Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time.

Background: Deep-learning-based reconstruction (DLR) improves the quality of magnetic resonance (MR) images which allows faster acquisitions. The aim of this study was to compare the image quality of standard and accelerated T2 weighted turbo-spin-echo (TSE) images of the prostate reconstructed with and without DLR and to find associations between perceived image quality and calculated image characteristics.

Methods: In a cohort of 47 prospectively enrolled consecutive patients referred for bi-parametric prostate magnetic resonance imaging (MRI), two T2-TSE acquisitions in the transverse plane were acquired on a 3T scanner-a standard T2-TSE sequence and a short sequence accelerated by a factor of two using compressed sensing (CS).

Reduced Radiation Exposure Protocol during Computer Tomography of the Left Atrium Prior to Catheter Ablation in Patients with Atrial Fibrillation.

(1) Background: Computer tomography (CT) is an imaging modality used in the pre-planning of radiofrequency catheter ablation (RFA) procedure in patients with cardiac arrhythmias. However, it is associated with a considerable ionizing radiation dose for patients. This study aims to develop and validate low-dose CT scanning protocols of the left atrium (LA) for RFA guidance.

Iterative machine learning based rotational alignment of brain 3D CT data.

The optimal rotational alignment of brain Computed Tomography (CT) images to a required standard position has a crucial importance for both automatic and manual diagnostic analysis. In this contribution, we present a novel two-step iterative approach for the automatic 3D rotational alignment of brain CT data. The angles of axial and coronal rotations are determined by an unsupervised by localisation of the Midsagittal Plane (MSP) method.

Deep-learning-based fully automatic spine centerline detection in CT data.

In this contribution, we present a fully automatic approach, that is based on two convolution neural networks (CNN) together with a spine tracing algorithm utilizing a population optimization algorithm. Based on the evaluation of 130 CT scans including heavily distorted and complicated cases, it turned out that this new combination enables fast and robust detection with almost 90% of correctly determined spinal centerlines with computing time of fewer than 20 seconds.

Learning-based vertebra localization and labeling in 3D CT data of possibly incomplete and pathological spines.

Background And Objective: We present a fully automatic system based on learning approaches, which aims to localization and identification (labeling) of vertebrae in 3D computed tomography (CT) scans of possibly incomplete spines in patients with bone metastases and vertebral compressions.

Methods: The framework combines a set of 3D algorithms for i) spine detection using a convolution neural network (CNN) ii) spinal cord tracking based on combination of a CNN and a novel growing sphere method with a population optimization, iii) intervertebral discs localization using a novel approach of spatially variant filtering of intensity profiles and iv) vertebra labeling using a CNN-based classification combined with global dynamic optimization.

Results: The proposed algorithm has been validated in testing databases, including also a publicly available dataset.

Synthesis of modified amorphous manganese oxide using low-cost sugars and biochars: Material characterization and metal(loid) sorption properties.

In this study, amorphous Mn oxides (AMOs) and their composites with biochar (BC) were synthesized using different sugars (glucose, sucrose, and molasses), and their sorption efficiency toward Zn(II), Cd(II), and As(V) was tested. Additionally, detailed characterization of synthesized materials using various solid-state analysis methods (e.g.

Stability and stabilizing efficiency of Mg-Fe layered double hydroxides and mixed oxides in aqueous solutions and soils with elevated As(V), Pb(II) and Zn(II) contents.

Although the mechanisms of metal(loid) removal from aqueous solutions using LDHs (layered double hydroxides) and mixed oxides (thermally treated LDHs; CLDHs) have been studied, research dealing with their stability, stabilizing efficiency and remediation potential for contaminated soils remains scarce. We present a complex study investigating the stabilizing efficiency of Mg-Fe LDHs and CLDHs at different conditions, including aqueous solutions and real soils with highly elevated As(V), Pb(II) and Zn(II) concentrations. All studied materials showed excellent (ad)sorption efficiency for As(V), Pb(II) and Zn(II) in aqueous solutions.

Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data.

This paper aims to address the segmentation and classification of lytic and sclerotic metastatic lesions that are difficult to define by using spinal 3D Computed Tomography (CT) images obtained from highly pathologically affected cases. As the lesions are ill-defined and consequently it is difficult to find relevant image features that would enable detection and classification of lesions by classical methods of texture and shape analysis, the problem is solved by automatic feature extraction provided by a deep Convolutional Neural Network (CNN). Our main contributions are: (i) individual CNN architecture, and pre-processing steps that are dependent on a patient data and a scan protocol - it enables work with different types of CT scans; (ii) medial axis transform (MAT) post-processing for shape simplification of segmented lesion candidates with Random Forest (RF) based meta-analysis; and (iii) usability of the proposed method on whole-spine CTs (cervical, thoracic, lumbar), which is not treated in other published methods (they work with thoracolumbar segments of spine only).

AMOchar: Amorphous manganese oxide coating of biochar improves its efficiency at removing metal(loid)s from aqueous solutions.

A novel sorbent made from biochar modified with an amorphous Mn oxide (AMOchar) was compared with pure biochar, pure AMO, AMO+biochar mixtures and biochar+birnessite composite for the removal of various metal(loid)s from aqueous solutions using adsorption and solid-state analyses. In comparison with the pristine biochar, both Mn oxide-biochar composites were able to remove significantly greater quantities of various metal(loid)s from the aqueous solutions, especially at a ratio 2:1 (AMO:biochar). The AMOchar proved most efficient, removing almost 99, 91 and 51% of Pb, As and Cd, respectively.

Whole-body low-dose computed tomography in multiple myeloma staging: Superior diagnostic performance in the detection of bone lesions, vertebral compression fractures, rib fractures and extraskeletal findings compared to radiography with similar radiation exposure.

The primary objective of the present prospective study was to compare the diagnostic performance of conventional radiography (CR) and whole-body low-dose computed tomography (WBLDCT) with a comparable radiation dose reconstructed using hybrid iterative reconstruction technique, in terms of the detection of bone lesions, skeletal fractures, vertebral compressions and extraskeletal findings. The secondary objective was to evaluate lesion attenuation in relation to its size. A total of 74 patients underwent same-day skeletal survey by CR and WBLDCT.

Sub-milliSievert ultralow-dose CT colonography with iterative model reconstruction technique.

Purpose. The purpose of this study was to evaluate the technical and diagnostic performance of sub-milliSievert ultralow-dose (ULD) CT colonograpy (CTC) in the detection of colonic and extracolonic lesions. Materials and Methods.

Submilisievert ultralow-dose CT colonography using iterative reconstruction technique: a feasibility study.

Background: Computed tomography (CT) colonography is a well established modality for the examination of symptomatic patients as well as in screening. Recent technical advances in improving image quality by iterative reconstruction contribute to the reduction of the radiation dose which is a major concern in CT imaging.

Purpose: To evaluate image quality of ultralow-dose submilisievert CT colonography using hybrid iterative reconstruction technique.

Novel registration-based framework for CT angiography in lower legs.

Proper subtraction and visualization of contrast-enhanced blood vessels in lower extremities using computed tomography angiography (CTA) is based on precise masking of all non-contrasted structures in the area, and it is the main prerequisite for correct diagnosis and decision on treatment for peripheral arterial occlusive disease (PAOD). Because of possible motion of patients during the CTA examination, precise elimination of non-contrasted tissues, including bones, calcifications, and soft tissue, is still very challenging for lower legs, that is, from knees to toes. We propose novel registration-based framework for detection and correction of the motion in lower legs, which typically occurs between and during CTA pre-contrast and post-contrast acquisitions.

Improved disparity based image processing in 3D CT subtractive angiography.

The paper describes a set of approaches and routines designed to improve results in CT based 3D subtractive angiography of lower extremities via better global locally defined image data registration. Starting from the generic concept of 3D disparity-based flexible registration, modifications of this idea are made founded on prior anatomical knowledge, as segmentation into individual bone areas, their rigid registration followed by constrained flexible registration, and flexible registration of soft tissue volumes. After final subtraction, fusion of the individually derived volumes into the full volume of extremities provides the medically assessable results.

Evaluation of left atrial function by multidetector computed tomography before left atrial radiofrequency-catheter ablation: comparison of a manual and automated 3D volume segmentation method.

Introduction: The purpose of this study was to compare a manual and automated 3D volume segmentation tool for evaluation of left atrial (LA) function by 64-slice multidetector-CT (MDCT).

Methods And Materials: In 33 patients with paroxysmal atrial fibrillation a MDCT scan was performed before radiofrequency-catheter ablation. Atrial function (minimal volume (LAmin), maximal volume (LAmax), stroke volume (SV), ejection fraction (EF)) was evaluated by two readers using a manual and an automatic tool and measurement time was evaluated.

Phantom-less QCT BMD system as screening tool for osteoporosis without additional radiation.

Purpose: Phantom-less bone mineral density (PLBMD) systems are easily integrated into the CT workflow for non-dedicated Quantitative CT (QCT) BMD measurements in thoracic and abdominal scans. This in vivo retrospective study aims to determine accuracy and precision of the PLBMD option located on the Extended Brilliance Workspace (Philips Medical Systems, Cleveland, OH, US) from both cross-sectional and longitudinal image data.

Materials And Methods: The cross-sectional comparison with phantom-based QCT BMD was performed for 82 patients (61 female, 21 male) with a mean age of (63.