Surpassing light inhomogeneities in structured-illumination microscopy with FlexSIM.

Super-resolution structured-illumination microscopy (SIM) is a powerful technique that allows one to surpass the diffraction limit by up to a factor two. Yet, its practical use is hampered by its sensitivity to imaging conditions which makes it prone to reconstruction artefacts. In this work, we present FlexSIM, a flexible SIM reconstruction method capable to handle highly challenging data. Specifically, we demonstrate the ability of FlexSIM to deal with the distortion of patterns, the high level of noise encountered in live imaging, as well as out-of-focus fluorescence. Moreover, we show that FlexSIM achieves state-of-the-art performance over a variety of open SIM datasets.

Calibration Method Based on Virtual Gear Artefact for Computer Vision Measuring Instrument of Fine Pitch Gear.

The verification of the correctness, adaptability, and robustness of software systems in modern precision measurement instruments is of great significance. Due to the difficulty in processing and calibrating high-precision fine-pitch gear artefacts, the function verification and accuracy calibration of vision measurement instruments for the fine-pitch gear have become a challenge. The calibration method of the gear vision measurement system based on the virtual gear artefact involves two steps, namely obtaining and applying the virtual artefact. The obtained virtual gear artefact has the same geometric features, error features, and image edge features as the real artefact. The calibration method based on the virtual artefact can complete the correctness verification of the gear vision measurement system, and is superior to the traditional methods in adaptability verification, robustness verification, and fault analysis. In a test, the characteristic error of the virtual gear artefact could be reproduced with the original shape in the evaluation results of the computer vision gear measurement (CVGM) system, while the reproduction error did not exceed 1.9 µm. This can meet the requirements of the verification of the gear vision measurement software. The application of the virtual gear artefact can significantly improve the accuracy and robustness of the computer vision measuring instrument of the fine-pitch gear.

Effect of metal artefact reduction level on the assessment of dental implant positioning by cone-beam computed tomography.

OBJECTIVES: This study evaluated the effect of metal artefact reduction (MAR) level and tube current on the assessment of dental implant positioning relative to the mandibular canal (MC) through cone-beam computed tomography (CBCT). METHODS: Titanium dental implants were placed in dried mandibles at 0.5-mm superior to the MC (group 1/n = 8) and 0.5-mm inside the MC with perforation of the cortex (group 2/n = 10). CBCT scans were obtained with different levels of MAR (off, medium, and high) and 2 tube currents (4 and 8 mA). Four examiners analysed the images and scored the contact between the implant and the MC using a 5-point scale. Sensitivity, specificity, area under receiver operating characteristic curve (ROC), and frequency of scores were calculated. Data were compared with analysis of variance 2-way and Tukey's test and scores with Chi-square test. RESULTS: Specificity and area under ROC curve decreased significantly when MAR level was high compared with MAR-medium and MAR-off. The frequency of score 3 (inconclusive) was the highest, and scores 1 and 5 (definitely no contact and definitely contact, respectively) were the lowest with MAR-high, regardless of the tube current. When MAR was off, there were higher frequencies of scores 1 and 5. CONCLUSIONS: The level of MAR influences the assessment of the relationship between the dental implant and the MC. MAR-high led to lower diagnostic accuracy compared with MAR-medium and off. ADVANCES IN KNOWLEDGE: This article shows that high level of MAR can interfere in the diagnostic of dental implant positioning relative to the MC, decreasing its accuracy.

Exploring the microstructure of hydrated collagen hydrogels under scanning electron microscopy.

Collagen hydrogels are a rapidly expanding platform in bioengineering and soft materials engineering for novel applications focused on medical therapeutics, medical devices and biosensors. Observations linking microstructure to material properties and function enables rational design strategies to control this space. Visualisation of the microscale organisation of these soft hydrated materials presents unique technical challenges due to the relationship between hydration and the molecular organisation of a collagen gel. Scanning electron microscopy is a robust tool widely employed to visualise and explore materials on the microscale. However, investigation of collagen gel microstructure is difficult without imparting structural changes during preparation and/or observation. Electrons are poorly propagated within liquid-phase materials, limiting the ability of electron microscopy to interrogate hydrated gels. Sample preparation techniques to remove water induce artefactual changes in material microstructure particularly in complex materials such as collagen, highlighting a critical need to develop robust material handling protocols for the imaging of collagen hydrogels. Here a collagen hydrogel is fabricated, and the gel state explored under high-vacuum (10-6  Pa) and low-vacuum (80-120 Pa) conditions, and in an environmental SEM chamber. Visualisation of collagen fibres is found to be dependent on the degree of sample hydration, with higher imaging chamber pressures and humidity resulting in decreased feature fidelity. Reduction of imaging chamber pressure is used to induce evaporation of gel water content, revealing collagen fibres of significantly larger diameter than observed in samples dehydrated prior to imaging. Rapid freezing and cryogenic handling of the gel material is found to retain a porous 3D structure following sublimation of the gel water content. Comparative analysis of collagen hydrogel materials demonstrates the care needed when preparing hydrogel samples for electron microscopy.

Relative growth rate (RGR) and other confounded variables: mathematical problems and biological solutions.

BACKGROUND: Relative growth rate (RGR) has a long history of use in biology. In its logged form, RGR = ln[(M + ΔM)/M], where M is size of the organism at the commencement of the study, and ΔM is new growth over time interval Δt. It illustrates the general problem of comparing non-independent (confounded) variables, e.g. (X + Y) vs. X. Thus, RGR depends on what starting M(X) is used even within the same growth phase. Equally, RGR lacks independence from its derived components, net assimilation rate (NAR) and leaf mass ratio (LMR), as RGR = NAR × LMR, so that they cannot legitimately be compared by standard regression or correlation analysis. FINDINGS: The mathematical properties of RGR exemplify the general problem of 'spurious' correlations that compare expressions derived from various combinations of the same component terms X and Y. This is particularly acute when X >> Y, the variance of X or Y is large, or there is little range overlap of X and Y values among datasets being compared. Relationships (direction, curvilinearity) between such confounded variables are essentially predetermined and so should not be reported as if they are a finding of the study. Standardizing by M rather than time does not solve the problem. We propose the inherent growth rate (IGR), lnΔM/lnM, as a simple, robust alternative to RGR that is independent of M within the same growth phase. CONCLUSIONS: Although the preferred alternative is to avoid the practice altogether, we discuss cases where comparing expressions with components in common may still have utility. These may provide insights if (1) the regression slope between pairs yields a new variable of biological interest, (2) the statistical significance of the relationship remains supported using suitable methods, such as our specially devised randomization test, or (3) multiple datasets are compared and found to be statistically different. Distinguishing true biological relationships from spurious ones, which arise from comparing non-independent expressions, is essential when dealing with derived variables associated with plant growth analyses.

Do We Appropriately Detect and Understand Singlet Oxygen Possibly Generated in Advanced Oxidation Processes by Electron Paramagnetic Resonance Spectroscopy?

Electron paramagnetic resonance (EPR) spectroscopy using sterically hindered amine is extensively applied to detect singlet oxygen (1O2) possibly generated in advanced oxidation processes. However, EPR-detectable 1O2 signals were observed in not only the 1O2-dominated hydrogen peroxide (H2O2)/hypochlorite (NaClO) reaction but surprisingly also the 1O2-absent Fe(II)/H2O2, UV/H2O2, and ferrate [Fe(VI)] process with even stronger intensities. By taking advantage of the characteristic reaction between 1O2 and 9,10-diphenyl-anthracene and near-infrared phosphorescent emission of 1O2, 1O2 was excluded in the Fe(II)/H2O2, UV/H2O2, and Fe(VI) process. The false detection of 1O2 was ascribed to the direct oxidation of hindered amine to piperidyl radical by reactive species [e.g., •OH and Fe(VI)/Fe(V)/Fe(IV)] via hydrogen transfer, followed by molecular oxygen addition (forming a piperidylperoxyl radical) and back reaction with piperidyl radical to generate a nitroxide radical, as evidenced by the successful identification of a piperidyl radical intermediate at 100 K and theoretical calculations. Moreover, compared to the highly oxidative species (e.g., •OH and high-valence Fe), the much lower reactivity of 1O2 and the profound nonradiative relaxation of 1O2 in H2O resulted it too selective and inefficient in organic contaminant destruction. This study demonstrated that EPR-based 1O2 detection could be remarkably misled by common oxidative species and thereby jeopardize the understandings on 1O2.

Improving MR image quality with a multi-task model, using convolutional losses.

PURPOSE: During the acquisition of MRI data, patient-, sequence-, or hardware-related factors can introduce artefacts that degrade image quality. Four of the most significant tasks for improving MRI image quality have been bias field correction, super-resolution, motion-, and noise correction. Machine learning has achieved outstanding results in improving MR image quality for these tasks individually, yet multi-task methods are rarely explored. METHODS: In this study, we developed a model to simultaneously correct for all four aforementioned artefacts using multi-task learning. Two different datasets were collected, one consisting of brain scans while the other pelvic scans, which were used to train separate models, implementing their corresponding artefact augmentations. Additionally, we explored a novel loss function that does not only aim to reconstruct the individual pixel values, but also the image gradients, to produce sharper, more realistic results. The difference between the evaluated methods was tested for significance using a Friedman test of equivalence followed by a Nemenyi post-hoc test. RESULTS: Our proposed model generally outperformed other commonly-used correction methods for individual artefacts, consistently achieving equal or superior results in at least one of the evaluation metrics. For images with multiple simultaneous artefacts, we show that the performance of using a combination of models, trained to correct individual artefacts depends heavily on the order that they were applied. This is not an issue for our proposed multi-task model. The model trained using our novel convolutional loss function always outperformed the model trained with a mean squared error loss, when evaluated using Visual Information Fidelity, a quality metric connected to perceptual quality. CONCLUSION: We trained two models for multi-task MRI artefact correction of brain, and pelvic scans. We used a novel loss function that significantly improves the image quality of the outputs over using mean squared error. The approach performs well on real world data, and it provides insight into which artefacts it detects and corrects for. Our proposed model and source code were made publicly available.

Stimulation artefact on EEG trace with BIS monitoring during D-wave recording.

The D-wave reflects the corticospinal fibre potentials and is frequently recorded intraoperatively for intramedullary spinal tumours to ensure maximum safe resection. A 38-year-old male had an intramedullary spinal cord tumour surgically removed while being monitored with D-wave from a single distal electrode technique. Total intravenous anaesthesia (TIVA) with propofol (without a muscle relaxant and with intermittent intravenous fentanyl boluses) was used for the maintenance of anaesthesia guided by processed electroencephalography (EEG) using a bispectral index (BIS®) monitor. Regular spike artefacts were observed in the EEG signal recorded by the BIS® monitor during the application of the single-pulse transcranial electrical stimulus and were used as a visual indicator of stimulus delivery. Finally, we propose a novel method of confirming stimulus delivery during D-wave recording based on stimulation artefacts in the EEG signal recorded by the BIS® monitor.

Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges.

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements. With the recent success of deep learning in many research fields, there is great potential to apply deep learning for MR image enhancement, and recent publications have demonstrated promising results. Motivated by the rapidly growing literature in this area, in this review paper, we provide a comprehensive overview of deep learning-based methods for post-processing MR images to enhance image quality and correct image artefacts. We aim to provide researchers in MRI or other research fields, including computer vision and image processing, a literature survey of deep learning approaches for MR image enhancement. We discuss the current limitations of the application of artificial intelligence in MRI and highlight possible directions for future developments. In the era of deep learning, we highlight the importance of a critical appraisal of the explanatory information provided and the generalizability of deep learning algorithms in medical imaging.

Artefact Profiling: Panomics Approaches for Understanding the Materiality of Written Artefacts.

This review explains the strategies behind genomics, proteomics, metabolomics, metallomics and isotopolomics approaches and their applicability to written artefacts. The respective sub-chapters give an insight into the analytical procedure and the conclusions drawn from such analyses. A distinction is made between information that can be obtained from the materials used in the respective manuscript and meta-information that cannot be obtained from the manuscript itself, but from residues of organisms such as bacteria or the authors and readers. In addition, various sampling techniques are discussed in particular, which pose a special challenge in manuscripts. The focus is on high-resolution, non-targeted strategies that can be used to extract the maximum amount of information about ancient objects. The combination of the various omics disciplines (panomics) especially offers potential added value in terms of the best possible interpretations of the data received. The information obtained can be used to understand the production of ancient artefacts, to gain impressions of former living conditions, to prove their authenticity, to assess whether there is a toxic hazard in handling the manuscripts, and to be able to determine appropriate measures for their conservation and restoration.

Restoring statistical validity in group analyses of motion-corrupted MRI data.

Motion during the acquisition of magnetic resonance imaging (MRI) data degrades image quality, hindering our capacity to characterise disease in patient populations. Quality control procedures allow the exclusion of the most affected images from analysis. However, the criterion for exclusion is difficult to determine objectively and exclusion can lead to a suboptimal compromise between image quality and sample size. We provide an alternative, data-driven solution that assigns weights to each image, computed from an index of image quality using restricted maximum likelihood. We illustrate this method through the analysis of quantitative MRI data. The proposed method restores the validity of statistical tests, and performs near optimally in all brain regions, despite local effects of head motion. This method is amenable to the analysis of a broad type of MRI data and can accommodate any measure of image quality.

Carbon-film-based Zernike phase plates with smooth thickness gradient for phase-contrast transmission electron microscopy with reduced fringing artefacts.

Phase plates (PPs) in transmission electron microscopy (TEM) improve the contrast of weakly scattering objects under in-focus imaging conditions. A well-established PP type is the Zernike (Z)PP, which consists of a thin amorphous carbon (aC) film with a microscaled hole in the centre. The mean inner potential of the aC film is exploited to shift the phase of the scattered electrons while the unscattered electrons in the zero-order beam propagate through the hole and remain unaffected. However, the abrupt thickness increase at the hole edge induces an abrupt change of the phase-shift distribution and leads to fringing, that is, intensity oscillations around imaged objects, in TEM images. In this work, we have used focused-ion-beam milling to fabricate ZPPs with abrupt and graded thickness profiles around the centre hole. Depending on the thickness gradient and inner hole radius, graded-ZPP-TEM images of an aC/vacuum interface and bundles of carbon nanotubes (CNTs) show strongly reduced fringing. Image simulations were performed with ZPP-phase-shift distributions derived from measured thickness profiles of graded ZPPs, which show good agreement with the experimental images. Fringing artefacts, that is, intensity oscillations around imaged objects, are strongly reduced for Zernike phase plates with a graded thickness profile around the centre hole. Focused-ion-beam milling is used to fabricate graded Zernike phase plates with specific inner hole radius and thickness gradients. The phase-shift distribution is obtained from measured thickness profiles around the centre hole. Image simulations based on experimentally measured thickness/phase-shift distributions show good agreement with experimental Zernike phase-plate TEM images.

Value of immunohistochemistry in crushed areas of pulmonary neuroendocrine carcinoma.

Immunohistochemical demonstration of neuroendocrine differentiation is often performed in routine diagnostic practice for lung neuroendocrine carcinoma. However, these carcinomas are often crushed, especially on small specimens. The value of immunohistochemistry on crushed areas is not known. We aimed to assess the value of immunohistochemical markers in crushed areas. We performed a retrospective study of 299 patients with a diagnosis of pulmonary neuroendocrine carcinoma. We showed that the markers TTF-1, synaptophysin, chromogranin A, CD56, and INSM1 were more often negative in crushed areas compared with well-preserved areas. The proliferation index with anti-Ki67 was decreased but remained on average around 90%. For all markers, the percentage of labeled cells was lower than in the preserved areas. Finally, we show that cases without labeling in the crushed areas and maintained labeling in the non-crushed areas have a lower percentage of labeling than cases without this labeling mismatch. Finally, there were no false positives of these stains. Neuroendocrine markers are valid in crushed areas when positive. However, the percentage of labeled cells may be lower than on preserved areas and lead to false negatives. Finally, the proliferation index, although decreased, remains close to that on preserved areas.

"Trapped labelled spins"-related signal on arterial spin labelling in the assessment of flow-diverted aneurysms: preliminary experience.

PURPOSE: To investigate ASL-MRI features of flow-diverted aneurysms, review their haemodynamic surrogates, and discuss their pertinent clinical implications. METHODS: Retrospective single institutional analysis was performed on the clinical and imaging data of patients who underwent digital subtraction angiography (DSA) and ASL-MRI after endovascular flow diversion for cerebral aneurysms. Pseudo-continuous ASL-MRI was performed with post-label delays of 1525-1800 ms. Intra-aneurysmal "trapped labelled spins" (TLS)-related hypersignal, as seen on cerebral blood flow (CBF)-weighted maps of ASL-MRI, was investigated. Intermodality equivalence with DSA [O'Kelly-Marotta (OKM) grading for occlusion], 3D-TOF-MRA, and 3D spin-echo T1-weighted ("black-blood") images was assessed. RESULTS: Ten cases were included. "TLS" signal was demonstrable in 7/8 (87.5%) of the DSA-visible flow-diverted aneurysms (OKM grade B3, n = 6; OKM grade A3, n = 2). No TLS was seen in both OKM-D (excluded) aneurysms. TLS was not visualised in an OKM-B3 aneurysm with < 3 mm opacifying remnant. 3D-TOF-MRA and ASL-MRI were discordant at 5 instances (45.4%; TOF-MRA false negative, n = 4; false positive, n = 1). Loss of flow void on black-blood images corresponded to the absence of TLS and vice versa in all cases but one. CONCLUSION: "Trapped labelled spins"-related signal on ASL-MRI occurs in patent large aneurysms that have undergone successful endovascular flow diversion. This phenomenon likely represents an interplay of a multitude of haemodynamic factors including decelerated intra-aneurysmal inflow and outflow restriction. Serial intra-saccular TLS signal changes may hold diagnostic value, including contexts where 3D-TOF-MRA interpretation becomes dubious. "Trapped labelled spins"-related signal as a non-invasive proxy marker of aneurysm patency can possibly obviate unnecessary DSA.

Truncation effect reduction for fast iterative reconstruction in cone-beam CT.

BACKGROUND: Iterative reconstruction for cone-beam computed tomography (CBCT) has been applied to improve image quality and reduce radiation dose. In a case where an object's actual projection is larger than a flat panel detector, CBCT images contain truncated data or incomplete projections, which degrade image quality inside the field of view (FOV). In this work, we propose truncation effect reduction for fast iterative reconstruction in CBCT imaging. METHODS: The volume matrix size of the FOV and the height of projection images were extrapolated to a suitable size. These extended projections were reconstructed by fast iterative reconstruction. Moreover, a smoothing parameter for noise regularization in iterative reconstruction was modified to reduce the accumulated error while processing. The proposed work was evaluated by image quality measurements and compared with conventional filtered backprojection (FBP). To validate the proposed method, we used a head phantom for evaluation and preliminarily tested on a human dataset. RESULTS: In the experimental results, the reconstructed images from the head phantom showed enhanced image quality. In addition, fast iterative reconstruction can be run continuously while maintaining a consistent mean-percentage-error value for many iterations. The contrast-to-noise ratio of the soft-tissue images was improved. Visualization of low contrast in the ventricle and soft-tissue images was much improved compared to those from FBP using the same dose index of 5 mGy. CONCLUSIONS: Our proposed method showed satisfactory performance to reduce the truncation effect, especially inside the FOV with better image quality for soft-tissue imaging. The convergence of fast iterative reconstruction tends to be stable for many iterations.

Age variations and population over-coverage: Is low mortality among migrants merely a data artefact?

The migrant mortality advantage has been observed extensively, but its authenticity is debated. In particular, concerns persist that the advantage is an artefact of the data, generated by the problems of recording mobility among foreign-born populations. Here, we build on the intersection of two recent developments: the first showing substantial age variation in the advantage-a deep U-shaped advantage at peak migration ages-and the second showing high levels of population over-coverage, the principal source of data artefact, at the same ages. We use event history analysis of Sweden's population registers (2010-15) to test whether this over-coverage can explain age variation in the migrant mortality advantage. We document its U-shape in Sweden and, crucially, demonstrate that large mortality differentials persist after adjusting for estimated over-coverage. Our findings contribute to ongoing debate by demonstrating that the migrant mortality advantage is real and by ruling out one of its primary mechanisms.

Quantification of Error Sources with Inertial Measurement Units in Sports.

BACKGROUND: Inertial measurement units (IMUs) offer the possibility to capture the lower body motions of players of outdoor team sports. However, various sources of error are present when using IMUs: the definition of the body frames, the soft tissue artefact (STA) and the orientation filter. Methods to minimize these errors are currently being used without knowing their exact influence on the various sources of errors. The goal of this study was to present a method to quantify each of the sources of error of an IMU separately. METHODS: An optoelectronic system was used as a gold standard. Rigid marker clusters (RMCs) were designed to construct a rigid connection between the IMU and four markers. This allowed for the separate quantification of each of the sources of error. Ten subjects performed nine different football-specific movements, varying both in the type of movement, and in movement intensity. RESULTS: The error of the definition of the body frames (11.3-18.7 deg RMSD), the STA (3.8-9.1 deg RMSD) and the error of the orientation filter (3.0-12.7 deg RMSD) were all quantified separately for each body segment. CONCLUSIONS: The error sources of IMU-based motion analysis were quantified separately. This allows future studies to quantify and optimize the effects of error reduction techniques.

Metrology Benchmarking of 3D Scanning Sensors Using a Ceramic GD&T-Based Artefact.

The use of non-contact scanning equipment in metrology and in dimensional and geometric inspection applications is increasing due to its ease of use, the speed and density of scans, and the current costs. In fact, these technologies are becoming increasingly dominant in the industrial environment, thus moving from reverse engineering applications to metrological applications. However, this planned transfer requires actions to ensure the achievable accuracy by providing traceability of measurements. In the present study, a comparison between the devices is carried out and a specific standard artefact is designed, equipped with multiple ceramic optically friendly entities, and allowing a wide variety of geometric dimensioning and tolerancing (GD&T). Four different 3D scanning sensors are used in the experimentation. Three of them are based on laser triangulation, and the fourth is a structured blue light sensor (fringe pattern projection). The standard artefact is calibrated with a high accuracy, using a coordinate measuring machine (CMM) and probing sensors. With this CMM, reference values of multiple predefined GD&T are obtained. The evaluation methodology maximises the accuracy of each device in measuring the dimensions of the artefact due to the good dimensional (milling and turning), surface (control of machining variables), and the dimensional and spatial distribution characteristics. The procedure also includes the same treatment of the captured point clouds (trimming, filtering, and best-fit algorithm, etc.) in each of the four 3D scanning sensors considered. From this process, very reliable measurements of the maximum achievable accuracy of each device (deviations from the CMM measurements) are finally obtained, and a multi-characteristic comparison between the four sensors is performed, also with high reliability.

Endoscopy Artefact Detection by Deep Transfer Learning of Baseline Models.

To visualise the tumours inside the body on a screen, a long and thin tube is inserted with a light source and a camera at the tip to obtain video frames inside organs in endoscopy. However, multiple artefacts exist in these video frames that cause difficulty during the diagnosis of cancers. In this research, deep learning was applied to detect eight kinds of artefacts: specularity, bubbles, saturation, contrast, blood, instrument, blur, and imaging artefacts. Based on transfer learning with pre-trained parameters and fine-tuning, two state-of-the-art methods were applied for detection: faster region-based convolutional neural networks (Faster R-CNN) and EfficientDet. Experiments were implemented on the grand challenge dataset, Endoscopy Artefact Detection and Segmentation (EAD2020). To validate our approach in this study, we used phase I of 2,200 frames and phase II of 331 frames in the original training dataset with ground-truth annotations as training and testing dataset, respectively. Among the tested methods, EfficientDet-D2 achieves a score of 0.2008 (mAPd[Formula: see text]0.6+mIoUd[Formula: see text]0.4) on the dataset that is better than three other baselines: Faster-RCNN, YOLOv3, and RetinaNet, and competitive to the best non-baseline result scored 0.25123 on the leaderboard although our testing was on phase II of 331 frames instead of the original 200 testing frames. Without extra improvement techniques beyond basic neural networks such as test-time augmentation, we showed that a simple baseline could achieve state-of-the-art performance in detecting artefacts in endoscopy. In conclusion, we proposed the combination of EfficientDet-D2 with suitable data augmentation and pre-trained parameters during fine-tuning training to detect the artefacts in endoscopy.

T2-PROPELLER Compared to T2-FRFSE for Image Quality and Lesion Detection at Prostate MRI.

PURPOSE: The primary objective was to compare T2-FRFSE and T2-PROPELLER sequences for image quality. The secondary objective was to compare the ability to detect prostate lesions at MRI in the presence and absence of motion artefact using the 2 sequences. METHODS: 99 patients underwent 3 T MRI examination of the prostate, including T2-FRFSE and T2-PROPELLER sequences. All patients underwent prostate biopsy. Two independent readers rated overall image quality, presence of motion artefact, and blurring for both sequences using a 5-point Likert scale. Scores were compared for the whole group and for subgroups with and without significant motion artefact. Outcome for lesion detection at an MRI threshold of PI-RADS score ≥3 was compared between T2-FRFSE and T2-PROPELLER. RESULTS: The overall image quality was not significantly different between T2-FRFSE and T2-PROPELLER sequences (3.74 vs. 3.93, p = 0.275). T2-PROPELLER recorded a lesser degree of motion artefact (score 4.53 vs. 3.78, p <0.0001), but demonstrated greater image blurring (score 3.29 vs. 3.73, p <0.001). However, in a subgroup of patients with significant motion artefact on T2-FRFSE, the T2-PROPELLER sequence demonstrated significantly higher image quality (3.46 vs. 2.49, p <0.001). T2-FRFSE and T2-PROPELLER showed comparable positive predictive values for lesion detection at 93.2% and 97.7%, respectively. CONCLUSIONS: T2-PROPELLER provides higher quality imaging in the presence of motion artefact, but T2-FRFSE is preferred in the absence of motion. T2-PROPELLER is therefore recommended as a secondary T2 sequence when imaging requires repeat acquisition due to motion artefact.