................................................................................................. - 4 - 
TYPE OF STUDY ............................................................................................................................. - 4 - 
SAMPLE SIZE PLANNING ................................................................................................................... - 4 - 
4. 
TEST METHOD ................................................................................................................... - 5 - 
STUDY POPULATION ....................................................................................................................... - 5 - 
INCLUSION CRITERIA ....................................................................................................................... - 5 - 
EXCLUSION CRITERIA ...................................................................................................................... - 5 - 
COURSE OF STUDY ......................................................................................................................... - 5 - 
RISKS .......................................................................................................................................... - 5 - 
EFFECT/BENEFITS .......................................................................................................................... - 6 - 
ABANDONMENT CRITERIA ............................................................................................................... - 6 - 
5. 
ETHICAL AND LEGAL ASPECTS ............................................................................................ - 6 - 
6. 
PRIVACY ............................................................................................................................ - 7 - 
7. 
PLANNED RECOVERY ......................................................................................................... - 7 - 
8. 
REFERENCES ...................................................................................................................... - 7 - 
 
 
 

Study protocol for the acquisition and frequency spectroscopic evaluation of broadband clinical 
ultrasound raw data for tissue and pathology differentiation using neural networks 
Department of Medicine I, University Hospital Carl Gustav Carus at TU Dresden  
 
 
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1. 
Summary 
 
Background 
Ultrasound examinations have become an integral part of everyday clinical practice. 
Ultrasound imaging impresses with a combination of availability, freedom from radiation and 
high diagnostic gain. So far, however, it is above all the diagnostic gain that must be critically 
questioned, as ultrasound examinations are strongly dependent on the examiner. Especially 
for the evaluation of ultrasound images, a wealth of experience is necessary. A more objective 
evaluation is offered by the technique of quantitative ultrasound (QUS). A quantitative 
evaluation of so-called RF data (radiofrequency data) enables a more accurate diagnosis that 
is independent of the assessor. The gain in information is similar to the change from black-
and-white television to color television. Physically, this is based on the individual, frequency-
dependent sound reflection of tissues and pathologies depending on their biomechanical 
properties. This makes it possible, for example, to automate the segmentation of ultrasound 
images or the early detection of hepatic steatosis. Previous systems are based on narrowband 
piezo transducers, which is why a broadband analysis of tissue properties in the clinical setting 
has not yet been carried out. The approach is thus like promising ex-vivo studies, such as Gare 
et al., 2022,  Nguyen et al., 2021 and    Gangeh     et al., 2014 . 
 
Goal 
Within the framework of basic research, multicenter prospective, anonymized, broadband raw 
ultrasound data are to be collected for the first time in a common database. The basis for this 
database is the cooperation of several German centers (previously planned: University 
Hospital Dresden, Municipal Hospital Dresden, University Hospital Leipzig, University Hospital 
Halle). These data will be used to evaluate a trained neural network, which can classify liver 
fibrosis based on RF data. 
 
To collect the necessary amounts of data, further centers are to be included in the course of 
the process. Thus, the study builds on previous analyses from retrospective data from the 
University Hospital Dresden (ethics application BO-EK-534122022) and is intended to test 
some of the promising preliminary results in a prospective approach. 
 
Design 
The study is designed as a prospective, multicenter, experimental study. Female and male 
patients will be included who will receive a sonographic examination with reliable ground 
truth (depending on the local standard procedures of the center e.g. biopsy, contrast-
enhanced ultrasound [CEUS], elastography, CT, MRI). In order not to create any additional 
effort, only patients with planned further clinical examinations are included. The data will then 
be used for validation by the already trained neural networks. 
The prerequisites for participation are existing consent, the age of majority and no minor 
interventions on the liver within the last 14 days or within the last 14 days. Exclusion from 
major intervention within the last 6 months (see also 4. Test ).  
2. 
Objective of the study 
 

Study protocol for the acquisition and frequency spectroscopic evaluation of broadband clinical 
ultrasound raw data for tissue and pathology differentiation using neural networks 
Department of Medicine I, University Hospital Carl Gustav Carus at TU Dresden  
 
 
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The aim of the study is to collect liver-specific ultrasound raw data and images, collect them 
in the form of a multicenter, anonymized, local database and subsequently evaluate them 
spectroscopes using methods like convolutional neural networks (CNN). The networks were 
trained on retrospective data from a separate ethics application (BO-EK-534122022) and are 
to be validated in these data primarily, but not exclusively. Should a sufficient amount of data 
arise to enable sufficient training, the use of the data for the training of the Networks would 
also be a goal. 
 
General objective 
Demonstrate that frequency spectroscopic analysis of raw ultrasound (RF) data enables the 
detection, classification, and differentiation of different tissue-specific entities. 
 
Initial more specific hypotheses: 
1. The evaluation of RF data of the liver by neural networks with liver elastography results 
as a reference standard enables the detection and classification of degrees of steatosis, 
fibrosis and cirrhosis. 
2. Neural networks trained on RF data are better than networks trained on B-scans at 
detecting degrees of steatosis, fibrosis, and cirrhosis as measured in AUC. 
3. The diagnostic certainty of the degree of steatosis, fibrosis and cirrhosis of a neural 
network trained on RF data depends on the bandwidth of the US signal provided 
4. The evaluation of RF data of the liver by neural networks with histology results as a 
reference standard enables the detection and classification of hepatic cavities. 
5. The evaluation of RF data of the liver by neural networks with results of contrast 
ultrasonography as a reference standard enables the detection and classification of 
hepatic cavities. 
6. Neural networks trained on RF data are better than networks trained on B-scans in 
classifying liver cavities. 
3. 
Study design 
 
The study is designed as a prospective, multicenter, experimental study. Female and male 
patients undergoing sonographic examination with biopsy, CEUS or elastography will be 
included. It is important to ensure that the corresponding examinations would also be carried 
out independently of the study so as not to create any additional effort for the patient. The 
only difference is the additional systemic acquisition of raw ultrasound data, which is not 
always stored in conventional examinations.   
 
Type of study 
• prospective, multicenter, experimental study 
 
Sample size planning 
For the validation of the networks, a sample size of at least 200 patients is targeted. After 
completion of the data collection, each degree of fibrosis should be present about 20 times in 
order to enable a valid performance of the network for each degree of fibrosis. If this is not 
achieved, data collection will continue for 100 additional patients and then the distribution 
will be reviewed again. This is repeated until the target minimum number is reached. For focal 

Study protocol for the acquisition and frequency spectroscopic evaluation of broadband clinical 
ultrasound raw data for tissue and pathology differentiation using neural networks 
Department of Medicine I, University Hospital Carl Gustav Carus at TU Dresden  
 
 
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lesions we plan to include 50 patients per lesion. If this criterion is not met, data acquisition 
continuous. 
4. 
Test Method 
 
Study population 
Female and male subjects over 18 years of age who are undergoing biopsy, CEUS, or 
elastography as part of an ultrasound scan. 
 
Inclusion Criteria 
• Age over 18 years 
• Subject's capacity to give consent 
• Signed declaration of consent by the subject 
 
Exclusion criteria 
• minor intervention (e.g. biopsy) performed less than a week ago in the same area 
• CEUS carried out less than a day ago 
• Major intervention on the liver (e.g. resection) in the history 
 
Course of study 
The required RF data is recorded shortly before the actual investigations, which provide us 
with the required ground truth. The data acquisition is carried out with approved devices from 
everyday clinical use. Specifically, Clarius' C3HD3 US handheld models are used as a low-
frequency abdominal transducer and Clarius' L15HD3 as a high-frequency linear transducer. 
The data from the two transducers are combined in the evaluation to simulate broadband 
recordings. In the so-called RF mode, both B-scans and ultrasonic raw data are recorded in the 
form of RF data. The raw data is only recorded within a predefined region of interest (ROI). 
The data is then temporarily stored locally on a mobile device as an interface to the ultrasound 
device and manually copied from there to a local network drive of the University Hospital 
Dresden. The mobile devices are not connected to the Internet and are protected from 
unauthorized access by a password. Access to the network drive is only permitted to persons 
named in the ethics application. On the local drive, the patient age, patient gender and the 
diagnosis from the doctor's letter are then assigned to the images. Subsequently, this 
combined data set is anonymized and stored for evaluation. The test persons are assigned a 
number that does not allow any conclusions to be drawn about their identity and is only used 
for assignment within the anonymous data set.  
Additional centers will record their data in the same form. Once anonymization and local 
storage have been completed, the data is manually inserted into the Dresden dataset via local 
data carriers. 
 
Risks 
Due to the ultrasound systems used, no particular risks are to be expected. All devices used in 
the study are certified according to European safety standards for electrical equipment and 
approved for clinical use. The examinations carried out as part of the study are similar to 
normal clinical ultrasound examinations. Since these are carried out anyway, an additional 

Study protocol for the acquisition and frequency spectroscopic evaluation of broadband clinical 
ultrasound raw data for tissue and pathology differentiation using neural networks 
Department of Medicine I, University Hospital Carl Gustav Carus at TU Dresden  
 
 
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burden for the patients is inherently excluded. In addition, the ultrasound examination is 
considered non-ionizing and neither physically nor psychologically stressful. The total 
additional time required for the subject is approximately 5 minutes (2.5 minutes per 
ultrasound transducer).  
 
Effect/Benefits 
The raw data collected by the study can provide much more information about the examined 
tissue than the B-scans previously used for evaluation. In addition to information on reflected 
sound intensity, this RF data also contains frequency information. This makes it possible to 
create new methods for the classification and segmentation of different tissues or entities in 
ultrasound. In the future, this could replace examinations such as biopsies, CEUS and 
elastography with classic ultrasound examinations. This would spare patients physical strain 
and risks and make the everyday life of the treating physicians easier. Furthermore, it is 
possible to color-code different tissues in the ultrasound image, which would make them 
easier to differentiate. This would increase the accuracy of ultrasound scans, especially for 
less experienced examiners. 
The anonymized raw data sets of several clinics are to be merged into a public database. This 
will enable further follow-up studies based on the data collected. 
 
Abandonment Criteria  
The study will be stopped immediately at the request of the subject. At the same time, no 
events are to be expected from the study itself that would cause an abortion. Subjects will be 
informed that they can terminate their participation in the study without giving reasons and 
without any disadvantages. 
5. 
Ethical and legal aspects 
 
The planning, implementation and exploitation of the project and the project results shall be 
carried out in accordance with the relevant ethical and scientific standards. The applicants 
thus commit themselves to comply with the requirements of the Memorandum on 
Safeguarding Good Scientific Practice of the German Research Foundation (DFG), the 
Declaration of Helsinki and the State and Federal Data Protection Acts, as well as the General 
Data Protection Regulation of the European Union (EU GDPR). In addition, the clinically active 
colleagues involved follow the professional regulations for physicians of the responsible State 
Medical Association in the current versions. The curriculum was submitted to the Ethics 
Committee of the University Hospital Dresden for consultation before the start of the study. 
The names of the test persons and all other confidential information are subject to the medical 
confidentiality of the treating physicians and the provisions of the General Data Protection 
Regulation or the Federal Data Protection Laws (GDPR or BDSG).  
The planned study is not a study according to MPG or AMG. Before participating in the study, 
interested participants will be informed in detail about the study by the study staff in writing 
and orally and will be given the opportunity to ask questions about the study. With the study 
information, participants will receive the contact details of a study staff member so that they 
can contact them at any time. Participants will be informed that they can terminate their 
participation in the study without giving any reason and without any disadvantages.  

Study protocol for the acquisition and frequency spectroscopic evaluation of broadband clinical 
ultrasound raw data for tissue and pathology differentiation using neural networks 
Department of Medicine I, University Hospital Carl Gustav Carus at TU Dresden  
 
 
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The study is insured for up to €10,000,000 through public liability insurance. The 
corresponding proof from Marsh Medical Consulting GmbH is enclosed with the application.  
 
6. 
Privacy 
The procedure was coordinated with the data protection officer of the University Hospital (see 
DSB consultation result). No concerns were raised with regard to data protection. 
 
Once enrolled in the study, subjects will receive a study participation ID that does not contain 
any personal identifying information. Since the subject's identity is no longer relevant to the 
study after the combination of ultrasound data and the clinical parameters such as age, gender 
and the diagnosis from the doctor's letter, the name of the subject is not noted in connection 
with the study ID or the data collected. Only the information sheets contain the names of the 
test subjects. These are only kept in paper form and stored in a lockable filing cabinet in the 
EKFZ together with other study-related forms.  
 
All image data recorded during the study will be stored anonymously in local network servers 
of the University Hospital Dresden. The data collected during the study will be used exclusively 
for the purposes indicated and will not be shared with third parties. However, the local raw 
data will be stored for a maximum of ten years until the end of the evaluation, in accordance 
with the retention period specified by the German Research Foundation (DFG) or in 
accordance with the legally prescribed archiving period for clinical studies. After the end of 
the retention period, the files are deleted from all data carriers as well as data sheets 
(questionnaires, declarations of consent, etc.) are destroyed via the clinic's internal data 
protection-compliant document destruction.  
 
All project staff and partners involved are subject to the European General Data Protection 
Regulation.  
 
7. 
Planned recovery 
 
The central benefit of the study is the generation of a raw clinical ultrasound data set and the 
testing of new examination methods, especially on the basis of artificial intelligence. The plan 
is to use the data in a medical doctoral thesis and to create a public ultrasound raw database. 
In addition, the results will be presented at recognized national and international conferences. 
The results will be published in an internationally recognized journal.  
 
8. 
References  
 
Gangeh, M. J., Sadeghi-Naini, A., Diu, M., Tadayyon, H., Kamel, M. S., & Czarnota, G. J. (2014). 
Categorizing extent of tumor cell death response to cancer therapy using quantitative 
ultrasound spectroscopy and maximum mean discrepancy. IEEE Transactions on Medical 
Imaging, 33(6), 1390–1400. https://doi.org/10.1109/TMI.2014.2312254 

Study protocol for the acquisition and frequency spectroscopic evaluation of broadband clinical 
ultrasound raw data for tissue and pathology differentiation using neural networks 
Department of Medicine I, University Hospital Carl Gustav Carus at TU Dresden  
 
 
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Gare, G. R., Li, J., Joshi, R., Magar, R., Vaze, M. P., Yousefpour, M., Rodriguez, R. L., & Galeotti, 
J. M. (2022). W-Net: Dense and diagnostic semantic segmentation of subcutaneous and 
breast tissue in ultrasound images by incorporating ultrasound RF waveform data. 
Medical Image Analysis, 76, 102326. https://doi.org/10.1016/J.MEDIA.2021.102326 
Mento, F., Soldati, G., Prediletto, R., Demi, M., & Demi, L. (2020). Quantitative Lung Ultrasound 
Spectroscopy Applied to the Diagnosis of Pulmonary Fibrosis: The First Clinical Study. IEEE 
Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(11), 2265–2273. 
https://doi.org/10.1109/TUFFC.2020.3012289 
Nguyen, T. N., Podkowa, A. S., Park, T. H., Miller, R. J., Do, M. N., & Oelze, M. L. (2021). Use of 
a convolutional neural network and quantitative ultrasound for diagnosis of fatty liver. 
Ultrasound 
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