A deep learning algorithm for the detection of aortic dissection on non-contrast-enhanced computed tomography via the identification and segmentation of the true and false lumens of the aorta.

Background: Aortic dissection is a life-threatening clinical emergency, but it is often missed and misdiagnosed due to the limitations of diagnostic technology. In this study, we developed a deep learning-based algorithm for identifying the true and false lumens in the aorta on non-contrast-enhanced computed tomography (NCE-CT) scans and to ascertain the presence of aortic dissection. Additionally, we compared the diagnostic performance of this algorithm with that of radiologists in detecting aortic dissection.

Direct abdominal vein thrombus imaging (DATI): a contrast-free black-blood MR technique for the diagnosis of abdominal vein thrombosis.

Objectives: To develop and evaluate a direct abdominal vein thrombus imaging (DATI) technique, based on a respiratory navigating SPACE sequence with DANTE black-blood preparation, for diagnosing abdominal vein thrombosis (AVT) without the use of exogenous contrast agents.

Methods: We prospectively enrolled 10 healthy subjects and 28 suspected AVT patients who underwent DATI scans on 3.0 T MRI.

Black-Blood Magnetization Prepared 2 Rapid Acquisition Gradient Echoes: A Fast and Three-Dimensional MR Black-Blood T Mapping Technique for Quantitative Assessment of Atherosclerosis and Venous Thrombosis.

Background: Blood flow signals may be a confounder in quantifying T values of plaque or thrombus and how to realize black-blood T mapping remains a challenge task.

Purpose: To develop a fast and three-dimensional black-blood T mapping technique for quantitative assessment of atherosclerosis and venous thrombosis.

Study Type: Sequence development and optimization via phantoms and volunteers as well as pilot prospective.

Advances in volumetric bioprinting.

The three-dimensional (3D) bioprinting technologies are suitable for biomedical applications owing to their ability to manufacture complex and high-precision tissue constructs. However, the slow printing speed of current layer-by-layer (bio)printing modality is the major limitation in biofabrication field. To overcome this issue, volumetric bioprinting (VBP) is developed.

Cross-Domain Unpaired Learning for Low-Dose CT Imaging.

Supervised deep-learning techniques with paired training datasets have been widely studied for low-dose computed tomography (LDCT) imaging with excellent performance. However, the paired training datasets are usually difficult to obtain in clinical routine, which restricts the wide adoption of supervised deep-learning techniques in clinical practices. To address this issue, a general idea is to construct a pseudo paired training dataset based on the widely available unpaired data, after which, supervised deep-learning techniques can be adopted for improving the LDCT imaging performance by training on the pseudo paired training dataset.

Thrombus Signal on T1-Weighted Black-Blood MR Predicts Outcomes of Catheter-Directed Thrombolysis in Acute Deep Vein Thrombosis.

Objectives:  Catheter-directed thrombolysis (CDT) is an effective therapy for acute deep vein thrombosis (DVT). However, predicting the CDT outcomes remains elusive. We hypothesized that the thrombus signal on T1-weighted black-blood magnetic resonance (MR) can provide insight into CDT outcomes in acute DVT patients.

Water excitation with LIBRE pulses in three-dimensional variable flip angle fast spin echo for fat-free and large field of view imaging at 3 tesla.

Purpose: To develop and evaluate a sequence in which water excitation with lipid insensitive binomial off-resonant radio frequency excitation (LIBRE) pulses is incorporated into three-dimensional (3D) variable flip angle fast spin echo (LIBRE-vf-FSE) for fat-free and large field of view imaging at 3 Tesla (T).

Materials And Methods: Numerical simulation was conducted to optimize the parameters of LIBRE pulses, including the flip angle, pulse duration, and frequency offset, for maximizing the fat suppression effect of the proposed LIBRE-vf-FSE sequence. The sequence was then implemented at 3 T and assessed in phantoms, lower extremity imaging of 8 healthy volunteers, and head/neck imaging of 5 healthy volunteers.

Black-blood Venous Imaging (BBVI): A Contrast-Free and High-Resolution Magnetic Resonance Approach for Diagnosing IVCS - a Proof of Concept Study.

Background: Iliac vein compression syndrome (IVCS) diagnosis heavily relies on an imaging test. However, non-invasive and contrast-free imaging test for the diagnosis of IVCS remains a big challenge. To address this issue, this prospective study aimed to assess the image quality and diagnostic performance of a magnetic resonance imaging technique, black-blood venous imaging (BBVI), in detecting IVCS by comparing it with contrast-enhanced computed tomography venography (CTV) and using invasive digital subtraction angiography (DSA) as the reference.

A Cascaded Multi-Task Generative Framework for Detecting Aortic Dissection on 3-D Non-Contrast-Enhanced Computed Tomography.

Contrast-enhanced computed tomography (CE-CT) is the gold standard for diagnosing aortic dissection (AD). However, contrast agents can cause allergic reactions or renal failure in some patients. Moreover, AD diagnosis by radiologists using non-contrast-enhanced CT (NCE-CT) images has poor sensitivity.

Knee Bone and Cartilage Segmentation Based on a 3D Deep Neural Network Using Adversarial Loss for Prior Shape Constraint.

Fast and accurate segmentation of knee bone and cartilage on MRI images is becoming increasingly important in the orthopaedic area, as the segmentation is an essential prerequisite step to a patient-specific diagnosis, optimising implant design and preoperative and intraoperative planning. However, manual segmentation is time-intensive and subjected to inter- and intra-observer variations. Hence, in this study, a three-dimensional (3D) deep neural network using adversarial loss was proposed to automatically segment the knee bone in a resampled image volume in order to enlarge the contextual information and incorporate prior shape constraints.

Deep Learning for Accurate Segmentation of Venous Thrombus from Black-Blood Magnetic Resonance Images: A Multicenter Study.

Objective: Deep vein thrombosis (DVT) is the third-largest cardiovascular disease, and accurate segmentation of venous thrombus from the black-blood magnetic resonance (MR) images can provide additional information for personalized DVT treatment planning. Therefore, a deep learning network is proposed to automatically segment venous thrombus with high accuracy and reliability.

Methods: In order to train, test, and external test the developed network, total images of 110 subjects are obtained from three different centers with two different black-blood MR techniques (i.

A Cascaded Deep Learning Framework for Detecting Aortic Dissection Using Non-contrast Enhanced Computed Tomography.

Aortic dissection (AD) is a rare but potentially fatal disease with high mortality. The aim of this study is to synthesize contrast enhanced computed tomography (CE-CT) images from non-contrast CT (NCE-CT) images for detecting aortic dissection. In this paper, a cascaded deep learning framework containing a 3D segmentation network and a synthetic network was proposed and evaluated.

Carotid arterial wall MRI of apolipoprotein e-deficient mouse at 7 T using DANTE-prepared variable-flip-angle rapid acquisition with relaxation enhancement.

Purpose: To optimize a sequence combining the delay alternating with nutation for tailored excitation (DANTE) preparative module with the variable-flip-angle rapid acquisition with relaxation enhancement (VF-RARE) sequence (DANTE-VF-RARE) and to investigate its feasibility for vessel wall imaging in Apolipoprotein E-Deficient (ApoE) mouse at 7 Tesla (T).

Materials And Methods: Specific T1/T2 values were used for producing a sharper vessel wall in the variable-flip-angle optimization scheme. The DANTE RF pulse flip angle and pulse train length were optimized for maximizing the wall-lumen contrast.

Positive Contrast Susceptibility MR Imaging Using GPU-based Primal-Dual Algorithm.

The susceptibility-based positive contrast MR technique was applied to estimate arbitrary magnetic susceptibility distributions of the metallic devices using a kernel deconvolution algorithm with a regularized L-1 minimization. Previously, the first-order primal-dual (PD) algorithm could provide a faster reconstruction time to solve the L-1 minimization, compared with other methods. Here, we propose to accelerate the PD algorithm of the positive contrast image using the multi-core multi-thread feature of graphics processor units (GPUs).

Evaluation of left ventricular strain in patients with arrhythmia based on the 3T MR temporal parallel acquisition technique.

Most of the current studies on myocardial strain are mainly applied in patients with sinus rhythm because the image quality of arrhythmias obtained with conventional scanning sequences does not meet diagnostic needs. Here, we intend to assess left ventricular (LV) global myocardial strain in patients with arrhythmias with 3 Tesla magnetic resonance (MR) and a new cine sequence. Thirty-three patients with arrhythmia and forty-eight subjects with sinus rhythm were enrolled in the study.

Multicenter Study on the Diagnostic Performance of Native-T1 Cardiac Magnetic Resonance of Chronic Myocardial Infarctions at 3T.

Background: Preclinical studies and pilot patient studies have shown that chronic infarctions can be detected and characterized from cardiac magnetic resonance without gadolinium-based contrast agents using native-T1 maps at 3T. We aimed to investigate the diagnostic capacity of this approach for characterizing chronic myocardial infarctions (MIs) in a multi-center setting.

Methods: Patients with a prior MI (n=105) were recruited at 3 different medical centers and were imaged with native-T1 mapping and late gadolinium enhancement (LGE) at 3T.

Comparison Between the Diagnostic Performance of 1.5 T and 3.0 T field Strengths for Detecting Deep Vein Thrombosis Using Magnetic Resonance Black-Blood Thrombus Imaging.

Background: Magnetic resonance (MR) black-blood thrombus imaging (BTI) is an accurate diagnostic technique for detecting deep vein thrombosis (DVT) but to date there have been no studies comparing the diagnostic performance and consistency of this technique at different field strengths. In this study, we evaluated and compared the diagnostic performance of BTI for detecting DVT at 1.5 T and 3.

Susceptibility-based MR Imaging of Nitinol Stent.

Conventional MR techniques have difficulty to accurately localize the stent position and access the stent restenosis because of the effects of susceptibility and radiofrequency (RF) shielding artifacts caused by stent mesh. Previous studies have demonstrated that a susceptibility-based positive contrast MR method exhibits excellent efficacy for visualizing MR compatible metal devices by taking advantage of their high magnetic susceptibility. However, the method is not evaluated in the visualization of stents.

Three Dimensional Positive Contrast Susceptibility Fast Spin Echo MR Imaging with Variable Excitation Pulses and PD Algorithm.

A susceptibility-based positive contrast MR technique is applied to image the MR compatible metallic devices by solving a regularized ℓ1 minimization problem. However, the previous SE/FSE sequence is used for the data acquisition which can result in high SAR and low sampling efficiency in 3D imaging. Therefore, a 3D single slab 3D FSE sequence with slab selective and variable excitation pulse is proposed to implement 3D positive contrast MR imaging for low SAR and acquiring high-resolution 3D images within a shorter timeframe.

Grading of hepatocellular carcinoma based on diffusion weighted images with multiple b-values using convolutional neural networks.

Purpose: To effectively grade hepatocellular carcinoma (HCC) based on deep features derived from diffusion weighted images (DWI) with multiple b-values using convolutional neural networks (CNN).

Materials And Methods: Ninety-eight subjects with 100 pathologically confirmed HCC lesions from July 2012 to October 2018 were included in this retrospective study, including 47 low-grade and 53 high-grade HCCs. DWI was performed for each subject with a 3.

Positive-contrast susceptibility imaging based on first-order primal-dual optimization.

Purpose: To achieve faster reconstruction and better imaging quality of positive-contrast MRI based on the susceptibility mapping by incorporating a primal-dual (PD) formulation.

Methods: The susceptibility-based positive contrast MR technique was applied to estimate arbitrary magnetic susceptibility distributions of the metallic devices using a kernel deconvolution algorithm with a regularized minimization. The regularized positive-contrast inversion problem and its PD formulation were derived.

Positive visualization of MR-compatible nitinol stent using a susceptibility-based imaging technique.

Background: MR-compatible metallic stents have been widely used for the treatment of arterial occlusive diseases. However, conventional MR techniques have difficulty in accurately localizing the stent position and access the stent restenosis because of the susceptibility and radiofrequency (RF) shielding artifacts caused by the stent mesh. Previous studies have demonstrated that a susceptibility-based positive contrast MR method exhibits excellent efficacy for visualizing MR compatible metal devices.

Erratum to "Magnetic Resonance Imaging of Atherosclerosis Using CD81-Targeted Microparticles of Iron Oxide in Mice".

[This corrects the article DOI: 10.1155/2015/758616.].