Nuclear Medicine

Deep learning image reconstruction improves 40 keV virtual monoenergetic image quality in rectal cancer.

BACKGROUND: Accurate preoperative evaluation of rectal cancer is essential for staging and treatment planning. Low-energy virtual monoenergetic imaging (VMI) enhances iodine contrast in dual-energy computed tomography (DECT) but increases image noise. Deep learning image reconstruction (DLIR) may mitigate this issue, but its effectiveness for 40 keV VMI in rectal cancer is underexplored. OBJECTIVE...

Machine learning for automated differentiation of parkinson's disease and its mimics using ¹²³I-mIBG scintigraphy: insights from a multicentre real-world cohort (ITA-mIBG study).

PURPOSE: To assess machine learning (ML) classifiers trained on harmonised multicentre ¹²³I-mIBG planar scintigraphy for differentiating Parkinson's disease (PD) from non-PD parkinsonian syndromes and to determine whether early imaging alone may ensure accurate discrimination. METHODS: This retrospective study included patients with suspected PD who underwent early (~ 15 min) and delayed (~ 240 mi...

Preoperative prediction of lymph node metastasis and prognosis in gastric cancer patients: A machine learning model based on 18F-FDG metabolic parameters.

OBJECTIVE: To construct and validate a model for predicting lymph node metastasis (LNMs) of gastric cancer (GC) based on 18F-FDG PET/CT multi-paramete...

SupReMix: Supervised contrastive learning for medical imaging regression with mixup.

In medical image analysis, regression plays a critical role in computer-aided diagnosis. It enables quantitative measurements such as age prediction f...

Software-based de-filtering restores quantitative accuracy in Clarity2D-enhanced whole-body bone scintigraphy.

OBJECTIVE: To determine whether software-based de-filtering can restore the quantitative accuracy of the bone scan index (BSI) and the number of hot s...

Diagnostic accuracy of ¹⁸F-FDG PET/CT radiomics for non-invasive prediction of PD-L1 expression in non-small cell lung cancer: A systematic review and meta-analysis.

To evaluate the diagnostic performance, methodological quality, and clinical feasibility of ¹⁸F-FDG PET/CT-based radiomics machine learning models for...

Combining the prognostic values of entropy-based heterogeneity features from 18 F-fluorodeoxyglucose PET and transmission computed tomography using machine learning in patients with lung adenocarcinoma undergoing curative surgery.

OBJECTIVE: The objective of this study is to evaluate the combined prognostic values of 18 F-fluorodeoxyglucose ( 18 F-FDG) PET and computed tomograph...

Recent Advances in Musculoskeletal Radiology: Bridging Innovation and Clinical Application.

Recent advances in musculoskeletal (MSK) radiology have markedly improved diagnostic accuracy through innovations in MRI, CT, and artificial intellige...

Support vector machine classification of 18F-FDG PET scans across subtypes of amyotrophic lateral sclerosis.

PURPOSE: While 18F-FDG PET imaging has demonstrated diagnostic value in people with Amyotrophic Lateral Sclerosis (PwALS) and group-level differences ...

Evaluating the efficacy of the ResNet50 deep learning model utilizing thyroid scintigraphy images for predicting the outcomes of initial iodine-131 therapy in patients with Graves' disease.

BACKGROUND: Graves' disease (GD), a leading cause of hyperthyroidism, exhibits heterogeneous responses to iodine-131 therapy, underscoring the need fo...

Global trends and collaboration networks in radiology: A bibliometric analysis of the 500 most-cited articles in web of science.

OBJECTIVE: This study examined global research trends in Radiology, Nuclear Medicine, and Medical Imaging by analyzing the 500 most-cited articles in ...

Multimodality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma.

PURPOSE: Clinical target volume (CTV) delineation for involved-site radiation therapy (ISRT) in Hodgkin lymphoma (HL) is time-consuming because of the...

Effect of a Robot Pet Companion on the Mood of Older Adults Receiving Family Caregiver Support: Pilot RCT.

BACKGROUND AND OBJECTIVES: Use of companion robot pets to reduce social isolation and loneliness in older people is well-established. Outcomes associa...

Deep learning algorithm assisting diagnosis of prostate cancer extracapsular extension based on [18F]PSMA-1007 PET/CT and multiparametric MRI: A multicenter study.

BACKGROUND: This study aimed to develop and validate deep learning (DL) models based on multiparametric MRI (mpMRI) and [18F]PSMA-1007 PET/CT to predi...

Automated Segmentation of Liver and Liver Tumors with SwinUNETR and UNET Neural Networks on 18F-FDG PET/CT.

OBJECTIVES: To develop and evaluate automated segmentation models for the liver and hepatic tumors on 18F-fluorodeoxyglucose positron emission tomogra...

CT-free attenuation correction of 13N-ammonia cardiac PET images using conditional denoising diffusion implicit model with logarithmic linear normalization.

BACKGROUND AND OBJECTIVE: CT-based attenuation correction (CT-AC) is commonly used in cardiac PET but introduces additional radiation exposure, which ...

The evolving landscape of screening, management and treatment strategies of aortic stenosis in ATTR amyloidosis.

Transthyretin amyloid cardiomyopathy (ATTR-CM) and aortic stenosis (AS) frequently coexist in elderly patients, particularly men, creating a complex c...

Comparison of spectral performance of three dual-energy CT scanners equipped with a deep-learning image reconstruction algorithm and one photon counting CT scanner: A phantom study.

PURPOSE: The purpose of this study was to compare the spectral performance of three dual-energy CT (DECT) scanners and one photon-counting CT (PCCT) s...

Impact of disease-modifying therapy on [99mTc]Tc-DPD SPECT/CT markers in transthyretin cardiac amyloidosis enabled by artificial intelligence.

PURPOSE: Transthyretin cardiac amyloidosis (ATTR-CM) is a progressive, underdiagnosed disease with high morbidity and mortality. While disease-modifyi...

Analysis of microplastics and nanoplastics emerged from polyethylene bags and polyethylene terephthalate bottles by an artificial intelligence-enabled tool.

This study presents a comprehensive and comparative analysis of the emergence of micro- and nanoplastics (MNPs) from polyethylene (PE) plastic bags an...