Quantification Errors Research Articles

Metal artifact reduction for improving quantitative SPECT/CT imaging.

This study aimed to evaluate the effect of the metal artifact reduction (MAR) method on quantitative single-photon emission computed tomography (SPECT)/computed tomography (CT) to reveal the usefulness of MAR in patients with metal implants. We performed a phantom experiment simulating patients with artificial hip prostheses using SPECT/CT equipped with the iterative MAR (iMAR). The phantom was filled with Tc-99m solution (29.5kBq/mL). For the CT scan conditions, tube current time products were applied to obtain volume CT dose indexes (CTDIvols) of 1.4, 2.8, and 5.6mGy. Six types of quantitative SPECT images were reconstructed using data from different doses of CT processed with and without iMAR for CT attenuation correction. Thirty circular regions of interest (ROIs) were placed in each of the dark-band artifactareas, the white-streak artifactareas, and the non-artifact areas. We calculated radioactivity concentrations from quantitative SPECT images with and without iMAR to evaluate the quantitative accuracy. The differences of the effect of iMAR with different CT doses were also evaluated. The results obtained using CT data with a CTDIvol of 2.8mGy are described below. For quantitative SPECT data without iMAR, we observed the underestimation of radioactivity concentration in the dark-band artifactareas and overestimation in the white-streak artifactareas. We observed quantification errors ranging from - 41.1% to + 20.0% without iMAR, depending on the ROI localization. When iMAR was used, these errors were reduced to a range of - 22.8% to + 14.2%. The mean absolute error from the true value in the artifact regions was also significantly reduced from 4.00 to 1.74kBq/mL. In the non-artifact areas, the radioactivity concentrations obtained from the quantitative SPECT data with and without iMAR were equivalent to the true value and did not differ significantly between the two conditions. Similar results were observed for procedures with CTDIvols of 1.4 and 5.6mGy. This study indicated that iMAR could improve the quantitative accuracy of SPECT/CT independent of the CT dose. iMAR can serve as a practicable technique for quantitative SPECT/CT in patients with metal implants.

Metabolic Labeling of Clostridioides difficile Proteins.

The introduction of stable isotopes in vivo via metabolic labeling approaches (SILAC or 15N-labeling) allows, after combination of differentially treated labeled and unlabeled cells or protein extracts, for correction of protein quantification errors implemented during elaborated sample preparation workflows. The SILAC-based approach uses heavy arginine and lysine to incorporate the label into bacterial strains and cell lines, whereas 15N-metabolic labeling is achieved by cultivation in 15N-salt containing media. In case of Clostridioides difficile, the lack in arginine and lysine auxotrophy as well as the Stickland dominated metabolism makes metabolic labeling challenging. Here, a step-by-step guideline for the metabolic labeling of C. difficile is described, which combines cultivation in liquid 15N-substituted medium followed by cultivation steps on solid 15N-substituted medium. The described procedure results in a label incorporation rate higher than 97%. Cells prepared by the following method can be used as standard for relative quantification approaches of, e.g., the membrane or surface proteome of C. difficile.

Pseudo-Random Key Stream Generation Algorithm for Encryption Purposes

For both chaos-based stream ciphers and chaos-based block ciphers, key streams have a crucial influence on their security. A well designed pseudo-chaotic number generator (PCNG) that exhibits both chaotic properties and pseudo-randomness is a good candidate for creating the cryptographic key stream for encryption purposes. PCNGs are based on multiple chaotic maps. Since the majority of the chaotic maps are defined using real numbers, most of the proposed PCNGs use floating-point notations. However, this data type, especially the double-precision notation, has disadvantages of high computation cost and inefficient resource utilization. Also, the quantification errors may undermine the reliability of the produced key stream. To overcome these drawbacks, a key stream generation algorithm using a PCNG scheme is proposed in this paper. The PCNG is based on reformulated skew tent maps over a 32-bit integer field. It not only reduces the resource utilization from the hardware perspective, but also ensures the key stream performance over various operation platforms. Furthermore, the proposed PCNG uses a parameter changeable strategy, which can help to expand the key space, and thus increases the immunity against the brute-force attack. The quality of the key stream produced by the PCNG has been tested in a stream cipher. The analysis and the obtained test results have demonstrated that the proposed PCNG is secure and reliable to generate cryptographic key streams for encryption purposes.

Accuracy limitations for composition analysis by XPS using relative peak intensities: LiF as an example

Although precision in XPS can be excellent, allowing small changes to be easily observed, obtaining an accurate absolute elemental composition of a solid material from relative peak intensities is generally much more problematical, involving many factors such as background removal, differing analysis depths at different photoelectron kinetic energies, possible angular distribution effects, calibration of the instrument transmission function, and variations in the distribution of photoelectron intensity between “main” peaks (those usually used for analysis) and associated substructure following the main peak, as a function of the chemical bonding of the elements concerned. The last item, coupled with the use of photoionization cross sections and/or relative sensitivity factors (RSFs), is the major subject of this paper, though it is necessary to consider the other items also, using LiF as a test case. The results show that the above issues, which are relevant to differing degrees in most XPS analyses, present significant challenges to highly accurate XPS quantification. LiF, using the Li1s and F1s XPS peaks, appears, at first sight, to be an ideal case for high accuracy. Only 1s core levels are involved, removing any possible angular effects, and it is a wide bandgap material, resulting in the main Li1s and F1s peaks being well separated from the following scattered electron backgrounds. There are, however, two serious complications: (1) the main F1s and F2s levels have a major loss of intensity diverted into satellite substructure spread over ∼100 eV KE from the main line, whereas the Li1s level has very much less diversion of intensity; (2) there is serious overlap of the substructure from F2s (∼30 eV BE) with the main line of Li1s at ∼56 eV. We report here a detailed analysis of the LiF XPS, plus a supporting theory analysis of losses of intensity from Li1s and F1s to satellite structure, based on the cluster models of LiF. We conclude that, if the overlap from the F2s substructure is correctly subtracted from Li1s, and the intensity from satellites for F1s and Li1s properly estimated, the atomic composition of the single crystal LIF may be recovered to within 5%, using the photoionization cross sections of Scofield, inelastic mean free path lengths based on Tanuma, Powell, and Penn, and the calibrated instrument transmission function. This refutes the claim by Wagner et al., based on their empirical determination of RSFs, (which applied only to the instruments and the analysis procedure they used, in 1981) that Scofield values are too low in general and, for Li1s in particular, are low by a factor of ∼2. This is important because Wagner-based RSFs (sometimes modified and sometimes not) are still embedded in quantification software on modern commercial instruments, and so analysts need to be aware of how those RSFs were obtained/modified. Incorrect use can lead to large quantification errors.

Improved PET/MRI attenuation correction in the pelvic region using a statistical decomposition method on T2-weighted images

BackgroundAttenuation correction of PET/MRI is a remaining problem for whole-body PET/MRI. The statistical decomposition algorithm (SDA) is a probabilistic atlas-based method that calculates synthetic CTs from T2-weighted MRI scans. In this study, we evaluated the application of SDA for attenuation correction of PET images in the pelvic region.Materials and methodTwelve patients were retrospectively selected from an ongoing prostate cancer research study. The patients had same-day scans of [11C]acetate PET/MRI and CT. The CT images were non-rigidly registered to the PET/MRI geometry, and PET images were reconstructed with attenuation correction employing CT, SDA-generated CT, and the built-in Dixon sequence-based method of the scanner. The PET images reconstructed using CT-based attenuation correction were used as ground truth.ResultsThe mean whole-image PET uptake error was reduced from − 5.4% for Dixon-PET to − 0.9% for SDA-PET. The prostate standardized uptake value (SUV) quantification error was significantly reduced from − 5.6% for Dixon-PET to − 2.3% for SDA-PET.ConclusionAttenuation correction with SDA improves quantification of PET/MR images in the pelvic region compared to the Dixon-based method.

Quantifying Structural Uncertainty in Paleoclimate Data Assimilation With an Application to the Last Millennium

AbstractPaleoclimate reconstruction relies on estimates of spatiotemporal relationships among climate quantities to interpolate between proxy data. This work quantifies how structural uncertainties in those relationships translate to uncertainties in reconstructions of past climate. We develop and apply a data assimilation uncertainty quantification approach to paleoclimate networks and observational uncertainties representative of data for the last millennium. We find that structural uncertainties arising from uncertain spatial covariance relationships typically contribute 10% of the total uncertainty in reconstructed temperature variability at small (∼200 km), continental, and hemispheric length scales, with larger errors (50% or larger) in regions where long‐range climate covariances are least certain. These structural uncertainties contribute far more to errors in uncertainty quantification, sometimes by a factor of 5 or higher. Accounting for and reducing uncertainties in climate model dynamics and resulting covariance relationships will improve paleoclimate reconstruction accuracy.

Quantitative determination of mutton adulteration with single-copy nuclear genes by real-time PCR

Mitochondrial genes were generally adopted for PCR-based meat adulteration authentication due to their excellent specificity to species and numerous copies in one cell. However, the number of mitochondrial gene copies varies according to cells and tissues, which leads to quantification errors for meat adulteration. To address this problem, single-copy nuclear genes were selected to develop a quantitative method for identifying mutton adulteration in this study. Both single-copy genes specific to sheep species and single-copy reference genes show good linearity between Ct values and series diluted DNA concentrations, with the correlation coefficients of 0.9999 and 0.9993, respectively. Meanwhile, a constant (correction factor) was introduced to transform DNA concentrations into mutton proportions in adulterated meat. With this method, simulated mutton-pork, mutton-chicken and mutton-duck adulteration samples could be accurately quantified with the recovery rates of 89.56%, 107.13% and 95.20%, respectively.

An Adaptive Bayesian Melding Method for Reliability Evaluation Via Limited Failure Data: An Application to the Servo Turret

In the early stage of product development, reliability evaluation is an indispensable step before launching a product onto the market. It is not realistic to evaluate the reliability of a new product by a host of reliability tests due to the limiting factors of time and test costs. Evaluating the reliability of products in a short time is a challenging problem. In this paper, an approach is proposed that combines a group of experts’ judgments and limited failure data. Novel features of this approach are that it can reflect various kinds of information without considering the individual weight and reduces aggregation error in the uncertainty quantification of multiple inconsistent pieces of information. First, an expert system is established by the Bayesian best–worst method and fuzzy logic inference, which collects and aggregates a group of expert opinions to estimate the reliability improvement factor. Then, an adaptive Bayesian melding method is investigated to generate a posterior by inaccurate prior knowledge and limited test data; this method is made more computationally efficient by implementing an improved sampling importance resampling algorithm. Finally, an application for the reliability evaluation of a subsystem of a CNC lathe is discussed to illustrate the framework, which is shown to validate the reasonability and robustness of our proposal.

Comparison of thresholding techniques for quantifying portland cement hydrates using synchrotron microtomography

X-ray computed tomography (CT) has become increasingly popular for non-destructive investigation of cementitious systems. Differentiating solid phases in the reconstructed image volume has been reported as a challenge due to similar greyscale values, thus limiting x-ray CT to qualitative or semi-quantitative analysis for solid phases. The goal of this research was to investigate image preprocessing and segmentation algorithms in quantifying synthesized hydration products. C-S-H, calcium hydroxide, monosulfate, and ettringite were created in the laboratory for investigation. In this research, two local segmentation algorithms, Markov random fields and watershed, and one global segmentation algorithm, Gaussian histogram deconstruction, were studied for their ability to quantify the phases. It was found that misclassification of voxels occurred when using the Gaussian deconstruction. An average reduction of 50%, and as high as 180%, in the quantification errors were observed when using local segmentation algorithms.

Quantitative perfusion-CMR is significantly influenced by the placement of the arterial input function

The aim of this study is to provide a systematic assessment of the influence of the position on the arterial input function (AIF) for perfusion quantification. In 39 patients with a wide range of left ventricular function the AIF was determined using a diluted contrast bolus of a cardiac magnetic resonance imaging in three left ventricular levels (basal, mid, apex) as well as aortic sinus (AoS). Time to peak signal intensities, baseline corrected peak signal intensity and upslopes were determined and compared to those obtained in the AoS. The error induced by sampling the AIF in a position different to the AoS was determined by Fermi deconvolution. The time to peak signal intensity was strongly correlated (r2 > 0.9) for all positions with a systematic earlier arrival in the basal (− 2153 ± 818 ms), the mid (− 1429 ± 928 ms) and the apical slice (− 450 ± 739 ms) relative to the AoS (all p < 0.001). Peak signal intensity as well as upslopes were strongly correlated (r2 > 0.9 for both) for all positions with a systematic overestimation in all positions relative to the AoS (all p < 0.001 and all p < 0.05). Differences between the positions were more pronounced for patients with reduced ejection fraction. The error of averaged MBF quantification was 8%, 13% and 27% for the base, mid and apex. The location of the AIF significantly influences core parameters for perfusion quantification with a systematic and ejection fraction dependent error. Full quantification should be based on obtaining the AIF as close as possible to the myocardium to minimize these errors.

Blinded rechecking of sputum smear microscopy performance in public health facilities in Tigray region, Northern Ethiopia: Retrospective cross sectional study.

IntroductionTuberculosis disease is the leading cause of death worldwide along with HIV/AIDS. Sputum smear microscopy plays an essential role for initial TB diagnosis and treatment follow up. But, misdiagnosis of sputum smear microscopy revealed a high economical crisis and missing of active TB cases. This study was aimed to determine blinded rechecking of sputum smear microscopy performance in public health facilities in Tigray region, Northern Ethiopia.Materials and methodsA cross sectional retrospective study was conducted from January, 2017 to December, 2018 year. Data was collected retrospectively using electronic and paper based in Tigray health research institute. The data was analyzed using the SPSS version 25 software. The sensitivity, specificity, positive predictive value, and negative predictive value of the smear readings were calculated using 2X2 contingency table. The reading agreement between the microscopic center and reference center was determined using kappa statistics.ResultsA total of 23,456 blinded rechecked smear results were reviewed. In average, the performances of sputum smear quality were 61%, 68%, 64%, 66%, 62% and 75% for specimen quality, staining quality, smear size, smear thickness, smear evenness and smear cleanliness respectively. Of the total error (0.48%) reported, 0.25%, 0.19% and 0.085% were false positive, false negative and quantification errors respectively. The concordance rate of health facilities for smear reading was increased to 90% by the end of 2018. Overall, the sensitivity, specificity, PPV, and NPV of the smear readings were 95%, 99.7%, 93% and 99.8% respectively. Likewise, the smear reading agreement was also perfect with kappa value, 0.87.ConclusionThe overall performance of public health facilities for blinded rechecking of smear microscopy was satisfactory. But, the high false positive and false negative errors found calls for continuous evaluation and monitoring of the health facilities by reference center.

Truncation compensation and metallic dental implant artefact reduction in PET/MRI attenuation correction using deep learning-based object completion

The susceptibility of MRI to metallic objects leads to void MR signal and missing information around metallic implants. In addition, body truncation occurs in MR imaging for large patients who exceed the transaxial field-of-view of the scanner. Body truncation and metal artefacts translate to incomplete MRI-derived attenuation correction (AC) maps, consequently resulting in large quantification errors in PET imaging. In this work, we propose a deep learning-based approach to predict the missing information/regions in MR images affected by metallic artefacts and/or body truncation aiming at reducing quantification errors in PET/MRI. Twenty-five whole-body (WB) co-registered PET, CT, and MR images were used for training and evaluation of the object completion approach. CT-based attenuation corrected PET images were considered as reference for the quantitative evaluation of the proposed approach. Its performance was compared to the 3-class segmentation-based AC approach (containing background air, soft-tissue and lung) obtained from MR images. The metal-induced artefacts affected 8.1 ± 1.8% of the volume of the head region when using the 3-class AC maps. This error reduced to 0.9 ± 0.5% after application of object completion on MR images. Consequently, quantification errors in PET images reduced from −57.5 ± 11% to −18.5 ± 5% in the head region after metal artefact correction. The percentage of the torso volume affected by body truncation in the 3-class AC maps reduced from 9.8 ± 1.9% to 0.6 ± 0.3% after truncation compensation. PET quantification errors in the affected regions were also reduced from −45.5 ± 10% to −9.5 ± 3% after truncation compensation. The quantitative results demonstrated promising performance of the proposed approach towards the completion of MR images corrupted by metal artefacts and/or body truncation in the context of WB PET/MR imaging.

Quantification and characterizing of soil microstructure features by image processing technique

An analyzing program SMAS based on digital image processing technique is developed for quantifying soil microstructure. By using SMAS, a series of geometrical and morphological indexes of soil particles/pores in microscale can be quantitatively determined. Three examples of using SMAS to quantify the microstructure features are shown. The analyzing results indicate that the developed program can effectively identify the morphology of soil particle and pore and accurately extract the soil microstructure indexes. A classification criterion for particle shape category is proposed based on the obtained values of morphology ratio and roundness. Moreover, effects of magnification and observation area of SEM images on the quantitative analysis results are discussed. It is important to select an appropriate magnification and observation area can cover as much structural information as possible while with high imaging quality. A recommendation approach is to stitch several images with relatively high magnification to one large image for quantification. Moreover, performing multiple scans on different zones of interest and then making comparative analysis is also an effective way to reduce quantification errors in microstructure observation. The findings of this investigation would be valuable for improving the reliability of quantitative characterization of soil microstructure on the basis of SEM images.

Quantification of mRNA Expression Using Single-Molecule Nanopore Sensing.

RNA quantification methods are broadly used in life science research and in clinical diagnostics. Currently, real-time reverse transcription polymerase chain reaction (RT-qPCR) is the most common analytical tool for RNA quantification. However, in cases of rare transcripts or inhibiting contaminants in the sample, an extensive amplification could bias the copy number estimation, leading to quantification errors and false diagnosis. Single-molecule techniques may bypass amplification but commonly rely on fluorescence detection and probe hybridization, which introduces noise and limits multiplexing. Here, we introduce reverse transcription quantitative nanopore sensing (RT-qNP), an RNA quantification method that involves synthesis and single-molecule detection of gene-specific cDNAs without the need for purification or amplification. RT-qNP allows us to accurately quantify the relative expression of metastasis-associated genes MACC1 and S100A4 in nonmetastasizing and metastasizing human cell lines, even at levels for which RT-qPCR quantification produces uncertain results. We further demonstrate the versatility of the method by adapting it to quantify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA against a human reference gene. This internal reference circumvents the need for producing a calibration curve for each measurement, an imminent requirement in RT-qPCR experiments. In summary, we describe a general method to process complicated biological samples with minimal losses, adequate for direct nanopore sensing. Thus, harnessing the sensitivity of label-free single-molecule counting, RT-qNP can potentially detect minute expression levels of RNA biomarkers or viral infection in the early stages of disease and provide accurate amplification-free quantification.

Monte Carlo SEM-EDS Nano-Microanalysis Strategy of Historical Mineral Pigments: The Simulation of the Egyptian Blue from Pompeii (Italy) as an Example

A correct determination of the mineral and chemical composition of specimens is of the utmost importance to answer questions regarding the Cultural Heritage field. Because of the preciousness and often very low quantity of sample available, with textures and sizes in the nano-to-micrometric range, scanning electron microscopy (SEM) combined with energy-dispersive X-ray spectroscopy (EDS) is one of the most suited and exploited nano-microanalytical techniques. In these cases, to avoid severe mistakes and quantification errors in SEM-EDS, it is mandatory to consider several effects related to the transport of electrons and X-rays in the material, which in turn are dependent on the SEM-EDS setup. In the present work, a Monte Carlo SEM-EDS nano-microanalytical simulation strategy is proposed and applied to a practical selected case. The Egyptian blue mineral pigment, which is found in Pompeian murals, is used here as an example and model system to show the effects of real size variations (0.1–10 µm), basic geometrical shapes of the pigment (prismatic and spherical) and typical SEM setups, sample holders and substrates. The simulations showed a great—sometimes not intuitive—dependence of the X-ray intensity on the thickness and shape of the samples and SEM-EDS parameters, thereby influencing the analysis and quantification. The critical overview of the results allowed the determination of the correct procedure and technical SEM-EDS parameters and indicated how to apply the Monte Carlo simulation strategy to other Cultural Heritage cases.

Quantitative analysis using external standards with a benchtop NMR spectrometer

This paper investigates the use of benchtop NMR spectrometers for quantitative analysis with external standards. Specifically, it focuses on the measurement of aqueous samples with analyte concentrations ranging from 30 mM to 1.7 M and electrical conductivity of up to 84mScm-1 using a 43 MHz instrument. It is demonstrated that measurements using the PULCON method cannot achieve an average error in quantification of <4% with the benchtop NMR tested here unless the standard and analyte are very similar. Our analysis indicates that this comparatively large error arises from the fixed tuning and matching of the benchtop spectrometer. We confirm that for moderately dilute samples (less than 0.2 M), the integral area of the solvent peak is suitable for use as an internal standard to mitigate this error. Furthermore, a round robin study demonstrates that the second major source of uncertainty in these measurements arises from the manual processing of the spectra by different analysts. Here we propose heuristics for manual baseline and phase correction to reduce this analyst-dependent error to about 3 %. We also demonstrate that semi-automated quantification using qGSD is able to achieve similar accuracy of integration, but with reduced sensitivity to the processing of the operator.

Classification and quantification of cracks in concrete structures using deep learning image-based techniques

Visual inspection has been the most widely used technique for monitoring concrete structures in service. Inspectors visually evaluate defects based on experience, skill, and engineering judgment. However, this process is subjective, laborious, time-consuming, and hampered by demanding access to numerous parts of complex structures. Accordingly, the present study proposes a nearly automated inspection model based on image processing and deep learning for detecting defects in typically inaccessible areas of concrete structures. Results indicate that using the Keras classifier combined with Otsu image processing can achieve superior classification accuracy of 97.63%, 96.5%, and 96.17% for training, validation, and testing data, respectively, along with low quantification error of 1.5%, 5% and 2% for the crack length, width, and angle of orientation, respectively. The type of structural damage and its severity are identified based on the allowed range of concrete crack width for different structures, including buildings and bridges based on different international standards and codes. The proposed method can deploy unmanned aerial vehicle image acquisition to offer a nearly automated inspection platform for the colossal backlog of aging concrete structures.

A realistic phantom of the human head for PET-MRI

BackgroundThe combination of positron emission tomography (PET) and magnetic resonance imaging (MRI) (PET-MRI) is a unique hybrid imaging modality mainly used in oncology and neurology. The MRI-based attenuation correction (MRAC) is crucial for correct quantification of PET data. A suitable phantom to validate quantitative results in PET-MRI is currently missing. In particular, the correction of attenuation due to bone is usually not verified by commonly available phantoms. The aim of this work was, thus, the development of such a phantom and to explore whether such a phantom might be used to validate MRACs.MethodVarious materials were investigated for their attenuation and MR properties. For the substitution of bone, water-saturated gypsum plaster was used. The attenuation of 511 keV annihilation photons was regulated by addition of iodine. Adipose tissue was imitated by silicone and brain tissue by agarose gel, respectively. The practicability with respect to the comparison of MRACs was checked as follows: A small flask inserted into the phantom and a large spherical phantom (serving as a reference with negligible error in MRAC) were filled with the very same activity concentration. The activity concentration was measured and compared using clinical protocols on PET-MRI and different built-in and offline MRACs. The same measurements were carried out using PET-CT for comparison.ResultsThe phantom imitates the human head in sufficient detail. All tissue types including bone were detected as such so that the phantom-based comparison of the quantification accuracy of PET-MRI was possible. Quantitatively, the activity concentration in the brain, which was determined using different MRACs, showed a deviation of about 5% on average and a maximum deviation of 11% compared to the spherical phantom. For PET-CT, the deviation was 5%.ConclusionsThe comparatively small error in quantification indicates that it is possible to construct a brain PET-MRI phantom that leads to MR-based attenuation-corrected images with reasonable accuracy.

Semi-automated transcription and scoring of autobiographical memory narratives.

Autobiographical memory studies conducted with narrative methods are onerous, requiring significant resources in time and labor. We have created a semi-automated process that allows autobiographical transcribing and scoring methods to be streamlined. Our paper focuses on the Autobiographical Interview (AI; Levine, Svoboda, Hay, Winocur, & Moscovitch, Psychology and Aging, 17, 677-89, 2002), but this method can be adapted for other narrative protocols. Specifically, here we lay out a procedure that guides researchers through the four main phases of the autobiographical narrative pipeline: (1) data collection, (2) transcribing, (3) scoring, and (4) analysis. First, we provide recommendations for incorporating transcription software to augment human transcribing. We then introduce an electronic scoring procedure for tagging narratives for scoring that incorporates the traditional AI scoring method with basic keyboard shortcuts in Microsoft Word. Finally, we provide a Python script that can be used to automate counting of scored transcripts. This method accelerates the time it takes to conduct a narrative study and reduces the opportunity for error in narrative quantification. Available open access on GitHub ( https://github.com/cMadan/scoreAI ), our pipeline makes narrative methods more accessible for future research.

Fast quantitative three-dimensional ultrashort echo time (UTE) Cones magnetic resonance imaging of major tissues in the knee joint using extended sprial sampling.

The purpose of this study is to investigate the effect of extending the spiral sampling window on quantitative 3D ultrashort echo time (UTE) Cones imaging of major knee joint tissues including articular cartilage, menisci, tendons and ligaments at 3 T. Nine cadaveric human whole knee specimens were imaged on a 3 T clinical MRI scanner. A series of quantitative 3D UTE Cones imaging biomarkers including T2 *, T1 , adiabatic T1ρ , magnetization transfer ratio (MTR) and macromolecular fraction (MMF) were estimated using spiral sampling trajectories with various durations. Errors in UTE MRI biomarkers as a function of sampling time were evaluated using a nonstretched spiral trajectory as a reference standard. No significant differences were observed by increasing the spiral sampling window from 1116 to 2232 μs in the calculated T2 *, T1 , adiabatic T1ρ , MTR and MMF, as all P-values were over .05 as assessed by ANOVA with two-sided Dunnett's test. Although extending the sampling window results in signal loss for short T2 components, there was limited effect on the calculated quantitative biomarkers, with error percentages typically smaller than 5% in all the evaluated tissues. The total scan time can be reduced by up to 54% with quantification errors of less than 5% in any evaluated major tissue in the knee joint, suggesting that 3D UTE Cones MRI techniques can be greatly accelerated by using a longer spiral sampling window without causing additional quantitative bias.