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Journal of Medical Imaging

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https://www.readbyqxmd.com/read/29250571/estimability-index-for-volume-quantification-of-homogeneous-spherical-lesions-in-computed-tomography
#1
Ehsan Samei, Marthony Robins, Baiyu Chen, Greeshma Agasthya
Volume of lung nodules is an important biomarker, quantifiable from computed tomography (CT) images. The usefulness of volume quantification, however, depends on the precision of quantification. Experimental assessment of precision is time consuming. A mathematical estimability model was used to assess the quantification precision of CT nodule volumetry in terms of an index ([Formula: see text]), incorporating image noise and resolution, nodule properties, and segmentation software. The noise and resolution were characterized in terms of noise power spectrum and task transfer function...
July 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29250570/estimating-detectability-index-in-vivo-development-and-validation-of-an-automated-methodology
#2
Taylor Brunton Smith, Justin Solomon, Ehsan Samei
This study's purpose was to develop and validate a method to estimate patient-specific detectability indices directly from patients' CT images (i.e., in vivo). The method extracts noise power spectrum (NPS) and modulation transfer function (MTF) resolution properties from each patient's CT series based on previously validated techniques. These are combined with a reference task function (10-mm disk lesion with [Formula: see text] HU contrast) to estimate detectability indices for a nonprewhitening matched filter observer model...
July 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29250569/influence-of-radiology-expertise-on-the-perception-of-nonmedical-images
#3
Brendan Kelly, Louise A Rainford, Mark F McEntee, Eoin C Kavanagh
Identifying if participants with differing diagnostic accuracy and visual search behavior during radiologic tasks also differ in nonradiologic tasks is investigated. Four clinician groups with different radiologic experience were used: a reference expert group of five consultant radiologists, four radiology registrars, five senior house officers, and six interns. Each of the four clinician groups is known to have significantly different performance in the identification of pneumothoraces in chest x-ray. Each of the 20 participants was shown 6 nonradiologic images (3 maps and 3 sets of geometric shapes) and was asked to perform search tasks...
July 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29430481/robotic-drill-guide-positioning-using-known-component-3d-2d-image-registration
#4
Thomas Yi, Vignesh Ramchandran, Jeffrey H Siewerdsen, Ali Uneri
A method for x-ray image-guided robotic instrument positioning is reported and evaluated in preclinical studies of spinal pedicle screw placement with the aim of improving delivery of transpedicle K-wires and screws. The known-component (KC) registration algorithm was used to register the three-dimensional patient CT and drill guide surface model to intraoperative two-dimensional radiographs. Resulting transformations, combined with offline hand-eye calibration, drive the robotically held drill guide to target trajectories defined in the preoperative CT...
April 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29430480/further-characterization-of-changes-in-axial-strain-elastograms-due-to-the-presence-of-slippery-tumor-boundaries
#5
Christopher Uff, Leo Garcia, Jeremie Fromageau, Aabir Chakraborty, Neil Dorward, Jeffrey Bamber
Elastography measures tissue strain, which can be interpreted under certain simplifying assumptions to be representative of the underlying stiffness distribution. This is useful in cancer diagnosis where tumors tend to have a different stiffness to healthy tissue and has also shown potential to provide indication of the degree of bonding at tumor-tissue boundaries, which is clinically useful because of its dependence on tumor pathology. We consider the changes in axial strain for the case of a symmetrical model undergoing uniaxial compression, studied by characterizing changes in tumor contrast transfer efficiency (CTE), inclusion to background strain contrast and strain contrast generated by slip motion, as a function of Young's modulus contrast and applied strain...
April 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29392162/combining-intraoperative-ultrasound-brain-shift-correction-and-augmented-reality-visualizations-a-pilot-study-of-eight-cases
#6
Ian J Gerard, Marta Kersten-Oertel, Simon Drouin, Jeffery A Hall, Kevin Petrecca, Dante De Nigris, Daniel A Di Giovanni, Tal Arbel, D Louis Collins
We present our work investigating the feasibility of combining intraoperative ultrasound for brain shift correction and augmented reality (AR) visualization for intraoperative interpretation of patient-specific models in image-guided neurosurgery (IGNS) of brain tumors. We combine two imaging technologies for image-guided brain tumor neurosurgery. Throughout surgical interventions, AR was used to assess different surgical strategies using three-dimensional (3-D) patient-specific models of the patient's cortex, vasculature, and lesion...
April 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29392161/on-the-fly-augmented-reality-for-orthopedic-surgery-using-a-multimodal-fiducial
#7
Sebastian Andress, Alex Johnson, Mathias Unberath, Alexander Felix Winkler, Kevin Yu, Javad Fotouhi, Simon Weidert, Greg Osgood, Nassir Navab
Fluoroscopic x-ray guidance is a cornerstone for percutaneous orthopedic surgical procedures. However, two-dimensional (2-D) observations of the three-dimensional (3-D) anatomy suffer from the effects of projective simplification. Consequently, many x-ray images from various orientations need to be acquired for the surgeon to accurately assess the spatial relations between the patient's anatomy and the surgical tools. We present an on-the-fly surgical support system that provides guidance using augmented reality and can be used in quasiunprepared operating rooms...
April 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29376105/psnet-prostate-segmentation-on-mri-based-on-a-convolutional-neural-network
#8
Zhiqiang Tian, Lizhi Liu, Zhenfeng Zhang, Baowei Fei
Automatic segmentation of the prostate on magnetic resonance images (MRI) has many applications in prostate cancer diagnosis and therapy. We proposed a deep fully convolutional neural network (CNN) to segment the prostate automatically. Our deep CNN model is trained end-to-end in a single learning stage, which uses prostate MRI and the corresponding ground truths as inputs. The learned CNN model can be used to make an inference for pixel-wise segmentation. Experiments were performed on three data sets, which contain prostate MRI of 140 patients...
April 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29376104/pairwise-domain-adaptation-module-for-cnn-based-2-d-3-d-registration
#9
Jiannan Zheng, Shun Miao, Z Jane Wang, Rui Liao
Accurate two-dimensional to three-dimensional (2-D/3-D) registration of preoperative 3-D data and intraoperative 2-D x-ray images is a key enabler for image-guided therapy. Recent advances in 2-D/3-D registration formulate the problem as a learning-based approach and exploit the modeling power of convolutional neural networks (CNN) to significantly improve the accuracy and efficiency of 2-D/3-D registration. However, for surgery-related applications, collecting a large clinical dataset with accurate annotations for training can be very challenging or impractical...
April 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29340290/tailoring-four-dimensional-cone-beam-ct-acquisition-settings-for-fiducial-marker-based-image-guidance-in-radiation-therapy
#10
Peng Jin, Niek van Wieringen, Maarten C C M Hulshof, Arjan Bel, Tanja Alderliesten
Use of four-dimensional cone-beam CT (4D-CBCT) and fiducial markers for image guidance during radiation therapy (RT) of mobile tumors is challenging due to the trade-off among image quality, imaging dose, and scanning time. This study aimed to investigate different 4D-CBCT acquisition settings for good visibility of fiducial markers in 4D-CBCT. Using these 4D-CBCTs, the feasibility of marker-based 4D registration for RT setup verification and manual respiration-induced motion quantification was investigated...
April 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29340289/automatic-segmentation-method-of-pelvic-floor-levator-hiatus-in-ultrasound-using-a-self-normalizing-neural-network
#11
Ester Bonmati, Yipeng Hu, Nikhil Sindhwani, Hans Peter Dietz, Jan D'hooge, Dean Barratt, Jan Deprest, Tom Vercauteren
Segmentation of the levator hiatus in ultrasound allows the extraction of biometrics, which are of importance for pelvic floor disorder assessment. We present a fully automatic method using a convolutional neural network (CNN) to outline the levator hiatus in a two-dimensional image extracted from a three-dimensional ultrasound volume. In particular, our method uses a recently developed scaled exponential linear unit (SELU) as a nonlinear self-normalizing activation function, which for the first time has been applied in medical imaging with CNN...
April 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29322072/plan-in-2-d-execute-in-3-d-an-augmented-reality-solution-for-cup-placement-in-total-hip-arthroplasty
#12
Javad Fotouhi, Clayton P Alexander, Mathias Unberath, Giacomo Taylor, Sing Chun Lee, Bernhard Fuerst, Alex Johnson, Greg Osgood, Russell H Taylor, Harpal Khanuja, Mehran Armand, Nassir Navab
Reproducibly achieving proper implant alignment is a critical step in total hip arthroplasty procedures that has been shown to substantially affect patient outcome. In current practice, correct alignment of the acetabular cup is verified in C-arm x-ray images that are acquired in an anterior-posterior (AP) view. Favorable surgical outcome is, therefore, heavily dependent on the surgeon's experience in understanding the 3-D orientation of a hemispheric implant from 2-D AP projection images. This work proposes an easy to use intraoperative component planning system based on two C-arm x-ray images that are combined with 3-D augmented reality (AR) visualization that simplifies impactor and cup placement according to the planning by providing a real-time RGBD data overlay...
April 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29285519/characterization-and-correction-of-intraoperative-soft-tissue-deformation-in-image-guided-laparoscopic-liver-surgery
#13
Jon S Heiselman, Logan W Clements, Jarrod A Collins, Jared A Weis, Amber L Simpson, Sunil K Geevarghese, T Peter Kingham, William R Jarnagin, Michael I Miga
Laparoscopic liver surgery is challenging to perform due to a compromised ability of the surgeon to localize subsurface anatomy in the constrained environment. While image guidance has the potential to address this barrier, intraoperative factors, such as insufflation and variable degrees of organ mobilization from supporting ligaments, may generate substantial deformation. The severity of laparoscopic deformation in humans has not been characterized, and current laparoscopic correction methods do not account for the mechanics of how intraoperative deformation is applied to the liver...
April 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29250568/selecting-electrode-configurations-for-image-guided-cochlear-implant-programming-using-template-matching
#14
Dongqing Zhang, Yiyuan Zhao, Jack H Noble, Benoit M Dawant
Cochlear implants (CIs) are neural prostheses that restore hearing using an electrode array implanted in the cochlea. After implantation, the CI processor is programmed by an audiologist. One factor that negatively impacts outcomes and can be addressed by programming is cross-electrode neural stimulation overlap (NSO). We have proposed a system to assist the audiologist in programming the CI that we call image-guided CI programming (IGCIP). IGCIP permits using CT images to detect NSO and recommend deactivation of a subset of electrodes to avoid NSO...
April 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29430479/toward-quantitative-quasistatic-elastography-with-a-gravity-induced-deformation-source-for-image-guided-breast-surgery
#15
Rebekah H Griesenauer, Jared A Weis, Lori R Arlinghaus, Ingrid M Meszoely, Michael I Miga
Biomechanical breast models have been employed for applications in image registration and diagnostic analysis, breast augmentation simulation, and for surgical and biopsy guidance. Accurate applications of stress-strain relationships of tissue within the breast can improve the accuracy of biomechanical models that attempt to simulate breast deformations. Reported stiffness values for adipose, glandular, and cancerous tissue types vary greatly. Variations in reported stiffness properties have been attributed to differences in testing methodologies and assumptions, measurement errors, and natural interpatient differences in tissue elasticity...
January 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29430478/use-of-clinical-mri-maximum-intensity-projections-for-improved-breast-lesion-classification-with-deep-convolutional-neural-networks
#16
Natalia Antropova, Hiroyuki Abe, Maryellen L Giger
Deep learning methods have been shown to improve breast cancer diagnostic and prognostic decisions based on selected slices of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). However, incorporation of volumetric and temporal components into DCE-MRIs has not been well studied. We propose maximum intensity projection (MIP) images of subtraction MRI as a way to simultaneously include four-dimensional (4-D) images into lesion classification using convolutional neural networks (CNN). The study was performed on a dataset of 690 cases...
January 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29430477/automated-segmentation-of-hyperreflective-foci-in-spectral-domain-optical-coherence-tomography-with-diabetic-retinopathy
#17
Idowu Paul Okuwobi, Wen Fan, Chenchen Yu, Songtao Yuan, Qinghuai Liu, Yuhan Zhang, Bekalo Loza, Qiang Chen
We propose an automated segmentation method to detect, segment, and quantify hyperreflective foci (HFs) in three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT). The algorithm is divided into three stages: preprocessing, layer segmentation, and HF segmentation. In this paper, a supervised classifier (random forest) was used to produce the set of boundary probabilities in which an optimal graph search method was then applied to identify and produce the layer segmentation using the Sobel edge algorithm...
January 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29430476/determination-and-verification-of-the-x-ray-spectrum-of-a-ct-scanner
#18
Ahmad Ibrahim Hassan, Martin Skalej, Helmut Schlattl, Christoph Hoeschen
The accuracy of Monte Carlo (MC) simulations in estimating the computed tomography radiation dose is highly dependent on the proprietary x-ray source information. To address this, this study develops a method to precisely estimate the x-ray spectrum and bowtie (BT) filter thickness of the x-ray source based on physical measurements and calculations. The static x-ray source of the CT localizer radiograph was assessed to measure the total filtration at the isocenter for the x-ray spectrum characterization and the BT profile (air-kerma values as a function of fan angle)...
January 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29430475/h-scan-analysis-of-thyroid-lesions
#19
Gary R Ge, Rosa Laimes, Joseph Pinto, Jorge Guerrero, Himelda Chavez, Claudia Salazar, Roberto J Lavarello, Kevin J Parker
The H-scan analysis of ultrasound images is a matched-filter approach derived from analysis of scattering from incident pulses in the form of Gaussian-weighted Hermite polynomial functions. This framework is applied in a preliminary study of thyroid lesions to examine the H-scan outputs for three categories: normal thyroid, benign lesions, and cancerous lesions within a total group size of 46 patients. In addition, phantoms comprised of spherical scatterers are analyzed to establish independent reference values for comparison...
January 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29430474/characterization-and-validation-of-the-thorax-phantom-lungman-for-dose-assessment-in-chest-radiography-optimization-studies
#20
Sunay Rodríguez Pérez, Nicholas William Marshall, Lara Struelens, Hilde Bosmans
This work concerns the validation of the Kyoto-Kagaku thorax anthropomorphic phantom Lungman for use in chest radiography optimization. The equivalence in terms of polymethyl methacrylate (PMMA) was established for the lung and mediastinum regions of the phantom. Patient chest examination data acquired under automatic exposure control were collated over a 2-year period for a standard x-ray room. Parameters surveyed included exposure index, air kerma area product, and exposure time, which were compared with Lungman values...
January 2018: Journal of Medical Imaging
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