Quantitative Image Quality Metrics Research Articles

Improved motion correction in brain MRI using 3D radial trajectory and projection moment analysis.

To develop a generalized rigid body motion correction method in 3D radial brain MRI to deal with continuous motion pattern through projection moment analysis. An assumption was made that the multichannel coil moves with the head, which was achieved by using a flexible head coil. A two-step motion correction scheme was proposed to directly extract the motion parameters from the acquired k-space data using the analysis of center-of-mass with high noise robustness, which were used for retrospective motion correction. A recursive least-squares model was introduced to recursively estimate the motion parameters for every single spoke, which used the smoothness of motion and resulted in high temporal resolution and low computational cost. Five volunteers were scanned at 3 T using a 3D radial multidimensional golden-means trajectory with instructed motion patterns. The performance was tested through both simulation and in vivo experiments. Quantitative image quality metrics were calculated for comparison. The proposed method showed good accuracy and precision in both translation and rotation estimation. A better result was achieved using the proposed two-step correction compared to traditional one-step correction without significantly increasing computation time. Retrospective correction showed substantial improvements in image quality among all scans, even for stationary scans. The proposed method provides an easy, robust, and time-efficient tool for motion correction in brain MRI, which may benefit clinical diagnosis of uncooperative patients as well as scientific MRI researches.

Rapid motion estimation and correction using self-encoded FID navigators in 3D radial MRI.

To develop a self-navigated motion compensation strategy for 3D radial MRI that can compensate for continuous head motion by measuring rigid body motion parameters with high temporal resolution from the central k-space acquisition point (self-encoded FID navigator) in each radial spoke. A forward model was created from low-resolution calibration data to simulate the effect of relative motion between the coil sensitivity profiles and the underlying object on the self-encoded FID navigator signal. Trajectory deviations were included in the model as low spatial-order field variations. Three volunteers were imaged at 3 T using a modified 3D gradient-echo sequence acquired with a Kooshball trajectory while performing abrupt and continuous head motion. Rigid body-motion parameters were estimated from the central k-space signal of each spoke using a least-squares fitting algorithm. The accuracy of self-navigated motion parameters was assessed relative to an established external tracking system. Quantitative image quality metrics were computed for images with and without retrospective correction using external and self-navigated motion measurements. Self-encoded FID navigators achieved mean absolute errors of 0.69 ± 0.82 mm and 0.73 ± 0.87° relative to external tracking for maximum motion amplitudes of 12 mm and 10°. Retrospective correction of the 3D radial data resulted in substantially improved image quality for both abrupt and continuous motion paradigms, comparable to external tracking results. Accurate rigid body motion parameters can be rapidly obtained from self-encoded FID navigator signals in 3D radial MRI to continuously correct for head movements. This approach is suitable for robust neuroanatomical imaging in subjects that exhibit patterns of large and frequent motion.

Image quality in whole-body MRI using the MY-RADS protocol in a prospective multi-centre multiple myeloma study

BackgroundThe Myeloma Response Assessment and Diagnosis System (MY-RADS) guidelines establish a standardised acquisition and analysis pipeline for whole-body MRI (WB-MRI) in patients with myeloma. This is the first study to assess image quality in a multi-centre prospective trial using MY-RADS.MethodsThe cohort consisted of 121 examinations acquired across ten sites with a range of prior WB-MRI experience, three scanner manufacturers and two field strengths. Image quality was evaluated qualitatively by a radiologist and quantitatively using a semi-automated pipeline to quantify common artefacts and image quality issues. The intra- and inter-rater repeatability of qualitative and quantitative scoring was also assessed.ResultsQualitative radiological scoring found that the image quality was generally good, with 94% of examinations rated as good or excellent and only one examination rated as non-diagnostic. There was a significant correlation between radiological and quantitative scoring for most measures, and intra- and inter-rater repeatability were generally good.When the quality of an overall examination was low, this was often due to low quality diffusion-weighted imaging (DWI), where signal to noise ratio (SNR), anterior thoracic signal loss and brain geometric distortion were found as significant predictors of examination quality.ConclusionsIt is possible to successfully deliver a multi-centre WB-MRI study using the MY-RADS protocol involving scanners with a range of manufacturers, models and field strengths. Quantitative measures of image quality were developed and shown to be significantly correlated with radiological assessment. The SNR of DW images was identified as a significant factor affecting overall examination quality.Trial registrationClinicalTrials.gov, NCT03188172, Registered on 15 June 2017.Critical relevance statementGood overall image quality, assessed both qualitatively and quantitatively, can be achieved in a multi-centre whole-body MRI study using the MY-RADS guidelines.Key points• A prospective multi-centre WB-MRI study using MY-RADS can be successfully delivered.• Quantitative image quality metrics were developed and correlated with radiological assessment.• SNR in DWI was identified as a significant predictor of quality, allowing for rapid quality adjustment.Graphical

Evaluation of commercially available point-of-care ultrasound for automated optic nerve sheath measurement

BackgroundMeasurement of the optic nerve sheath diameter (ONSD) via ultrasonography has been proposed as a non-invasive metric of intracranial pressure that may be employed during in-field patient triage. However, first responders are not typically trained to conduct sonographic exams and/or do not have access to an expensive ultrasound device. Therefore, for successful deployment of ONSD measurement in-field, we believe that first responders must have access to low-cost, portable ultrasound and be assisted by artificial intelligence (AI) systems that can automatically interpret the optic nerve sheath ultrasound scan.We examine the suitability of five commercially available, low-cost, portable ultrasound devices that can be combined with future artificial intelligence algorithms to reduce the training required for and cost of in-field optic nerve sheath diameter measurement. This paper is focused on the quality of the images generated by these low-cost probes. We report results of a clinician preference survey and compare with a lab analysis of three quantitative image quality metrics across devices. We also examine the suitability of the devices in a hypothetical far-forward deployment using operators unskilled in ultrasound, with the assumption of a future onboard AI video interpreter.ResultsWe find statistically significant differences in clinician ranking of the devices in the following categories: “Image Quality”, “Ease of Acquisition”, “Software”, and “Overall ONSD”. We show differences in signal-to-noise ratio, generalized contrast-to-noise ratio, point-spread function across the devices. These differences in image quality result in a statistically significant difference in manual ONSD measurement. Finally, we show that sufficiently wide transducers can capture the optic nerve sheath during blind (no visible B-mode) scans performed by operators unskilled in sonography.ConclusionsUltrasound of the optic nerve sheath has the potential to be a convenient, non-invasive, point-of-injury or triage measure for elevated intracranial pressure in cases of traumatic brain injury. When transducer width is sufficient, briefly trained operators may obtain video sequences of the optic nerve sheath without guidance. This data suggest that unskilled operators are able to achieve the images needed for AI interpretation. However, we also show that image quality differences between ultrasound probes may influence manual ONSD measurements.

Hybrid compressive spectral imaging with coded aperture optimization based on coherence minimization

Compressive spectral imaging (CSI) obtains spectral images through reconstruction from under-sampled low-dimensional projections with spatial and spectral coding. However, the simultaneous realization of high spatial–spectral resolution is challenged due to limitations of a single CSI architecture. In this paper, a hybrid CSI system combining the coded aperture snapshot spectral imaging (CASSI) and the spectrometer-based single-pixel spectral imaging (SPSI), with the corresponding coded aperture optimization for both constituent systems are proposed. The hybrid CSI system sustains the complementary advantages of the spatial resolution in CASSI and the spectral fidelity in SPSI, ensuring high reconstruction quality in both spatial and spectral dimensions with increased sampling efficiency. The derivable matrix operation of the hybrid CSI system is formulated; meanwhile, based on coherence minimization, the individual optimization on the coded apertures in the CASSI or the SPSI system, and the joint optimization on the coded apertures in the hybrid system are demonstrated. The reconstruction is further improved with the optimization, indicated by the quantitative image quality metrics and the point spectra. Compared to the conventional unoptimized CASSI, the optimized hybrid CSI system improves the PSNR for more than 7 dB. This work may introduce opportunities for multipath compressive imaging systems toward high-efficiency, high-resolution future.

Review and Analysis of Tumour Detection and Image Quality Analysis in Experimental Breast Microwave Sensing.

This review evaluates the methods used for image quality analysis and tumour detection in experimental breast microwave sensing (BMS), a developing technology being investigated for breast cancer detection. This article examines the methods used for image quality analysis and the estimated diagnostic performance of BMS for image-based and machine-learning tumour detection approaches. The majority of image analysis performed in BMS has been qualitative and existing quantitative image quality metrics aim to describe image contrast-other aspects of image quality have not been addressed. Image-based diagnostic sensitivities between 63 and 100% have been achieved in eleven trials, but only four articles have estimated the specificity of BMS. The estimates range from 20 to 65%, and do not demonstrate the clinical utility of the modality. Despite over two decades of research in BMS, significant challenges remain that limit the development of this modality as a clinical tool. The BMS community should utilize consistent image quality metric definitions and include image resolution, noise, and artifacts in their analyses. Future work should include more robust metrics, estimates of the diagnostic specificity of the modality, and machine-learning applications should be used with more diverse datasets and with robust methodologies to further enhance BMS as a viable clinical technique.

Performance evaluation of 67 denoising filters in ultrasound images: A systematic comparison analysis

AbstractNoise corrupts ultrasound images and degrades spatial and contrast resolutions. Hence, it is challenging to characterize the lesions precisely using ultrasound images. The present study aims to evaluate 67 denoising filters and select the best one for ultrasound image denoising. Seven test images were synthesized to evaluate the performance of filters at three different noise levels. Eleven full‐reference quantitative image quality metrics (IQMs) were employed to evaluate the performance of the filters. A new filter evaluation method, Rank Analysis, was introduced and utilized at each noise level. The ten best filters with the smallest mean rank in all noise levels were defined for further analysis on real ultrasound images. The Rank Analysis was also employed for real ultrasound images, and filters were evaluated based on 14 IQMs (11 full‐reference and three no‐reference). Finally, the best filter was defined using the repeated measures analysis statistical test. According to the Rank Analysis results, the Spatial correlation (SCorr) filter obtained the best results with the mean rank scores±SD of 1 ± 0, which was significantly better than the other nine filters (p < 0.001). The second‐best results were achieved by three filters, Bitonic, most homogeneous neighborhood, and Lee diffusion (p < 0.05). We concluded that SCorr is the best filter for ultrasound image denoising. It can be used in the pre‐processing step before segmentation and diagnostic procedures. In addition, a new filter evaluation method, Rank Analysis, was introduced in this study, which is easy to use, fast, and provides reliable results. So, it can be used to evaluate newly developed filters in the future studies.

Deep-Learning-Based Contrast Synthesis From MRF Parameter Maps in the Knee Joint.

Magnetic resonance fingerprinting (MRF) is a method to speed up acquisition of quantitative MRI data. However, MRF does not usually produce contrast-weighted images that are required by radiologists, limiting reachable total scan time improvement. Contrast synthesis from MRF could significantly decrease the imaging time. To improve clinical utility of MRF by synthesizing contrast-weighted MR images from the quantitative data provided by MRF, using U-nets that were trained for the synthesis task utilizing L1- and perceptual loss functions, and their combinations. Retrospective. Knee joint MRI data from 184 subjects from Northern Finland 1986 Birth Cohort (ages 33-35, gender distribution not available). A 3 T, multislice-MRF, proton density (PD)-weighted 3D-SPACE (sampling perfection with application optimized contrasts using different flip angle evolution), fat-saturated T2-weighted 3D-space, water-excited double echo steady state (DESS). Data were divided into training, validation, test, and radiologist's assessment sets in the following way: 136 subjects to training, 3 for validation, 3 for testing, and 42 for radiologist's assessment. The synthetic and target images were evaluated using 5-point Likert scale by two musculoskeletal radiologists blinded and with quantitative error metrics. Friedman's test accompanied with post hoc Wilcoxon signed-rank test and intraclass correlation coefficient. The statistical cutoff P <0.05 adjusted by Bonferroni correction as necessary was utilized. The networks trained in the study could synthesize conventional images with high image quality (Likert scores 3-4 on a 5-point scale). Qualitatively, the best synthetic images were produced with combination of L1- and perceptual loss functions and perceptual loss alone, while L1-loss alone led to significantly poorer image quality (Likert scores below 3). The interreader and intrareader agreement were high (0.80 and 0.92, respectively) and significant. However, quantitative image quality metrics indicated best performance for the pure L1-loss. Synthesizing high-quality contrast-weighted images from MRF data using deep learning is feasible. However, more studies are needed to validate the diagnostic accuracy of these synthetic images. 4. Stage 1.

Performance of automatic exposure control on dose and image quality: comparison between slot-scanning and flat-panel digital radiography systems.

EOSedge™* (EOS Imaging, Paris, France) is an X-ray imaging system using automatic exposure control (AEC) with tube current modulation, in order to optimize dose deposition inpatients. This study aims at characterizing EOSedge organ dose deposition in comparison to a digital radiography (DR) system and the previous EOS system (EOS-1stgeneration), in relation to their respective image qualitylevels. Organ doses were measured in an anthropomorphic female adult phantom and a 5-year-old pediatric phantom using optically stimulated luminescence (OSL) dosimeters, which were carefully calibrated within the studied energy range. Organ doses were recorded on the EOSedge and the Fuji Visionary DRF (Fujifilm Medical Systems U.S.A., Inc, Lexington, MA). The resulting effective doses were compared to the EOS-1st-generation values present in the literature. Image quality assessment was carried out on end-user images. Quantitative image quality metrics were computed for all tested modalities on a quality assurance phantom. Qualitative assessment of EOSedge image quality was based on anthropomorphic phantom acquisitions against the EOS-1st-generation system, and on clinical images against the tested DRsystem. For a full-spine exam, and on the female adult phantom (respectively, the pediatric phantom), an effective dose of 92μSv (respectively, 32μSv) was obtained on EOSedge, and 572μSv (respectively, 179μSv) on the DR system; these values were compared to effective dose values of 290μSv (respectively, 200μSv) from the literature on EOS-1stgeneration, leading to an effective dose reduction factor of 6 with respect to the DR system, and of 3-6 with respect to EOS-1stgeneration. EOSedge provides the best compromise between contrast-to-noise ratio (CNR) and dose, with more consistent CNR values than the other tested modalities, in a range of attenuation from 10 to 40cm of poly(methyl methacrylate) (PMMA). Within this range, EOSedge is also comparable to DR for 10 and 20cm of PMMA, and better than DR for 30 and 40cm of PMMA, both in terms of spatial resolution and low-contrast detection. The anatomical landmarks of interest in the follow-up of spinal deformities can be detected in all testedmodalities. Results showed that EOSedge provides significant dose reduction factors for full spine imaging in both adults and children compared to the other tested modalities, without compromising image quality. We believe that this work could help raise awareness on the capabilities of modern X-ray systems, when equipped with appropriate AEC strategies, to perform ultra-low-dose, long-axisimages.

Real-time shimming with FID navigators.

To implement a method for real-time field control using rapid FID navigator (FIDnav) measurements and evaluate the efficacy of the proposed approach for mitigating dynamic field perturbations and improving -weighted image quality. FIDnavs were embedded in a gradient echo sequence and a subject-specific linear calibration model was generated on the scanner to facilitate rapid shim updates in response to measured FIDnav signals. To confirm the accuracy of FID-navigated field updates, phantom and volunteer scans were performed with online updates of the scanner B0 shim settings. To evaluate improvement in -weighted image quality with real-time shimming, 10 volunteers were scanned at 3T while performing deep-breathing and nose-touching tasks designed to modulate the B0 field. Quantitative image quality metrics were compared with and without FID-navigated field control. An additional volunteer was scanned at 7T to evaluate performance at ultra-high field. Applying measured FIDnav shim updates successfully compensated for applied global and linear field offsets in phantoms and across all volunteers. FID-navigated real-time shimming led to a substantial reduction in field fluctuations and a consequent improvement in -weighted image quality in volunteers performing deep-breathing and nose-touching tasks, with 7.57% ± 6.01% and 8.21% ± 10.90% improvement in peak SNR and structural similarity, respectively. FIDnavs facilitate rapid measurement and application of field coefficients for slice-wise B0 shimming. The proposed approach can successfully counteract spatiotemporal field perturbations and substantially improves -weighted image quality, which is important for a variety of clinical and research applications, particularly at ultra-high field.

Single projection driven real-time multi-contrast (SPIDERM) MR imaging using pre-learned spatial subspace and linear transformation

Objective. To develop and test the feasibility of a novel Single ProjectIon DrivEn Real-time Multi-contrast (SPIDERM) MR imaging technique that can generate real-time 3D images on-the-fly with flexible contrast weightings and a low latency. Approach. In SPIDERM, a ‘prep’ scan is first performed, with sparse k-space sampling periodically interleaved with the central k-space line (navigator data), to learn a subject-specific model, incorporating a spatial subspace and a linear transformation between navigator data and subspace coordinates. A ‘live’ scan is then performed by repeatedly acquiring the central k-space line only to dynamically determine subspace coordinates. With the ‘prep’-learned subspace and ‘live’ coordinates, real-time 3D images are generated on-the-fly with computationally efficient matrix multiplication. When implemented based on a multi-contrast pulse sequence, SPIDERM further allows for data-driven image contrast regeneration to convert real-time contrast-varying images into contrast-frozen images at user’s discretion while maintaining motion states. Both digital phantom and in-vivo experiments were performed to evaluate the technical feasibility of SPIDERM. Main results. The elapsed time from the input of the central k-space line to the generation of real-time contrast-frozen 3D images was approximately 45 ms, permitting a latency of 55 ms or less. Motion displacement measured from SPIDERM and reference images showed excellent correlation (). Geometric variation from the ground truth in the digital phantom was acceptable as demonstrated by pancreas contour analysis (Dice ≥ 0.84, mean surface distance ≤ 0.95 mm). Quantitative image quality metrics showed good consistency between reference images and contrast-varying SPIDREM images in in-vivo studies (mean ). Significance. SPIDERM is capable of generating real-time multi-contrast 3D images with a low latency. An imaging framework based on SPIDERM has the potential to serve as a standalone package for MR-guided radiation therapy by offering adaptive simulation through a ‘prep’ scan and real-time image guidance through a ‘live’ scan.

Principal Component Analysis Based Hybrid Speckle Noise Reduction Technique for Medical Ultrasound Imaging

Ultrasound imaging is the safest and most widely used medical imaging technique available today. The main disadvantage of ultrasound imaging is the presence of speckle noise in its images that may obscure pathological changes in the body and makes diagnosis more challenging. Therefore, many techniques were proposed to reduce speckle and improve image quality. Unfortunately, variations of their performance with different scan parameters and due to their methodologies make it hard to choose which one to adopt in clinical practice. In this work, we consider the problem of combining the information from multiple speckle filters and propose the use of principal component analysis to find the optimal set of weights that would retain the most information and hence would better represent the data in the final image. The new technique is implemented to process ultrasound images collected from a research system and the outcomes are compared to the individual techniques and their average using quantitative image quality metrics. The proposed technique has potential for utilization in clinical settings to provide consistently better-quality combined images that may help improve diagnostic accuracy.

Speckle Reduction in Medical Ultrasound Imaging based on Visual Perception Model

Ultrasound imaging technology is one of the most important clinical imaging modalities due to its safety, low cost, in addition to its versatile applications. The main technical problem in this technology is its noisy appearance due to the presence of speckle, which makes reading imaging more difficult. In this study, a new method of speckle reduction in medical ultrasound images is proposed based on adaptive shifting of the contrast sensitivity function of human vision using a bias field map estimated from the original image. The aim of this work is to have an effective image enhancement strategy that reduces speckle while preserving diagnostically useful image features and allowing practical implementation in real-time for medical ultrasound imaging applications. The new method is used to improve the visual perception of image quality of ultrasound images by adding a local brightness bias to the areas with speckle noise. This allows the variations in image pixels due to speckle noise to be better perceived by the human observer because of the visual perception model. The performance of the proposed method is objectively assessed using quantitative image quality metrics and compared to previous methods. Furthermore, given that image quality perception is subjective, the level of added bias is controlled by a single parameter that accommodates the different needs for different users and applications. This method has potential to offer better viewing conditions of ultrasound images, which translates to higher diagnostic accuracy.

Quantitative Image Quality Metrics of the Low-Dose 2D/3D Slot Scanner Compared to Two Conventional Digital Radiography X-ray Imaging Systems.

The aim of this study was to determine the quantitative image quality metrics of the low-dose 2D/3D EOS slot scanner X-ray imaging system (LDSS) compared with conventional digital radiography (DR) X-ray imaging systems. The effective detective quantum efficiency (eDQE) and effective noise quantum equivalent (eNEQ) were measured using chest and knee protocols. Methods: A Nationwide Evaluation of X-ray Trends (NEXT) of a chest adult phantom and a PolyMethylmethacrylate (PMMA) phantom were used for the chest and knee protocols, respectively. Quantitative image quality metrics, including effective normalised noise power spectrum (eNNPS), effective modulation transfer function (eMTF), eDQE and eNEQ of the LDSS and DR imaging systems were assessed and compared. Results: In the chest acquisition, the LDSS imaging system achieved significantly higher eNEQ and eDQE than the DR imaging systems at lower and higher spatial frequencies (0.001 ≤ p ≤ 0.044). For the knee acquisition, the LDSS imaging system also achieved significantly higher eNEQ and eDQE than the DR imaging systems at lower and higher spatial frequencies (0.001 ≤ p ≤ 0.002). However, there was no significant difference in eNEQ and eDQE between DR systems 1 and 2 at lower and higher spatial frequencies (0.10 < p < 1.00) for either chest or knee protocols. Conclusion: The LDSS imaging system performed well compared to the DR systems. Thus, we have demonstrated that the LDSS imaging system has the potential to be used for clinical diagnostic purposes.

Effect of Noise Adaptive Wavelet Filter on Diagnostic Performance in Stroke Perfusion

Background: Computed tomography perfusion (CTP) images include more noise than routine clinic computed tomography (CT) images. Singular value decomposition based deconvolution algorithms are widely used for obtaining several functional perfusion maps. Recently block circulant singular value decomposition algorithms become popular for its superior property of immunity to contrast bolus lag. It is well known from literature that these algorithms are very sensitive to noise. There are a lot of examples of noise reduction filters in the literature as well as commercial ones. Functional maps which help physicians in the diagnostic process can be obtained with better image quality by de-noising CTP images with adaptive noise reduction filters. Objective: In this study, the effect of a noise adaptive wavelet filtering method on diagnostic performance on CTP stroke patient images is investigated. Method: Images of acute stroke patients were de-noised by this method and their diagnostic value were evaluated by visual means, peak signal-to-noise ratio and time intensity profile metrics. An observer evaluation study was carried out in order to validate quantitative image quality metrics. The results are compared with Gaussian and a bilateral filter based filtering method called TIPS (Time Intensity Profile Similarity) on same images sets to benchmark proposed method. Results: The diagnostic value of the images obtained from noise adaptive wavelet filtering method were better than Gaussian filter method and were compatible with a wellknown time intensity profile similarity bilateral filter method. Diagnostic performance of the both observers were improved compared to both Gaussian and TIPS methods. Conclusion: The noise adaptive wavelet filter method succeeded to reduce noise while preserving details contained in the contrast bolus. Its final effect on the timeintensity profiles and generated perfusion maps are compatible with the literature and showed improvements on diagnostic performance on specificity and overall accuracy when compared to other methods.

Accelerating Brain 3D T1-Weighted Turbo Field Echo MRI Using Compressed Sensing-Sensitivity Encoding (CS-SENSE)

PurposeTo evaluate the clinical application of the accelerated 3D T1-weighted turbo field echo (T1W-TFE) using the compressed sensing-sensitivity encoding (CS-SENSE) and identify the appropriate acceleration factor. Methods33 healthy controls (HC), 10 multiple sclerosis (MS) and 10 Alzheimer's disease (AD) patients were prospectively recruited. A conventional 3D T1W-TFE sequence and accelerated sequences with CS-SENSE factors of 3, 4.5, 6 and with SENSE factors of 3, 4.5 were acquired for all participants on a 3.0T MR system. The visual evaluation was independently assessed by two experienced radiologists. Quantitative image quality metrics and intraclass correlation coefficients (ICCs) between the conventional and the accelerated sequences were performed at the voxel level. Group comparisons were performed between HC and AD or MS patients. ResultsThere were no significant differences in the visual image quality metrics between conventional sequence and CS-SENSE factor of 3. The sequences with CS-SENSE factor of 6 and SENSE factors of 3, 4.5 showed significantly decreased overall image quality. The ICC values based on the voxel level of each accelerated scan and conventional scan were high (>0.9, 85.2%). For different accelerated sequences, AD and MS patients showed consistent results with the conventional sequence in gray matter atrophy when compared to HC. ConclusionsCS-SENSE factor of 3 is the appropriate parameter to accelerate the 3D T1W-TFE (65% time reduction) with preserved visual image quality. The voxel-based analysis demonstrated high ICCs for brain volume measurements in the majority of brain regions, implying the feasibility of the accelerated technique.

Spiral 3-Dimensional T1-Weighted Turbo Field Echo: Increased Speed for Magnetization-Prepared Gradient Echo Brain Magnetic Resonance Imaging.

Spiral magnetic resonance imaging acquisition may enable improved image quality and higher scan speeds than Cartesian trajectories. We tested the performance of four 3D T1-weighted (T1w) TFE sequences (magnetization-prepared gradient echo magnetic resonance sequence) with isotropic spatial resolution for brain imaging at 1.5 T in a clinical patient cohort based on qualitative and quantitative image quality metrics. Two prototypical spiral TFE sequences (spiral 1.0 and spiral 0.85) and a Cartesian compressed sensing technology accelerated TFE sequence (CS 2.5; acceleration factor of 2.5) were compared with a conventional (reference standard) Cartesian parallel imaging accelerated TFE sequence (SENSE; acceleration factor of 1.8). The SENSE (5:52 minutes), CS 2.5 (3:17 minutes), and spiral 1.0 (2:16 minutes) sequences all had identical spatial resolutions (1.0 mm). The spiral 0.85 (3:47 minutes) had a higher spatial resolution (0.85 mm). The 4 TFE sequences were acquired in 41 patients (20 with and 21 without contrast media). Three readers rated qualitative image quality (12 categories) and selected their preferred sequence for each patient. Two readers performed quantitative analysis whereby 6 metrics were derived: contrast-to-noise ratio for white and gray matter (CNRWM/GM), contrast ratio for gray matter-CSF (CRGM/CSF), and white matter-CSF (CRWM/CSF); and coefficient of variations for gray matter (CVGM), white matter (CVWM), and CSF (CVCSF). Friedman tests with post hoc Nemenyi tests, exact binomial tests, analysis of variance with post hoc Dunnett tests, and Krippendorff alphas were computed. Concerning qualitative analysis, the CS 2.5 sequence significantly outperformed the SENSE in 4/1 (with/without contrast) categories, whereas the spiral 1.0 and spiral 0.85 showed significantly improved scores in 10/9and 7/7 categories, respectively (P's < 0.001-0.039). The spiral 1.0 was most frequently selected as the preferred sequence (reader 1, 10/15 times; reader 2, 9/12 times; reader 3, 11/13times [with/without contrast]). Interreader agreement ranged from substantial to almost perfect (alpha = 0.615-0.997). Concerning quantitative analysis, compared with the SENSE, the CS 2.5 had significantly better scores in 2 categories (CVWM, CVCSF) and worse scores in 2 categories (CRGM/CSF, CRWM/CSF), the spiral 1.0 had significantly improved scores in 4 categories (CNRWM/GM, CRGM/CSF, CRWM/CSF, CVWM), and the spiral 0.85 had significantly better scores in 2 categories (CRGM/CSF, CRWM/CSF). Spiral T1w TFE sequences may deliver high-quality clinical brain imaging, thus matching the performance of conventional parallel imaging accelerated T1w TFEs. Imaging can be performed at scan times as short as 2:16 minutes per sequence (61.4% scan time reduction compared with SENSE). Optionally, spiral imaging enables increased spatial resolution while maintaining the scan time of a Cartesian-based acquisition schema.

Impact of the non-negativity constraint in model-based iterative reconstruction from CT data.

Model-based iterative reconstruction is a promising approach to achieve dose reduction without affecting image quality in diagnostic x-ray computed tomography (CT). In the problem formulation, it is common to enforce non-negative values to accommodate the physical non-negativity of x-ray attenuation. Using this a priori information is believed to be beneficial in terms of image quality and convergence speed. However, enforcing non-negativity imposes limitations on the problem formulation and the choice of optimization algorithm. For these reasons, it is critical to understand the value of the non-negativity constraint. In this work, we present an investigation that sheds light on the impact of this constraint. We primarily focus our investigation on the examination of properties of the converged solution. To avoid any possibly confounding bias, the reconstructions are all performed using a provably converging algorithm started from a zero volume. To keep the computational cost manageable, an axial CT scanning geometry with narrow collimation is employed. The investigation is divided into five experimental studies that challenge the non-negativity constraint in various ways, including noise, beam hardening, parametric choices, truncation, and photon starvation. These studies are complemented by a sixth one that examines the effect of using ordered subsets to obtain a satisfactory approximate result within 50 iterations. All studies are based on real data, which come from three phantom scans and one clinical patient scan. The reconstructions with and without the non-negativity constraint are compared in terms of image similarity and convergence speed. In select cases, the image similarity evaluation is augmented with quantitative image quality metrics such as the noise power spectrum and closeness to a known ground truth. For cases with moderate inconsistencies in the data, associated with noise and bone-induced beam hardening, our results show that the non-negativity constraint offers little benefit. By varying the regularization parameters in one of the studies, we observed that sufficient edge-preserving regularization tends to dilute the value of the constraint. For cases with strong data inconsistencies, the results are mixed: the constraint can be both beneficial and deleterious; in either case, however, the difference between using the constraint or not is small relative to the overall level of error in the image. The results with ordered subsets are encouraging in that they show similar observations. In terms of convergence speed, we only observed one major effect, in the study with data truncation; this effect favored the use of the constraint, but had no impact on our ability to obtain the converged solution without constraint. Our results did not highlight the non-negativity constraint as being strongly beneficial for diagnostic CT imaging. Altogether, we thus conclude that in some imaging scenarios, the non-negativity constraint could be disregarded to simplify the optimization problem or to adopt other forward projection models that require complex optimization machinery to be used together with non-negativity.

Evaluation of the Visibility and Artifacts of Seven Common Fiducials for Image-guided Radiation Therapy

The implantation of high-contrast fiducial markers in or near low-contrast gastrointestinal tumors improves the accuracy of stereotactic body radiation therapy (SBRT) delivery. The purpose of this work was to evaluate the visibility and artifacts of several commercially available fiducial markers in CT simulation, CT-on-Rails, and cone-beam CT (CBCT) images for IGRT. Seven of the most commonly used fiducials in SBRT were selected from four of the most prominent vendors. The fiducials varied in material composition (gold, platinum, carbon), shape (cylinder, coil, notched), diameter (0.28-1.0 mm), and length (3.0-20.0 mm) as summarized in the Table below. Each fiducial was placed within 4 mm of bolus located within a 13 cm thick virtual water phantom. Each fiducial was then imaged with a CT scanner used for simulation, and a CT-on-Rails scanner and an onboard CBCT system used for treatment set-up. Image acquisition parameters were set according to clinical protocols with slice thickness ranging from 2-3 mm, voxel sizes from 0.91-0.98 mm, and tube potential between 120-125 kVp. Fiducial visibility was evaluated in terms of contrast (ΔHU) and the Michelson visibility metric (V). Artifacts were quantified in terms of the relative streak artifacts level (rSAL) and the relative standard deviation (rStdDev) with larger values indicating more severe artifacts. The rSAL metric quantifies streak artifacts in terms of the image total variation relative to a uniform reference image. Maximum contrast and artifacts were observed for the 0.75 mm diameter platinum fiducial for all three imaging systems. Minimum contrast and median artifacts (rSAL) were observed for the smallest diameter gold fiducials. The carbon fiducial produced the least severe image artifacts while also ranking in the upper half in terms of Michelson contrast. Quantitative results for CBCT are presented in the Table. Similar results were observed for the simulation CT and CT-on-Rails systems. Qualitatively, fiducial contrast and artifacts were correlated with the quantitative image quality metrics. Visibility and artifacts were assessed for seven commercial fiducials. Visibility and artifacts were greatest for the largest diameter platinum fiducial evaluated. Carbon fiducials, which are MRI compatible and the preferred option for proton therapy, are a promising option for photon beam RT due to the least severe artifacts and superior contrast compared to two popular gold fiducials.Abstract 3716; Table 1FiducialContrastArtifactsMaterial, shape, diameter x length mm2ΔHUVrStdDevrSALPt, coil, 0.75 x 547981.07.52.7Au, cylinder, 0.43 x 517100.73.50.9Au, coil, 0.35 x 1013790.52.40.5Pt, coil, 0.35 x 1013240.42.00.4C, cylinder, 1.0 x 39980.51.60.3Au, notched, 0.28 x 109460.22.30.5Au, notched, 0.28 x 208650.22.60.5 Open table in a new tab

Experimental demonstration of imaging hidden objects in opaque liquid-based media by fusion of single-shot multiview polarized and unpolarized speckle images

Turbid water-based liquids are common scattering media within which imaging objects is challenging, since scattering particles of various origins and sizes may cause severe image degradation, resulting in poor object recovery and resolution. Therefore, counteracting the nontrivial effects of light scattering is a primary challenge, limiting the utility of optical imaging within liquid media. In this work, we sought to circumvent the optical degradation in turbid liquid media and to recover high-quality images of hidden objects by averaging polarized speckle images projected from several directions through a lens array. Inspired by astronomy imaging techniques, the averaging process utilized a Shift-and-Add (SAA) algorithm developed to reconstruct atmospherically-degraded solar images. Both linear and circular polarization strategies were then applied to further improve image reconstruction. Experiments were carried out using two targets, each having a distinct geometrical shape, embedded within turbid liquid media comprising a range of dilutions of commercially available cow's milk in seawater. The media were irradiated with a polarized laser beam, after which multiple polarized speckled images were captured with a CCD camera. Offline, images were first shifted to a common center and then fused, using the SAA algorithm, to yield reconstructed images of each hidden object. Quantitative image quality metrics, including SNR, entropy, and sharpness were applied to evaluate quality of the reconstructed images. The reconstructed images obtained demonstrate successful adaptation of our methodology for the identification of hidden objects within turbid media. To the best of our knowledge, this is the first report to demonstrate successful imaging of hidden object images within turbid liquid media by averaging multiple polarized speckle projections.