Feasibility of noise-reduction reconstruction technology based on non-local-mean principle in SiPM-PET/CT.

Quantitative values of positron emission tomography (PET) images using non-local-mean in a silicon photomultiplier (SiPM)-PET/computed tomography (CT) system with phantom and clinical images. The evaluation was conducted on a National Electrical Manufacturers Association body phantom with micro-spheres (4, 5, 6, 8, 10, 13 mm) and clinical images using the SiPM-PET/CT system. The signal-to-background ratio of the phantom was set to 4, and all PET image data was obtained and reconstructed using three-dimensional ordered subset expectation maximization, time-of-flight, point-spread function, and a 4-mm Gaussian filter (GF) and clear adaptive low-noise method (CaLM) in mild, standard, and strong intensities.

Evaluation of an artificial intelligence U-net algorithm for pulmonary nodule tracking on chest computed tomography images.

Objectives: To apply image registration in the follow up of lung nodules and verify the feasibility of automatic tracking of lung nodules using an artificial intelligence (AI) method.

Methods: For this retrospective, observational study, patients with pulmonary nodules 5-30 mm in diameter on computed tomography (CT) and who had at least six months follow-up were identified. Two radiologists defined a 'correct' cuboid circumscribing each nodule which was used to judge the success/failure of nodule tracking.

Impact of list-mode reconstruction and image-space point spread function correction on PET image contrast and quantitative value using SiPM-based PET/CT system.

We evaluate the effects of list-mode reconstruction and the image-space point spread function (iPSF) on the contrast and quantitative values of positron emission tomography (PET) images using a SiPM-PET/CT system. The evaluation is conducted on an NEMA body phantom and clinical images using a Cartesion Prime SiPM-PET/CT system. The signal-to-background ratio (SBR) of the phantom is set to 2, 4, 6, and 8, and all the PET image data are obtained and reconstructed using 3D-OSEM, time-of-flight, iPSF (-/ +), and a 4-mm Gaussian filter with several iterations.

A Time-of-Flight Image Sensor Using 8-Tap P-N Junction Demodulator Pixels.

This paper presents a time-of-flight image sensor based on 8-Tap P-N junction demodulator (PND) pixels, which is designed for hybrid-type short-pulse (SP)-based ToF measurements under strong ambient light. The 8-tap demodulator implemented with multiple p-n junctions used for modulating the electric potential to transfer photoelectrons to eight charge-sensing nodes and charge drains has an advantage of high-speed demodulation in large photosensitive areas. The ToF image sensor implemented using 0.

Development and performance evaluation of a deep learning lung nodule detection system.

Background: Lung cancer is the leading cause of cancer-related deaths throughout the world. Chest computed tomography (CT) is now widely used in the screening and diagnosis of lung cancer due to its effectiveness. Radiologists must identify each small nodule shadow from 3D volume images, which is very burdensome and often results in missed nodules.

An 8-Tap CMOS Lock-In Pixel Image Sensor for Short-Pulse Time-of-Flight Measurements.

An 8-tap CMOS lock-in pixel image sensor that has seven carrier-capturing and a draining time window was developed for short-pulse time-of-flight (TOF) measurements. The proposed pixel for the short-pulse TOF measurements has seven consecutive time-gating windows, each of which has the width of 6 ns, which is advantageous for high-resolution range imaging, particularly for relatively longer distances (>5 m) and under high ambient light operations. In order to enhance the depth resolution, a technique for the depth-adaptive time-gating-number assignment (DATA) for the short-pulse TOF measurement is proposed.

[Optimization of Scan Parameters for Patient without Breath Hold in Chest by Computed Tomography].

This study aims to establish optimal scan parameters by high temporal resolution computed tomography (CT) scan for emergency patients who cannot hold their breath. First, we investigated scan parameters that can reduce the effect of motion by evaluating motion artifacts from the moving phantom scan and the temporal sensitivity profile (TPS) measurement. Second, we confirmed the standard deviation (SD) of the CT values as well as the operating time and exposure dose.