Visual Image Quality Assessment Research Articles

Natureness‐preserving tone mapping operator based on improved guided filter and adaptive Gamma curve

AbstractMapping high dynamic range (HDR) images to low dynamic range (LDR) display devices while performing enhancement processing is a challenging task. To address the issues of chromatic drift, the imbalance between preserving naturalness and enhancing details during the mapping process, this paper proposed a tone mapping operator (TMO) based on an improved guided filter with multi‐layered processing. Firstly, the operator tackled the problem of insufficient extraction of detail layer information in complex scene images by redefining the formula of the guided filter and introducing adaptive regularization parameters. This enhancement enabled more effective extraction of details compared to traditional fixed‐parameter guided filters. Secondly, the algorithm employed a centre‐surround approach to subdivide and processed the detail layers individually, effectively suppressing halos and artefacts. Thirdly, image entropy was utilized to guide the fusion and enhancement processing of the detail layers, preventing excessive or insufficient enhancement. Finally, this paper proposed an adaptive gamma correction algorithm and a chromatic consistency algorithm to improve overall contrast and hue saturation. The results demonstrate convincing performance in terms of subjective visual evaluation and image quality assessment.

Relationship between the visual evaluation of pathology visibility and the physical measure of low contrast detail detectability in neonatal chest radiography

IntroductionThe detectability of low contrast detail (LCD) is a method used to assess image quality (IQ) in neonatal radiography; however, there is a lack of data on the relationship between LCD detectability and visual IQ. The study aims at investigating the relationship between the LCD detectability and visual IQ and pathology visibility (PV). MethodsSeveral acquisition parameters were employed to obtain a group of images from a neonatal Gammex chest phantom. Three observers applied relative visual grading analysis (VGA) for assessing the IQ and PV. A simulated pneumothorax visibility (PNV) and simulated hyaline membrane disease visibility (HMV) represented PV. Next, a CDRAD 2.0 phantom was radiographed utilising the same acquisition protocols, and several paired images were obtained. With the use of CDRAD analyser software, the detectability of LCD was assessed and expressed by an image quality figure inverse (IQFiinv) metric. The correlation between the IQFinv and each of IQ, PNV and HMV was examined. ResultsThe physical measure (IQFinv) and the visual assessment of IQ were shown to be strongly correlated (r = 0.95; p < 0.001). Using Pearson's correlation, the IQFinv, PNV, and HMV were found to be strongly correlated (r = 0.94; p < 0.001) and (r = 0.92; p < 0.001), correspondingly. ConclusionResults of the study show that physical measures of LCD detectability utilising the CDRAD 2.0 phantom is strongly corelated with visual IQ and PV (PNV and HMV) and can be used to evaluate IQ when undertaking neonatal chest radiography (CXR). Implications for practiceThis study establishes the feasibility of utilising the physical measure (IQFinv) and the CDRAD 2.0 phantom in routine quality assurance and neonatal CXR optimisation studies.

Exploration of the total-body PET/CT reconstruction protocol with ultra-low 18F-FDG activity over a wide range of patient body mass indices

PurposeThe purpose of this study was to investigate the image quality and diagnostic performance of different reconstructions over a wide range of patient body mass indices (BMIs) obtained by total-body PET/CT with ultra-low 18F-FDG activity (0.37 MBq/kg).MethodsA total of 63 patients who underwent total-body PET/CT with ultra-low activity (0.37 MBq/kg) 18F-FDG were enrolled. Patients were grouped by their BMIs. Images were reconstructed with the following two algorithms: the ordered subset expectation maximization (OSEM) algorithm (2, 3 iterations), both with time of flight (TOF) and point spread function (PSF) corrections (hereinafter referred as OSEM2, OSEM3) and HYPER Iterative algorithm (β-values of 0.3, 0.4, 0.5, 0.6) embedded TOF and PSF technologies (hereinafter referred as HYPER0.3, HYPER0.4, HYPER0.5 and HYPER0.6, respectively). Subjective image quality was assessed by two experienced nuclear medicine physicians according to the Likert quintile, including overall image quality, image noise and lesion conspicuity. The standard deviation (SD) and signal-to-noise ratio (SNR) of the liver, and maximum standard uptake value (SUVmax), peak standard uptake value (SUVpeak), tumour background ratio (T/N) and the largest diameter of lesions were quantitatively analysed by a third reader who did not participate in the subjective image assessment.ResultsIncreased noise was associated with increased BMI in all reconstruction groups. Significant differences occurred in the liver SNR among BMI categories of OSEM reconstructions (P < 0.001) but no difference was seen in the HYPER Iterative reconstructions between any of the BMI categories (P > 0.05). With the increase in BMI, overall image quality and image noise scores decreased significantly in all reconstructions, but there was no statistically significant difference of lesion conspicuity. The overall image quality score of the obese group was not qualified (score = 2.7) in OSEM3, while the others were qualified. The lesion conspicuity scores were significantly higher in HYPER Iterative reconstructions and lower in OSEM2 than in OSEM3 (all P < 0.05). The values of SUVmax, SUVpeak and T/N in HYPER0.3, HYPER0.4 and HYPER0.5 were higher than those in OSEM3. In different reconstructions, there was a correlation between lesion size (median, 1.55 cm; range, 0.7–11.0 cm) and SUVpeak variation rate compared to OSEM3 (r = 0.388, − 0.515, − 0.495, − 0.464, and − 0.423, respectively, and all P < 0.001).ConclusionConsidering the image quality and lesion analysis in 18F-FDG total-body PET/CT with ultra-low activity injection, OSEM reconstructions with 3 iterations meet the clinical requirements in patients with BMI < 30. In patients with BMI ≥ 30, it is recommended that the HYPER Iterative algorithm (β-value of 0.3–0.5) be used to ensure consistent visual image quality and quantitative assessment.

Reproducibility of Longitudinal Changes in Cortical Thickness Determined by Surface-Based Morphometry Between Non-Accelerated and Accelerated MR Imaging.

Scan acceleration such as parallel imaging reduces scan time, but shorter scan time may reduce the signal-to-noise ratio and affect image quality. The reproducibility of longitudinal changes in the brain structure between non-accelerated and accelerated imaging by surface-based analysis is unclear. To determine the reproducibility of longitudinal changes in cortical thickness, measured by surface-based morphometry, between non-accelerated and accelerated structural T1 -weighted imaging in the healthy elderly and those with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Retrospective. Fifty healthy elderly subjects (age=73 ± 5 years, 29 females, 21 males), 54 MCI patients (age=71 ± 7 years, 23 females, 31 males), and 8 AD patients (age=78 ± 6 years, 6 females, 2 males). 3 T, magnetization-prepared rapid gradient-echo. Longitudinal changes in cortical thickness estimated by the longitudinal stream in FreeSurfer from 2-year interval data, and visual assessment of image quality by three radiologists. Intraclass correlation coefficient (ICC) and Kruskal-Wallis test. A P value <0.05 was considered significant. Healthy elderly subjects, MCI patients, and AD patients showed different patterns in the ICC maps. For the smoothing of 20 mm full width at half maximum, the mean ICC was 0.45 overall (healthy elderly, 0.33; MCI patients, 0.49; AD patients, 0.31). The within-subject SDs of the symmetrized percent changes were similar between healthy elderly subjects (mean, 1.3%/year) and MCI patients (mean, 1.3%/year) but larger in AD patients (mean, 1.7%/year). Image quality did not significantly differ per group (P=0.18). The results of this study indicate the reproducibility of longitudinal changes in cortical thickness measured by surface-based morphometry between non-accelerated and accelerated imaging, and that the reproducibility varies by disease and region. 3 TECHNICAL EFFICACY: Stage 1.

Reliability of Changes in Brain Volume Determined by Longitudinal Voxel-Based Morphometry.

Longitudinal magnetic resonance imaging (MRI) studies have become increasingly important to assess the changes in brain morphology during normal aging and neurodegenerative disorders. However, the reliability of longitudinal morphometric changes has not been fully evaluated. To examine the reliability of longitudinal (2-year) changes in brain morphology determined by longitudinal voxel-based morphometry (VBM) in healthy elderly subjects, patients with mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD). Retrospective analysis. Twenty-four healthy elderly subjects, 28 MCI patients, and 16 AD patients. A 1.5 T, magnetization-prepared rapid gradient-echo. Longitudinal (2-year) changes in gray matter volume determined by longitudinal VBM processing, and visual assessment of image quality. Intraclass correlation coefficient (ICC) and Kruskal-Wallis test. The ICC maps differed among the three groups. The mean ICC was 0.81 overall (0.86 for healthy elderly subjects, 0.75 for MCI patients, and 0.76 for AD patients). The reliability was good to excellent (ICC, 0.60-1.00) for 92% of voxels (99% for healthy elderly subjects, 83% for MCI patients, and 83% for AD patients). The image quality differed significantly among the three groups (P< 0.05). These results indicate that the reliability of longitudinal gray matter volume changes by VBM is good to excellent for most voxels. However, reliability may be affected by the disease, possibly due to differences in head motion during imaging. Evidence Level 3 Technical Efficacy Stage 1.

Projection Invariant Feature and Visual Saliency-Based Stereoscopic Omnidirectional Image Quality Assessment

In this article, we propose a quality assessment model-based on the projection invariant feature and the visual saliency for Stereoscopic Omnidirectional Images (SOIs). Firstly, the projection invariant monocular and binocular features of SOI are derived from the Scale-Invariant Feature Transform (SIFT) points to tackle the inconsistency between the stretched projection formats and the viewports. Secondly, the visual saliency model, which combines the chrominance and contrast perceptual factors, is used to facilitate the prediction accuracy. Thirdly, according to the characteristics of the panoramic image, we generate the weight map and utilize it as a location prior, which can be adapted to different projection formats. Finally, the proposed SOI quality assessment model fuses the projection invariant features, visual saliency, and location prior. Experimental results on both the NingBo University SOI Database (NBU-SOID) and Stereoscopic OmnidirectionaL Image quality assessment Database (SOLID) demonstrate the proposed metric on equi-rectangular projection format outperforms the state-of-the-art schemes, the pearson linear correlation coefficient and spearman rank order correlation coefficient performance are 0.933 and 0.933 on SOLID, and 0.907 and 0.910 on NBU-SOID, respectively. Meanwhile, the proposed algorithm is extended to another five representative projection formats and achieves superior performance.

An effective coded exposure photography framework using optimal fluttering pattern generation

Motion blurring often occurs during camera imaging process due to the relative motion between the scene and imaging sensor, which would degrade the quality of captured image. In this paper, an effective coded exposure photography framework is proposed to help obtain better motion deblurring results. An integrated optimal fluttering pattern generation criterion with several factors is designed to guarantee the limited frequency information loss using coded exposure photography. Furthermore, an effective fluttering code sequence search algorithm is put forward to obtain the optimal code sequence quickly with perfect balance consideration of global search and local search. The differential evolution algorithm is utilized to carry out effective global searching. Meanwhile, simulate annealing algorithm is introduce to implement supplementary local optimal search, which could help avoid falling into local optimizations and reduce the iteration numbers significantly. Various groups of synthetic and real-world deblurring experiments are carried out to verify the effectiveness of the designed imaging framework. Experimental results demonstrate that the optimal fluttering pattern could be calculated quickly by the presented approach and the deblurring images have superior subjective visual perception and objective image quality assessment performance.

Highly Accelerated Real-Time Free-Breathing Cine CMR for Patients With a Cardiac Implantable Electronic Device

To develop a 16-fold accelerated real-time, free-breathing cine cardiovascular magnetic resonance (CMR) pulse sequence with compressed sensing reconstruction and test whether it is capable of producing clinically acceptable summed visual scores (SVS) and accurate left ventricular ejection fraction (LVEF) in patients with a cardiac implantable electronic device (CIED). A 16-fold accelerated real-time cine CMR pulse sequence was developed using gradient echo readout, Cartesian k-space sampling, and compressed sensing. We scanned 13 CIED patients (mean age = 59 years; 9/4 males/females) using clinical standard, breath-hold cine and real-time, free-breathing cine. Two clinical readers performed a visual assessment of image quality in four categories (conspicuity of endocardial wall at end diastole, temporal fidelity of wall motion, any artifact level on the heart, noise) using a five-point Likert scale (1: worst; 3: clinically acceptable; 5: best). SVS was calculated as the sum of 4 individual scores, where 12 was defined as clinical acceptable. The Wilcoxon signed-rank test was performed to compare SVS, and the Bland-Altman analysis was conducted to evaluate the agreement of LVEF. Median scan time was 3.7 times shorter for real-time (3.5 heartbeats per slice) than clinical standard (13 heartbeats per slice, excluding nonscanning time between successive breath-hold acquisitions). Median SVS was not significantly different between clinical standard (15.0) and real-time (14.5). The mean difference in LVEF was -2% (4.7% of mean), and the limits of agreement was 5.8% (13.5% of mean). This study demonstrates that the proposed real-time cine method produces clinically acceptable SVS and relatively accurate LVEF in CIED patients.

Optimization of [18F]PSMA-1007 PET-CT using regularized reconstruction in patients with prostate cancer

BackgroundProstate-specific membrane antigen (PSMA) radiotracers such as [18F]PSMA-1007 used with positron emission tomography-computed tomography (PET-CT) is promising for initial staging and detection of recurrent disease in prostate cancer patients. The block-sequential regularization expectation maximization algorithm (BSREM) is a new PET reconstruction algorithm, which provides higher image contrast while also reducing noise. The aim of the present study was to evaluate the influence of different acquisition times and different noise-suppressing factors in BSREM (β values) in [18F]PSMA-1007 PET-CT regarding quantitative data as well as a visual image quality assessment.We included 35 patients referred for clinical [18F]PSMA-1007 PET-CT. Four megabecquerels per kilogramme were administered and imaging was performed after 120 min. Eighty-four image series per patient were created with combinations of acquisition times of 1–4 min/bed position and β values of 300–1400. The noise level in normal tissue and the contrast-to-noise ratio (CNR) of pathological uptakes versus the local background were calculated. Image quality was assessed by experienced nuclear medicine physicians.ResultsThe noise level in the liver, spleen, and muscle was higher for low β values and low acquisition times (written as activity time products (ATs = administered activity × acquisition time)) and was minimized at maximum AT (16 MBq/kg min) and maximum β (1400). There was only a small decrease above AT 10. The median CNR increased slowly with AT from approximately 6 to 12 and was substantially lower at AT 4 and higher at AT 14–16. At AT 4–6, many images were regarded as being of unacceptable quality. For AT 8, β values of 700–900 were considered of acceptable quality.ConclusionsAn AT of 8 (for example as in our study, 4 MB/kg with an acquisition time of 2 min) with a β value of 700 performs well regarding noise level, CNR, and visual image quality assessment.

Deep Learning to Automate Reference-Free Image Quality Assessment of Whole-Heart MR Images.

To develop and characterize an algorithm that mimics human expert visual assessment to quantitatively determine the quality of three-dimensional (3D) whole-heart MR images. In this study, 3D whole-heart cardiac MRI scans from 424 participants (average age, 57 years ± 18 [standard deviation]; 66.5% men) were used to generate an image quality assessment algorithm. A deep convolutional neural network for image quality assessment (IQ-DCNN) was designed, trained, optimized, and cross-validated on a clinical database of 324 (training set) scans. On a separate test set (100 scans), two hypotheses were tested: (a) that the algorithm can assess image quality in concordance with human expert assessment as assessed by human-machine correlation and intra- and interobserver agreement and (b) that the IQ-DCNN algorithm may be used to monitor a compressed sensing reconstruction process where image quality progressively improves. Weighted κ values, agreement and disagreement counts, and Krippendorff α reliability coefficients were reported. Regression performance of the IQ-DCNN was within the range of human intra- and interobserver agreement and in very good agreement with the human expert (R 2 = 0.78, κ = 0.67). The image quality assessment during compressed sensing reconstruction correlated with the cost function at each iteration and was successfully applied to rank the results in very good agreement with the human expert. The proposed IQ-DCNN was trained to mimic expert visual image quality assessment of 3D whole-heart MR images. The results from the IQ-DCNN were in good agreement with human expert reading, and the network was capable of automatically comparing different reconstructed volumes.Supplemental material is available for this article.© RSNA, 2020.

SAR Image Despeckling Based on Combination of Fractional-Order Total Variation and Nonlocal Low Rank Regularization

Regularization method is an effective tool for synthetic aperture radar (SAR) image despeckling. Design of the effective regularization terms describing the image priors plays a vital role in this kind of method. In this article, a new combinational regularization model for speckle reduction (CRM-SR) is proposed, in which a regularization term is elaborately designed to contain both a fractional-order total variation (FrTV) regularization and a nonlocal low rank (NLR) regularization. The new regularization model inherits both the advantages of FrTV and NLR and improves the performance of SAR despeckling and, therefore, better preserves the edges and geometrical features of the images during the despeckling process. An efficient algorithm based on alternating direction optimization is derived to solve the proposed combinational regularization model. Experimental results show that the proposed model can effectively remove SAR image speckle and preserve the geometrical features of images according to both subjective visual assessment of image quality and objective evaluation.

Can the anode heel effect be used to optimise radiation dose and image quality for AP pelvis radiography?

IntroductionA study was conducted to determine whether the anode heel effect can be used to influence optimisation of radiation dose and image quality (IQ) for AP pelvis radiography. MethodsATOM dosimetry phantom and an anthropomorphic phantom were positioned for AP pelvis. Using a CR system, images were acquired and doses were measured with phantom feet toward anode and then feet toward cathode. Exposure factors (kVp, mAs and SID) were systematically generated using a factorial design. Images were scored visually for quality using relative visual grading together with a 3 point Likert scale. Signal to noise ratio was also calculated as a physical measure of image quality. Dosimetry data were collected for the ovaries and testes. ResultsThe optimum technique for male, which resulted in lower dose and suitable image quality, was with feet positioned toward the anode (0.80 ± 0.03 mGy; SNR of 38 ± 2.9; visual IQ score 3.13 ± 0.35). The optimum technique for female was with feet toward anode (0.23 ± 0.02 mGy; SNR of 34.7 ± 2.6; visual IQ score 3.15 ± 0.26). kVp had the biggest effect on both visual and physical image quality metrics (p < 0.001) for both tube orientations, whereas SID had the lowest effect on both visual and physical image quality metrics compared with mAs and kVp (p < 0.001). The effect of SID on the SNR was not significant (p > 0.05) with feet toward anode. ConclusionPositioning the patient with feet toward the anode, as opposed to the cathode, has no adverse effect on visual image quality assessment but it does have an effect on physical image quality. Implications for practiceThis study would add a new clinical concept in positioning of AP pelvis radiography especially for male positioning.

Human Visual Perception-Based Multi-Exposure Fusion Image Quality Assessment

Compared with ordinary single exposure images, multi-exposure fusion (MEF) images are prone to color imbalance, detail information loss and abnormal exposure in the process of combining multiple images with different exposure levels. In this paper, we proposed a human visual perception-based multi-exposure fusion image quality assessment method by considering the related perceptual features (i.e., color, dense scale invariant feature transform (DSIFT) and exposure) to measure the quality degradation accurately, which is closely related to the symmetry principle in human eyes. Firstly, the L1 norm of chrominance components between fused images and the designed pseudo images with the most severe color attenuation is calculated to measure the global color degradation, and the color saturation similarity is added to eliminate the influence of color over-saturation. Secondly, a set of distorted images under different exposure levels with strong edge information of fused image is constructed through the structural transfer, thus DSIFT similarity and DSIFT saturation are computed to measure the local detail loss and enhancement, respectively. Thirdly, Gauss exposure function is used to detect the over-exposure or under-exposure areas, and the above perceptual features are aggregated with random forest to predict the final quality of fused image. Experimental results on a public MEF subjective assessment database show the superiority of the proposed method with the state-of-the-art image quality assessment models.

A Heterogeneous AdaBoost Ensemble Based Extreme Learning Machines for Imbalanced Data

Extreme learning machine (ELM) is an effective learning algorithm for the single hidden layer feed-forward neural network (SLFN). It is diversified in the form of kernels or feature mapping functions, while achieving a good learning performance. It is agile in learning and often has good performance, including kernel ELM and Regularized ELM. Dealing with imbalanced data has been a long-term focus for the learning algorithms to achieve satisfactory analytical results. It is obvious that the unbalanced class distribution imposes very challenging obstacles to implement learning tasks in real-world applications, including online visual tracking and image quality assessment. This article addresses this issue through advanced diverse AdaBoost based ELM ensemble (AELME) for imbalanced binary and multiclass data classification. This article aims to improve classification accuracy of the imbalanced data. In the proposed method, the ensemble is developed while splitting the trained data into corresponding subsets. And different algorithms of enhanced ELM, including regularized ELM and kernel ELM, are used as base learners, so that an active learner is constructed from a group of relatively weak base learners. Furthermore, AELME is implemented by training a randomly selected ELM classifier on a subset, chosen by random re-sampling. Then, the labels of unseen data could be predicted using the weighting approach. AELME is validated through classification on real-world benchmark datasets.

Computer Vision in Precipitation Nowcasting: Applying Image Quality Assessment Metrics for Training Deep Neural Networks

This paper presents a viewpoint from computer vision to the radar echo extrapolation task in the precipitation nowcasting domain. Inspired by the success of some convolutional recurrent neural network models in this domain, including convolutional LSTM, convolutional GRU and trajectory GRU, we designed a new sequence-to-sequence neural network structure to leverage these models in a realistic data context. In this design, we decreased the numbers of channels in high abstract recurrent layers rather than increasing them. We formulated the task as a problem of encoding five radar images and predicting 10 steps ahead at the pixel level, and found that using only the common mean squared error can misguide the training and mislead the testing. Especially, the image quality of last predictions usually degraded rapidly. As a solution, we employed some visual image quality assessment techniques including Structural Similarity (SSIM) and multi-scale SSIM to train our models. Experimental results show that our structure was more tolerant to increasing uncertainty in the data, and the use of image quality metrics can significantly reduce the blurry image issue. Moreover, we found that using SSIM was very effective and a combination of SSIM with mean squared error and mean absolute error yielded the best prediction quality.

Quantitative and Qualitative Improvement of Low-Count [68Ga]Citrate and [90Y]Microspheres PET Image Reconstructions Using Block Sequential Regularized Expectation Maximization Algorithm.

There are several important positron emission tomography (PET) imaging scenarios that require imaging with very low photon statistics, for which both quantitative accuracy and visual quality should not be neglected. For example, PET imaging with the low photon statistics is closely related to active efforts to significantly reduce radiation exposure from radiopharmaceuticals. We investigated two examples of low-count PET imaging: (a) imaging [90Y]microsphere radioembolization that suffers the very small positron emission fraction of Y-90's decay processes, and (b) cancer imaging with [68Ga]citrate with uptake time of 3-4 half-lives, necessary for visualizing tumors. In particular, we investigated a type of penalized likelihood reconstruction algorithm, block sequential regularized expectation maximization (BSREM), for improving both image quality and quantitative accuracy of these low-count PET imaging cases. The NEMA/IEC Body phantom filled with aqueous solution of Y-90 or Ga-68 was scanned to mimic the low-count scenarios of corresponding patient data acquisitions on a time-of-flight (TOF) PET/magnetic resonance imaging system. Contrast recovery, background variation, and signal-to-noise ratio were evaluated in different sets of count densities using both conventional TOF ordered subset expectation (TOF-OSEM) and TOF-BSREM algorithms. The regularization parameter, beta, in BSREM that controls the tradeoff between image noise and resolution was evaluated to find a value for improved confidence in image interpretation. Visual quality assessment of the images obtained from patients administered with [68Ga]citrate (n = 6) was performed. We also made preliminary visual image quality assessment for one patient with [90Y]microspheres. In Y-90 imaging, the effect of 511-keV energy window selection for minimizing the number of random events was also evaluated. Quantitatively, phantom images reconstructed with TOF-BSREM showed improved contrast recovery, background variation, and signal-to-noise ratio values over images reconstructed with TOF-OSEM. Both phantom and patient studies of delayed imaging of [68Ga]citrate show that TOF-BSREM with beta = 500 gives the best tradeoff between image noise and image resolution based on visual assessment by the readers. The NEMA-IQ phantom study with [90Y]microspheres shows that the narrow energy window (460-562keV) recovers activity concentrations in small spheres better than the regular energy window (425-650keV) with the beta value of 2000 using the TOF-BSREM algorithm. For the images obtained from patients with [68Ga]citrate using TOF-BSREM with beta = 500, the visual analogue scale (VAS) was improved by 17% and the Likert score was increased by 1 point on average, both in comparison to corresponding scores for images reconstructed using TOF-OSEM. Our investigation shows that the TOF-BSREM algorithm improves the image quality and quantitative accuracy in low-count PET imaging scenarios. However, the beta value in this algorithm needed to be adjusted for each radiopharmaceutical and counting statistics at the time of scans.

Analysis wavefront propagating in free space based on the Zernike polynomials and Gauss-Laguerre modes expansion

The Zernike orthogonal polynomials are often used to describe the wavefront. The coefficients of the wavefront expansion in terms of Zernike polynomials allow us to determine the root-mean-square error of the deviation from the ideal wavefront. For visual assessment of image quality a spread point function is used. In this paper, we use the Fresnel transform to propagate the initial wavefront in free space and show how the coefficients of the Zernike polynomials and Gauss-Laguerre modes change. In modern ophthalmology, aberrometers are used to measure the wavefront distortions on the surface of elements of the optical system of a human eye. In this paper, we investigate the possibility of reconstructing the original field on the basis of the wavefront obtained at a certain distance.

High resolution isotropic diffusion imaging in post-mortem neonates: a feasibility study

To investigate the potential of advanced diffusion weighted imaging (DWI) in post-mortem MRI (PMMR) at 3T. We acquired PMMR brain and body imaging in 12 neonates, mean gestational age 33.4 weeks (range 29-37 weeks) at 3T and 1.5T. Head and body diffusion imaging at 1.5T consisted of bipolar diffusion encoding and single-shot spin-echo echo-planar imaging (SE-EPI) for acquisition (echo time (TE) 96 ms; repetition time (TR) 2700 ms; voxel size 1.8 x 1.8 mm in-plane with slice thickness 5 mm; b-values of 500 and 1000 s/mm2 applied in three orthogonal directions; total acquisition time 2:12). A whole-body 3T diffusion imaging protocol using monopolar diffusion encoding and simultaneous multislice EPI acquisition with gradients applied in 12 uniformly distributed directions was obtained (TE 53.4 ms; TR 5600 ms; 1.8 mm isotropic; multiband factor 2; b-values of 250, 750, 1250 and 1750 s/mm2; acquisition time 2:09 for a single b-value). There was significant improvement in image quality in multiband, multislice diffusion PMMR protocol. On visual assessment of image quality, 1.5T DWI scored poorly (mean 2.4 SD ± 0.47), and all 3T b-values individually scored significantly higher (p < 0.001) apart from b = 250 s/mm2 which was not significantly different. Recent advances in diffusion sequences and hardware utilising higher field strengths and gradient performance allows whole-body diffusion PMMR imaging at high resolution with improved image quality compared to the current clinical approach. We have demonstrated feasibility of a multislice, multiband quantitative diffusion imaging sequence in the perinatal post-mortem setting. This will allow more detailed and quantitative clinical PMMR investigations using diffusion MRI in the future.

Optimization and validation of accelerated golden-angle radial sparse MRI reconstruction with self-calibrating GRAPPA operator gridding.

Golden-angle radial sparse parallel (GRASP) MRI reconstruction requires gridding and regridding to transform data between radial and Cartesian k-space. These operations are repeatedly performed in each iteration, which makes the reconstruction computationally demanding. This work aimed to accelerate GRASP reconstruction using self-calibrating GRAPPA operator gridding (GROG) and to validate its performance in clinical imaging. GROG is an alternative gridding approach based on parallel imaging, in which k-space data acquired on a non-Cartesian grid are shifted onto a Cartesian k-space grid using information from multicoil arrays. For iterative non-Cartesian image reconstruction, GROG is performed only once as a preprocessing step. Therefore, the subsequent iterative reconstruction can be performed directly in Cartesian space, which significantly reduces computational burden. Here, a framework combining GROG with GRASP (GROG-GRASP) is first optimized and then compared with standard GRASP reconstruction in 22 prostate patients. GROG-GRASP achieved approximately 4.2-fold reduction in reconstruction time compared with GRASP (∼333 min versus ∼78 min) while maintaining image quality (structural similarity index ≈ 0.97 and root mean square error ≈ 0.007). Visual image quality assessment by two experienced radiologists did not show significant differences between the two reconstruction schemes. With a graphics processing unit implementation, image reconstruction time can be further reduced to approximately 14 min. The GRASP reconstruction can be substantially accelerated using GROG. This framework is promising toward broader clinical application of GRASP and other iterative non-Cartesian reconstruction methods. Magn Reson Med 80:286-293, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Comparison of fat suppression capability for chest MR imaging with Dixon, SPAIR and STIR techniques at 3 Tesla MR system

PurposeTo directly compare fat suppression efficacy of Dixon, spectral attenuated inversion recovery (SPAIR) and short tau inversion recovery (STIR) techniques for a 3T MR system. Materials and methodsNineteen consecutive patients (11 men, 8 women; mean age 67.9years) underwent chest MR imaging. Contrast-noise ratio (CNR) between muscle and fat (CNRFat) was calculated by ROI measurement. Then, two radiologists used a five-point scale for visual assessment of image quality, fat suppression capability and lesion detection. Next, the quantitative calculations obtained with all three techniques were compared by means of Tukey's honest significant difference (HSD) test. Inter-observer agreements were assessed by kappa statistics and χ2 test. Fisher's least significant difference (LSD) test was used for comparison among all scores. ResultsCNRFat of SPAIR were significantly higher than those of Dixon and STIR (p<0.001). Inter-observer agreements were assessed as significantly moderate, substantial or almost perfect (0.51<κ<0.89, p<0.0001). Total image quality and fat suppression capability of Dixon and STIR were significantly higher than those of SPAIR (p<0.001). ConclusionDixon technique has better fat suppression capability on T2-weighted turbo SE imaging than SPAIR technique on chest MR imaging examined with a 3T MR system, although it can't substitute STIR technique on fat suppression and lesion visualization.