Nuclear Medicine

PSMA-PET improves deep learning-based automated CT kidney segmentation.

UNLABELLED: For dosimetry of radiopharmaceutical therapies, it is essential to determine the volume of relevant structures exposed to therapeutic radiation. For many radiopharmaceuticals, the kidneys represent an important organ-at-risk. To reduce the time required for kidney segmentation, which is often still performed manually, numerous approaches have been presented in recent years to apply dee...

Classification of Pulmonary Nodules in 2-[F]FDG PET/CT Images with a 3D Convolutional Neural Network.

PURPOSE: 2-[F]FDG PET/CT plays an important role in the management of pulmonary nodules. Convolutional neural networks (CNNs) automatically learn features from images and have the potential to improve the discrimination between malignant and benign pulmonary nodules. The purpose of this study was to develop and validate a CNN model for classification of pulmonary nodules from 2-[F]FDG PET images.

Deep-learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co-registered to MRI to identify the epileptogenic zone in focal epilepsy.

OBJECTIVE: Normal interictal [ F]FDG-PET can be predicted from the corresponding T1w MRI with Generative Adversarial Networks (GANs). A technique we c...

Robust deep learning-based PET prognostic imaging biomarker for DLBCL patients: a multicenter study.

OBJECTIVE: To develop and independently externally validate robust prognostic imaging biomarkers distilled from PET images using deep learning techniq...

Robot-Assisted Parathyroidectomy Using Indocyanine Green (ICG) Fluorescence in Primary Hyperparathyroidism.

: Surgical treatment for primary hyperparathyroidism (PHPT) has evolved from bilateral exploration through a long transcervical incision to focused pa...

The Clinical Added Value of Breast Cancer Imaging Using Hybrid PET/MR Imaging.

Dedicated MR imaging is highly performant for the evaluation of the primary lesion and should regularly be added to whole-body PET/MR imaging for the ...

Respiratory signal estimation for cardiac perfusion SPECT using deep learning.

BACKGROUND: Respiratory motion induces artifacts in reconstructed cardiac perfusion SPECT images. Correction for respiratory motion often relies on a ...

Prediction of lymphoma response to CAR T cells by deep learning-based image analysis.

Clinical prognostic scoring systems have limited utility for predicting treatment outcomes in lymphomas. We therefore tested the feasibility of a deep...

Multi-institutional PET/CT image segmentation using federated deep transformer learning.

BACKGROUND AND OBJECTIVE: Generalizable and trustworthy deep learning models for PET/CT image segmentation necessitates large diverse multi-institutio...

Deep learning in neuroimaging of epilepsy.

In recent years, artificial intelligence, particularly deep learning (DL), has demonstrated utility in diverse areas of medicine. DL uses neural netwo...

Feasibility of a deep learning algorithm to achieve the low-dose Ga-FAPI/the fast-scan PET images: a multicenter study.

OBJECTIVES: Our work aims to study the feasibility of a deep learning algorithm to reduce the Ga-FAPI radiotracer injected activity and/or shorten the...

Deep learning-based combination of [18F]-FDG PET and CT images for producing pulmonary perfusion image.

BACKGROUND: The main application of [18F] FDG-PET ( FDG-PET) and CT images in oncology is tumor identification and quantification. Combining PET and C...

Deep learning based automated delineation of the intraprostatic gross tumour volume in PSMA-PET for patients with primary prostate cancer.

PURPOSE: With the increased use of focal radiation dose escalation for primary prostate cancer (PCa), accurate delineation of gross tumor volume (GTV)...

Deep learning enhanced ultra-fast SPECT/CT bone scan in patients with suspected malignancy: quantitative assessment and clinical performance.

. To evaluate the clinical performance of deep learning-enhanced ultrafast single photon emission computed tomography/computed tomography (SPECT/CT) b...

The use of weather nowcasting convolutional neural network extrapolators in cardiac PET imaging.

INTRODUCTION: Algorithms to predict short-term changes in local weather modalities have been used in meteorology for many years. These algorithms pred...

Reduction of SPECT acquisition time using deep learning: A phantom study.

Single photon emission computed tomography (SPECT) procedures are characterized by long acquisition time to acquire diagnostically acceptable image da...

Deep learning model for automatic image quality assessment in PET.

BACKGROUND: A variety of external factors might seriously degrade PET image quality and lead to inconsistent results. The aim of this study is to expl...

A Two-Branch Neural Network for Short-Axis PET Image Quality Enhancement.

The axial field of view (FOV) is a key factor that affects the quality of PET images. Due to hardware FOV restrictions, conventional short-axis PET sc...

Predicting FDG-PET Images From Multi-Contrast MRI Using Deep Learning in Patients With Brain Neoplasms.

BACKGROUND: F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is valuable for determining presence of viable tumor, but is limited by geo...

Reducing scan time in Lu planar scintigraphy using convolutional neural network: A Monte Carlo simulation study.

PURPOSE: The aim of this study was to reduce scan time in Lu planar scintigraphy through the use of convolutional neural network (CNN) to facilitate ...