Clinical efficacy of DSA-based features in predicting outcomes of acupuncture intervention on upper limb dysfunction following ischemic stroke.

Background And Objectives: This study aimed to employ machine learning techniques to predict the clinical efficacy of acupuncture as an intervention for patients with upper limb motor dysfunction following ischemic stroke, as well as to assess its potential utility in clinical practice.

Methods: Medical records and digital subtraction angiography (DSA) imaging data were collected from 735 ischemic stroke patients with upper limb motor dysfunction who were treated with standardized acupuncture at two hospitals. Following the initial screening, 314 patient datasets that met the inclusion criteria were selected.

Accelerated 3D MR neurography of the brachial plexus using deep learning-constrained compressed sensing.

Objectives: To explore the use of deep learning-constrained compressed sensing (DLCS) in improving image quality and acquisition time for 3D MRI of the brachial plexus.

Methods: Fifty-four participants who underwent contrast-enhanced imaging and forty-one participants who underwent unenhanced imaging were included. Sensitivity encoding with an acceleration of 2 × 2 (SENSE4x), CS with an acceleration of 4 (CS4x), and DLCS with acceleration of 4 (DLCS4x) and 8 (DLCS8x) were used for MRI of the brachial plexus.

Effectiveness of simultaneous multislice accelerated readout-segmented echo planar imaging for the assessment of rectal cancer.

Purpose: To investigate the effectiveness of simultaneous multislice (SMS) accelerated readout-segmented echo planar imaging (RESOLVE) DWI for assessing rectal cancer in the clinic.

Method: Sixty consecutive histologically proven rectal cancer patients were enrolled. They all received MRI examinations, including both SMS-RESOLVE and RESOLVE sequences.

Rapid 3D breath-hold MR cholangiopancreatography using deep learning-constrained compressed sensing reconstruction.

Objectives: To compare the image quality of three-dimensional breath-hold magnetic resonance cholangiopancreatography with deep learning-based compressed sensing reconstruction (3D DL-CS-MRCP) to those of 3D breath-hold MRCP with compressed sensing (3D CS-MRCP), 3D breath-hold MRCP with gradient and spin-echo (3D GRASE-MRCP) and conventional 2D single-shot breath-hold MRCP (2D MRCP).

Methods: In total, 102 consecutive patients who underwent MRCP at 3.0 T, including 2D MRCP, 3D GRASE-MRCP, 3D CS-MRCP, and 3D DL-CS-MRCP, were prospectively included.

[Application of Deep Learning Reconstruction Algorithm in Low-Dose Thin-Slice Liver CT of Healthy Volunteers].

Objective: To explore the clinical feasibility of applying deep learning (DL) reconstruction algorithm in low-dose thin-slice liver CT examination of healthy volunteers by comparing the reconstruction algorithm based on DL, filtered back projection (FBP) reconstruction algorithm and iterative reconstruction (IR) algorithm.

Methods: A standard water phantom with a diameter of 180 mm was scanned, using the 160 slice multi-detector CT scanning of United Imaging Healthcare, to compare the noise power spectrums of DL, FBP and IR algorithms. 100 healthy volunteers were prospectively enrolled, with 50 assigned to the normal dose group (ND) and 50 to the low dose group (LD).

[Application of Automated Machine Learning Based on Radiomics Features of T2WI and RS-EPI DWI to Predict Preoperative T Staging of Rectal Cancer].

Objective: To explore the radiomics features of T2 weighted image (T2WI) and readout-segmented echo-planar imaging (RS-EPI) plus difusion-weighted imaging (DWI), to develop an automated mahchine-learning model based on the said radiomics features, and to test the value of this model in predicting preoperative T staging of rectal cancer.

Methods: The study retrospectively reviewed 131 patients who were diagnosed with rectal cancer confirmed by the pathology results of their surgical specimens at West China Hospital of Sichuan University between October, 2017 and December, 2018. In addition, these patients had preoperative rectal MRI.

[Application of MRI-based Radiomics Models in the Assessment of Hepatic Metastasis of Rectal Cancer].

Obejective: To explore the clinical value of using radiomics models based on different MRI sequences in the assessment of hepatic metastasis of rectal cancer.

Methods: 140 patients with pathologically confirm edrectal cancer were included in the study. They underwent baseline magnetic resonance imaging (MRI) between April 2015 and May 2018 before receiving any treatment.

[The Application Value of Artificial Intelligence-based Filtering and Interpolated Image Reconstruction Algorithm in Abdominal Magnetic Resonance Image Denoising].

Objective: To compare the noise reduction performance of conventional filtering and artificial intelligence-based filtering and interpolation (AIFI) and to explore for optimal parameters of applying AIFI in the noise reduction of abdominal magnetic resonance imaging (MRI).

Methods: Sixty patients who underwent upper abdominal MRI examination in our hospital were retrospectively included. The raw data of T1-weighted image (T1WI), T2-weighted image (T2WI), and dualecho sequences were reconstructed with two image denoising techniques, conventional filtering and AIFI of different levels of intensity.

[Noise Reduction Effect of Deep-learning-based Image Reconstruction Algorithms in Thin-section Chest CT].

Objective: To evaluate the noise reduction effect of deep learning-based reconstruction algorithms in thin-section chest CT images by analyzing images reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and deep learning image reconstruction (DLIR) algorithms.

Methods: The chest CT scan raw data of 47 patients were included in this study. Images of 0.

[Application of 3.0T Time-of-flight Magnetic Resonance Angiography with Sparse Undersampling and Iterative Reconstruction in the Diagnosis of Unruptured Intracranial Aneurysms].

Objective: To evaluate the diagnostic value of 3.0T time-of-flight MR angiography with sparse undersampling and iterative reconstruction (TOFu-MRA) for unruptured intracranial aneurysms (UIAs) on the basis of using digital subtraction angiography (DSA) as the reference standard.

Methods: A total of 65 patients with suspected UIAs were prospectively enrolled and all patients underwent TOFu-MRA and DSA.

Deep learning trained algorithm maintains the quality of half-dose contrast-enhanced liver computed tomography images: Comparison with hybrid iterative reconstruction: Study for the application of deep learning noise reduction technology in low dose.

Purpose: This study compares the image and diagnostic qualities of a DEep Learning Trained Algorithm (DELTA) for half-dose contrast-enhanced liver computed tomography (CT) with those of a commercial hybrid iterative reconstruction (HIR) method used for standard-dose CT (SDCT).

Methods: This study enrolled 207 adults, and they were divided into two groups: SDCT and low-dose CT (LDCT). SDCT was reconstructed using the HIR method (SDCT), and LDCT was reconstructed using both the HIR method (LDCT) and DELTA (LDCT).

[The Application Value of One-stop CT Myocardial Perfusion Imaging in the Evaluation of Patients with Severe Coronary Artery Stenosis].

Purpose "One-stop" CT myocardial perfusion imaging (CT-MPI) was compared with cardiac magnetic resonance(CMR) to investigate its application value in evaluating patients with severe coronary artery stenosis.Fifty patients with coronary artery stenosis≥90% of at least one major coronary arteries comfirmed by coronary angiography (CAG) in the department of cardiology in our hospital, who referred for coronary artery stent implantation were prospectively enrolled. All the patients underwent "One-stop" CT-MPI within a week before surgery, among which 22 patients underwent CMR examination simultaneously.

Achieving Low Radiation Dose in "One-Stop" Myocardial Computed Tomography Perfusion Imaging in Coronary Artery Disease Using 16-cm Wide Detector CT.

Rationale And Objectives: To investigate the feasibility of "one-stop" myocardial computed tomography perfusion (CTP) imaging (combined anatomy, perfusion, and function) in coronary artery disease using 16-cm wide detector CT, compared to conventional coronary CT angiography (CCTA).

Materials And Methods: 442 patients with suspected coronary artery disease were randomly divided into two groups. Patients in group A underwent "one-stop" CTP, whereas group B underwent conventional CCTA.

[Automated Estimation of Stenosis Severity in Coronary Computed Tomography Angiography].

Objective: To determine the value of automated detection in computed tomography angiography (CTA) for cases with greater than 70% coronary stenosis.

Methods: Fifty-seven patients who had both coronary CTA and digital subtraction angiography (DSA) were retrospectively recruited in this study. The patients were categorized into two groups using a cutoff value of 70% stenosis in DSA.

Dynamic contrast-enhanced MR imaging of rectal cancer using a golden-angle radial stack-of-stars VIBE sequence: comparison with conventional contrast-enhanced 3D VIBE sequence.

Purpose: To compare conventional 3D volumetric-interpolated breath-hold examination (C-VIBE) sequence image quality to that of golden-angle radial stack-of stars acquisition scheme (R-VIBE) in rectal cancer patients.

Methods: Seventy-eight patients had undergone pre-contrast C-VIBE, followed by DCE-MRI with R-VIBE and post-contrast C-VIBE in the visualization of rectal cancer. The first phase and the last phase of R-VIBE sequence were compared with pre-contrast and post-contrast C-VIBE sequences, respectively.