Self-supervised parametric map estimation for multiplexed PET with a deep image prior.

Multiplexed positron emission tomography (mPET) imaging allows simultaneous observation of physiological and pathological information from multiple tracers in a single PET scan. Although supervised deep learning has demonstrated superior performance in mPET image separation compared to purely model-based methods, acquiring large amounts of paired single-tracer data and multi-tracer data for training poses a practical challenge and needs extended scan durations for patients. In addition, the generalisation ability of the supervised learning framework is a concern, as the patient being scanned and their tracer kinetics may potentially fall outside the training distribution.

Deep learned triple-tracer multiplexed PET myocardial image separation.

Introduction: In multiplexed positron emission tomography (mPET) imaging, physiological and pathological information from different radiotracers can be observed simultaneously in a single dynamic PET scan. The separation of mPET signals within a single PET scan is challenging due to the fact that the PET scanner measures the sum of the PET signals of all the tracers. The conventional multi-tracer compartment modeling (MTCM) method requires staggered injections and assumes that the arterial input functions (AIFs) of each tracer are known.

Kinetic model-informed deep learning for multiplexed PET image separation.

Background: Multiplexed positron emission tomography (mPET) imaging can measure physiological and pathological information from different tracers simultaneously in a single scan. Separation of the multiplexed PET signals within a single PET scan is challenging due to the fact that each tracer gives rise to indistinguishable 511 keV photon pairs, and thus no unique energy information for differentiating the source of each photon pair.

Methods: Recently, many applications of deep learning for mPET image separation have been concentrated on pure data-driven methods, e.

Pain Reduction of Ibuprofen Sodium Dihydrate Alone and in Combination with Acetaminophen in an Untreated Endodontic Pain Model: A Randomized, Double-blind Investigation.

Introduction: Most pain studies have been based on a postsurgical, third molar model using ibuprofen (IBU)/acetaminophen (APAP). Studies have found quicker onset of pain relief with a newer formulation of IBU - ibuprofen sodium dihydrate (ISD). The purpose of this study was to compare pain reduction of ISD/APAP to ISD in an acute endodontic pain model of untreated patients experiencing moderate to severe pain with symptomatic apical periodontitis.

Synthesized Image Reconstruction for Post-Reconstruction Resolution Recovery.

Resolution recovery (RR) techniques in positron emission tomography (PET) imaging aim to mitigate spatial resolution losses and related inaccuracies in quantification by using a model of the system's point spread function (PSF) during reconstruction or post-processing. However, including PSF modeling in fully 3-D image reconstruction is far from trivial as access to the scanner-specific forward and back-projectors is required, along with access to the 3-D sinogram data. Hence, post-reconstruction RR methods, such as the Richardson-Lucy (RL) algorithm, can be more practical.

Efficacy of the TuttleNumbNow Intraosseous Method for Pulpal Anesthesia in the Mandibular First Molar: A Prospective, Randomized, Crossover Study.

Introduction: Previous studies on intraosseous (IO) anesthesia as a primary injection have shown high success rates. The TuttleNumbNow (TNN; Orem, UT) is a new primary IO injection technique that has not been scientifically evaluated. Therefore, the purpose of this prospective randomized, crossover study was to evaluate the anesthetic efficacy of the TNN IO technique using the Septoject Evolution needle (Septodont, Saint-Maur-des-Fosses, France) compared with buccal infiltration for pulpal anesthesia in mandibular first molars.

Transcranial magnetic stimulation over supramarginal gyrus stimulates primary motor cortex directly and impairs manual dexterity: implications for TMS focality.

Based on human motor cortex, the effective spatial resolution of transcranial magnetic stimulation (TMS) is often described as 5-20 mm, because small changes in TMS coil position can have large effects on motor-evoked potentials (MEPs). MEPs are often studied at rest, with muscles relaxed. During muscle contraction and movement, corticospinal excitability is higher, thresholds for effective stimulation are lower, and MEPs can be evoked from larger regions of scalp, so the effective spatial resolution of TMS is larger.

Non-rigid motion-compensated 3D whole-heart T mapping in a hybrid 3T PET-MR system.

Purpose: Simultaneous PET-MRI improves inflammatory cardiac disease diagnosis. However, challenges persist in respiratory motion and mis-registration between free-breathing 3D PET and 2D breath-held MR images. We propose a free-breathing non-rigid motion-compensated 3D T -mapping sequence enabling whole-heart myocardial tissue characterization in a hybrid 3T PET-MR system and provides non-rigid respiratory motion fields to correct also simultaneously acquired PET data.

Deep Learned Segmentations of Inflammation for Novel ⁹⁹Tc-maraciclatide Imaging of Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is an autoimmune disease that causes joint pain, stiffness, and erosion. Power Doppler ultrasound and MRI are imaging modalities used in detecting and monitoring the disease, but they have limitations. ⁹⁹Tc-maraciclatide gamma camera imaging is a novel technique that can detect joint inflammation at all sites in a single examination and has been shown to correlate with power Doppler ultrasound.

Success of Pulpal Anesthesia Following Buccal Infiltration of the Maxillary First Molar With 1.8 mL and 3.6 mL of 4% Articaine With 1:100,000 Epinephrine: A Prospective, Randomized Crossover Study.

Objective: The purpose of this prospective, randomized crossover study was to compare the peak incidence of success, onset, and incidence over time of pulpal anesthesia in maxillary first molars following a buccal infiltration of 1.8 mL or 3.6 mL of 4% articaine with 1:100 000 epinephrine.

Subcortical contributions to the sense of body ownership.

The sense of body ownership (i.e. the feeling that our body or its parts belong to us) plays a key role in bodily self-consciousness and is believed to stem from multisensory integration.

Articaine Infiltrations of the Mandibular Lateral Incisor-Is It Volume or Location of the Infiltrations That Affect Success? A Prospective, Randomized Crossover Study.

Introduction: A combination labial infiltration (1.8 mL) plus lingual infiltration (1.8 mL) of 4% articaine with 1:100,000 epinephrine in the mandibular lateral incisor was found superior to a labial infiltration of 1.

No reduction in motor-evoked potential amplitude during the rubber hand illusion.

Introduction: In the rubber hand illusion (RHI), touches are applied to a fake hand at the same time as touches are applied to a participant's real hand that is hidden in a congruent position. Synchronous (but not asynchronous) tactile stimulation of the two hands may induce the sensation that the fake hand is the participant's own. As such, the illusion is commonly used to examine the sense of body ownership.

AI for PET image reconstruction.

Image reconstruction for positron emission tomography (PET) has been developed over many decades, with advances coming from improved modelling of the data statistics and improved modelling of the imaging physics. However, high noise and limited spatial resolution have remained issues in PET imaging, and state-of-the-art PET reconstruction has started to exploit other medical imaging modalities (such as MRI) to assist in noise reduction and enhancement of PET's spatial resolution. Nonetheless, there is an ongoing drive towards not only improving image quality, but also reducing the injected radiation dose and reducing scanning times.

Smaller preferred interpersonal distance for joint versus parallel action.

During social interaction, humans prefer to keep a certain distance between themselves and other individuals. This preferred 'interpersonal distance' (IPD) is known to be sensitive to social context, and in the present study we aimed to further investigate the extent to which IPD is affected by the specific type of social interaction. In particular, we focused on the contrast between joint actions, where two or more individuals coordinate their actions in space and time to achieve a shared goal, and parallel actions, where individuals act alongside each other but individually.

Pre-training via Transfer Learning and Pretext Learning a Convolutional Neural Network for Automated Assessments of Clinical PET Image Quality.

Positron emission tomography (PET) using a fraction of the usual injected dose would reduce the amount of radioligand needed, as well as the radiation dose to patients and staff, but would compromise reconstructed image quality. For performing the same clinical tasks with such images, a clinical (rather than numerical) image quality assessment is essential. This process can be automated with convolutional neural networks (CNNs).

Pepinemab antibody blockade of SEMA4D in early Huntington's disease: a randomized, placebo-controlled, phase 2 trial.

SIGNAL is a multicenter, randomized, double-blind, placebo-controlled phase 2 study (no. NCT02481674) established to evaluate pepinemab, a semaphorin 4D (SEMA4D)-blocking antibody, for treatment of Huntington's disease (HD). The trial enrolled a total of 265 HD gene expansion carriers with either early manifest (EM, n = 179) or late prodromal (LP, n = 86) HD, randomized (1:1) to receive 18 monthly infusions of pepinemab (n = 91 EM, 41 LP) or placebo (n = 88 EM, 45 LP).

Memory-Efficient Training for Fully Unrolled Deep Learned PET Image Reconstruction with Iteration-Dependent Targets.

We propose a new version of the forward-backward splitting expectation-maximisation network (FBSEM-Net) along with a new memory-efficient training method enabling the training of fully unrolled implementations of 3D FBSEM-Net. FBSEM-Net unfolds the maximum expectation-maximisation algorithm and replaces the regularisation step by a residual convolutional neural network. Both the gradient of the prior and the regularisation strength are learned from training data.