Inhomogeneity Correction Research Articles

TU‐H‐206‐04: An Effective Homomorphic Unsharp Mask Filtering Method to Correct Intensity Inhomogeneity in Daily Treatment MR Images

Purpose:The daily treatment MRIs acquired on MR‐IGRT systems, like diagnostic MRIs, suffer from intensity inhomogeneity issue, associated with B1 and B0 inhomogeneities. An improved homomorphic unsharp mask (HUM) filtering method, automatic and robust body segmentation, and imaging field‐of‐view (FOV) detection methods were developed to compute the multiplicative slow‐varying correction field and correct the intensity inhomogeneity. The goal is to improve and normalize the voxel intensity so that the images could be processed more accurately by quantitative methods (e.g., segmentation and registration) that require consistent image voxel intensity values.Methods:HUM methods have been widely used for years. A body mask is required, otherwise the body surface in the corrected image would be incorrectly bright due to the sudden intensity transition at the body surface. In this study, we developed an improved HUM‐based correction method that includes three main components: 1) Robust body segmentation on the normalized image gradient map, 2) Robust FOV detection (needed for body segmentation) using region growing and morphologic filters, and 3) An effective implementation of HUM using repeated Gaussian convolution.Results:The proposed method was successfully tested on patient images of common anatomical sites (H/N, lung, abdomen and pelvis). Initial qualitative comparisons showed that this improved HUM method outperformed three recently published algorithms (FCM, LEMS, MICO) in both computation speed (by 50+ times) and robustness (in intermediate to severe inhomogeneity situations). Currently implemented in MATLAB, it takes 20 to 25 seconds to process a 3D MRI volume.Conclusion:Compared to more sophisticated MRI inhomogeneity correction algorithms, the improved HUM method is simple and effective. The inhomogeneity correction, body mask, and FOV detection methods developed in this study would be useful as preprocessing tools for many MRI‐related research and clinical applications in radiotherapy.Authors have received research grants from ViewRay and Varian.

TH‐CD‐206‐01: Expectation‐Maximization Algorithm‐Based Tissue Mixture Quantification for Perfusion MRI

Purpose:To investigate the feasibility of estimating the tissue mixture perfusions and quantifying cerebral blood flow change in arterial spin labeled (ASL) perfusion MR images.Methods:The proposed perfusion MR image analysis framework consists of 5 steps: (1) Inhomogeneity correction was performed on the T1‐ and T2‐weighted images, which are available for each studied perfusion MR dataset. (2) We used the publicly available FSL toolbox to strip off the non‐brain structures from the T1‐ and T2‐weighted MR images. (3) We applied a multi‐spectral tissue‐mixture segmentation algorithm on both T1‐ and T2‐structural MR images to roughly estimate the fraction of each tissue type ‐ white matter, grey matter and cerebral spinal fluid inside each image voxel. (4) The distributions of the three tissue types or tissue mixture across the structural image array are down‐sampled and mapped onto the ASL voxel array via a co‐registration operation. (5) The presented 4‐dimensional expectation‐maximization (4D‐EM) algorithm takes the down‐sampled three tissue type distributions on perfusion image data to generate the perfusion mean, variance and percentage images for each tissue type of interest.Results:Experimental results on three volunteer datasets demonstrated that the multi‐spectral tissue‐mixture segmentation algorithm was effective to initialize tissue mixtures from T1‐ and T2‐weighted MR images. Compared with the conventional ASL image processing toolbox, the proposed 4D‐EM algorithm not only generated comparable perfusion mean images, but also produced perfusion variance and percentage images, which the ASL toolbox cannot obtain. It is observed that the perfusion contribution percentages may not be the same as the corresponding tissue mixture volume fractions estimated in the structural images.Conclusion:A specific application to brain ASL images showed that the presented perfusion image analysis method is promising for detecting subtle changes in tissue perfusions, which is valuable for the early diagnosis of certain brain diseases, e.g. multiple sclerosis.

TU‐H‐206‐03: Characterizing B1 Inhomogeneities in DCE MRI

Purpose:Dynamic Contrast Enhanced (DCE) MRI is a valuable technique for measuring perfusion and permeability characteristics of tumors. Exogenous contrast concentrations are calculated based on changes in T1 measured using fast 3D gradient echo (FLASH) sequences. However, the slab selective pulses used in 3D MRI may result in B1 inhomogeneities across the volume of interest that can lead to errors in T1 and thus the estimated gadolinium concentration. We compared three FLASH DCE sequences (GRE, TWIST, and VIBE) to determine their signal homogeneity across slices and the accuracy in calculating T1 using acquisitions with variable flip angles.Methods:The sequences were tested at 3 T on a Siemens mMR (VB20P) using a doped water phantom 3.75 g/L NiSO4 ‐ 6H2O + 5 g/L NaCl (T1 = 104 ms) and a 2% agar, 0.67% NaCl phantom (T1= 1.71 s). 2D EPI B1 maps and inversion recovery T1maps were acquired for ground truth. 3D MRI was acquired at different flip angles to generate a T1 map. Regions of interest were drawn to measure signal inside the phantoms as a function of slice position. The T1 for each slice ROI was fit to the FLASH steady‐state model of magnetization.Results:Based on the data, GRE gave the most uniform signal homogeneity and T1 values in the middle slices of the 3D volume. The 3D VIBE sequence had the largest region of signal inhomogeneity compared to the 3D GRE and TWIST sequences. VIBE's B1 inhomogeneity is inconsistent at low flip angles. However, VIBE resulted in more slices with T1 values similar to the ground truth.Conclusion:The central 1/3 of the slices yielded signals that result in T1 fits consistent with the ground truth. However, the remaining slices required some form of B1 inhomogeneity correction for quantitative DCE analysis.The research was supported in part by NIH NCI Grant R01CA159471.

SU‐G‐IeP1‐01: A Novel MRI Post‐Processing Algorithm for Visualization of the Prostate LDR Brachytherapy Seeds and Calcifications Based On B0 Field Inhomogeneity Correction and Hough Transform

Purpose:This study aims at developing an MRI‐only workflow for post‐implant dosimetry of the prostate LDR brachytherapy seeds. The specific goal here is to develop a post‐processing algorithm to produce positive contrast for the seeds and prostatic calcifications and differentiate between them on MR images.Methods:An agar‐based phantom incorporating four dummy seeds (I‐125) and five calcifications of different sizes (from sheep cortical bone) was constructed. Seeds were placed arbitrarily in the coronal plane. The phantom was scanned with 3T Philips Achieva MR scanner using an 8‐channel head coil array. Multi‐echo turbo spin echo (ME‐TSE) and multi‐echo gradient recalled echo (ME‐GRE) sequences were acquired. Due to minimal susceptibility artifacts around seeds, ME‐GRE sequence (flip angle=15; TR/TE=20/2.3/2.3; resolution=0.7×0.7×2mm3) was further processed.The induced field inhomogeneity due to the presence of titaniumencapsulated seeds was corrected using a B0 field map. B0 map was calculated using the ME‐GRE sequence by calculating the phase difference at two different echo times. Initially, the product of the first echo and B0 map was calculated. The features corresponding to the seeds were then extracted in three steps: 1) the edge pixels were isolated using “Prewitt” operator; 2) the Hough transform was employed to detect ellipses approximately matching the dimensions of the seeds and 3) at the position and orientation of the detected ellipses an ellipse was drawn on the B0‐corrected image.Results:The proposed B0‐correction process produced positive contrast for the seeds and calcifications. The Hough transform based on Prewitt edge operator successfully identified all the seeds according to their ellipsoidal shape and dimensions in the edge image.Conclusion:The proposed post‐processing algorithm successfully visualized the seeds and calcifications with positive contrast and differentiates between them according to their shapes. Further assessments on more realistic phantoms and patient study are required to validate the outcome.

SU‐D‐207A‐04: Use of Gradient Echo Plural Contrast Imaging (GEPCI) in MR‐Guided Radiation Therapy: A Feasibility Study Targeting Brain Treatment

Purpose:To implement the Gradient Echo Plural Contrast Imaging(GEPCI) technique in MRI‐simulation for radiation therapy and assess the feasibility of using GEPCI images with advanced inhomogeneity correction in MRI‐guided radiotherapy for brain treatment.Methods:An optimized multigradient‐echo GRE sequence (TR=50ms;TE1=4ms;delta_TE=4ms;flip angle=300,11 Echoes) was developed to generate both structural (T1w and T2*w) and functional MRIs (field and susceptibility maps) from a single acquisition. One healthy subject (Subject1) and one post‐surgical brain cancer patient (Subject2) were scanned on a Philips Ingenia 1.5T MRI used for radiation therapy simulation. Another healthy subject (Subject3) was scanned on a 0.35T MRI‐guided radiotherapy (MR‐IGRT) system (ViewRay). A voxel spread function (VSF) was used to correct the B0 inhomogeneities caused by surgical cavities and edema for Subject2. GEPCI images and standard radiotherapy planning MRIs for this patient were compared focusing the delineation of radiotherapy target region.Results:GEPCI brain images were successfully derived from all three subjects with scan times of <7 minutes. The images derived for Subjects1&2 demonstrated that GEPCI can be applied and combined into radiotherapy MRI simulation. Despite low field, T1‐weighted and R2* images were successfully reconstructed for Subject3 and were satisfactory for contour and target delineation. The R2* distribution of grey matter (center=12,FWHM=4.5) and white matter (center=14.6, FWHM=2) demonstrated the feasibility for tissue segmentation and quantification. The voxel spread function(VSF) corrected surgical site related inhomogeneities for Subject2. R2* and quantitative susceptibility map(QSM) images for Subject2 can be used to quantitatively assess the brain structure response to radiation over the treatment course.Conclusion:We implemented the GEPCI technique in MRI‐simulation and in MR‐IGRT system for radiation therapy. The images demonstrated that it is feasible to adopt this technique in radiotherapy for structural delineation. The preliminary data also enable the opportunity for quantitative assessment of radiation response of the target region and normal tissue.

WE‐DE‐201‐09: Performance of HDR Ring Surface Applicator Printed by 3D Printer

Purpose:The commercially available Leipzig‐style Cone for High Dose Rate (HDR) Brachytherapy has a steep depth dose curve and a non‐uniform dose distribution. This work shows the performance of a Ring Surface Applicator created using a 3D printer that can generate a better dose distribution. Calculated doses were verified with film measurement.Methods:The water equivalent red‐ABS plastic was used to print the Ring Surface Applicator which hosts three catheters: a center piece with a straight catheter and two concentric rings with diameters of 3.5 and 5.5 cm. Gafchromic EBT2 film, Epson Expression 10000 flatbed scanner, and the online software at radiochromic.com were used to analyze the measured data. 10cm×10cm piece of film was sandwiched between two 15×10×5cm3 polystyrene phantoms. The applicator was positioned directly on top of the phantom. Measurement was done using dwell time and positions calculated by Eclipse BrachyVision treatment planning system (RTP).Results:Depth dose curve was generated from the plan and measurement. The results show that the measured and calculated depth dose were in agreement (<3%) from surface to 4mm depth. A discrepancy of 6% was observed at 5 mm depth, where the dose is typically prescribed to. For depths deeper than 5 mm, the measured doses were lower than those calculated by Eclipse BrachyVision. This can be attributed to a combination of simple calculation algorithm using TG‐43 and the lack of inhomogeneity correction. Dose profiles at 5 mm depth were also generated from TPS calculation and measured with film. The measured and calculated profiles are similar. Consistent with the depth dose curve, the measured dose is lower than the calculated.Conclusion:Our results showed that the Ring Surface Applicator, printed using 3D printer, can generate more uniform dose distribution within the target volume and can be safely used in the clinic.

Test-retest reliability of rapid whole body and compartmental fat volume quantification on a widebore 3T MR system in normal-weight, overweight, and obese subjects.

To measure the test-retest reliability of rapid (<15 min) whole body and visceral fat volume quantification in normal and obese subjects on a widebore 3T MR system and compare it with conventional manual segmentation. Thirty participants (body mass index [BMI] 20.1-48.6 kg/m2 ) underwent two whole-body magnetic resonance imaging (MRI) examinations on a widebore 3T machine using a 2-point Dixon technique. Phase sensitive reconstruction and intensity inhomogeneity correction produced quantitative datasets of total adipose tissue (TAT), abdominal subcutaneous adipose tissue (ASAT), and visceral adipose tissue (VAT). The quantification was performed automatically using nonrigid atlas-based segmentation and compared with manual segmentation (SliceOmatic). The mean TAT was 31.74 L with a coefficient of variation (CV) of 0.79% and a coefficient of repeatability (CR) of 0.49 L. The ASAT was 7.92 L with a CV of 2.98% and a CR of 0.46 L. There was no significant difference in the semiautomated and manually segmented VAT (P = 0.73) but there were differences in the reliability of the two techniques. The mean semiautomated VAT was 2.56 L, CV 1.8%, and CR 0.09 L compared to the mean manually segmented VAT of 3.12 L, where the CV was 6.3% and the CR was 0.39 L. Rapid semiautomated whole body and compartmental fat volume quantification can be derived from a widebore 3T system, for a range of body sizes including obese patients, with "almost perfect" test-retest reliability. J. Magn. Reson. Imaging 2016;44:1464-1473.

SU‐F‐T‐53: Treatment Planning with Inhomogeneity Correction for Intraoperative Radiotherapy Using KV X‐Ray Beams

Purpose:The current standard in dose calculation for intraoperative radiotherapy (IORT) using the ZEISS Intrabeam 50 kV x‐ray system is based on depth dose measurements in water and no heterogeneous tissue effect has been taken into account. We propose an algorithm for pre‐treatment planning including inhomogeneity correction based on data of depth dose measurements in various tissue phantoms for kV x‐rays.Methods:Direct depth dose measurements were made in air, water, inner bone and cortical bone phantoms for the Intrabeam 50 kV x‐rays with a needle applicator. The data were modelled by a function of power law combining exponential with different parameters. Those phantom slabs used in the measurements were scanned to obtain CT numbers. The x‐ray beam initiated from the source isocenter is ray‐traced through tissues. The corresponding doses will be deposited/assigned at different depths. On the boundary of tissue/organ changes, the x‐ray beam will be re‐traced in new tissue/organ starting at an equivalent depth with the same dose. In principle, a volumetric dose distribution can be generated if enough directional beams are traced. In practice, a several typical rays traced may be adequate in providing estimates of maximum dose to the organ at risk and minimum dose in the target volume.Results:Depth dose measurements and modeling are shown in Figure 1. The dose versus CT number is shown in Figure 2. A computer program has been written for Kypho‐IORT planning using those data. A direct measurement through 2 mm solid water, 2 mm inner bone, and 1 mm solid water yields a dose rate of 7.7 Gy/min. Our calculation shows 8.1±0.4 Gy/min, consistent with the measurement within 5%.Conclusion:The proposed method can be used to more accurately calculate the dose by taking into account the heterogeneous effect. The further validation includes comparison with Monte Carlo simulation.

Automated quantification of abdominal adiposity by magnetic resonance imaging.

To develop a fully-automated algorithm to process axial magnetic resonance imaging (MRI) slices for quantifying abdominal visceral, subcutaneous and total adipose tissues, i.e., VAT, SAT, and TAT, without human intervention or prior knowledge. Fat regions in single MRI slice or sequence (20 slices) were identified with image processing techniques including region-growing, inhomogeneity correction, fuzzy c-means clustering, and active contours segmentation. The MR images of 85 subjects (60 males and 25 females), whose body mass index (BMI) values ranged from 19.96 to 40.35 kg/m2 , were analyzed using the fully-automated algorithm-the automatic method developed in the research and the widely used semi-automated software (sliceOmatic® Tomovision, Inc.)-the reference method. The proposed automated method showed good performance against the reference method to quantify adipose tissues in both single umbilical slice and MRI sequence. The square of the Pearson correlation coefficients (R2 ) based on the results generated from the two methods for VAT/SAT/TAT were 0.977/0.998/0.997 for single slice data and 0.995/0.999/0.999 for volumetric data. The intra-class correlation of visceral adipose tissue (VAT) between the three operators was 0.939 in the reference method, which was improved to 0.999 in the automatic method. The adipose tissue measurements in the slice at Lumbar 3 vertebra have the highest correlation with the total fat volumes across the entire abdomen. The fully-automated algorithm presented in the paper provides an accurate and reliable assessment of abdominal fat without human intervention. Am. J. Hum. Biol. 28:757-766, 2016. © 2016Wiley Periodicals, Inc.

B0-adjusted and sensitivity-encoded spectral localization by imaging (BASE-SLIM) in the human brain in vivo

Spectral localization by imaging (SLIM) based magnetic resonance spectroscopy (MRS) provides a framework that overcomes major limitations of current MRS techniques, which allow only rectangular voxel shapes that do not conform to the shapes of brain structures or lesions. However, the restrictive assumption of compartmental homogeneity in SLIM can lead to localization errors, thus its applications have been very limited to date. SLIM-based localization is subject to errors due to inhomogeneous B0 and B1 fields, particularly in organs with complex compartmental geometry including the human brain. The limitations of SLIM were overcome through the development and implementation of B0-adjusted and sensitivity-encoded SLIM (BASE-SLIM) that includes corrections for inhomogeneities of both B0 and B1 fields throughout the volume of interest. In this study, we demonstrate significantly improved localization accuracy in compartments with arbitrary shapes and reliable quantification of metabolite concentrations in gray and white matter of the human brain using the BASE-SLIM technique.

INTENSITY INHOMOGENEITY CORRECTION OF MACULAR OCT USING N3 AND RETINAL FLATSPACE.

As optical coherence tomography (OCT) has increasingly become a standard modality for imaging the retina, automated algorithms for processing OCT data have become necessary to do large scale studies looking for changes in specific layers. To provide accurate results, many of these algorithms rely on the consistency of layer intensities within a scan. Unfortunately, OCT data often exhibits inhomogeneity in a given layer's intensities, both within and between images. This problem negatively affects the performance of segmentation algorithms and little prior work has been done to correct this data. In this work, we adapt the N3 framework for intensity inhomogeneity correction, which was originally developed to correct MRI data, to work for macular OCT data. We first transform the data to a flattened macular space to create a template intensity profile for each layer giving us an accurate initial estimate of the gain field. N3 will then produce a smoothly varying field to correct the data. We show that our method is able to both accurately recover synthetically generated gain fields and improves the stability of the layer intensities.

Detection of changes in the ventral tegmental area of patients with schizophrenia using neuromelanin-sensitive MRI

The ventral tegmental area (VTA), one of the neuromelanin-containing dopaminergic nuclei in the midbrain, is closely related to the pathogenesis of schizophrenia, but its imaging findings remain unclear. Here, we investigated whether neuromelanin-sensitive MRI can detect alterations in the signals of the VTA of patients with schizophrenia. Neuromelanin-sensitive T1-weighted images at 3 T were obtained in 14 patients with schizophrenia and 22 healthy individuals. After signal inhomogeneity correction, brain signal rescaling of the images, and spatial normalization, signal intensity of the VTA and substantia nigra pars compacta (SNc) was automatically measured using the regions-of-interest constructed from the images of the healthy individuals. The normalized signal intensity of the VTA in patients with schizophrenia (median ± quartile deviation, 998 ± 10) was significantly decreased compared with that in healthy controls (1018 ± 15) (P=0.010), whereas that of the SNc was not significantly different between the groups (1093 ± 6 and 1098 ± 10, respectively, P=0.84). The VTA/SNc ratio was also significantly lower in patients with schizophrenia (0.916 ± 0.007) than in the control participants (0.934 ± 0.010) (P=0.010). In addition, the scores of the Scale for the Assessment of Positive Symptoms showed an independent negative correlation with VTA signaling (r=-0.69, P=0.012). Neuromelanin-sensitive MRI enables direct visualization of the selective signal attenuation in the VTA, which was correlated with positive symptoms, in patients with schizophrenia.

Intensity Inhomogeneity Correction of Structural MR Images: A Data-Driven Approach to Define Input Algorithm Parameters.

Intensity non-uniformity (INU) in magnetic resonance (MR) imaging is a major issue when conducting analyses of brain structural properties. An inaccurate INU correction may result in qualitative and quantitative misinterpretations. Several INU correction methods exist, whose performance largely depend on the specific parameter settings that need to be chosen by the user. Here we addressed the question of how to select the best input parameters for a specific INU correction algorithm. Our investigation was based on the INU correction algorithm implemented in SPM, but this can be in principle extended to any other algorithm requiring the selection of input parameters. We conducted a comprehensive comparison of indirect metrics for the assessment of INU correction performance, namely the coefficient of variation of white matter (CVWM), the coefficient of variation of gray matter (CVGM), and the coefficient of joint variation between white matter and gray matter (CJV). Using simulated MR data, we observed the CJV to be more accurate than CVWM and CVGM, provided that the noise level in the INU-corrected image was controlled by means of spatial smoothing. Based on the CJV, we developed a data-driven approach for selecting INU correction parameters, which could effectively work on actual MR images. To this end, we implemented an enhanced procedure for the definition of white and gray matter masks, based on which the CJV was calculated. Our approach was validated using actual T1-weighted images collected with 1.5 T, 3 T, and 7 T MR scanners. We found that our procedure can reliably assist the selection of valid INU correction algorithm parameters, thereby contributing to an enhanced inhomogeneity correction in MR images.

Improvement of water saturation shift referencing by sequence and analysis optimization to enhance chemical exchange saturation transfer imaging

PurposeTo optimize B0-field inhomogeneity correction for chemical exchange saturation transfer (CEST) imaging by investigating different water saturation shift referencing (WASSR) Z-spectrum shapes and different frequency correction techniques. MethodsWASSR Z-spectra were simulated for different B1-fields and pulse durations (PD). Two parameter settings were used for further simulations and experiments (WASSR1: B1=0.1 μT, PD=50ms; WASSR2: B1=0.3 μT, PD=40ms). Four frequency correction techniques were investigated: 1) MinW: Minimum of the spline-interpolated WASSR-spectrum; 2) MSCF: maximum symmetry center frequency algorithm; 3) PMSCF: further development of MSCF algorithm; 4) BFit: fit with Bloch equations. Performance of frequency correction was assessed with Monte-Carlo simulations and in-vivo MR examinations in the brain and intervertebral disks. ResultsDifferent shapes of WASSR-Z-spectra were obtained by changing B1 and PD including spectra with one (1-Peak) or two (2-Peak) minima. WASSR1 resulted in 1-Peak WASSR-spectrum, whereas WASSR2 resulted in 2-Peak WASSR-spectrum. Both Monte-Carlo simulations and in-vivo MR examinations revealed highest accuracy of field-inhomogeneity correction with WASSR1 combined with PMSCF or BFit. ConclusionUsing a WASSR sequence, which results in a Z-spectrum with a single absorption peak, in combination with advanced postprocessing algorithms enables improved B0-field inhomogeneity correction for CEST imaging.

Comparison of selected dose calculation algorithms in radiotherapy treatment planning for tissues with inhomogeneities

Inhomogeneity correction is recommended for accurate dose calculation in radiotherapy treatment planning since human body are highly inhomogeneous with the presence of bones and air cavities. However, each dose calculation algorithm has its own limitations. This study is to assess the accuracy of five algorithms that are currently implemented for treatment planning, including pencil beam convolution (PBC), superposition (SP), anisotropic analytical algorithm (AAA), Monte Carlo (MC) and Acuros XB (AXB). The calculated dose was compared with the measured dose using radiochromic film (Gafchromic EBT2) in inhomogeneous phantoms. In addition, the dosimetric impact of different algorithms on intensity modulated radiotherapy (IMRT) was studied for head and neck region. MC had the best agreement with the measured percentage depth dose (PDD) within the inhomogeneous region. This was followed by AXB, AAA, SP and PBC. For IMRT planning, MC algorithm is recommended for treatment planning in preference to PBC and SP. The MC and AXB algorithms were found to have better accuracy in terms of inhomogeneity correction and should be used for tumour volume within the proximity of inhomogeneous structures.

Evaluating the impact of U.S. Historical Climatology Network homogenization using the U.S. Climate Reference Network

AbstractNumerous inhomogeneities including station moves, instrument changes, and time of observation changes in the U.S. Historical Climatological Network (USHCN) complicate the assessment of long‐term temperature trends. Detection and correction of inhomogeneities in raw temperature records have been undertaken by NOAA and other groups using automated pairwise neighbor comparison approaches, but these have proven controversial due to the large trend impact of homogenization in the United States. The new U.S. Climate Reference Network (USCRN) provides a homogenous set of surface temperature observations that can serve as an effective empirical test of adjustments to raw USHCN stations. By comparing nearby pairs of USHCN and USCRN stations, we find that adjustments make both trends and monthly anomalies from USHCN stations much more similar to those of neighboring USCRN stations for the period from 2004 to 2015 when the networks overlap. These results improve our confidence in the reliability of homogenized surface temperature records.

Does hydration status affect MRI measures of brain volume or water content?

To determine whether differences in hydration state, which could arise from routine clinical procedures such as overnight fasting, affect brain total water content (TWC) and brain volume measured with magnetic resonance imaging (MRI). Twenty healthy volunteers were scanned with a 3T MR scanner four times: day 1, baseline scan; day 2, hydrated scan after consuming 3L of water over 12 hours; day 3, dehydrated scan after overnight fasting of 9 hours, followed by another scan 1 hour later for reproducibility. The following MRI data were collected: T2 relaxation (for TWC measurement), inversion recovery (for T1 measurement), and 3D T1 -weighted (for brain volumes). Body weight and urine specific gravity were also measured. TWC was calculated by fitting the T2 relaxation data with a nonnegative least-squares algorithm, with corrections for T1 relaxation and image signal inhomogeneity and normalization to ventricular cerebrospinal fluid. Brain volume changes were measured using SIENA. TWC means were calculated within 14 tissue regions. Despite indications of dehydration as demonstrated by increases in urine specific gravity (P = 0.03) and decreases in body weight (P = 0.001) between hydrated and dehydrated scans, there was no measurable change in TWC (within any brain region) or brain volume between hydration states. We demonstrate that within a range of physiologic conditions commonly encountered in routine clinical scans (no pretreatment with hydration, well hydrated before MRI, and overnight fasting), brain TWC and brain volumes are not substantially affected in a healthy control cohort. J. Magn. Reson. Imaging 2016;44:296-304.

Computed tomography imaging parameters for inhomogeneity correction in radiation treatment planning.

Modern treatment planning systems provide accurate dosimetry in heterogeneous media (such as a patient' body) with the help of tissue characterization based on computed tomography (CT) number. However, CT number depends on the type of scanner, tube voltage, field of view (FOV), reconstruction algorithm including artifact reduction and processing filters. The impact of these parameters on CT to electron density (ED) conversion had been subject of investigation for treatment planning in various clinical situations. This is usually performed with a tissue characterization phantom with various density plugs acquired with different tube voltages (kilovoltage peak), FOV reconstruction and different scanners to generate CT number to ED tables. This article provides an overview of inhomogeneity correction in the context of CT scanning and a new evaluation tool, difference volume dose-volume histogram (DVH), dV-DVH. It has been concluded that scanner and CT parameters are important for tissue characterizations, but changes in ED are minimal and only pronounced for higher density materials. For lungs, changes in CT number are minimal among scanners and CT parameters. Dosimetric differences for lung and prostate cases are usually insignificant (<2%) in three-dimensional conformal radiation therapy and < 5% for intensity-modulated radiation therapy (IMRT) with CT parameters. It could be concluded that CT number variability is dependent on acquisition parameters, but its dosimetric impact is pronounced only in high-density media and possibly in IMRT. In view of such small dosimetric changes in low-density medium, the acquisition of additional CT data for financially difficult clinics and countries may not be warranted.

FCM Clustering Algorithms for Segmentation of Brain MR Images

The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR) brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF), Gray Matter (GM), and White Matter (WM), has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzyc-means (FCM) clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.

MRI-Based Attenuation Correction for PET/MRI Using Multiphase Level-Set Method.

Inaccuracy in MR image-based attenuation correction (MR-AC) leads to errors in quantification and the misinterpretation of lesions in brain PET/MRI studies. To resolve this problem, we proposed an improved ultrashort echo time MR-AC method that was based on a multiphase level-set algorithm with main magnetic field (B0) inhomogeneity correction. We also assessed the feasibility of this level-set-based MR-AC method (MR-AC(level)), compared with CT-AC and MR-AC provided by the manufacturer of the PET/MRI scanner (MR-AC(mMR)). Ten healthy volunteers and 20 Parkinson disease patients underwent(18)F-FDG and(18)F-fluorinated-N-3-fluoropropyl-2-β-carboxymethoxy-3-β-(4-iodophenyl)nortropane ((18)F-FP-CIT) PET scans, respectively, using both PET/MRI and PET/CT scanners. The level-set-based segmentation algorithm automatically delimited air, bone, and soft tissue from the ultrashort echo time MR images. For the comparison, MR-AC maps were coregistered to reference CT. PET sinogram data obtained from PET/CT studies were then reconstructed using the CT-AC, MR-AC(mMR), and MR-AC(level) maps. The accuracies of SUV, SUVr (SUV and its ratio to the cerebellum), and specific-to-nonspecific binding ratios obtained using MR-AC(level) and MR-AC(mMR) were compared with CT-AC using region-of-interest- and voxel-based analyses. There was remarkable improvement in the segmentation of air cavities and bones and the quantitative accuracy of PET measurement using the level set. Although the striatal and cerebellar activities in (18)F-FP-CIT PET and frontal activity in (18)F-FDG PET were significantly underestimated by the MR-AC(mMR), the MR-AC(level) provided PET images almost equivalent to the CT-AC images. PET quantification error was reduced by a factor of 3 using MR-AC(level) (SUV error < 10% in MR-AC(level) and < 30% in MR-AC(mMR) [version VB18P], and < 5% in MR-AC(level) and < 15% in MR-AC(mMR) [VB20P]). The results of this study indicate that our new multiphase level-set-based MR-AC method improves the quantitative accuracy of brain PET in PET/MRI studies.