A5030122428- Advanced Radiotherapy Techniques
- Medical Imaging Techniques and Applications
- Advanced X-ray and CT Imaging
- Radiomics and Machine Learning in Medical Imaging
- Radiation Therapy and Dosimetry
- Medical Imaging and Analysis
- Lung Cancer Diagnosis and Treatment
- Medical Image Segmentation Techniques
- COVID-19 diagnosis using AI
- AI in cancer detection
- Cerebrovascular and Carotid Artery Diseases
- COVID-19 Clinical Research Studies
- Radiation Dose and Imaging
- Surgical Simulation and Training
- Digital Radiography and Breast Imaging
- Anatomy and Medical Technology
- Retinal Imaging and Analysis
- Nuclear Physics and Applications
- MRI in cancer diagnosis
- CNS Lymphoma Diagnosis and Treatment
- Lymphoma Diagnosis and Treatment
- Head and Neck Cancer Studies
- Long-Term Effects of COVID-19
- Radiation Detection and Scintillator Technologies
- Augmented Reality Applications
Magna Graecia University
2015-2024
Karlsruhe Institute of Technology
2023-2024
German Cancer Research Center
2020
Heidelberg University
2020
Zero to Three
2020
DKFZ-ZMBH Alliance
2020
Massachusetts General Hospital
2011-2015
Athinoula A. Martinos Center for Biomedical Imaging
2015
Harvard University
2011-2013
University of Wollongong
2012
Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number diversity variety segmentation algorithms, consensus lacking as which automated method works best for certain applications. Segmentation challenges are good approach unbiased evaluation comparison algorithms.In this work, we describe present results Head Neck Auto-Segmentation Challenge 2015, satellite event at Medical Image Computing Computer Assisted Interventions (MICCAI) 2015...
Accurate delineation of organs at risk (OARs) is a precondition for intensity modulated radiation therapy. However, manual OARs time consuming and prone to high interobserver variability. Because image artifacts low contrast between different structures, however, the number available approaches autosegmentation structures in head-neck area still rather low. In this project, new approach automated segmentation CT images that combine robustness multiatlas-based with flexibility geodesic active...
Abstract In-room imaging is a prerequisite for adaptive proton therapy. The use of onboard cone-beam computed tomography (CBCT) imaging, which routinely acquired patient position verification, can enable daily dose reconstructions and plan adaptation decisions. Image quality deficiencies though, hamper calculation accuracy make corrections CBCTs necessity. This study compared three methods to correct create synthetic CTs that are suitable calculations. CBCTs, planning repeated (rCT) from 33...
External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-tissue contrast provided magnetic resonance imaging (MRI) makes it a valuable modality diagnosing and these However, in to computed tomography (CT) imaging, appearance of catheters, through which radiation sources are inserted reach cancerous tissue later on, often variable across images. This paper reports, first time, new deep-learning-based...
Breast cancer is the most frequent in women worldwide and late diagnosis often adversely affects prognosis of disease. Radiotherapy commonly used to treat breast cancer, reducing risk recurrence after surgery. However, eradication radioresistant cells, including stem remains main challenge radiotherapy. Recently, lipid droplets (LDs) have been proposed as functional markers also being involved increased cell tumorigenicity. LD biogenesis a multistep process requiring various enzymes,...
Multiatlas based segmentation is largely used in many clinical and research applications. Due to its good performances, it has recently been included some commercial platforms for radiotherapy planning surgery guidance. Anyway, date, a software with no restrictions about the anatomical district image modality still missing. In this paper we introduce plastimatch mabs, an open source that can be any automatic segmentation.plastimatch mabs workflow consists of two main parts: (1) offline...
Abstract Cone-beam computed tomography (CBCT)- and magnetic resonance (MR)-images allow a daily observation of patient anatomy but are not directly suited for accurate proton dose calculations. This can be overcome by creating synthetic CTs (sCT) using deep convolutional neural networks. In this study, we compared sCTs based on CBCTs MRs head neck (H&N) cancer patients in terms image quality calculation accuracy. A dataset 27 H&N-patients, treated with therapy (PT), containing...
The coronavirus disease 19 (COVID-19) pandemic is having a dramatic impact on society and healthcare systems. In this complex scenario, lung computerized tomography (CT) may play an important prognostic role. However, datasets released so far present limitations that hamper the development of tools for quantitative analysis. paper, we open-source CT dataset comprising information 50 COVID-19-positive patients. volumes are provided along with (i) automatic threshold-based annotation obtained...
Time-resolved 4D cone beam-computed tomography (4D-CBCT) allows a daily assessment of patient anatomy and respiratory motion. However, 4D-CBCTs suffer from imaging artifacts that affect the CT number accuracy prevent accurate proton dose calculations. Deep learning can be used to correct numbers generate synthetic CTs (sCTs) enable CBCT-based calculations.In this work, sparse view were converted into 4D-sCT utilizing deep convolutional neural network (DCNN). 4D-sCTs evaluated in terms image...
Primary Central Nervous System Lymphoma (PCNSL) is an aggressive neoplasm with a poor prognosis. Although therapeutic progresses have significantly improved Overall Survival (OS), number of patients do not respond to HD-MTX-based chemotherapy (15-25%) or experience relapse (25-50%) after initial response. The reasons underlying this response therapy are unknown. Thus, there urgent need develop predictive models for PCNSL. In study, we investigated whether radiomics features can improve...
Purpose: The aim of the study was to evaluate dosimetric impact low-Z and high-Z metallic implants on IMRT plans. Methods: Computed tomography (CT) scans three patients were analyzed effects due presence Titanium (low-Z), Platinum Gold (high-Z) inserts. To eliminate artifacts in CT images, a sinogram-based metal artifact reduction algorithm applied. dose calculations performed both uncorrected corrected images using commercial planning system (convolution/superposition algorithm) an in-house...
Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging diagnostic quality. Cone-beam CT (CBCT) can provide these daily images, but x-ray scattering limits CBCT-image quality and hampers dose calculation accuracy. The purpose this study was to generate CBCT-based synthetic CTs using a deep convolutional neural network (DCNN) investigate image clinical suitability for calculations in patients.A dataset 33 thoracic patients, containing CBCTs, same-day repeat...
Artifacts affect 4D CT images due to breathing irregularities or incorrect phase identification. The purpose of this study is the reduction artifacts in sorted images. assumption that use multiple respiratory related signals may reduce uncertainties and increase robustness identification.Multiple were provided by infrared 3D localization a configuration markers placed on thoracoabdominal surface. Multidimensional K-means clustering was used for retrospective image sorting, which based marker...