Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions.

Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm2). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data.

Influence of Magnetic Field Strength on Intravoxel Incoherent Motion Parameters in Diffusion MRI of the Calf.

Background: The purpose of this study was to investigate the dependence of Intravoxel Incoherent Motion (IVIM) parameters measured in the human calf on B0. Methods: Diffusion-weighted image data of eight healthy volunteers were acquired using five b-values (0-600 s/mm2) at rest and after muscle activation at 0.55 and 7 T. The musculus gastrocnemius mediale (GM, activated) was assessed. The perfusion fraction f and diffusion coefficient D were determined using segmented fits. The dependence on field strength was assessed using Student's t-test for paired samples and the Wilcoxon signed-rank test. A biophysical model built on the three non-exchanging compartments of muscle, venous blood, and arterial blood was used to interpret the data using literature relaxation times. Results: The measured perfusion fraction of the GM was significantly lower at 7 T, both for the baseline measurement and after muscle activation. For 0.55 and 7 T, the mean f values were 7.59% and 3.63% at rest, and 14.03% and 6.92% after activation, respectively. The biophysical model estimations for the mean proton-density-weighted perfusion fraction were 3.37% and 6.50% for the non-activated and activated states, respectively. Conclusions: B0 may have a significant effect on the measured IVIM parameters. The blood relaxation times suggest that 7 T IVIM may be arterial-weighted whereas 0.55 T IVIM may exhibit an approximately equal weighting of arterial and venous blood.

Impact of velocity- and acceleration-compensated encodings on signal dropout and black-blood state in diffusion-weighted magnetic resonance liver imaging at clinical TEs.

PURPOSE: The study aims to develop easy-to-implement concomitant field-compensated gradient waveforms with varying velocity-weighting (M1) and acceleration-weighting (M2) levels and to evaluate their efficacy in correcting signal dropouts and preserving the black-blood state in liver diffusion-weighted imaging. Additionally, we seek to determine an optimal degree of compensation that minimizes signal dropouts while maintaining blood signal suppression. METHODS: Numerically optimized gradient waveforms were adapted using a novel method that allows for the simultaneous tuning of M1- and M2-weighting by changing only one timing variable. Seven healthy volunteers underwent diffusion-weighted magnetic resonance imaging (DWI) with five diffusion encoding schemes (monopolar, velocity-compensated (M1 = 0), acceleration-compensated (M1 = M2 = 0), 84%-M1-M2-compensated, 67%-M1-M2-compensated) at b-values of 50 and 800 s/mm2 at a constant echo time of 70 ms. Signal dropout correction and apparent diffusion coefficients (ADCs) were quantified using regions of interest in the left and right liver lobe. The blood appearance was evaluated using two five-point Likert scales. RESULTS: Signal dropout was more pronounced in the left lobe (19%-42% less signal than in the right lobe with monopolar scheme) and best corrected by acceleration-compensation (8%-10% less signal than in the right lobe). The black-blood state was best with monopolar encodings and decreased significantly (p < 0.001) with velocity- and/or acceleration-compensation. The partially M1-M2-compensated encoding schemes could restore the black-blood state again. Strongest ADC bias occurred for monopolar encodings (difference between left/right lobe of 0.41 µm2/ms for monopolar vs. < 0.12 µm2/ms for the other encodings). CONCLUSION: All of the diffusion encodings used in this study demonstrated suitability for routine DWI application. The results indicate that a perfect value for the level of M1-M2-compensation does not exist. However, among the examined encodings, the 84%-M1-M2-compensated encodings provided a suitable tradeoff.

Feature-guided deep learning reduces signal loss and increases lesion CNR in diffusion-weighted imaging of the liver.

PURPOSE: This research aims to develop a feature-guided deep learning approach and compare it with an optimized conventional post-processing algorithm in order to enhance the image quality of diffusion-weighted liver images and, in particular, to reduce the pulsation-induced signal loss occurring predominantly in the left liver lobe. METHODS: Data from 40 patients with liver lesions were used. For the conventional approach, the best-suited out of five examined algorithms was chosen. For the deep learning approach, a U-Net was trained. Instead of learning "gold-standard" target images, the network was trained to optimize four image features (lesion CNR, vessel darkness, data consistency, and pulsation artifact reduction), which could be assessed quantitatively using manually drawn ROIs. A quality score was calculated from these four features. As an additional quality assessment, three radiologists rated different features of the resulting images. RESULTS: The conventional approach could substantially increase the lesion CNR and reduce the pulsation-induced signal loss. However, the vessel darkness was reduced. The deep learning approach increased the lesion CNR and reduced the signal loss to a slightly lower extent, but it could additionally increase the vessel darkness. According to the image quality score, the quality of the deep-learning images was higher than that of the images obtained using the conventional approach. The radiologist ratings were mostly consistent with the quantitative scores, but the overall quality ratings differed among the readers. CONCLUSION: Unlike the conventional algorithm, the deep-learning algorithm increased the vessel darkness. Therefore, it may be a viable alternative to conventional algorithms.

Comparison of Diagnostic Performance and Image Quality between Topup-Corrected and Standard Readout-Segmented Echo-Planar Diffusion-Weighted Imaging for Cholesteatoma Diagnostics.

This study compares the diagnostic performance and image quality of single-shot turbo spin-echo DWI (tseDWI), standard readout-segmented DWI (rsDWI), and a modified rsDWI version (topupDWI) for cholesteatoma diagnostics. Thirty-four patients with newly suspected unilateral cholesteatoma were examined on a 1.5 Tesla MRI scanner. Diagnostic performance was evaluated by calculating and comparing the sensitivity and specificity using histopathological results as the standard of reference. Image quality was independently reviewed by two readers using a 5-point Likert scale evaluating image distortions, susceptibility artifacts, image resolution, lesion conspicuity, and diagnostic confidence. Twenty-five cholesteatomas were histologically confirmed after surgery and originated in the study group. TseDWI showed the highest sensitivity with 96% (95% confidence interval (CI): 88-100%), followed by topupDWI with 92% (95% CI: 81-100%) for both readers. The sensitivity for rsDWI was 76% (95% CI: 59-93%) for reader 1 and 84% (95% CI: 70-98%) for reader 2, respectively. Both tseDWI and topupDWI revealed a specificity of 100% (95% CI: 66-100%) and rsDWI of 89% (95% CI: 52-100%). Both tseDWI and topupDWI showed fewer image distortions and susceptibility artifacts compared to rsDWI. Image resolution was consistently rated best for topupDWI, followed by rsDWI, which both outperformed tseDWI. TopupDWI and tseDWI showed comparable results for lesions' conspicuity and diagnostic confidence, both outperforming rsDWI. Modified readout-segmented DWI using the topup-correction method is preferable to standard rsDWI and may be regarded as an accurate alternative to single-shot turbo spin-echo DWI in cholesteatoma diagnostics.

Influence of saturation effects on biexponential liver intravoxel incoherent motion.

PURPOSE: Studies on intravoxel incoherent motion (IVIM) imaging in the liver have been carried out with different acquisition protocols. The number of acquired slices and the distances between slices can influence IVIM measurements due to saturation effects, but these effects have often been disregarded. This study investigated differences in biexponential IVIM parameters between two slice settings. METHODS: Fifteen healthy volunteers (21-30 years) were examined at a field strength of 3 T. Diffusion-weighted images of the abdomen were acquired with 16 b values (0-800 s/mm2 ), with four slices for the few slices setting and 24-27 slices for the many slices setting. Regions of interest were manually drawn in the liver. The data were fitted with a monoexponential signal curve and a biexponential IVIM curve, and biexponential IVIM parameters were determined. The dependence on the slice setting was assessed with Student's t test for paired samples (normally distributed IVIM parameters) and the Wilcoxon signed-rank test (non-normally distributed parameters). RESULTS: None of the parameters were significantly different between the settings. For few slices and many slices, respectively, the mean values (SDs) for D $$ D $$ were 1.21 µm 2 / ms $$ 1.21{\upmu \mathrm{m}}^2/\mathrm{ms} $$ ( 0.19 µm 2 / ms $$ 0.19\kern0.3em {\upmu \mathrm{m}}^2/\mathrm{ms} $$ ) and 1.20 µm 2 / ms $$ 1.20{\upmu \mathrm{m}}^2/\mathrm{ms} $$ ( 0.11 µm 2 / ms $$ 0.11\kern0.3em {\upmu \mathrm{m}}^2/\mathrm{ms} $$ ); for f $$ f $$ they were 29.7% (6.2%) and 27.7% (3.6%); and for D * $$ {D}^{\ast } $$ they were 8.76 ⋅ 10 - 2 mm 2 / s $$ 8.76\cdot {10}^{-2}{\mathrm{mm}}^2/\mathrm{s} $$ ( 4.54 ⋅ 10 - 2 mm 2 / s $$ 4.54\cdot {10}^{-2}\kern0.3em {\mathrm{mm}}^2/\mathrm{s} $$ ) and 8.71 ⋅ 10 - 2 mm 2 / s $$ 8.71\cdot {10}^{-2}{\mathrm{mm}}^2/\mathrm{s} $$ ( 4.06 ⋅ 10 - 2 mm 2 / s $$ 4.06\cdot {10}^{-2}\kern0.3em {\mathrm{mm}}^2/\mathrm{s} $$ ). CONCLUSION: Biexponential IVIM parameters in the liver are comparable among IVIM studies that use different slice settings, with mostly negligible saturation effects. However, this may not hold for studies that use much shorter TR.

Reduction of the cardiac pulsation artifact and improvement of lesion conspicuity in flow-compensated diffusion images in the liver-A quantitative evaluation of postprocessing algorithms.

PURPOSE: To enhance image quality of flow-compensated diffusion-weighted liver MRI data by increasing the lesion conspicuity and reducing the cardiac pulsation artifact using postprocessing algorithms. METHODS: Diffusion-weighted image data of 40 patients with liver lesions had been acquired at 1.5 T. These data were postprocessed with 5 different algorithms (weighted averaging, p-mean, percentile, outlier exclusion, and exception set). Four image properties of the postprocessed data were evaluated for optimizing the algorithm parameters. These properties were the lesion to tissue contrast-to-noise ratio (CNR), the reduction of the cardiac pulsation artifact, the data consistency, and the vessel darkness. They were combined into a total quality score ( Q total , $$ {Q}_{\mathrm{total}}, $$ set to 1 for the trace-weighted reference image), which was used to rate the image quality objectively. RESULTS: The weighted averaging algorithm performed best according to the total quality score ( Q total = 1.111 ± 0.067 $$ {Q}_{\mathrm{total}}=1.111\pm 0.067 $$ ). The further ranking was outlier exclusion algorithm ( Q total = 1.086 ± 0.061 $$ {Q}_{\mathrm{total}}=1.086\pm 0.061 $$ ), p-mean algorithm ( Q total = 1.045 ± 0.049 $$ {Q}_{\mathrm{total}}=1.045\pm 0.049 $$ ), percentile algorithm ( Q total = 1.012 ± 0.049 $$ {Q}_{\mathrm{total}}=1.012\pm 0.049 $$ ), and exception set algorithm ( Q total = 0.957 ± 0.027 $$ {Q}_{\mathrm{total}}=0.957\pm 0.027 $$ ). All optimized algorithms except for the exception set algorithm corrected the pulsation artifact and increased the lesion CNR. Changes in Q total $$ {Q}_{\mathrm{total}} $$ were significant for all optimized algorithms except for the percentile algorithm. Liver ADC was significantly reduced (except for the exception set algorithm), particularly in the left lobe. CONCLUSION: Postprocessing algorithms should be used for flow-compensated liver DWI. The proposed weighted averaging algorithm seems to be suited best to increase the image quality of artifact-corrupted flow-compensated diffusion-weighted liver data.

Diffusion pore imaging in the presence of extraporal water.

Diffusion-weighted imaging (DWI) is a powerful, non-invasive tool which is widely used in clinical routine. Mostly, apparent diffusion coefficient maps are acquired, which cannot be related directly to cellular structure. More recently it was shown that DWI is able to reconstruct pore shapes using a specialized magnetic field gradient scheme so that cell size distributions may be obtained. So far, artificial systems have been used for experimental demonstration without extraporal signal components and relatively low gradient amplitudes. The aim of this study was to investigate the feasibility of diffusion pore imaging in the presence of extraporal fluids and to develop correction methods for the effects arising from extraporal signal contributions. Monte Carlo simulations and validation experiments on a 14.1 T NMR spectrometer equipped with a dedicated diffusion probe head were performed. Both by using a filter gradient approach suppressing extraporal signal components as well as by using post-processing methods relying on the Gaussian phase approximation, it was possible to reconstruct pore space functions in the presence of extraporal fluids with little to no deviations from the expectations. These results may be a significant step towards application of diffusion pore imaging to biological samples.

Differentiating Benign and Malignant Breast Lesions in Diffusion Kurtosis MRI: Does the Averaging Procedure Matter?

BACKGROUND: Diffusion kurtosis imaging (DKI) is used to differentiate between benign and malignant breast lesions. DKI fits are performed either on voxel-by-voxel basis or using volume-averaged signal. PURPOSE: Investigate and compare DKI parameters' diagnostic performance using voxel-by-voxel and volume-averaged signal fit approach. STUDY TYPE: Retrospective. STUDY POPULATION: A total of 104 patients, aged 24.1-86.4 years. FIELD STRENGTH/SEQUENCE: A 3 T Spin-echo planar diffusion-weighted sequence with b-values: 50 s/mm2 , 750 s/mm2 , and 1500 s/mm2 . Dynamic contrast enhanced (DCE) sequence. ASSESSMENT: Lesions were manually segmented by M.P. under supervision of S.O. (2 and 5 years of experience in breast MRI). DKI fits were performed on voxel-by-voxel basis and with volume-averaged signal. Diagnostic performance of DKI parameters D K (kurtosis corrected diffusion coefficient) and kurtosis K was compared between both approaches. STATISTICAL TESTS: Receiver operating characteristics analysis and area under the curve (AUC) values were computed. Wilcoxon rank sum and Students t-test tested DKI parameters for significant (P <0.05) difference between benign and malignant lesions. DeLong test was used to test the DKI parameter performance for significant fit approach dependency. Correlation between parameters of the two approaches was determined by Pearson correlation coefficient. RESULTS: DKI parameters were significantly different between benign and malignant lesions for both fit approaches. Median benign vs. malignant values for voxel-by-voxel and volume-averaged approach were 2.00 vs. 1.28 ( D K in µm2 /msec), 2.03 vs. 1.26 ( D K in µm2 /msec), 0.54 vs. 0.90 ( K ), 0.55 vs. 0.99 ( K ). AUC for voxel-by-voxel and volume-averaged fit were 0.9494 and 0.9508 ( D K ); 0.9175 and 0.9298 ( K ). For both, AUC did not differ significantly (P = 0.20). Correlation of values between the two approaches was very high (r = 0.99 for D K and r = 0.97 for K ). DATA CONCLUSION: Voxel-by-voxel and volume-averaged signal fit approach are equally well suited for differentiating between benign and malignant breast lesions in DKI. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Echo time dependence of biexponential and triexponential intravoxel incoherent motion parameters in the liver.

PURPOSE: Intravoxel incoherent motion (IVIM) studies are performed with different acquisition protocols. Comparing them requires knowledge of echo time (TE) dependencies. The TE-dependence of the biexponential perfusion fraction f is well-documented, unlike that of its triexponential counterparts f1 and f2 and the biexponential and triexponential pseudodiffusion coefficients D* , D1∗ , and D2∗ . The purpose was to investigate the TE-dependence of these parameters and to check whether the triexponential pseudodiffusion compartments are associated with arterial and venous blood. METHODS: Fifteen healthy volunteers (19-58 y; mean: 24.7 y) underwent diffusion-weighted imaging of the abdomen with 24 b-values (0.2-800 s/mm2 ) at TEs of 45, 60, 75, and 90 ms. Regions of interest (ROIs) were manually drawn in the liver. One set of bi- and triexponential IVIM parameters per volunteer and TE was determined. The TE-dependence was assessed with the Kruskal-Wallis test. RESULTS: TE-dependence was observed for f (P < .001), f1 (P = .001), and f2 (P < .001). Their median values at the four measured TEs were: f: 0.198/0.240/0.274/0.359, f1 : 0.113/0.139/0.146/0.205, f2 : 0.115/0.155/0.182/0.194. D, D* , D1∗ , and D2∗ showed no significant TE-dependence. Their values were: diffusion coefficient D (10-4 mm2 /s): 9.45/9.63/9.75/9.41, biexponential D* (10-2 mm2 /s): 5.26/5.52/6.13/5.82, triexponential D1∗ (10-2 mm2 /s): 1.73/2.91/2.25/2.51, triexponential D2∗ (mm2 /s): 0.478/1.385/0.616/0.846. CONCLUSION: f1 and f2 show similar TE-dependence as f, ie, increase with rising TE; an effect that must be accounted for when comparing different studies. The diffusion and pseudodiffusion coefficients might be compared without TE correction. Because of the similar TE-dependence of f1 and f2 , the triexponential pseudodiffusion compartments are most probably not associated to venous and arterial blood.

Direct imaging of white matter ultrashort T2∗ components at 7 Tesla.

PURPOSE: To demonstrate direct imaging of the white matter ultrashort T2∗ components at 7 Tesla using inversion recovery (IR)-enhanced ultrashort echo time (UTE) MRI. To investigate its characteristics, potentials and limitations, and to establish a clinical protocol. MATERIAL AND METHODS: The IR UTE technique suppresses long T2∗ signals within white matter by using adiabatic inversion in combination with dual-echo difference imaging. Artifacts arising at 7 T from long T2∗ scalp fat components were reduced by frequency shifting the IR pulse such that those frequencies were inverted likewise. For 8 healthy volunteers, the T2∗ relaxation times of white matter were then quantified. In 20 healthy volunteers, the UTE difference and fraction contrast were evaluated. Finally, in 6 patients with multiple sclerosis (MS), the performance of the technique was assessed. RESULTS: A frequency shift of -1.2 ppm of the IR pulse (i.e. towards the fat frequency) provided a good suppression of artifacts. With this, an ultrashort compartment of (68 ± 6) % with a T2∗ time of (147 ± 58) µs was quantified with a chemical shift of (-3.6 ± 0.5) ppm from water. Within healthy volunteers' white matter, a stable ultrashort T2∗ fraction contrast was calculated. For the MS patients, a significant fraction reduction in the identified lesions as well as in the normal-appearing white matter was observed. CONCLUSIONS: The quantification results indicate that the observed ultrashort components arise primarily from myelin tissue. Direct IR UTE imaging of the white matter ultrashort T2∗ components is thus feasible at 7 T with high quantitative inter-subject repeatability and good detection of signal loss in MS.

Nonparametric D-R1-R2 distribution MRI of the living human brain.

Diffusion-relaxation correlation NMR can simultaneously characterize both the microstructure and the local chemical composition of complex samples that contain multiple populations of water. Recent developments on tensor-valued diffusion encoding and Monte Carlo inversion algorithms have made it possible to transfer diffusion-relaxation correlation NMR from small-bore scanners to clinical MRI systems. Initial studies on clinical MRI systems employed 5D D-R1 and D-R2 correlation to characterize healthy brain in vivo. However, these methods are subject to an inherent bias that originates from not including R2 or R1 in the analysis, respectively. This drawback can be remedied by extending the concept to 6D D-R1-R2 correlation. In this work, we present a sparse acquisition protocol that records all data necessary for in vivo 6D D-R1-R2 correlation MRI across 633 individual measurements within 25 min-a time frame comparable to previous lower-dimensional acquisition protocols. The data were processed with a Monte Carlo inversion algorithm to obtain nonparametric 6D D-R1-R2 distributions. We validated the reproducibility of the method in repeated measurements of healthy volunteers. For a post-therapy glioblastoma case featuring cysts, edema, and partially necrotic remains of tumor, we present representative single-voxel 6D distributions, parameter maps, and artificial contrasts over a wide range of diffusion-, R1-, and R2-weightings based on the rich information contained in the D-R1-R2 distributions.

Ultra-High b-Value Diffusion-Weighted Imaging-Based Abbreviated Protocols for Breast Cancer Detection.

OBJECTIVES: Contrast-enhanced (CE) magnetic resonance imaging (MRI) is the most effective imaging modality for breast cancer detection. A contrast agent-free examination technique would be desirable for breast MRI screening. The purpose of this study was to evaluate the capability to detect and characterize suspicious breast lesions with an abbreviated, non-contrast-enhanced MRI protocol featuring ultra-high b-value diffusion-weighted imaging (DWI) compared with CE images. MATERIALS AND METHODS: The institutional review board-approved prospective study included 127 female subjects with different clinical indications for breast MRI. Magnetic resonance imaging examinations included DWI sequences with b-values of 1500 s/mm2 (b1500) and 2500 s/mm2 (b2500), native T1- and T2-weighted images, and CE sequences at 1.5 T and 3 T scanners. Two reading rounds were performed, including either the b1500 or the b2500 DWI in consecutive assessment steps: (A) maximum intensity projections (MIPs) of DWI, (B) DWI and apparent diffusion coefficient maps, (C) as (B) but with additional native T1- and T2-weighted images, and (D) as (C) but with additional CE images (full-length protocol). Two readers independently determined the presence of a suspicious lesion. Histological confirmation was obtained for conspicuous lesions, whereas the full MRI data set was obtained for inconspicuous and clearly benign lesions. Statistical analysis included calculation of diagnostic accuracy and interrater agreement via the intraclass correlation coefficient. RESULTS: The cohort comprised 116 cases with BI-RADS 1 findings and 138 cases with BI-RADS ≥2 findings, including 38 histologically confirmed malignancies. For (A), breasts without pathological findings could be recognized with high diagnostic accuracy (negative predictive value, ≥97.0%; sensitivity, ≥92.1% for both readers), but with a limited specificity (≥58.3%; positive predictive value, ≥28.6%). Within the native readings, approach (C) with b2500 performed best (negative predictive value, 99.5%; sensitivity, 97.4%; specificity, 88.4%). The intraclass correlation coefficient was between 0.683 (MIP b1500) and 0.996 (full protocol). CONCLUSIONS: A native abbreviated breast MRI protocol with advanced high b-value DWI might allow nearly equivalent diagnostic accuracy as CE breast MRI and seems to be well suited for lesion detection purposes.

Brain tissues have single-voxel signatures in multi-spectral MRI.

Since the seminal works by Brodmann and contemporaries, it is well-known that different brain regions exhibit unique cytoarchitectonic and myeloarchitectonic features. Transferring the approach of classifying brain tissues - and other tissues - based on their intrinsic features to the realm of magnetic resonance (MR) is a longstanding endeavor. In the 1990s, atlas-based segmentation replaced earlier multi-spectral classification approaches because of the large overlap between the class distributions. Here, we explored the feasibility of performing global brain classification based on intrinsic MR features, and used several technological advances: ultra-high field MRI, q-space trajectory diffusion imaging revealing voxel-intrinsic diffusion properties, chemical exchange saturation transfer and semi-solid magnetization transfer imaging as a marker of myelination and neurochemistry, and current neural network architectures to analyze the data. In particular, we used the raw image data as well to increase the number of input features. We found that a global brain classification of roughly 97 brain regions was feasible with gross classification accuracy of 60%; and that mapping from voxel-intrinsic MR data to the brain region to which the data belongs is possible. This indicates the presence of unique MR signals of different brain regions, similar to their cytoarchitectonic and myeloarchitectonic fingerprints.

Apparent exchange rate imaging: On its applicability and the connection to the real exchange rate.

PURPOSE: Water exchange between the intracellular and extracellular space can be measured using apparent exchange rate (AXR) imaging. The aim of this study was to investigate the relationship between the measured AXR and the geometry of diffusion restrictions, membrane permeability, and the real exchange rate, as well as to explore the applicability of AXR for typical human measurement settings. METHODS: The AXR measurements and the underlying exchange rates were simulated using the Monte Carlo method with different geometries, size distributions, packing densities, and a broad range of membrane permeabilities. Furthermore, the influence of SNR and sequence parameters was analyzed. RESULTS: The estimated AXR values correspond to the simulated values and show the expected proportionality to membrane permeability, except for fast exchange (ie, AXR>20-30s-1 ) and small packing densities. Moreover, it was found that the duration of the filter gradient must be shorter than 2·AXR-1 . In cell size and permeability distributions, AXR depends on the average surface-to-volume ratio, permeability, and the packing density. Finally, AXR can be reliably determined in the presence of orientation dispersion in axon-like structures with sufficient gradient sampling (ie, 30 gradient directions). CONCLUSION: Currently used experimental settings for in vivo human measurements are well suited for determining AXR, with the exception of single-voxel analysis, due to limited SNR. The detection of changes in membrane permeability in diseased tissue is nonetheless challenging because of the AXR dependence on further factors, such as packing density and geometry, which cannot be disentangled without further knowledge of the underlying cell structure.

Effect of compression garments on muscle perfusion in delayed-onset muscle soreness: A quantitative analysis using intravoxel incoherent motion MR perfusion imaging.

The aim of this prospective cohort study was to evaluate the effect of compression garments under resting conditions and after the induction of delayed-onset muscle soreness (DOMS) by MR perfusion imaging using intravoxel incoherent motion (IVIM). Magnetic resonance imaging of both lower legs of 16 volunteers was performed before and after standardized eccentric exercises that induced DOMS. A compression garment (21-22 mmHg) was worn during and for 6 h after exercise on one randomly selected leg. IVIM MR imaging, represented as total muscle perfusion D*f, perfusion fraction f and tissue diffusivity D, were compared between baseline and directly, 30 min, 6 h and 48 h after exhausting exercise with and without compression. Creatine kinase levels and T2-weighted images were acquired at baseline and after 48 h. DOMS was induced in the medial head of the gastrocnemius muscle (MGM) in all volunteers. Compression garments did not show any significant effect on IVIM perfusion parameters at any time point in the MGM or the tibialis anterior muscle (p > 0.05). Microvascular perfusion in the MGM increased significantly in both the compressed and noncompressed leg between baseline measurements and those taken directly after and 30 min after the exercise: the relative median f increased by 31.5% and 24.7% in the compressed and noncompressed leg, respectively, directly after the exercise compared with the baseline value. No significant change in tissue perfusion occurred 48 h after the induction of DOMS compared with baseline. It was concluded that compression garments (21-22 mmHg) do not alter microvascular muscle perfusion at rest, nor do they have any significant effect during the regeneration phase of DOMS. In DOMS, only a short-term effect of increased muscle perfusion (30 min after exercise) was observed, with normalization occurring during regeneration after 6-48 h. The normalization of perfusion independently of compression after 6 h may have implications for diagnostic and therapeutic strategies and for the better understanding of pathophysiological pathways in DOMS.

An optimized b-value distribution for triexponential intravoxel incoherent motion (IVIM) in the liver.

PURPOSE: To find an optimized b-value distribution for reproducible triexponential intravoxel incoherent motion (IVIM) exams in the liver. METHODS: A numeric optimization of b-value distributions was performed using the triexponential IVIM equation and 27 different IVIM parameter sets. Starting with an initially optimized distribution of 6 b-values, the number of b-values was increased stepwise. Each new b-value was chosen from a set of 64 predefined b-values based on the computed summed relative mean error of the fitted triexponential IVIM parameters. This process was repeated for up to 100 b-values. In simulations and in vivo measurements, optimized b-value distributions were compared to 4 representative distributions found in literature. RESULTS: The first 16 optimized b-values were 0, 0.3, 0.3, 70, 200, 800, 70, 1, 3.5, 5, 70, 1.2, 6, 45, 1.5, and 60 in units of s/mm2 . Low b-values were much more frequent than high b-values. The optimized b-value distribution resulted in a higher fit stability compared to distributions used in literature in both, simulation and in vivo measurements. Using more than 6 b-values, ideally 16 or more, increased the fit stability considerably. CONCLUSION: Using optimized b-values, the fit uncertainty in triexponential IVIM can be largely reduced. Ideally, 16 or more b-values should be acquired.

On the dependence of the cardiac motion artifact on the breathing cycle in liver diffusion-weighted imaging.

PURPOSE: The purpose of this study was to investigate whether the cardiac motion artifact that regularly appears in diffusion-weighted imaging of the left liver lobe might be reduced by acquiring images in inspiration, when the coupling between heart and liver might be minimal. MATERIALS AND METHODS: 43 patients with known or suspected focal liver lesions were examined at 1.5 T with breath hold acquisition, once in inspiration and once in expiration. Data were acquired with a diffusion-weighted echo planar imaging sequence and two b-values (b50 = 50 s/mm² and b800 = 800 s/mm²). The severity of the cardiac motion artifact in the left liver lobe was rated by two experienced radiologists for both b-values with a 5 point Likert scale. Additionally, the normalized signal S(b800)/S(b50) in the left liver lobe was computed. The Wilcoxon signed-rank test was used comparing the scores of the two readers obtained in inspiration and expiration, and to compare the normalized signal in inspiration and expiration. RESULTS: The normalized signal in inspiration was slightly higher than in expiration (0.349±0.077 vs 0.336±0.058), which would indicate a slight reduction of the cardiac motion artifact, but this difference was not significant (p = 0.24). In the qualitative evaluation, the readers did not observe a significant difference for b50 (reader 1: p = 0.61; reader 2: p = 0.18). For b800, reader 1 observed a significant difference of small effect size favouring expiration (p = 0.03 with a difference of mean Likert scores of 0.27), while reader 2 observed no significant difference (p = 0.62). CONCLUSION: Acquiring the data in inspiration does not lead to a markedly reduced cardiac motion artifact in diffusion-weighted imaging of the left liver lobe and is in this regard not to be preferred over acquiring the data in expiration.

Influence of residual fat signal on diffusion kurtosis MRI of suspicious mammography findings.

Recent studies showed the potential of diffusion kurtosis imaging (DKI) as a tool for improved classification of suspicious breast lesions. However, in diffusion-weighted imaging of the female breast, sufficient fat suppression is one of the main factors determining the success. In this study, the data of 198 patients examined in two study centres was analysed using standard diffusion and kurtosis evaluation methods and three DKI fitting approaches accounting phenomenologically for fat-related signal contamination of the lesions. Receiver operating characteristic curve analysis showed the highest area under the curve (AUC) for the method including fat correction terms (AUC = 0.85, p < 0.015) in comparison to the values obtained with the standard diffusion (AUC = 0.77) and kurtosis approach (AUC = 0.79). Comparing the two study centres, the AUC value improved from 0.77 to 0.86 (p = 0.036) using a fat correction term for the first centre, while no significant difference with no adverse effects was observed for the second centre (AUC 0.89 vs. 0.90, p = 0.95). Contamination of the signal in breast lesions with unsuppressed fat causing a reduction of diagnostic performance of diffusion kurtosis imaging may potentially be counteracted by proposed adapted evaluation methods.

Advanced Diffusion-Weighted Abdominal Imaging: Qualitative and Quantitative Comparison of High and Ultra-High b-Values for Lesion Detection and Image Quality.

INTRODUCTION: Magnetic resonance imaging (MRI) of the abdomen increasingly incorporates diffusion-weighted imaging (DWI) sequences. Whereas DWI can substantially aid in detecting and characterizing suspicious findings, it remains unclear to what extent the use of ultra-high b-value DWI might further be of aid for the radiologist especially when using DWI sequences with advanced processing. The target of this study was therefore to compare high and ultra-high b-value DWI in abdominal MRI examinations. METHODS: This institutional review board-approved, prospective study included abdominal MRI examinations of 70 oncologic patients (mean age, 58 years; range, 21-90 years) examined with a clinical 1.5 T MRI scanner (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany) with an advanced echo planar DWI sequence (b = 0, 50, 900, and 1500 s/mm) after ex vivo phantom and in vivo volunteer investigations. High b900 and ultra-high b1500 DWIs were compared by a qualitative reading for image quality and lesion conspicuity using a 5-point Likert scale with 2 radiologists as readers. The ratios of apparent signal intensities of suspicious lesions/normal tissue of the same organ (LNTRs) were calculated. Appropriate methods were used for statistical analysis, including Wilcoxon signed-rank test and κ statistic for interreader agreement analysis (P < 0.05/0.0125/0.005 after Bonferroni correction). RESULTS: Image quality was significantly increased with b900 as compared with b1500 DWI (P < 0.001) despite using an advanced DWI sequence. A total of 153 suspicious lesions were analyzed. Overall reader confidence for characterization/detection of malignant lesions and, correspondingly, the LNTR (mean, 2.7 ± 1.8 vs 2.4 ± 1.6) were significantly higher with b900 than with b1500 DWI (P < 0.001 and P < 0.001). The increased confidence of lesion recognition and LNTR in the b900 DWI remained significant qualitatively in lymphatic and hepatic lesions and quantitatively in lymphatic, pulmonal, and osseous lesions. CONCLUSIONS: Using high b-value DWI (900 s/mm) provided an improved image quality and also lesion conspicuity as compared with ultra-high b-value DWI (1500 s/mm) in oncologic abdominal examinations despite using advanced processing. Consequently, the value for additional ultra-high b-value DWI in oncologic examinations should be critically evaluated in future studies.