Distortion correction for high-resolution single-shot EPI DTI using a modified field-mapping method.

PURPOSE: The widely used single-shot EPI (SS-EPI) diffusion tensor imaging (DTI) suffers from strong image distortion due to B0 inhomogeneity, especially for high-resolution imaging. Traditional methods such as the field-mapping method and the top-up method have various deficiencies in high-resolution SS-EPI DTI distortion correction. This study aims to propose a robust distortion correction approach, which combines the advantages of traditional methods and overcomes their deficiencies, for high-resolution SS-EPI DTI. METHODS: The proposed correction method is based on the echo planar spectroscopic imaging field-mapping followed by an intensity correction procedure. To evaluate the efficacy of distortion correction, the proposed method was compared with the conventional field-mapping method and the top-up method, using a newly developed quantitative evaluation framework. The correction results were also compared with multi-shot EPI DTI to investigate whether the proposed method can provide high-resolution SS-EPI DTI with high geometric fidelity and high time efficiency. RESULTS: The results show that accurate field-mapping and intensity correction are critical to distortion correction in high-resolution SS-EPI DTI. The proposed method can provide more precise field maps and better correction results than the other two methods (p < 0.0001), and the corrected images show higher geometric fidelity than those from MS-EPI DTI. CONCLUSION: An effective method is proposed to reduce image distortion in high-resolution SS-EPI DTI. It is practical to achieve high-resolution DTI with high time efficiency and high structure accuracy using this method.

Passive radiofrequency shimming in the thighs at 3 Tesla using high permittivity materials and body coil receive uniformity correction.

PURPOSE: To explore the effects of high permittivity dielectric pads on the transmit and receive characteristics of a 3 Tesla body coil centered at the thighs, and their implications on image uniformity in receive array applications. THEORY AND METHODS: Transmit and receive profiles of the body coil with and without dielectric pads were simulated and measured in healthy volunteers. Parallel imaging was performed using sensitivity encoding (SENSE) with and without pads. An intensity correction filter was constructed from the measured receive profile of the body coil. RESULTS: Measured and simulated data show that the dielectric pads improve the transmit homogeneity of the body coil in the thighs, but decrease its receive homogeneity, which propagates into reconstruction algorithms in which the body coil is used as a reference. However, by correcting for the body coil reception profile this effect can be mitigated. CONCLUSION: Combining high permittivity dielectric pads with an appropriate body coil receive sensitivity filter improves the image uniformity substantially compared with the situation without pads. Magn Reson Med 76:1951-1956, 2016. © 2015 International Society for Magnetic Resonance in Medicine.

Consistent intensity inhomogeneity correction in water-fat MRI.

PURPOSE: To quantitatively and qualitatively evaluate the water-signal performance of the consistent intensity inhomogeneity correction (CIIC) method to correct for intensity inhomogeneities METHODS: Water-fat volumes were acquired using 1.5 Tesla (T) and 3.0T symmetrically sampled 2-point Dixon three-dimensional MRI. Two datasets: (i) 10 muscle tissue regions of interest (ROIs) from 10 subjects acquired with both 1.5T and 3.0T whole-body MRI. (ii) Seven liver tissue ROIs from 36 patients imaged using 1.5T MRI at six time points after Gd-EOB-DTPA injection. The performance of CIIC was evaluated quantitatively by analyzing its impact on the dispersion and bias of the water image ROI intensities, and qualitatively using side-by-side image comparisons. RESULTS: CIIC significantly ( P1.5T≤2.3×10-4,P3.0T≤1.0×10-6) decreased the nonphysiological intensity variance while preserving the average intensity levels. The side-by-side comparisons showed improved intensity consistency ( Pint⁡≤10-6) while not introducing artifacts ( Part=0.024) nor changed appearances ( Papp≤10-6). CONCLUSION: CIIC improves the spatiotemporal intensity consistency in regions of a homogenous tissue type.

EDIC intensity correction of electron diffraction.

Transmission electron microscopy (TEM) has recently become indispensable in determining crystal structures. The location of atoms in crystals can be determined using electron diffraction (ED) intensity data series if the diffracted intensities are directly proportional to the square of the structure factor (|Fhkl|2). However, due to the crystal thickness, the used electron wavelength and the potential misalignment of the measured crystal the detected intensities differ from the ideal values. A method, Electron Diffraction Intensity Correction (EDIC), and a computer program have been developed to recover the ideal |Fhkl|2 proportional intensities from experimental data for kinematic scattering, for further structure studies.

U2-Net and ResNet50-Based Automatic Pipeline for Bacterial Colony Counting.

In this paper, an automatic colony counting system based on an improved image preprocessing algorithm and convolutional neural network (CNN)-assisted automatic counting method was developed. Firstly, we assembled an LED backlighting illumination platform as an image capturing system to obtain photographs of laboratory cultures. Consequently, a dataset was introduced consisting of 390 photos of agar plate cultures, which included 8 microorganisms. Secondly, we implemented a new algorithm for image preprocessing based on light intensity correction, which facilitated clearer differentiation between colony and media areas. Thirdly, a U2-Net was used to predict the probability distribution of the edge of the Petri dish in images to locate region of interest (ROI), and then threshold segmentation was applied to separate it. This U2-Net achieved an F1 score of 99.5% and a mean absolute error (MAE) of 0.0033 on the validation set. Then, another U2-Net was used to separate the colony region within the ROI. This U2-Net achieved an F1 score of 96.5% and an MAE of 0.005 on the validation set. After that, the colony area was segmented into multiple components containing single or adhesive colonies. Finally, the colony components (CC) were innovatively rotated and the image crops were resized as the input (with 14,921 image crops in the training set and 4281 image crops in the validation set) for the ResNet50 network to automatically count the number of colonies. Our method achieved an overall recovery of 97.82% for colony counting and exhibited excellent performance in adhesion classification. To the best of our knowledge, the proposed "light intensity correction-based image preprocessing→U2-Net segmentation for Petri dish edge→U2-Net segmentation for colony region→ResNet50-based counting" scheme represents a new attempt and demonstrates a high degree of automation and accuracy in recognizing and counting single-colony and multi-colony targets.

Physics-based learning with channel attention for Fourier ptychographic microscopy.

Fourier ptychographic microscopy (FPM) is a computational imaging technology for large field-of-view, high resolution and quantitative phase imaging. In FPM, low-resolution intensity images captured with angle-varying illumination are synthesized in Fourier space with phase retrieval approaches. However, system errors such as pupil aberration and light-emitting diode (LED) intensity error seriously affect the reconstruction performance. In this article, we propose a physics-based neural network with channel attention for FPM reconstruction. With the channel attention module, which is introduced into physics-based neural networks for the first time, the spatial distribution of LED intensity can be adaptively corrected. Besides, the channel attention module is used to synthesize different Zernike modes and recover the pupil function. Detailed simulations and experiments are carried out to validate the effectiveness and robustness of the proposed method. The results demonstrate that our method achieves better performance in high-resolution complex field reconstruction, LED intensity correction and pupil function recovery compared with the state-of-art methods. The combination with deep neural network structures like channel attention modules significantly enhance the performance of physics-based neural networks and will promote the application of FPM in practical use.

Radiofrequency Bias Correction of Magnetization Prepared Rapid Gradient Echo MRI at 7.0 Tesla Using an External Reference in a Sequential Protocol.

At field strengths of 7 T and above, T1-weighted imaging of human brain suffers increasingly from radiofrequency (RF) B1 inhomogeneities. The well-known MP2RAGE (magnetization prepared two rapid acquisition gradient echoes) sequence provides a solution but may not be readily available for all MR systems. Here, we describe the implementation and evaluation of a sequential protocol to obtain normalized magnetization prepared rapid gradient echo (MPRAGE) images at 0.7, 0.8, or 0.9-mm isotropic spatial resolution. Optimization focused on the reference gradient-recalled echo (GRE) that was used for normalization of the MPRAGE. A good compromise between white-gray matter contrast and the signal-to-noise ratio (SNR) was reached at a flip angle of 3° and total scan time was reduced by increasing the reference voxel size by a factor of 8 relative to the MPRAGE resolution. The average intra-subject coefficient-of-variation (CV) in segmented white matter (WM) was 7.9 ± 3.3% after normalization, compared to 20 ± 8.4% before. The corresponding inter-subject average CV in WM was 7.6 ± 7.6% and 13 ± 7.8%. Maps of T1 derived from forward signal modelling showed no obvious bias after correction by a separately acquired flip angle map. To conclude, a non-interleaved acquisition for normalization of MPRAGE offers a simple alternative to MP2RAGE to obtain semi-quantitative purely T1-weighted images. These images can be converted to T1 maps, analogously to the established MP2RAGE approach. Scan time can be reduced by increasing the reference voxel size which has only a miniscule effect on image quality.

Spectrum Correction Using Modeled Panchromatic Image for Pansharpening.

Pansharpening is a method applied for the generation of high-spatial-resolution multi-spectral (MS) images using panchromatic (PAN) and multi-spectral images. A common challenge in pansharpening is to reduce the spectral distortion caused by increasing the resolution. In this paper, we propose a method for reducing the spectral distortion based on the intensity-hue-saturation (IHS) method targeting satellite images. The IHS method improves the resolution of an RGB image by replacing the intensity of the low-resolution RGB image with that of the high-resolution PAN image. The spectral characteristics of the PAN and MS images are different, and this difference may cause spectral distortion in the pansharpened image. Although many solutions for reducing spectral distortion using a modeled spectrum have been proposed, the quality of the outcomes obtained by these approaches depends on the image dataset. In the proposed technique, we model a low-spatial-resolution PAN image according to a relative spectral response graph, and then the corrected intensity is calculated using the model and the observed dataset. Experiments were conducted on three IKONOS datasets, and the results were evaluated using some major quality metrics. This quantitative evaluation demonstrated the stability of the pansharpened images and the effectiveness of the proposed method.

Comparison of optimized intensity correction methods for 23Na MRI of the human brain using a 32-channel phased array coil at 7 Tesla.

PURPOSE: To correct for the non-homogeneous receive profile of a phased array head coil in sodium magnetic resonance imaging (23Na MRI). METHODS: 23Na MRI of the human brain (n = 8) was conducted on a 7T MR system using a dual-tuned quadrature 1H/23Na transmit/receive birdcage coil, equipped with a 32-channel receive-only array. To correct the inhomogeneous receive profile four different methods were applied: (1) the uncorrected phased array image and an additionally acquired birdcage image as reference image were low-pass filtered and divided by each other. (2) The second method substituted the reference image by a support region. (3) By averaging the individually calculated receive profiles, a universal sensitivity map was obtained and applied. (4) The receive profile was determined by a pre-scanned large uniform phantom. The calculation of the sensitivity maps was optimized in a simulation study using the normalized root-mean-square error (NRMSE). All methods were evaluated in phantom measurements and finally applied to in vivo 23Na MRI data sets. The in vivo measurements were partial volume corrected and for further evaluation the signal ratio between the outer and inner cerebrospinal fluid compartments (CSFout:CSFin) was calculated. RESULTS: Phantom measurements show the correction of the intensity profile applying the given methods. Compared to the uncorrected phased array image (NRMSE = 0.46, CSFout:CSFin = 1.71), the quantitative evaluation of simulated and measured intensity corrected human brain data sets indicates the best performance utilizing the birdcage image (NRMSE = 0.39, CSFout:CSFin = 1.00). However, employing a support region (NRMSE = 0.40, CSFout:CSFin = 1.17), a universal sensitivity map (NRMSE = 0.41, CSFout:CSFin = 1.05) or a pre-scanned sensitivity map (NRMSE = 0.42, CSFout:CSFin = 1.07) shows only slightly worse results. CONCLUSION: Acquiring a birdcage image as reference image to correct for the receive profile demonstrates the best performance. However, when aiming to reduce acquisition time or for measurements without existing birdcage coil, methods that use a support region as reference image, a universal or a pre-scanned sensitivity map provide good alternatives for correction of the receive profile.

Echo Intensity Versus Muscle Function Correlations in Older Adults are Influenced by Subcutaneous Fat Thickness.

Recently, an equation that allows investigators to correct echo intensity for subcutaneous fat was developed. We evaluated correlations between uncorrected and corrected echo intensity versus measures of lower-extremity function. Twenty-three older adults (11 men, 12 women; mean age = 72 y) participated. B-Mode ultrasonography was used to quantify rectus femoris echo intensity and subcutaneous fat thickness. Knee extensor isometric peak torque and rate of torque development at 200 ms (RTD200) were determined (joint angle = 90°). Fast gait speed was evaluated at 10- and 400-m distances. Partial correlations between normalized peak torque, RTD200 and 10- and 400-m gait speed versus uncorrected echo intensity were weak and insignificant. Correction for subcutaneous fat strengthened the correlations (peak torque r = -0.500, RTD200 r= -0.425, 10-m r = -0.409, 400-m r = -0.410). Correcting echo intensity values for subcutaneous fat strengthened the associations with lower-extremity muscle function in older adults.

Reverse Intensity Correction for Raman Spectral Library Search.

A reverse intensity correction method was developed for spectral library searches to correct for instrument response without the side effect of magnifying the noise in the low responsivity region of test spectra. Instead of applying relative intensity correction to the sample test spectra to match the standardized library spectra, a reverse intensity correction is applied to the standardized library spectra to match the uncorrected sample spectrum. This simple procedural change improves library search performance, especially for dispersive charge-coupled device Raman analyzers using near-infrared excitations, where the instrument response often varies greatly across the spectral range, and signal-to-noise ratio in the low responsivity regions is typically poor.

A new CT reconstruction technique using adaptive deformation recovery and intensity correction (ADRIC).

PURPOSE: Sequential same-patient CT images may involve deformation-induced and non-deformation-induced voxel intensity changes. An adaptive deformation recovery and intensity correction (ADRIC) technique was developed to improve the CT reconstruction accuracy, and to separate deformation from non-deformation-induced voxel intensity changes between sequential CT images. MATERIALS AND METHODS: ADRIC views the new CT volume as a deformation of a prior high-quality CT volume, but with additional non-deformation-induced voxel intensity changes. ADRIC first applies the 2D-3D deformation technique to recover the deformation field between the prior CT volume and the new, to-be-reconstructed CT volume. Using the deformation-recovered new CT volume, ADRIC further corrects the non-deformation-induced voxel intensity changes with an updated algebraic reconstruction technique ("ART-dTV"). The resulting intensity-corrected new CT volume is subsequently fed back into the 2D-3D deformation process to further correct the residual deformation errors, which forms an iterative loop. By ADRIC, the deformation field and the non-deformation voxel intensity corrections are optimized separately and alternately to reconstruct the final CT. CT myocardial perfusion imaging scenarios were employed to evaluate the efficacy of ADRIC, using both simulated data of the extended-cardiac-torso (XCAT) digital phantom and experimentally acquired porcine data. The reconstruction accuracy of the ADRIC technique was compared to the technique using ART-dTV alone, and to the technique using 2D-3D deformation alone. The relative error metric and the universal quality index metric are calculated between the images for quantitative analysis. The relative error is defined as the square root of the sum of squared voxel intensity differences between the reconstructed volume and the "ground-truth" volume, normalized by the square root of the sum of squared "ground-truth" voxel intensities. In addition to the XCAT and porcine studies, a physical lung phantom measurement study was also conducted. Water-filled balloons with various shapes/volumes and concentrations of iodinated contrasts were put inside the phantom to simulate both deformations and non-deformation-induced intensity changes for ADRIC reconstruction. The ADRIC-solved deformations and intensity changes from limited-view projections were compared to those of the "gold-standard" volumes reconstructed from fully sampled projections. RESULTS: For the XCAT simulation study, the relative errors of the reconstructed CT volume by the 2D-3D deformation technique, the ART-dTV technique, and the ADRIC technique were 14.64%, 19.21%, and 11.90% respectively, by using 20 projections for reconstruction. Using 60 projections for reconstruction reduced the relative errors to 12.33%, 11.04%, and 7.92% for the three techniques, respectively. For the porcine study, the corresponding results were 13.61%, 8.78%, and 6.80% by using 20 projections; and 12.14%, 6.91%, and 5.29% by using 60 projections. The ADRIC technique also demonstrated robustness to varying projection exposure levels. For the physical phantom study, the average DICE coefficient between the initial prior balloon volume and the new "gold-standard" balloon volumes was 0.460. ADRIC reconstruction by 21 projections increased the average DICE coefficient to 0.954. CONCLUSION: The ADRIC technique outperformed both the 2D-3D deformation technique and the ART-dTV technique in reconstruction accuracy. The alternately solved deformation field and non-deformation voxel intensity corrections can benefit multiple clinical applications, including tumor tracking, radiotherapy dose accumulation, and treatment outcome analysis.

Surface Coil Intensity Correction in Magnetic Resonance Imaging in Spinal Metastases.

OBJECTIVE: To evaluate the clinical application of phased-array surface coil intensity correction in magnetic resonance imaging (MRI) in spinal metastases. METHODS: 3 phantoms and 50 patients with a corresponding total number of 80 spinal metastases were included in this study. Fast spin echo T1- and T2- weighted MRI with and without surface coil intensity correction was routinely performed for all phantoms and patients. Phantoms were evaluated by means of variance to mean ratio of signal intensity on both T1- and T2- weighted MRI obtained with and without surface coil intensity correction. Spinal metastases were evaluated by image quality scores; reading time per case on both T1- and T2- weighted MRI obtained with and without surface coil intensity correction. RESULTS: Spinal metastases were diagnosed more successfully on MRI with surface coil intensity correction than on MRI with conventional surface coil technique. The variance to mean ratio of signal intensity was 53.36% for original T1-weighted MRI and 53.58% for original T2-weighted MRI. The variance to mean ratio of signal intensity was reduced to 18.99% for T1-weighted MRI with surface coil intensity correction and 22.77% for T2-weighted MRI with surface coil intensity correction. The overall image quality scores (interface conspicuity of lesion and details of lesion) were significantly higher than those of the original MRI. The reading time per case was shorter for MRI with surface coil intensity correction than for MRI without surface coil intensity correction. CONCLUSIONS: Phased-array surface coil intensity correction in MRIs of spinal metastases provides improvements in image quality that leads to more successfully detection and assessment of spinal metastases than original MRI.

A Gauss-Newton approach to joint image registration and intensity correction.

We develop a new efficient numerical methodology for automated simultaneous registration and intensity correction of images. The approach separates the intensity correction term from the images being registered in a regularized expression. Our formulation is consistent with the existing non-parametric image registration techniques, however, an extra additive intensity correction term is carried throughout. An objective functional is formed for which the corresponding Hessian and Jacobian is computed and employed in a multi-level Gauss-Newton minimization approach. In this paper, our experiments are based on elastic regularization on the transformation and total variation on the intensity correction. Validations on dynamic contrast enhanced MR abdominal images for both real and simulated data verified the efficacy of the model. The pursued approach is flexible in which we can exploit various forms of regularization on the transformation and the intensity correction.