Habib Zaidi

Habib Zaidi
Hôpitaux Universitaires de Genève | HUG · Service de médecine nucléaire et imagerie moléculaire

FIEEE FAIMBE FAAPM FIOMP FAAIA FBIR
Head of PET Instrumentation & Neuroimaging Lab

About

865
Publications
167,720
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19,848
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Introduction
Professor Habib Zaidi is Chief physicist and head of the PET Instrumentation & Neuroimaging Lab at Geneva University Hospital and faculty member the University of Geneva. He is also a Professor at the University of Groningen (Netherlands), Adjunct Professor at the University of Southern Denmark, and Distinguished Professor at Obuda University (Hungary). His academic accomplishments have been well recognized by his peers and since he is a recipient of many awards and distinctions (h-index=79).

Publications

Publications (865)
Article
Full-text available
Introduction Providing tools for comprehensively evaluating scintigraphy images could enhance transthyretin amyloid cardiomyopathy (ATTR-CM) diagnosis. This study aims to automatically detect and score ATTR-CM in total body scintigraphy images using deep learning on multi-tracer, multi-scanner, and multi-center datasets. Methods In the current stu...
Article
Full-text available
Background Estimation of the radiation dose to the fetus is essential for the assessment of radiation risks and benefits to pregnant patients undergoing radiological examinations. During the past decade, the global twinning rate has soared resulting from embryo assistance and increased delivery age. However, to the best of our knowledge, radiation...
Article
Purpose The common approach for organ segmentation in hybrid imaging relies on coregistered CT (CTAC) images. This method, however, presents several limitations in real clinical workflows where mismatch between PET and CT images are very common. Moreover, low-dose CTAC images have poor quality, thus challenging the segmentation task. Recent advance...
Article
Full-text available
Purpose We aim to perform radiogenomic profiling of breast cancer tumors using dynamic contrast magnetic resonance imaging (MRI) for the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) genes. Methods The dataset used in the current study consists of imaging data of 922 biopsy-confirmed invasi...
Article
Full-text available
Background The radiation exposure of nuclear medicine personnel, especially concerning extremity doses, has been a significant focus over the past two decades. This study addresses the evolving practice of NM, particularly with the rise of radionuclide therapy and theranostic procedures, which involve a variety of radionuclides such as ⁶⁸Ga, ¹⁷⁷Lu,...
Article
Full-text available
Purpose. Positron emission tomography (PET) image quality can be affected by artifacts emanating from PET, computed tomography (CT), or artifacts due to misalignment between PET and CT images. Automated detection of misalignment artifacts can be helpful both in data curation and in facilitating clinical workflow. This study aimed to develop an expl...
Article
Full-text available
Background Coronary artery disease (CAD) has one of the highest mortality rates in humans worldwide. Single‐photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) provides clinicians with myocardial metabolic information non‐invasively. However, there are some limitations to interpreting SPECT images performed by physicians...
Article
Full-text available
Introduction: Multiplexed PET imaging revolutionized clinical decision-making by simultaneously capturing various radiotracer data in a single scan, enhancing diagnostic accuracy and patient comfort. Through a transformer-based deep learning, this study underscores the potential of advanced imaging techniques to streamline diagnosis and improve pa...
Conference Paper
Computed tomography pulmonary angiography (CTPA) is the gold standard for Pulmonary embolism diagnosis. This study proposed an augmented U-Net architecture, incorporating Long Short-Term Memory (LSTM) and attention mechanisms, for improved pulmonary embolism segmentation in CTPAs. Two online datasets, CAD-pe and FUMPE, containing a total of 126 ima...
Conference Paper
Pulmonary embolism (PE) poses a significant risk to patients and often presents with nonspecific symptoms, challenging its timely detection during visual examination of computed tomography (CT) scans. Leveraging convolutional neural networks (CNNs) can aid radiologists in precise PE detection, potentially averting delayed treatment and associated c...
Conference Paper
This study explores the segmentation efficacy of computed tomography (CT) images for partial volume correction (PVC) in positron emission tomography (PET). Employing three deep convolutional neural networks (DCNNs), we conducted supervised segmentation of multiple brain regions to ensure accurate anatomical information. A dataset of 100 CT images a...
Conference Paper
The integration of automated cardiac imaging analysis holds significant promise for expediting clinical diagnosis and enhancing patient outcomes. This study focuses on segmenting cardiac systolic abnormalities using a dataset comprising 1717 cardiac catheterization X-ray images. We investigate the effectiveness of ensemble learning methodologies in...
Conference Paper
This study delves into the domain of automated cardiac imaging analysis, with a specific focus on semantic segmentation of cardiac systolic abnormalities in catheterization X-ray images. We thoroughly evaluate the effectiveness of attention-based network methods in this regard, utilizing a dataset comprising 1717 cardiac catheterization X-ray image...
Conference Paper
Full-text available
The study aimed to evaluate the shielding effectiveness of a material comprised of 15V2O5−(55-x) Pb3O4−30TeO2−xNd2O2, with varied molar concentrations (x) from 0 to 40, against gamma rays. Assessments were made regarding the influence of increasing Nd2O3 concentrations and decreasing Pb3O4 concentrations on key attenuation parameters, encomp...
Article
Introduction We propose a fully automated framework to conduct a region-wise image quality assessment (IQA) on whole-body ¹⁸ F-FDG PET scans. This framework (1) can be valuable in daily clinical image acquisition procedures to instantly recognize low-quality scans for potential rescanning and/or image reconstruction, and (2) can make a significant...
Article
Full-text available
Simple Summary Lymphoma is a type of cancer that affects the immune system and can be difficult to diagnose and treat effectively, especially in the early stages. Current imaging methods, such as PET/CT scans, are valuable tools for diagnosing and monitoring the disease, but they have limitations in providing precise information for personalized tr...
Article
To predict Gleason Score (GS) using radiomic features from 68Ga-PSMA-PET/CT images in primary prostate cancer. 138 patients undergoing 68Ga-PSMA-PET/CT imaging were categorized based on GS, with GS above 4 + 3 as malignant and under 3 + 4 as benign tumors. radiomic features were extracted from tumors’ volume of interest in both PET and CT images, u...
Article
This study aimed at predicting four important immunohistochemical biomarkers, including estrogen receptor, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki67 (cell proliferation rate index)) in breast cancer using radiomic features derived from multicentric 18F-FDG PET/CT images. Sixty-two patients with locally ad...
Article
Full-text available
The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multi-segmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for personalized treatment strategies and improving patient outcomes. One hundred and twenty-six patients...
Chapter
In recent years, some new positron emission tomography (PET) radiopharmaceuticals have come to attention as possible alternatives to fluorine-18-fluoro-2-deoxyglucose (18F-FDG) PET. In this context, this chapter describes the role of PET with gallium-68 (68Ga)-Pentixafor, describing potential clinical indications, with interesting clinical cases. M...
Chapter
This chapter describes the interesting role of PET with radiolabeled fibroblast activation protein inhibitors (FAPIs), underlining the potential clinical indications. Furthermore, the emerging role of FAPI PET is highlighted with interesting clinical cases. In addition synthesis, pharmacokinetics and physiological distribution of FAPIs, and PET acq...
Article
Purpose: Non-small cell lung cancer is the most common subtype of lung cancer. Patient survival prediction using machine learning (ML) and radiomics analysis proved to provide promising outcomes. However, most studies reported in the literature focused on information extracted from malignant lesions. This study aims to explore the relevance and ad...
Preprint
Full-text available
Introduction: The common approach for organ segmentation in hybrid imaging relies on co-registered CT (CTAC) images. This method, however, presents several limitations in real clinical workflows where mismatch between PET and CT images are very common. Moreover, low-dose CTAC images have poor quality, thus challenging the segmentation task. Recent...
Article
Full-text available
Objective: Numerous studies have shown that gallium-68-labeled fibroblast activation protein inhibitor (68Ga-FAPI) positron emission tomography/computed tomography (PET/CT) scans would yield high intra-tumoral tracer uptake and low uptake in normal tissues as background, thus allowing for excellent visualization of lesions in the cancer microenvir...
Article
Full-text available
Background Low-dose ungated CT is commonly used for total-body PET attenuation and scatter correction (ASC). However, CT-based ASC (CT-ASC) is limited by radiation dose risks of CT examinations, propagation of CT-based artifacts and potential mismatches between PET and CT. We demonstrate the feasibility of direct ASC for multi-tracer total-body PET...
Preprint
Full-text available
The objective of this study was to develop an automated pipeline that enhances thyroid disease classification using thyroid scintigraphy images, aiming to decrease assessment time and increase diagnostic accuracy. Anterior thyroid scintigraphy images from 2,643 patients were collected and categorized into diffuse goiter (DG), multinodal goiter (MNG...
Article
Full-text available
Simple Summary Head and neck cancer is a serious health concern that affects millions of people across the globe. Predicting how patients will respond to therapy is critical for providing optimal care. To make these predictions, one may first manually identify tumour boundaries on medical imaging in order to obtain the necessary information. Manual...
Article
Full-text available
Background Overall Survival (OS) and Progression-Free Survival (PFS) analyses are crucial metrics for evaluating the efficacy and impact of treatment. This study evaluated the role of clinical biomarkers and dosimetry parameters on survival outcomes of patients undergoing ⁹⁰Y selective internal radiation therapy (SIRT). Materials/Methods This prel...
Article
Full-text available
Introduction Amyloid-β (Aβ) plaques is a significant hallmark of Alzheimer's disease (AD), detectable via amyloid-PET imaging. The Fluorine-18-Fluorodeoxyglucose ([¹⁸F]FDG) PET scan tracks cerebral glucose metabolism, correlated with synaptic dysfunction and disease progression and is complementary for AD diagnosis. Dual-scan acquisitions of amyloi...
Article
Full-text available
Purpose: This study aims to elucidate the role of quantitative SSTR-PET metrics and clinicopathological biomarkers in the progression-free survival (PFS) and overall survival (OS) of neuroendocrine tumors (NETs) treated with peptide receptor radionuclide therapy (PRRT). Methods: A retrospective analysis including 91 NET patients (M47/F44; age 66 ye...
Article
Full-text available
To develop a robust segmentation model, encoding the underlying features/structures of the input data is essential to discriminate the target structure from the background. To enrich the extracted feature maps, contrastive learning and self-learning techniques are employed, particularly when the size of the training dataset is limited. In this work...
Conference Paper
Introduction: The potential of amyloid PET radiotracers, such as 18F-Florbetapir (FBP) and 18F-Flutemetamol (FMM), in the non-invasive detection of amyloid plaques, a key pathological feature in Alzheimer's disease (AD), has been demonstrated in previous studies. Detectable presence of amyloid plaques can precede noticeable cognitive decline by app...
Article
Full-text available
This review casts a spotlight on intraoperative positron emission tomography (PET) scanners and the distinctive challenges they confront. Specifically, these systems contend with the necessity of partial coverage geometry, essential for ensuring adequate access to the patient. This inherently leans them towards limited-angle PET imaging, bringing a...
Article
Full-text available
Radiopharmaceutical therapy (RPT) is a rapidly developing field of nuclear medicine, with several RPTs already well established in the treatment of several different types of cancers. However, the current approaches to RPTs often follow a somewhat inflexible “one size fits all” paradigm, where patients are administered the same amount of radioactiv...
Article
Full-text available
Purpose The aim of this study was to compare the organ doses assessed through a digital phantom‐based and a patient specific‐based dosimetric tool in adult routine thorax computed tomography (CT) examinations with reference to physical dose measurements performed in anthropomorphic phantoms. Methods Two Monte Carlo based dose calculation tools wer...
Preprint
Full-text available
Purpose Non-small cell lung cancer (NSCLC) is the most common subtype of lung cancer. Patient survival prediction using machine learning and radiomics analysis proved to provide promising outcomes. However, most studies reported in the literature focused on information extracted from malignant lesions. This study aims to explore the relevance and a...
Article
Full-text available
Background Contrast‐enhanced computed tomography (CECT) provides much more information compared to non‐enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is usually missing on public datasets and not standardized in the clinic even in the same region and langu...
Article
Full-text available
Purpose This study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC). Methods We included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET...
Article
Full-text available
This study investigated the impact of ComBat harmonization on the reproducibility of radiomic features extracted from magnetic resonance images (MRI) acquired on different scanners, using various data acquisition parameters and multiple image pre-processing techniques using a dedicated MRI phantom. Four scanners were used to acquire an MRI of a non...
Article
Full-text available
Manual segmentation poses a time-consuming challenge for disease quantification, therapy evaluation, treatment planning, and outcome prediction. Convolutional neural networks (CNNs) hold promise in accurately identifying tumor locations and boundaries in PET scans. However, a major hurdle is the extensive amount of supervised and annotated data nec...
Article
Full-text available
Background Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID‐19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi‐institutional cohort of patients with COVID...
Article
Full-text available
Purpose Total metabolic tumor volume (TMTV) segmentation has significant value enabling quantitative imaging biomarkers for lymphoma management. In this work, we tackle the challenging task of automated tumor delineation in lymphoma from PET/CT scans using a cascaded approach. Methods Our study included 1418 2-[¹⁸F]FDG PET/CT scans from four diffe...
Article
Full-text available
To derive and validate an effective machine learning and 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 and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID‐19; 9657 other lung diseases in...
Article
Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of...