Improved precision of noise estimation in CT with a volume-based approach.
Ontology highlight
ABSTRACT:
SUBMITTER: Wisselink HJ
PROVIDER: S-EPMC8429536 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Cardiovascular diseases (CVDs) are one of the leading members of non-communicable diseases. An early diagnosis is essential for effective treatment, to reduce hospitalization time and health care costs. Nowadays, an exercise stress test on an ergometer is used to identify CVDs. To improve the accuracy of diagnostics, the hemodynamic status and parameters of a person can be investigated. For hemodynamic management, thoracic electrical bioimpedance has recently been used. This technique offers beat-to-beat stroke volume calculation but suffers from an artifact-sensitive signal that makes such measurements difficult during movement. We propose a new method based on a gated recurrent unit (GRU) neural network and the ECG signal to improve the measurement of bioimpedance signals, reduce artifacts and calculate hemodynamic parameters. We conducted a study with 23 subjects. The new approach is compared to ensemble averaging, scaled Fourier linear combiner, adaptive filter, and simple neural networks. The GRU neural network performs better with single artifact events than shallow neural networks (mean error -0.0244, mean square error 0.0181 for normalized stroke volume). The GRU network is superior to other algorithms using time-correlated data for the exercise stress test.
| S-EPMC9612153 | biostudies-literature
Project description:Local noise power spectra (NPS) have been commonly calculated to represent the noise properties of CT imaging systems, but their properties are significantly affected by the utilized calculation schemes. In this study, the effects of varied calculation parameters on the local NPS were analyzed, and practical suggestions were provided regarding the estimation of local NPS for clinical CT scanners. The uniformity module of a Catphan phantom was scanned with a Philips Brilliance 64 slice CT simulator with varied scanning protocols. Images were reconstructed using FBP and iDose4 iterative reconstruction with noise reduction levels 1, 3, and 6. Local NPS were calculated and compared for varied region of interest (ROI) locations and sizes, image background removal methods, and window functions. Additionally, with a predetermined NPS as a ground truth, local NPS calculation accuracy was compared for computer simulated ROIs, varying the aforementioned parameters in addition to ROI number. An analysis of the effects of these varied calculation parameters on the magnitude and shape of the NPS was conducted. The local NPS varied depending on calculation parameters, particularly at low spatial frequencies below ~ 0.15 mm-1. For the simulation study, NPS calculation error decreased exponentially as ROI number increased. For the Catphan study the NPS magnitude varied as a function of ROI location, which was better observed when using smaller ROI sizes. The image subtraction method for background removal was the most effective at reducing low-frequency background noise, and produced similar results no matter which ROI size or window function was used. The PCA background removal method with a Hann window function produced the closest match to image subtraction, with an average percent difference of 17.5%. Image noise should be analyzed locally by calculating the NPS for small ROI sizes. A minimum ROI size is recommended based on the chosen radial bin size and image pixel dimensions. As the ROI size decreases, the NPS becomes more dependent on the choice of background removal method and window function. The image subtraction method is most accurate, but other methods can achieve similar accuracy if certain window functions are applied. All dependencies should be analyzed and taken into account when considering the interpretation of the NPS for task-based image quality assessment.
| S-EPMC5690921 | biostudies-literature
Project description:BackgroundThe acoustic startle reflex (ASR) is a rapid, involuntary movement to sound, found in many species. The ASR can be modulated by external stimuli and internal state, making it a useful tool in many disciplines. ASR data collection and interpretation varies greatly across laboratories making comparisons a challenge.New methodHere we investigate the animal movement associated with a startle in mouse (CBA/CaJ). Movements were simultaneously captured with high-speed video and a piezoelectric startle plate. We also use simple mathematical extrapolations to convert startle data (force) into center of mass displacement ("height"), which incorporates the animal's mass.ResultsStartle plate force data revealed a stereotype waveform associated with a startle that contained three distinct peaks. This waveform allowed researchers to separate trials into 'startles' and 'no-startles' (termed 'manual classification). Fleiss' kappa and Krippendorff"s alpha (0.865 for both) indicate very good levels of agreement between researchers. Further work uses this waveform to develop an automated startle classifier. The automated classifier compares favorably with manual classification. A two-way ANOVA reveals no significant difference in the magnitude of the 3 peaks as classified by the manual and automated methods (P1: p=0.526, N1: p=0.488, P2: p=0.529).Comparison with existing method(s)The ability of the automated classifier was compared with three other commonly used classification methods; the automated classifier far outperformed these methods.ConclusionsThe improvements made allow researchers to automatically separate startle data from noise, and normalize for an individual animal's mass. These steps ease inter-animal and inter-laboratory comparisons of startle data.
| S-EPMC4560645 | biostudies-literature
Project description:BACKGROUND:To define a new coefficient to be used in the formula (Volume?=?L x H x W x Coefficient) that better estimates prostate volume using dimensions of fresh prostates from patients who had transrectal ultrasound (TRUS) imaging prior to prostatectomy. METHODS:The prostate was obtained from 153 patients, weighed and measured to obtain length (L), height (H), and width (W). The density was determined by water displacement to calculate volume. TRUS data were retrieved from patient charts. Linear regression analyses were performed to compare various prostate volume formulas, including the commonly used ellipsoid formula and newly introduced bullet-shaped formula. RESULTS:By relating measured prostate volumes from fresh prostates to TRUS-estimated prostate volumes, 0.66 was the best fitting coefficient in the (L x H x W x Coefficient) equation. This newfound coefficient combined with outlier removal yielded a linear equation with an R2 of 0.64, compared to 0.55 and 0.60, for the ellipsoid and bullet, respectively. By comparing each of the measured vs. estimated dimensions, we observed that the mean prostate height and length were overestimated by 11.1 and 10.8% using ultrasound (p?<?0.05), respectively, while the mean width was similar (p?>?0.05). Overall, the ellipsoid formula underestimates prostate volumes by 18%, compared to an overestimation of 4.6 and 5.7% for the bullet formula and the formula using our coefficient, respectively. CONCLUSIONS:This study defines, for the first time, a coefficient based on freshly resected prostates as a reference to estimate volumes by imaging. Our findings support a bullet rather than an ellipsoid prostate shape. Moreover, substituting the coefficient commonly used in the ellipsoid formula by our calculated coefficient in the equation estimating prostate volume by TRUS, provides a more accurate value of the true prostate volume.
| S-EPMC6657110 | biostudies-literature
Project description:Partial volume effects (PVE) present a source of confound for the analysis of functional imaging data. Correction for PVE requires estimates of the partial volumes (PVs) present in an image. These estimates are conventionally obtained via volumetric segmentation, but such an approach may not be accurate for complex structures such as the cortex. An alternative is to use surface-based segmentation, which is well-established within the literature. Toblerone is a new method for estimating PVs using such surfaces. It uses a purely geometric approach that considers the intersection between a surface and the voxels of an image. In contrast to existing surface-based techniques, Toblerone is not restricted to use with any particular structure or modality. Evaluation in a neuroimaging context has been performed on simulated surfaces, simulated T1-weighted MRI images and finally a Human Connectome Project test-retest dataset. A comparison has been made to two existing surface-based methods; in all analyses Toblerone's performance either matched or surpassed the comparator methods. Evaluation results also show that compared to an existing volumetric method (FSL FAST), a surface-based approach with Toblerone offers improved robustness to scanner noise and field non-uniformity, and better inter-session repeatability in brain volume. In contrast to volumetric methods, a surface-based approach negates the need to perform resampling which is advantageous at the resolutions typically used for neuroimaging.
| S-EPMC7291325 | biostudies-literature
Project description:AimTo assess the precision of preoperative ultrasonography (US)-determined prostate volume and to propose formulas for improving it.MethodsThis retrospective study comprised 155 consecutive men who underwent open prostatectomy for benign prostatic hyperplasia (BPH) between 2013 and 2019. Preoperative prostate volume was estimated by either abdominal US (AUS) (n = 92) or transrectal US (TRUS) (n = 63), and was compared with the weight of surgically enucleated tissue at a conversion rate of 1 ml (US) = 1 g tissue. Statistical analysis was conducted and a novel formula for prostate volume was constructed.ResultsThe median prostate volumes by AUS and TRUS were 140 ml [interquartile ratio (IQR) 111-182] and 108 ml (IQR 93-120), respectively. Enucleated tissue weight was lower than the AUS assessment by a median difference of 50 g (IQR 28.7-75.7; p < 0.001), and lower than the TRUS assessment by a median difference of 27 g, IQR 10-43, p < 0.001). Using a cutoff of 80 ml, 30 (33%) AUS patients and 23 (36%) TRUS patients underwent unneeded open procedures. Mathematical calculations revealed two formulas that significantly adjusted for the actual weight: 1.082*Age + 0.523*AUS - 53.845 for AUS and 0.138*age + 2.22*prostate-specific antigen + 0.453*TRUS + 11.682 for TRUS (p < 0.001). These formulas increased the overall US prostate volume accuracy from 65% to 85%.ConclusionAssessment of prostate volume by US is imprecise for decision-making of whether to perform open simple prostatectomy for BPH. Our novel formulas may enhance stratification of patients with prostatic enlargement to a more optimal surgical approach. Future studies in larger cohorts are needed to substantiate our results.
| S-EPMC7887671 | biostudies-literature
Project description:This work evaluates the accuracy and precision of the Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline, developed to identify and minimize common sources of methodological variability including: thermal noise, Gibbs ringing artifacts, Rician bias, EPI and eddy current induced spatial distortions, and motion-related artifacts. Following this processing pipeline, iterative parameter estimation techniques were used to derive diffusion parameters of interest based on the diffusion tensor and kurtosis tensor. We evaluated accuracy using a software phantom based on 36 diffusion datasets from the Human Connectome project and tested the precision by analyzing data from 30 healthy volunteers scanned three times within one week. Preprocessing with both DESIGNER or a standard pipeline based on smoothing (instead of noise removal) improved parameter precision by up to a factor of 2 compared to preprocessing with motion correction alone. When evaluating accuracy, we report average decreases in bias (deviation from simulated parameters) over all included regions for fractional anisotropy, mean diffusivity, mean kurtosis, and axonal water fraction of 9.7%, 8.7%, 4.2%, and 7.6% using DESIGNER compared to the standard pipeline, demonstrating that preprocessing with DESIGNER improves accuracy compared to other processing methods.
| S-EPMC6371781 | biostudies-literature
Project description:Background and objectiveComputed tomography perfusion (CTP) is widely used in the evaluation of acute ischemic stroke patients for endovascular thrombectomy (EVT). The stability of CTP core estimation is suboptimal and varies between software packages. We aimed to quantify the volumetric and spatial agreement between the CTP ischemic core and follow-up infarct for four ischemic core estimation approaches using syngo.via.MethodsWe included successfully reperfused, EVT-treated patients with baseline CTP and 24h follow-up diffusion weighted magnetic resonance imaging (DWI) (November 2017-September 2020). Data were processed with syngo.via VB40 using four core estimation approaches based on: cerebral blood volume (CBV)<1.2mL/100mL with and without smoothing filter, relative cerebral blood flow (rCBF)<30%, and rCBF<20%. The follow-up infarct was segmented on DWI.ResultsIn 59 patients, median estimated CTP core volumes for four core estimation approaches ranged from 12-39 mL. Median 24h follow-up DWI infarct volume was 11 mL. The intraclass correlation coefficient (ICC) showed moderate-good volumetric agreement for all approaches (range 0.61-0.76). Median Dice was low for all approaches (range 0.16-0.21). CTP core overestimation >10mL occurred least frequent (14/59 [24%] patients) using the CBV-based core estimation approach with smoothing filter.ConclusionsIn successfully reperfused patients who underwent EVT, syngo.via CTP ischemic core estimation showed moderate volumetric and spatial agreement with the follow-up infarct on DWI. In patients with complete reperfusion after EVT, the volumetric agreement was excellent. A CTP core estimation approach based on CBV<1.2 mL/100mL with smoothing filter least often overestimated the follow-up infarct volume and is therefore preferred for clinical decision making using syngo.via.
| S-EPMC9345340 | biostudies-literature
Project description:Identification of early ischemic changes (EIC) on noncontrast head CT scans performed within the first few hours of stroke onset may have important implications for subsequent treatment, though early stroke is poorly delimited on these studies. Lack of sharp lesion boundary delineation in early infarcts precludes manual volume measures, as well as measures using edge-detection or region-filling algorithms. We wished to test a hypothesis that image intensity inhomogeneity correction may provide a sensitive method for identifying the subtle regional hypodensity which is characteristic of early ischemic infarcts. A digital image analysis algorithm was developed using image intensity inhomogeneity correction (IIC) and intensity thresholding. Two different IIC algorithms (FSL and ITK) were compared. The method was evaluated using simulated infarcts and clinical cases. For synthetic infarcts, measured infarct volumes demonstrated strong correlation to the true lesion volume (for 20% decreased density "infarcts," Pearson r = 0.998 for both algorithms); both algorithms demonstrated improved accuracy with increasing lesion size and decreasing lesion density. In clinical cases (41 acute infarcts in 30 patients), calculated infarct volumes using FSL IIC correlated with the ASPECTS scores (Pearson r = 0.680) and the admission NIHSS (Pearson r = 0.544). Calculated infarct volumes were highly correlated with the clinical decision to treat with IV-tPA. Image intensity inhomogeneity correction, when applied to noncontrast head CT, provides a tool for image analysis to aid in detection of EIC, as well as to evaluate and guide improvements in scan quality for optimal detection of EIC.
| S-EPMC6719274 | biostudies-literature
Project description:BackgroundCochlear implant technology is a well-known approach to help deaf individuals hear speech again and can improve speech intelligibility in quiet conditions; however, it still has room for improvement in noisy conditions. More recently, it has been proven that deep learning-based noise reduction, such as noise classification and deep denoising autoencoder (NC+DDAE), can benefit the intelligibility performance of patients with cochlear implants compared to classical noise reduction algorithms.ObjectiveFollowing the successful implementation of the NC+DDAE model in our previous study, this study aimed to propose an advanced noise reduction system using knowledge transfer technology, called NC+DDAE_T; examine the proposed NC+DDAE_T noise reduction system using objective evaluations and subjective listening tests; and investigate which layer substitution of the knowledge transfer technology in the NC+DDAE_T noise reduction system provides the best outcome.MethodsThe knowledge transfer technology was adopted to reduce the number of parameters of the NC+DDAE_T compared with the NC+DDAE. We investigated which layer should be substituted using short-time objective intelligibility and perceptual evaluation of speech quality scores as well as t-distributed stochastic neighbor embedding to visualize the features in each model layer. Moreover, we enrolled 10 cochlear implant users for listening tests to evaluate the benefits of the newly developed NC+DDAE_T.ResultsThe experimental results showed that substituting the middle layer (ie, the second layer in this study) of the noise-independent DDAE (NI-DDAE) model achieved the best performance gain regarding short-time objective intelligibility and perceptual evaluation of speech quality scores. Therefore, the parameters of layer 3 in the NI-DDAE were chosen to be replaced, thereby establishing the NC+DDAE_T. Both objective and listening test results showed that the proposed NC+DDAE_T noise reduction system achieved similar performances compared with the previous NC+DDAE in several noisy test conditions. However, the proposed NC+DDAE_T only required a quarter of the number of parameters compared to the NC+DDAE.ConclusionsThis study demonstrated that knowledge transfer technology can help reduce the number of parameters in an NC+DDAE while keeping similar performance rates. This suggests that the proposed NC+DDAE_T model may reduce the implementation costs of this noise reduction system and provide more benefits for cochlear implant users.
| S-EPMC8587190 | biostudies-literature