Aims: Prognosis of lung mathology severity after Covid-19 infection using chest X-ray time series Background: We have been inspired by methods analysing time series of images in remote sensing for change detection. During the current Covid-19 pandemic, our motivation is to provide an automatic tool to predict severity of lung pathologies due to Covid-19. This can be done by analysing images of the same patient acquired at different dates. Since no analytical model is available, and also no accurate quantification tools can be used due to many unknowns about the pathology, feature-free methods are good candidates to analyse such temporal images. Objective: This contribution helps improving performances of medical structures facing the Covid-19 pandemic. The first impact is medical and social since more lives could be saved with a 92% rate of good prognosis. In addition to that, patients in intensive care units (up to 15%) could a posteriori suffer from less sequels due to an early and accurate prognosis of their PP. Moreover, accurate prognosis can lead to a better planning of patient’s transfer between units and hospitals, which is linked to the second claimed economical impact. Indeed, prognosis is linked to lower treatment costs due to an optimized predictive protocol using ragiological prognosis. Methods: Using Convolutional Neural Networks (CNN) in combination with Recurrent Neural Networks (RNN). Spatial and temporal features are combines to analyse image time series. A prognosis score is delivered indicating the severity of the pathology. Learning is made on a publicly available database. Results: When applied to radiological time-series, promising results are obtained with an accuracy rates higher than 92%. Sensitivity and specificity rates are also very interesting. Conclusion: Our method is segmentation-free, which makes it competitive with respect to other assessment methods relying on time-consuming lung segmentation algorithms. When applied on radiographic data, the proposed ProgNet architecture showed promising results with good classification performances, especially for ambiguous cases. Specifically, the reported low false positive rates are interesting for an accurate and personalised care workflow.
Objective: This study aims to investigate the use of 3D printing techniques for the fabrication of physical breast phantoms, suitable for conventional and phase contrast breast imaging. Such phantoms could provide essential information for the design, development and optimization of emerging X-ray imaging modalities. Materials and Methods: Physical phantoms were constructed using two 3D printing techniques: Fused Deposition Modeling and Stereolithography. Eight materials suitable for 3D printing, including thermoplastic filaments and photopolymer resins, were investigated for the optimal representation of breast tissues, based on their attenuation and refractive characteristics. The phantoms consisted of a 3D-printed mold, which was then manually filled with paraffin wax. Additionally, a 3D complex-patterned layer and details representing abnormalities were embedded in different depths. Images of the phantoms were obtained in attenuation and phase contrast mode. Experiments were conducted using an X-ray microfocus tube with Tungsten anode set to 55kVp, combined with a photon-counting detector. The distance between source and detector was 56.5cm. The images were acquired at different object-to-detector distances starting from 5cm up to 40cm in a free space propagation set-up. Results and Conclusion: Results show that among all combinations with paraffin used as an adipose substitute, phantoms created with the Stereolithography technique and resins (especially Flex) as glandular equivalent, were found to be more appropriate for both attenuation and phase contrast imaging. The edge enhancement effect was well observed in the experimental images acquired at 35cm object-to-detector distance, indicating the potential for improved feature visualization using this set-up in phase contrast compared to attenuation mode.
Aims: Digital retinal images are commonly used for hard exudates and lesion detection. These images are rarely noiseless and therefore before any further processing they should be underwent noise removal. Background: An efficient segmentation method is then needed to detect and discern the lesions from the retinal area. Objective: In this paper, a hybrid method is presented for digital retinal image processing for diagnosis and screening purposes. The aim of this study is to present a supervised/semi-supervised approach for exudate detection in fundus images and also to analyze the method to find the optimum structure. Methods: Ripplet transform and cycle spinning method is first used to remove the noises and artifacts. Results: The noises may be normal or any other commonly occurring forms such as salt and pepper. The image is transformed into fuzzy domain after it is denoised. Conclusion: A cellular learning automata model is used to detect any abnormality on the image which is related to a lesion. The automaton is created with an extra term as the rule updating term to improve the adaptability and efficiency of the cellular automata.Three main statistical criteria are introduced as the sensitivity, specificity and accuracy. A number of 50 retinal images with visually detection hard exudates and lesions are the experimental dataset for evaluation and validation of the method.
Background:Ultrasound has been widely used in clinical settings for the assessment of different types of Arthritis as well as in their management. This Review study assessed the diagnostic value of Ultrasonography in comparison with major types of Arthritis namely Osteoarthritis (OA), Rheumatoid Arthritis (RA), Gouty Arthritis (GA), Pseudogout (Calcium Pyrophosphate Deposition Disease - CPPD), Psoriatic Arthritis (PA), Infectious Arthritis (IA) and Spondyloarthritis (SA).Methodology:Computerized literature search of PubMed was conducted from 1990 to present, for publications in English on diagnostic ultrasonography and major types of arthritis. A total of 206 publications were identified. Experimental and clinical studies that focused on the ultrasound features of the major types of Arthritis were accepted. A total of 52 out of the 206 publications, met our search criteria. Among these, 12 studies focused on OA, 7 on RA, 7 on GA, 5 on CPPD, 10 on PA, 4 on IA, and 7 on SA. From all the studies, some distinctive US features are reviewed for each of the major arthritis. Some of the features were unique and some overlapped.Conclusion:Ultrasound may demonstrate the ability to differentiate between the major types of Arthritis on a basic level when combined with history and physical examination. This can prove to be beneficial in the early diagnosis of the major types of arthritis, but with few limitations. This review literature shows that Ultrasound can be very helpful in bed side analysis of the major types of arthritis as well as in differentiating between them, because this modality besides being non-invasive is also very cheap.
Background: The anatomical assessment of the arrangement of facial soft tissues has important applications in different fields from orthodontics to plastic surgery. One of the issues concerns the relationship between facial soft tissue thickness and skeletal class. Literature mainly deals with adult populations, whereas very few studies have been focused on children. Objective: This study aims at investigating the relationship between midline facial soft tissue thickness and skeletal classes in Italian pre-treatment orthodontic child patients. Methods: Lateral cephalometric X-ray films were obtained from 220 healthy Caucasoid children (91 males and 129 females), aged between 6 and 18 years (Class I: 41 males and 70 females; Class II: 18 males and 25 females; Class III: 32 males and 34 females). All the films were digitized and 14 soft tissue thicknesses were measured on the midface; in addition, the skeletal class was assessed according to the corrected ANB angle (ANBc). Differences in facial soft tissue thickness according to sex and skeletal class were assessed through two-way ANOVA test (p<0.01). Results: Statistically significant differences according to sex were found for labrale superius, stomion and labrale inferius, with thicker soft tissues in males than in females (p<0.01). Only measurements at labrale superius and gnathion showed statistically significant differences according to skeletal class, with thicker soft tissues in Class III children and thinner ones in Class II children (p<0.01). Conclusion: The limited number of investigations, as well as the differences in protocols, renders the comparison of results from different studies difficult, suggesting further investigations to enlighten this complex and debated anatomical issue.
Objective: To investigate and validate the role of the Knee-chest Decubitus position in fetal facial feature delineation by 4D Ultrasonography. Methods: Pregnant women were randomly divided into two groups: One group underwent knee-chest decubitus position prior to re-examination, and the second group underwent free activities like walking for 5, 15 or 30 minutes followed by a re-examination of the fetus. The acceptability of the fetal facial images following the two above mentioned activities was compared. Results: The Knee-chest Decubitus position was identified to be a more successful procedure for obtaining acceptable images. Additionally, it improved the fetal position and the resulting images were achieved significantly rapidly by this maneuver compared to free movement. Conclusion: The knee-chest decubitus position is simple, easy, safe and fast and thus of great convenience and promising for pregnant women.
Purpose: To evaluate aortic pulse wave velocities obtained by real-time phase-contrast (PC) MRI in comparison to cine PC MRI. Methods: Real-time PC MRI of eight healthy volunteers employed highly undersampled radial FLASH sequences and phase-sensitive image reconstructions by regularized nonlinear inversion (NLINV) at 40 ms temporal resolution and 1.3 mm in-plane resolution. Pulse wave velocities were analyzed for combinations of 2, 3 and 4 locations of aortic flow using time-to-upslope and cross-correlation methods. Results: For the time-to-upslope analysis mean pulse wave velocities ranged from 3.5 to 3.9 m s-1 for real-time PC MRI and from 3.5 to 3.8 m s-1 for cine PC MRI. A cross-correlation analysis of the same data resulted in 2.9 to 3.3 m s-1 and 3.3 to 3.7 m s-1, respectively. Conclusion: Real-time PC MRI determined aortic pulse wave velocities from single cardiac cycles in close correspondence to values obtained by cine PC MRI.
Purpose: To develop a method for T1 mapping at high spatial resolution and for multiple slices. Methods: The proposed method emerges as a single-shot inversion-recovery experiment which covers the entire spin- lattice relaxation process by serial acquisitions of highly undersampled radial FLASH images, either in single-slice or multi-slice mode. Serial image reconstructions are performed in time-reversed order and first involve regularized nonline- ar inversion (NLINV) to estimate optimum coil sensitivity profiles. Subsequently, the coil profiles are fixed for the calcu- lation of differently T1-weighted frames and the resulting linear inverse problem is solved by a conjugate gradient (CG) technique. T1 values are obtained by pixelwise fitting with a Deichmann correction modified for multi-slice applications. Results: T1 accuracy was validated for a reference phantom. For human brain, T1 maps were obtained at 0.5 mm resolu- tion for single-slice acquisitions and at 0.75 mm resolution for up to 5 simultaneous slices (5 mm thickness). Correspond- ing T1 maps of the liver were acquired at 1 mm and 1.5 mm resolution, respectively. All T1 values were in agreement with literature data. Conclusion: Inversion-recovery sequences with highly undersampled radial FLASH images and NLINV/CG reconstruc- tion allow for fast, robust and accurate T1 mapping at high spatial resolution and for multiple slices.
Objective: The exquisite bone detail offered by computed tomography makes it the ideal modality for evaluation of bone healing. However, few studies have investigated the normal computed tomographic appearance of the sternum after median sternotomy and, to the best of our knowledge, no computed tomographic classification of sternal healing has been proposed. Given the potential benefit of objective criteria, we propose a validated scoring classification of sternal healing using computed tomography for both clinical and investigational purposes. Methods: Computed tomography scans from 20 patients who underwent a median sternotomy were evaluated for sternal healing at either 3 or 6 months postoperatively. Five anatomic locations along the sternum were selected using defined criteria, and a 6-point quantitative scale was developed to evaluate sternal healing. Independent radiologists read and scored each of the 5 locations on the sternum. Inter- and intra-observer variability was assessed by calculating the kappa statistics to measure the reliability of the scoring algorithm. Results: Calculation of the kappa statistics indicated substantial agreement for intra-observer variability and substantial to almost perfect agreement for inter-observer variability. For intra-observer variability, the kappa statistics ranged from 0.591 to 0.802, and for inter-observer variability, the kappa statistics ranged from 0.590 to 0.969. When the two radiologists differed, the magnitude of the difference was no more than 1 or 2 points. Conclusion: This simple system of evaluating sternal healing had high inter- and intra-observer reliability. Therefore, it may be considered a valid method for assessing sternal osteosynthesis for both clinical and investigative purposes. Ultramini abstract: (49 words): Few studies have investigated the normal computed tomography appearance of the sternum after median sternotomy, and we knew of no computed tomography-based classification of sternal healing. Given the potential benefit of objective criteria, we designed and validated a scoring classification of sternal osteosynthesis for both clinical and investigational purposes.
Purpose: To evaluate the temporal accuracy of a self-consistent nonlinear inverse reconstruction method (NLINV) for real-time MRI using highly undersampled radial gradient-echo sequences and to present an open source framework for the motion assessment of real-time MRI methods. Methods: Serial image reconstructions by NLINV combine a joint estimation of individual frames and corresponding coil sensitivities with temporal regularization to a preceding frame. The temporal fidelity of the method was determined with a phantom consisting of water-filled tubes rotating at defined angular velocity. The conditions tested correspond to real- time cardiac MRI using SSFP contrast at 1.5 T (40 ms resolution) and T1 contrast at 3.0 T (33 ms and 18 ms resolution). In addition, the performance of a post-processing temporal median filter was evaluated. Results: NLINV reconstructions without temporal filtering yield accurate estimations as long as the speed of a small moving object corresponds to a spatial displacement during the acquisition of a single frame which is smaller than the object itself. Faster movements may lead to geometric distortions. For small objects moving at high velocity, a median filter may severely compromise the spatiotemporal accuracy. Conclusion: NLINV reconstructions offer excellent temporal fidelity as long as the image acquisition time is short enough to adequately sample ("freeze") the object movement. Temporal filtering should be applied with caution. The motion framework emerges as a valuable tool for the evaluation of real-time MRI methods.
When performing endovascular aortic repair (EVAR) procedures, clinicians sometimes encounter cases in which the indications for EVAR are beyond the instruction for use (IFU). In EVAR, proximal neck fixation is the most important factor. This report describes several techniques and tips for EVAR in cases presenting with a difficult short neck or angled neck, especially when using the Gore Excluder®. Endo-wedge techniques (EWT), including non-sheath-assisted EWT with sheath-assisted strategies, are presented. We describe the scrum technique, which is a special procedure that is only feasible when using the Gore Excluder®. In this technique, intentional wire bending around the proximal neck portion occurs by pushing from both sides of the guidewire. This technique is applicable in cases with a sharply angled neck. The scrum with EWT is a more precise strategy for cases with a short or angled neck. Other troublesome cases include limb occlusion caused by weak points in the Gore Excluder® body and legs. The very rare complication of stent-graft collapse is also reviewed.
Purpose: The efficacy and safety of percutaneous transhepatic portal embolization (PTPE) with dehydrated ethanol was determined by measuring the liver lobe volume before and after the procedure. Materials and Methods: A total of 38 patients (25 men, 13 women; mean age: 62.0 ± 10.8 years) who underwent PTPE with dehydrated ethanol between April 2005 and March 2011 participated in this study. Dehydrated ethanol containing 17% lipiodol was injected into the target portal vein branch under balloon occlusion, and the portal vein was subsequently embolized. The liver lobe volume was measured via contrast-enhanced computed tomography, and the percent increase in the unembolized lobe volume was then calculated. In addition, PTPE-related complications were surveyed, and the procedural safety was evaluated. Results: The mean percent increase in the unembolized lobe volume after PTPE was 33.8% ± 20.2%. The procedure could not be completed in one patient because of an insufficient increase in the unembolized lobe volume. No serious post- PTPE complications were observed. Conclusion: These data suggest that PTPE with dehydrated ethanol is a safe and effective method for enlarging the planned residual liver volume before extensive liver resection.
In this paper, a combined Fractal and Wavelet (CFW) compression algorithm targeting x-ray angiogram images is proposed. Initially, the image is decomposed using wavelet transform. The smoothness of the low frequency part of the image appears as an approximation image with higher self similarities, therefore, it is coded using a fractal coding technique. However, the rest of the image is coded using an adaptive wavelet thresholding technique. This model is implemented and its performance is compared with best performances of the available published algorithms. A data set containing 1000 x-ray angiograms is used to study the performance of the algorithm. A minimum compression ratio of 30 with a peak signal to noise ratio (PSNR) of 36 dB and percent diameter stenosis deviation of (<0.2%) was achieved. Results demonstrate the effectiveness of the proposed technique in obtaining a diagnostic quality of reconstructed images at very low bit rates.
In this paper, a polynomial fitting improved Bayesian approach is proposed for the reconstruction of volumetric metabolite images from long echo time (TE) whole brain proton magnetic resonance spectroscopic imaging (MRSI) data. The proposed algorithm uses a modified EM (expectation maximization) algorithm that takes into account the partial volume effects contained inside a thick slice MRSI. It incorporates high resolution volumetric magnetic resonance imaging (MRI) as a priori information. It further integrates the polynomial fitting method to smooth out artificial edges before the high resolution metabolite images are reconstructed. Our proposed reconstruction method has successfully extended our existing reconstruction of two dimensional (2D) metabolite images to 3D cases. The experimental results show that resolution enhanced volumetric metabolite images are reconstructed.
The quantitative analysis of blood vessel volumes from magnetic resonance angiograms (MRA) or μCT images is difficult and time-consuming. This fact, when combined with a study that involves multiple scans of multiple subjects, can represent a significant portion of research time. In order to enhance analysis options and to provide an automated and fast analysis method, we developed a software plugin for the ImageJ and Fiji image processing frameworks that enables the quick and reproducible volume quantification of blood vessel segments. The novel plugin named Volume Calculator (VolCal), accepts any binary (thresholded) image and produces a three-dimensional schematic representation of the vasculature that can be directly manipulated by the investigator. Using MRAs of the mouse hindlimb ischemia model, we demonstrate quick and reproducible blood vessel volume calculations with 95 - 98% accuracy. In clinical settings this software may enhance image interpretation and the speed of data analysis and thus enhance intervention decisions for example in peripheral vascular disease or aneurysms. In summary, we provide a novel, fast and interactive quantification of blood vessel volumes for single blood vessels or sets of vessel segments with particular focus on collateral formation after an ischemic insult.
Blood oxygenation level-dependent (BOLD) imaging is firmly established as a powerful tool for clinical and research studies in the brain. Though, borrowing from the brain methodology, the technique was early adapted for the heart, due to artifacts unique to myocardial imaging; cardiac and respiratory motion, flow and blood pool effects, and magnetic susceptibility effects arising from the adjacent lungs and bowel, and from the veins at the surface of the heart, cardiac application has largely been limited to research groups. In this paper we aim to focus on these factors affecting ease and reliability of BOLD myocardial imaging, and highlight advances which have the potential to make this methodology more widely accessible to clinicaland physiologybased practioners who may have an interest in exploring this area in collaboration with technical magnetic resonance imaging colleagues
Since language necessarily unfolds over time, language comprehension involves the processing of a sequentially ordered input. In this paper, we review neuroimaging findings on syntactic processing and word order variations in order to shed light on the neural bases of linguistic sequencing, focusing particularly on the role of prefrontal cortex (including Broca's region). On the basis of the full range of available data from several languages, we argue that different types of sequencing cues correlate with activation along an anterior-posterior gradient in frontal cortex, with highly local sequencing cues eliciting activation in the most posterior frontal regions (premotor cortex and the cytoarchitectonically corresponding frontal operculum) and less local sequencing cues (requiring relational comparisons between the current input element and the current sentence or discourse context, respectively) engendering activation in successively more anterior regions of the left inferior frontal gyrus. We argue that this neurocognitive gradient of linguistic sequence processing can be associated with a more general hierarchy of cognitive control in prefrontal cortex, which has also been shown to vary along an anterior-posterior gradient (1). We conclude that the processing of linguistic sequences and the concomitant extraction of information (e.g. semantic relations) from them, as an essential component of language processing, follows more general principles of neurocognitive organisation in prefrontal cortex.
While a clear variability of canonical word order across languages has been found, such a finding is not reflected in recent neuroimaging studies of language processing.Languages having a canonical word order of Subject-Object-Verb (SOV) in a sentence make up approximately 43% of world languages, while languages having a Subject-Verb-Object (SVO) word order make up approximately 37%.Sufficient attention has not been given to this typological difference in neuroimaging studies.In this article, we review neuroimaging studies of sentence processing to examine whether the typological difference of canonical word order in a sentence is represented in brain activation results or not.As a result of this literature survey, an effect from the difference in canonical word order was found to exist between SVO and SOV languages for brain activation during sentence comprehension.This effect was found mainly in the left inferior and middle frontal gyri, precentral gyrus, supplemental motor area, inferior and middle temporal gyri, temporal pole, hippocampus, and cerebellum.These results imply that a difference in canonical word order causes a different sentence processing pattern, as well as a different load in the working memory process.
Purpose: The incident at the Fukushima Daiichi nuclear power station in 2011 has again raised concerns with the public regarding radiation exposure, especially so in medical workers and patients undergoing treatment involving the use of radiation. Radioisotopes are currently used during sentinel node navigation surgery (SNNS) in operating rooms without radiation monitoring. To re-evaluate the safety issues, the potential effective dose (Epoten) from 99m Tc-tin (-Sn) colloid in breast cancer surgery was estimated and personal dose equivalents, Hp(10) and Hp(0.07), were measured during SNNS. Materials and methods: Seventeen breast cancer patients were enrolled. One day before SNNS, 99m Tc-Sn colloid was injected around the tumor and radiation exposure rates were measured using survey meters. Personal dose equivalents for the surgical workers were measured. Hp(10) and Hp(0.07) for the body and Hp(0.07) for the hands were recorded using semiconductor detectors and ring-type glass dosimeters. Results: The maximum Epoten was 29 μSv per 74 MBq injection. The maximum Hp(10) for the primary and assisting surgeons, nurse, and anesthetist was 3.7, 1.4, 0.3 and 0.6 μSv per SNNS, respectively. The maximum Hp(0.07) for the hands was 100 μSv. Maximum radiocontamination 20 times higher than background (0.05 μSv/h) was detected in bloody gauze. Conclusion: The workers' radiation dose exposure from SNNS was not high, although radiation management such as a temporary cooling off period may be required.
The current state of myocardial Blood-Oxygen-Level-Dependent (BOLD) MRI with balanced steady-state free precession (SSFP) approaches is reviewed. Initial studies forming the basis for SSFP-based detection of oxygenation changes beginning with whole blood studies, progressing through controlled studies that consider microcirculatory changes in oxygenation in skeletal muscle and kidney, culminating in basic myocardial studies are outlined. The theoretical basis to observe signal changes and the mechanisms that facilitate such observations are elucidated. Methods to overcome limitations in sensitivity are described.
