A5036836472- Advanced X-ray and CT Imaging
- Medical Imaging Techniques and Applications
- Advanced Radiotherapy Techniques
- Radiomics and Machine Learning in Medical Imaging
- Radiation Dose and Imaging
- Radiation Therapy and Dosimetry
- COVID-19 diagnosis using AI
- Radiopharmaceutical Chemistry and Applications
- Lung Cancer Diagnosis and Treatment
- Radiation Detection and Scintillator Technologies
- Lung Cancer Research Studies
- Hepatocellular Carcinoma Treatment and Prognosis
- Neuroendocrine Tumor Research Advances
- Nuclear Physics and Applications
- Head and Neck Cancer Studies
- Neuroblastoma Research and Treatments
- Radiation Effects in Electronics
- Electron and X-Ray Spectroscopy Techniques
- Digital Radiography and Breast Imaging
- Radioactive element chemistry and processing
University Hospital of Geneva
2018-2024
University of Michigan–Ann Arbor
2023-2024
Geneva College
2021-2023
Hôpital Beau-Séjour
2021
Abstract Objectives The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients. Methods In this study, 800, 170, and 171 pairs full-dose images were used as input/output training, test, external validation set, respectively, implement the prediction technique. A residual convolutional neural network was applied generate from images. quality predicted assessed root mean square error (RMSE),...
Abstract Purpose In the era of precision medicine, patient-specific dose calculation using Monte Carlo (MC) simulations is deemed gold standard technique for risk-benefit analysis radiation hazards and correlation with patient outcome. Hence, we propose a novel method to perform whole-body personalized organ-level dosimetry taking into account heterogeneity activity distribution, non-uniformity surrounding medium, anatomy deep learning algorithms. Methods We extended voxel-scale MIRD...
We aimed to analyze the prognostic power of CT-based radiomics models using data 14,339 COVID-19 patients.
Purpose The generalizability and trustworthiness of deep learning (DL)–based algorithms depend on the size heterogeneity training datasets. However, because patient privacy concerns ethical legal issues, sharing medical images between different centers is restricted. Our objective to build a federated DL-based framework for PET image segmentation utilizing multicentric dataset compare its performance with centralized DL approach. Methods from 405 head neck cancer patients 9 formed basis this...
Abstract Purpose Accurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical practice, MIRD formalisms are widely employed. However, with rapid advancement deep learning (DL) algorithms, there has been an increasing interest in leveraging calculation speed automation capabilities different tasks. We aimed to develop a hybrid transformer-based model that incorporates multiple voxel S -value (MSV) approach voxel-level [ 177...
Abstract Background Despite the prevalence of chest CT in clinic, concerns about unoptimized protocols delivering high radiation doses to patients still remain. This study aimed assess additional dose associated with overscanning and develop an automated deep learning-assisted scan range selection technique reduce patients. Results A significant (31 ± 24) mm was observed clinical setting for over 95% cases. The average Dice coefficient lung segmentation 0.96 0.97 anterior–posterior (AP)...
Accurate calculation of the absorbed dose delivered to tumor and normal tissues improves treatment gain factor, which is major advantage brachytherapy over external radiation therapy. To address simplifications TG-43 assumptions that ignore dosimetric impact medium heterogeneities, we proposed a deep learning (DL)-based approach, accuracy while requiring reasonable computation time.We developed Monte Carlo (MC)-based personalized dosimetry simulator (PBrDoseSim), deployed generate...
We aimed to construct a prediction model based on computed tomography (CT) radiomics features classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A total of 1110 were studied from publicly available dataset with 4-class severity scoring performed by radiologist (based CT images clinical features). The entire lungs segmented followed resizing, bin discretization radiomic extraction. utilized two feature selection algorithms, namely bagging random forest (BRF)...
We present a deep learning (DL)-based automated whole lung and COVID-19 pneumonia infectious lesions (COLI-Net) detection segmentation from chest computed tomography (CT) images. This multicenter/multiscanner study involved 2368 (347'259 2D slices) 190 (17 341 volumetric CT exams along with their corresponding manual of lungs lesions, respectively. All images were cropped, resized, the intensity values clipped normalized. A residual network non-square Dice loss function built upon TensorFlow...
Abstract Objective We propose a deep learning-guided approach to generate voxel-based absorbed dose maps from whole-body CT acquisitions. Methods The voxel-wise corresponding each source position/angle were calculated using Monte Carlo (MC) simulations considering patient- and scanner-specific characteristics (SP_MC). distribution in uniform cylinder was computed through MC calculations (SP_uniform). density map SP_uniform fed into residual neural network (DNN) predict SP_MC an image...
This study aims to elucidate the role of quantitative SSTR-PET metrics and clinicopathological biomarkers in progression-free survival (PFS) overall (OS) neuroendocrine tumors (NETs) treated with peptide receptor radionuclide therapy (PRRT).
Extracting water equivalent diameter (DW), as a good descriptor of patient size, from the CT localizer before spiral scan not only minimizes truncation errors due to limited field-of-view but also enables prior size-specific dose estimation well protocol optimization. This study proposed unified methodology measure shape, and attenuation parameters 2D anterior-posterior image using deep learning algorithms without need for labor-intensive vendor-specific calibration procedures.3D chest...
Selective internal radiation therapy with 90Y radioembolization aims to selectively irradiate liver tumours by administering radioactive microspheres under the theragnostic assumption that pre-therapy injection of 99mTc labelled macroaggregated albumin (99mTc-MAA) provides an estimation biodistribution, which is not always case. Due growing interest in dosimetry for personalized radionuclide therapy, a robust relationship between delivered and pre-treatment absorbed doses required. In this...
Abstract Purpose To derive and validate an effective radiomics-based model for differentiation of COVID-19 pneumonia from other lung diseases using a very large cohort patients. Methods We collected 19 private 5 public datasets, accumulating to 26,307 individual patient images (15,148 COVID-19; 9,657 with e.g. non-COVID-19 pneumonia, cancer, pulmonary embolism; 1502 normal cases). Images were automatically segmented validated deep learning (DL) the results carefully reviewed. first cropped...
Abstract To derive and validate an effective machine learning radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private five public datasets of chest CT images, accumulating 26 307 images (15 148 COVID‐19; 9657 including non‐COVID‐19 pneumonia, cancer, pulmonary embolism; 1502 normal cases). We tested 96 learning‐based models by cross‐combining four feature selectors (FSs) eight...
Computational phantom libraries have been developed over the years to enhance accuracy of Monte Carlo-based radiation dose calculations from radiological procedures. In this paper, we report on development an adult computational anthropomorphic library covering different body morphometries among 20-80 old population. The anatomical diversities populations are modeled based anthropometric parameters extracted National Health and Nutrition Examination Survey database, including standing...
The clinical value of x-ray computed tomography (CT) has skyrocketed in the last decade while at same time being main source medical exposure to population. Concerns regarding potential health hazards associated with use ionizing radiation were raised and an appropriate estimation absorbed dose patients is highly desired. In this work, we aim validate our developed Monte Carlo CT simulator using in-phantom measurements further assess impact personalized scan-related parameters on dosimetric...
Abstract Background: in this study, two proton beam delivery designs, i.e. passive scattering therapy (PSPT) and pencil scanning (PBS), were quantitatively compared terms of dosimetric indices. The GATE Monte Carlo (MC) particle transport code was used to simulate the system; developed simulation engines benchmarked with respect experimental measurements. Method: a water phantom system energy parameters using set depth-dose data range 120–235 MeV. To compare performance PSPT against PBS,...
Diagnostic imaging procedures require optimization depending on the medical task at hand, apparatus being used, and patient physical anatomical characteristics. The assessment of radiation dose associated risks plays a key role in safety quality management for protection purposes. In this work, we aim developing methodology personalized organ-level x-ray computed tomography (CT) imaging.Regional voxel models representing reference patient-specific computational phantoms were generated...
Abstract We aimed to construct a prediction model based on computed tomography (CT) radiomics features classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A total of 1110 were studied from publicly available dataset with 4-class severity scoring performed by radiologist (based CT images clinical features). scans preprocessed bin discretization resized, followed segmentation the entire lung extraction features. utilized two feature selection algorithms, namely...
To develop and validate a versatile Monte Carlo (MC)-based dose calculation engine to support MC-based verification of treatment planning systems (TPSs) quality assurance (QA) workflows in proton therapy. The GATE MC toolkit was used simulate fixed horizontal active scan-based beam delivery (SIEMENS IONTRIS). Within the nozzle, two primary secondary monitors have been designed enable comparison accuracy estimation from simulations with respect physical QA measurements. developed model...