Clinical utility of PET/MRI in multiple myeloma.

Introduction: This study aimed to evaluate the clinical utility of positron emission tomography/magnetic resonance imaging (PET/MRI), especially in comparison with PET/computed tomography (CT), which has been widely used in clinical practice in multiple myeloma.

Method: F-18 fluorodeoxyglucose PET/MRI and PET/ CT studies were done at baseline and when at least a partial response to treatment was achieved. These were done for newly-diagnosed myeloma patients who have not had more than 1 cycle of anti-myeloma treatment, or for relapsed and/or refractory myeloma patients before the start of next line of therapy.

Comorbid amyloid with cerebrovascular disease in domain-specific cognitive and neuropsychiatric disturbances: a cross-sectional memory clinic study.

Dementia is a multifactorial disorder that is likely influenced by both Alzheimer's disease (AD) and vascular pathologies. We evaluated domain-specific cognitive and neuropsychiatric dysfunction using a two-neuroimaging biomarker construct (beta-amyloid [Aβ] and cerebrovascular disease [CeVD]). We analyzed data from 216 memory clinic participants (mean age = 75.

Longitudinal associations between β-amyloid and cortical thickness in mild cognitive impairment.

How beta-amyloid accumulation influences brain atrophy in Alzheimer's disease remains contentious with conflicting findings. We aimed to elucidate the correlations of regional longitudinal atrophy with cross-sectional regional and global amyloid in individuals with mild cognitive impairment and no cognitive impairment. We hypothesized that greater cortical thinning over time correlated with greater amyloid deposition, particularly within Alzheimer's disease characteristic regions in mild cognitive impairment, and weaker or no correlations in those with no cognitive impairment.

Brain atrophy and white matter hyperintensities are independently associated with plasma neurofilament light chain in an Asian cohort of cognitively impaired patients with concomitant cerebral small vessel disease.

Introduction: Plasma neurofilament light chain (NfL) is a potential biomarker for neurodegeneration in Alzheimer's disease (AD), ischemic stroke, and non-dementia cohorts with cerebral small vessel disease (CSVD). However, studies of AD in populations with high prevalence of concomitant CSVD to evaluate associations of brain atrophy, CSVD, and amyloid beta (Aβ) burden on plasma NfL are lacking.

Methods: Associations were tested between plasma NfL and brain Aβ, medial temporal lobe atrophy (MTA) as well as neuroimaging features of CSVD, including white matter hyperintensities (WMH), lacunes, and cerebral microbleeds.

Comparison of quantitative parameters and radiomic features as inputs into machine learning models to predict the Gleason score of prostate cancer lesions.

Introduction: The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomography (PET), as inputs into machine learning (ML) to predict the Gleason scores (GS) of detected lesions for improved PCa lesion classification.

Methods: 20 biopsy-confirmed PCa subjects underwent imaging before radical prostatectomy.

PET image enhancement using artificial intelligence for better characterization of epilepsy lesions.

Introduction: [F]fluorodeoxyglucose ([F]FDG) brain PET is used clinically to detect small areas of decreased uptake associated with epileptogenic lesions, e.g., Focal Cortical Dysplasias (FCD) but its performance is limited due to spatial resolution and low contrast.

The Associations Between Intracranial Stenosis, Brain Amyloid-beta, and Cognition in a Memory Clinic Sample.

Background: Intracranial stenosis (ICS) and brain amyloid-beta (Aβ) have been associated with cognition and dementia. We aimed to investigate the association between ICS and brain Aβ and their independent and joint associations with cognition.

Methods: We conducted a cross-sectional study of 185 patients recruited from a memory clinic.

Evolving operational guidance and experiences for radiology and nuclear medicine facilities in response to and beyond the COVID-19 pandemic.

The resulting pandemic from the novel severe acute respiratory coronavirus 2, SARS-CoV-2 (COVID-19), continues to exert a strain on worldwide health services due to the incidence of hospitalization and mortality associated with infection. The aim of clinical services throughout the period of the pandemic and likely beyond to endemic infections as the situation stabilizes is to enhance safety aspects to mitigate transmission of COVID-19 while providing a high quality of service to all patients (COVID-19 positive and negative) while still upholding excellent medical standards. In order to achieve this, new strategies of clinical service operation are essential.

Comparison of the performances of machine learning and deep learning in improving the quality of low dose lung cancer PET images.

Purpose: To compare the performances of machine learning (ML) and deep learning (DL) in improving the quality of low dose (LD) lung cancer PET images and the minimum counts required.

Materials And Methods: 33 standard dose (SD) PET images, were used to simulate LD PET images at seven-count levels of 0.25, 0.

11C-Metomidate PET-CT versus adrenal vein sampling to subtype primary aldosteronism: a prospective clinical trial.

Objective: Adrenal vein sampling (AVS) is recommended to subtype primary aldosteronism, but it is technically challenging. We compared 11C-Metomidate-PET-computed tomography (PET-CT) and AVS for subtyping of primary aldosteronism.

Methods: Patients with confirmed primary aldosteronism underwent both AVS and 11C-Metomidate PET-CT (post-dexamethasone).

Validation of deep learning-based nonspecific estimates for amyloid burden quantification with longitudinal data.

Purpose: To validate our previously proposed method of quantifying amyloid-beta (Aβ) load using nonspecific (NS) estimates generated with convolutional neural networks (CNNs) using [F]Florbetapir scans from longitudinal and multicenter ADNI data.

Methods: 188 paired MR (T1-weighted and T2-weighted) and PET images were downloaded from the ADNI3 dataset, of which 49 subjects had 2 time-point scans. 40 Aβ- subjects with low specific uptake were selected for training.

Comparison of Three Automated Approaches for Classification of Amyloid-PET Images.

Automated amyloid-PET image classification can support clinical assessment and increase diagnostic confidence. Three automated approaches using global cut-points derived from Receiver Operating Characteristic (ROC) analysis, machine learning (ML) algorithms with regional SUVr values, and deep learning (DL) network with 3D image input were compared under various conditions: number of training data, radiotracers, and cohorts. 276 [C]PiB and 209 [F]AV45 PET images from ADNI database and our local cohort were used.

Cerebrovascular disease in suspected non-Alzheimer's pathophysiology and cognitive decline over time.

Background: The underlying cause of cognitive decline in individuals who are positive for biomarkers of neurodegeneration (N) but negative for biomarkers of amyloid-beta (A), designated as Suspected non-Alzheimer's pathophysiology (SNAP), remains unclear. We evaluate whether cerebrovascular disease (CeVD) is more prevalent in those with SNAP compared to A-N- and A+N+ individuals and whether CeVD is associated with cognitive decline over time in SNAP patients.

Methods: A total of 216 individuals from a prospective memory clinic cohort (mean [SD] age, 72.

The Association Between Standard Electrocardiography and Cerebral Small Vessel Disease in a Memory Clinic Study.

Background: P-wave terminal force in lead V1 (PTFV1) on electrocardiography has been associated with atrial fibrillation and ischemic stroke.

Objective: To investigate whether PTFV1 is associated with cerebral small vessel disease (CSVD) markers and etiological subtypes of cognitive impairment and dementia.

Methods: Participants were recruited from ongoing memory clinic study between August 2010 to January 2019.

Parametric mapping for 11C-metomidate PET-computed tomography imaging in the study of primary aldosteronism.

Objective: 11C-metomidate (11C-MTO) PET-computed tomography (CT) imaging has shown good sensitivity and specificity for the classification of bilateral or unilateral overexpression of aldosterone. This work seeks to investigate the usefulness of parametric maps via kinetic modeling of 11C-metomidate data into the clinical diagnosis pathway.

Methods: Twenty-five patients were injected with 172 ± 12 MBq of 11C-metomidate and a dynamic PET scan performed of the adrenal glands.

Comparison of metrics for the evaluation of medical segmentations using prostate MRI dataset.

Nine previously proposed segmentation evaluation metrics, targeting medical relevance, accounting for holes, and added regions or differentiating over- and under-segmentation, were compared with 24 traditional metrics to identify those which better capture the requirements for clinical segmentation evaluation. Evaluation was first performed using 2D synthetic shapes to highlight features and pitfalls of the metrics with known ground truths (GTs) and machine segmentations (MSs). Clinical evaluation was then performed using publicly-available prostate images of 20 subjects with MSs generated by 3 different deep learning networks (DenseVNet, HighRes3DNet, and ScaleNet) and GTs drawn by 2 readers.

Plasma P-tau181 to Aβ42 ratio is associated with brain amyloid burden and hippocampal atrophy in an Asian cohort of Alzheimer's disease patients with concomitant cerebrovascular disease.

Introduction: There is increasing evidence that phosphorylated tau (P-tau181) is a specific biomarker for Alzheimer's disease (AD) pathology, but its potential utility in non-White patient cohorts and patients with concomitant cerebrovascular disease (CeVD) is unknown.

Methods: Single molecule array (Simoa) measurements of plasma P-tau181, total tau, amyloid beta (Aβ)40 and Aβ42, as well as derived ratios were correlated with neuroimaging modalities indicating brain amyloid (Aβ+), hippocampal atrophy, and CeVD in a Singapore-based cohort of non-cognitively impaired (NCI; n = 43), cognitively impaired no dementia (CIND; n = 91), AD (n = 44), and vascular dementia (VaD; n = 22) subjects.

Results: P-tau181/Aβ42 ratio showed the highest area under the curve (AUC) for Aβ+ (AUC = 0.

Improved amyloid burden quantification with nonspecific estimates using deep learning.

Purpose: Standardized uptake value ratio (SUVr) used to quantify amyloid-β burden from amyloid-PET scans can be biased by variations in the tracer's nonspecific (NS) binding caused by the presence of cerebrovascular disease (CeVD). In this work, we propose a novel amyloid-PET quantification approach that harnesses the intermodal image translation capability of convolutional networks to remove this undesirable source of variability.

Methods: Paired MR and PET images exhibiting very low specific uptake were selected from a Singaporean amyloid-PET study involving 172 participants with different severities of CeVD.

Head-to-head comparison of amplified plasmonic exosome Aβ42 platform and single-molecule array immunoassay in a memory clinic cohort.

Background And Purpose: Various blood biomarkers reflecting brain amyloid-β (Aβ) load have recently been proposed with promising results. However, to date, no comparative study amongst blood biomarkers has been reported. Our objective was to examine the diagnostic performance and cost effectiveness of three blood biomarkers on the same cohort.

Validation of low-dose lung cancer PET-CT protocol and PET image improvement using machine learning.

Purpose: To conduct a simplified lesion-detection task of a low-dose (LD) PET-CT protocol for frequent lung screening using 30% of the effective PETCT dose and to investigate the feasibility of increasing clinical value of low-statistics scans using machine learning.

Methods: We acquired 33 SD PET images, of which 13 had actual LD (ALD) PET, and simulated LD (SLD) PET images at seven different count levels from the SD PET scans. We employed image quality transfer (IQT), a machine learning algorithm that performs patch-regression to map parameters from low-quality to high-quality images.

Evaluation of Multimodal Algorithms for the Segmentation of Multiparametric MRI Prostate Images.

Prostate segmentation in multiparametric magnetic resonance imaging (mpMRI) can help to support prostate cancer diagnosis and therapy treatment. However, manual segmentation of the prostate is subjective and time-consuming. Many deep learning monomodal networks have been developed for automatic whole prostate segmentation from T2-weighted MR images.

Brain amyloid β, cerebral small vessel disease, and cognition: A memory clinic study.

Objective: To evaluate the association between brain amyloid β (Aβ) and cerebral small vessel disease (CSVD) markers, as well as their joint effect on cognition, in a memory clinic study.

Methods: A total of 186 individuals visiting a memory clinic, diagnosed with no cognitive impairment, cognitive impairment no dementia (CIND), Alzheimer dementia (AD), or vascular dementia were included. Brain Aβ was measured by [C] Pittsburgh compound B-PET global standardized uptake value ratio (SUVR).

[Telemedicine monitoring for AMD patients].

Introduction: AMD follow-up is a public health issue in developed countries due to aging of the population and medical demographics. Telemedicine may be a means of improving follow-up.

Purpose: To compare the agreement between telemedicine and in-person consultations in terms of indications for intravitreal injections in exudative AMD patients.