Diffusion-weighting Gradients Research Articles

Development and Validation of Interpretable Machine Learning Models for Clinically Significant Prostate Cancer Diagnosis in Patients With Lesions of PI-RADS v2.1 Score ≥3.

For patients with PI-RADS v2.1 ≥ 3, prostate biopsy is strongly recommended. Due to the unsatisfactory positive rate of biopsy, improvements in clinically significant prostate cancer (csPCa) risk assessments are required. To develop and validate machine learning (ML) models based on clinical and imaging parameters for csPCa detection in patients with PI-RADS v2.1 ≥ 3. Retrospective. One thousand eighty-three patients with PI-RADS v2.1 ≥ 3, randomly split into training (70%, N = 759) and validation (30%, N = 324) datasets, and 147 patients enrolled prospectively for testing. 3.0 T scanners/T2-weighted fast spin echo sequence and DWI with diffusion-weighted single-shot gradient echo planar imaging sequence. The factors evaluated for csPCa detection were age, prostate specific antigen, prostate volume, and the diameter and location of the index lesion, PI-RADSv2.1. Five ML models for csPCa detection were developed: logistic regression (LR), extreme gradient boosting, random forest (RF), decision tree, and support vector machines. The csPCa was defined as Gleason grade ≥2. Univariable and multivariable LR analyses to identify parameters associated with csPCa. Area under the receiver operating characteristic curve (AUC), Brier score, and DeLong test were used to assess and compare the csPCa diagnostic performance with the LR model. The significance level was defined as 0.05. The RF model exhibited the highest AUC (0.880-0.904) and lowest Brier score (0.125-0.133) among the ML models in the validation and testing cohorts, however, there was no difference when compared to the LR model (P = 0.453 and 0.548). The sensitivity and negative predictive values in the validation and testing cohorts were 93.8%-97.6% and 82.7%-95.1%, respectively, at a threshold of 0.450 (99% sensitivity of the RF model). The RF model might help for assessing the risk of csPCa and preventing overdiagnosis and unnecessary biopsy for men with PI-RADSv2.1 ≥ 3. 3 TECHNICAL EFFICACY: Stage 2.

Facilitating diffusion tensor imaging of the brain during continuous gross head motion with first and second order motion compensating diffusion gradients.

To demonstrate the feasibility of motion compensating diffusion gradient schemes in the acquisition of quality diffusion tensor images (DTI) of the brain during continuous gross head motion. Five healthy subjects were scanned using a clinical 3 T MRI with and without continuous head motion. For one volunteer, DTI data was acquired using standard (M0) diffusion-weighted (DW) gradients, and first (M1) and second (M2) order gradient schemes that were previously developed for use in cardiac DTI. In four additional volunteers, DTI data was acquired with M0 and M2 gradients. DTI parameters were calculated and compared with established retrospective motion corrections. In the absence of motion, DTI parameters calculated from M0, M1, and M2 data were consistent. In the presence of motion, up to 44% of DW images acquired with M0 gradients were corrupted by signal dropout, compared to 0% of the M2 images. In voxelwise comparisons, DTI parameters calculated using motion-M0 data were elevated compared to reference data. Retrospective corrections for extreme motion applied to motion-M0 data did not improve consistency with reference data in cases where motion corrupted >15% of DW images. In contrast, DTI parameters calculated with motion-M2 data were consistent with reference data. This proof-of-principle study demonstrates that motion compensating diffusion gradients can mitigate artifacts because of continuous motion in DTI of the brain and offers promise for improved DTI accessibility. Further study will be necessary to determine the robustness of the approach in patient populations with high susceptibility to head motion.

Compensation of concomitant field effects in double diffusion encoding by means of added oscillating gradients

Maxwell or concomitant fields imprint additional phases on the transverse magnetization. This concomitant phase may cause severe image artifacts like signal voids or distort the quantitative parameters due to the induced intravoxel dephasing. In particular, double diffusion encoding (DDE) schemes with two pairs of bipolar diffusion-weighting gradients separated by a refocusing radiofrequency (RF) pulse are prone to concomitant field-induced artifacts. In this work, a method for reducing concomitant field effects in these DDE sequences based on additional oscillating gradients is presented. These oscillating gradient pulses obtained by constrained optimization were added to the original gradient waveforms. The modified sequences reduced the accumulated concomitant phase without significant changes in the original sequence characteristics. The proposed method was applied to a DDE acquisition scheme consisting of 60 pairs of diffusion wave vectors. For phantom as well as for in vivo experiments, a considerable increase in the signal-to-noise ratio (SNR) was obtained. For phantom measurements with a diffusion weighting of b = 2000 s/mm2 for each of the gradient pairs, an SNR increase of up to 40% was observed for a transversal slice that had a distance of 5 cm from the isocenter. For equivalent slice parameters, in vivo measurements in the brain of a healthy volunteer exhibited an increase in SNR of up to 35% for b = 750 s/mm2 for each weighting. These findings are supported by corresponding simulations, which also predict a positive effect on the SNR. In summary, the presented method leads to an SNR gain without additional RF refocusing pulses.

Simultaneous perfusion, diffusion, T2 *, and T1 mapping with MR fingerprinting.

Quantitative mapping of brain perfusion, diffusion, T2 *, and T1 has important applications in cerebrovascular diseases. At present, these sequences are performed separately. This study aims to develop a novel MRI technique to simultaneously estimate these parameters. This sequence to measure perfusion, diffusion, T2 *, and T1 mapping with magnetic resonance fingerprinting (MRF) was based on a previously reported MRF-arterial spin labeling (ASL) sequence, but the acquisition module was modified to include different TEs and presence/absence of bipolar diffusion-weighting gradients. We compared parameters derived from the proposed method to those derived from reference methods (i.e., separate sequences of MRF-ASL, conventional spin-echo DWI, and T2 * mapping). Test-retest repeatability and initial clinical application in two patients with stroke were evaluated. The scan time of our proposed method was 24% shorter than the sum of the reference methods. Parametric maps obtained from the proposed method revealed excellent image quality. Their quantitative values were strongly correlated with those from reference methods and were generally in agreement with values reported in the literature. Repeatability assessment revealed that ADC, T2 *, T1 , and B1 + estimation was highly reliable, with voxelwise coefficient of variation (CoV) <5%. The CoV for arterial transit time and cerebral blood flow was 16% ± 3% and 25% ± 9%, respectively. The results from the two patients with stroke demonstrated that parametric maps derived from the proposed method can detect both ischemic and hemorrhagic stroke. The proposed method is a promising technique for multi-parametric mapping and has potential use in patients with stroke.

Diffusion tensor imaging of fetal spinal cord: feasibility and gestational-age-related changes.

Diffusion tensor imaging (DTI) of the fetal brain is a relatively new technique that allows evaluation of white matter tracts of the central nervous system throughout pregnancy, as well as in certain pathological conditions. The objectives of this study were to evaluate the feasibility of DTI of the spinal cord in utero and to examine gestational-age (GA)-related changes in DTI parameters during pregnancy. This was a prospective study conducted between December 2021 and June 2022 in the LUMIERE Platform, Necker-Enfants Malades Hospital, Paris, France, as part of the LUMIERE SUR LE FETUS trial. Women with a pregnancy between 18 and 36 weeks of gestation without fetal or maternal abnormality were eligible for inclusion. Sagittal diffusion-weighted scans of the fetal spine were acquired, without sedation, using a 1.5-Tesla magnetic resonance imaging scanner. Theimaging parameters were as follows: 15 non-collinear direction diffusion-weighted magnetic-pulsed gradients with a b-value 700 s/mm2 and one B0 image without diffusion-weighting; slice thickness, 3 mm; field of view (FOV), 36 mm; phase FOV, 1.00; voxel size, 4.5 × 2.8 × 3 mm3 ; number of slices, 7-10; repetition time, 2800 ms; echo time, minimum; and total acquisition time, 2.3 min. DTI parameters, including fractional anisotropy (FA) and apparent diffusion coefficient (ADC), were extracted at the cervical, upper thoracic, lower thoracic and lumbar levels of the spinal cord. Cases with motion degradation and those with aberrant reconstruction of the spinal cord on tractography were excluded. Pearson's correlation analysis was performed to evaluate GA-related changes of DTI parameters during pregnancy. During the study period, 42 pregnant women were included at a median GA of 29.3 (range, 22.0-35.7) weeks. Five (11.9%) patients were not included in the analysis because of fetal movement. Two (4.8%) patients with aberrant tractography reconstruction were also excluded from analysis. Acquisition of DTI parameters was feasible in all remaining cases (35/35). Increasing GA correlated with increasing FA averaged over the entire fetal spinal cord (r, 0.37; P < 0.01), as well as at the individual cervical (r, 0.519; P < 0.01), upper thoracic (r, 0.468; P < 0.01), lower thoracic (r, 0.425; P = 0.02) and lumbar (r, 0.427; P = 0.02) levels. There was no correlation between GA and ADC averaged over the entire spinal cord (r, 0.01; P = 0.99) or at the individual cervical (r, -0.109; P = 0.56), upper thoracic (r, -0.226; P = 0.22), lower thoracic (r, -0.052; P = 0.78) or lumbar (r, -0.11; P = 0.95) levels. This study shows that DTI of the spinal cord is feasible in normal fetuses in typical clinical practice and allows extraction of DTI parameters of the spinal cord. There is a significant GA-related change in FA in the fetal spinal cord during pregnancy, which may result from decreasing water content as observed during myelination of fiber tracts occurring in utero. This study may serve as a basis for further investigation of DTI in the fetus, including research into its potential in pathological conditions that impact spinal cord development. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

3D diffusion MRI with twin navigator-based GRASE and comparison with 2D EPI for tractography in the human brain.

3Dpulse sequences enable high-resolution acquisition with a high SNRand ideal slice profiles, which, however, is particularly difficult for diffusion MRI (dMRI) due to the additional phase errors from diffusion encoding. We proposed a twin navigator-based 3D diffusion-weighted gradient spin-echo (GRASE) sequence to correct the phase errors between shots and between odd and even spin echoes for human whole-brain acquisition. We then compared the SNR of 3D GRASE and 2Dsimultaneous multi-slice EPIwithin the same acquisition time. We further tested the performance of 2D versus 3D acquisition at equivalent SNR on fiber tracking and microstructural mapping, using the diffusion tensor and high-order fiber orientation density-based metrics. The proposed twin navigator approach removed multi-shot phase errors to some extent in the whole brain dMRI, and the 2D navigator performed better than the 1D navigator. Comparisons of SNR between the 2D simultaneous multi-slice EPI and 3D GRASE sequences demonstrated that the SNR of the GRASE sequence was 1.4-1.5-fold higher than the EPI sequence at an equivalent scan time. More importantly, we found a significantly higher fiber cross-section in the cerebrospinal tract, as well as richer subcortical fibers (U-fibers) using the 3D GRASE sequence compared to 2D EPI. The twin navigator-based 3D diffusion-weighted-GRASE sequence minimized the multishot phase error and effectively improved the SNR for whole-brain dMRI acquisition. We found differences in fiber tracking and microstructural mapping between 2D and 3D acquisitions, possibly due to the different slice profiles.

A generalized order mixture model for tracing connectivity of white matter fascicles complexity in brain from diffusion MRI.

This paper focuses on tracing the connectivity of white matter fascicles in the brain. In particular, a generalized order algorithm based on mixture of non-central Wishart distribution model is proposed for this purpose. The proposed algorithm utilizes the generalization of integer order based approach with the mixture of non-central Wishart distribution model. Pseudo super anomalous behavior of water diffusion inside human brain is the prime motivation of the the present study. We have shown results on multiple synthetic simulations with fibers orientations in two and three directions in each voxel as well as experiments on real data. Synthetic simulations were performed with varying noise levels and diffusion weighting gradient i.e. $b-$values. The proposed model performed outstanding especially for distinguishing closely oriented fibers.

MRI Assessment of Brain Frailty and Clinical Outcome in Patients With Acute Posterior Perforating Artery Infarction.

Global brain health has gained increasing attention recently. Imaging markers of brain frailty have been related to functional outcomes in previous studies on anterior circulation; however, little data are available on imaging markers and posterior circulation. To investigate the impact of brain frailty on functional outcomes in patients with acute perforating artery infarction (PAI) of the posterior circulation. Prospective. One hundred patients (60.78 ± 9.51 years, 72% men) with acute posterior circulation PAI (determined by diffusion-weighted magnetic resonance imaging (MRI)/time-of-flight MR angiography). T1- and T2-weighted fast spin echo, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted echo planar, gradient echo (susceptibility-weight imaging), and 3D time-of-flight MR angiography sequences at 3.0 T. Periventricular and deep white matter hyperintensities (WMH), enlarged perivascular spaces (EPVS) in the basal ganglia and centrum semiovale area, lacunes, cerebral microbleeds (CMB), and total brain frailty score by calculating the above imaging characters were rated visually by three radiologists with 9, 10, and 11 years of experience and one neuroradiologist with 12. Infarction volume was assessed using baseline diffusion-weighted imaging (DWI) data obtained within 24 hours of symptom onset. A modified Rankin Scale (mRS) score >1 on day 90 defined an adverse functional outcome. Associations between the imaging markers of brain frailty and functional outcomes were assessed. Fisher's exact test, Mann-Whitney U test, and multivariable binary logistic regression. A P value <0.05 was considered statistically significant. Adverse prognoses (mRS > 1) were observed in 34 (34%) patients. Infarction volume, periventricular WMH, deep WMH, basal ganglia EPVS, CMB, and the brain frailty score were significantly associated with adverse functional outcomes. An increased brain frailty score was significantly associated with unfavorable mRS score on day 90 (odds ratio 1.773, 95% confidence interval 1.237-2.541). Advanced MRI imaging markers of brain frailty, individually or combined as a total brain frailty score, were associated with worse functional outcomes after acute posterior circulation PAI. 3 TECHNICAL EFFICACY: Stage 3.

Deep-Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi-Parametric MRI.

Deep learning for diagnosing clinically significant prostate cancer (csPCa) is feasible but needs further evaluation in patients with prostate-specific antigen (PSA) levels of 4-10 ng/mL. To explore diffusion-weighted imaging (DWI), alone and in combination with T2-weighted imaging (T2WI), for deep-learning-based models to detect and localize visible csPCa. Retrospective. One thousand six hundred twenty-eight patients with systematic and cognitive-targeted biopsy-confirmation (1007 csPCa, 621 non-csPCa) were divided into model development (N=1428) and hold-out test (N=200) datasets. DWI with diffusion-weighted single-shot gradient echo planar imaging sequence and T2WI with T2-weighted fast spin echo sequence at 3.0-T and 1.5-T. The ground truth of csPCa was annotated by two radiologists in consensus. A diffusion model, DWI and apparent diffusion coefficient (ADC) as input, and a biparametric model (DWI, ADC, and T2WI as input) were trained based on U-Net. Three radiologists provided the PI-RADS (version 2.1) assessment. The performances were determined at the lesion, location, and the patient level. The performance was evaluated using the areas under the ROC curves (AUCs), sensitivity, specificity, and accuracy. A P value <0.05 was considered statistically significant. The lesion-level sensitivities of the diffusion model, the biparametric model, and the PI-RADS assessment were 89.0%, 85.3%, and 90.8% (P=0.289-0.754). At the patient level, the diffusion model had significantly higher sensitivity than the biparametric model (96.0% vs. 90.0%), while there was no significant difference in specificity (77.0%. vs. 85.0%, P=0.096). For location analysis, there were no significant differences in AUCs between the models (sextant-level, 0.895 vs. 0.893, P=0.777; zone-level, 0.931 vs. 0.917, P=0.282), and both models had significantly higher AUCs than the PI-RADS assessment (sextant-level, 0.734; zone-level, 0.863). The diffusion model achieved the best performance in detecting and localizing csPCa in patients with PSA levels of 4-10 ng/mL. 3 TECHNICAL EFFICACY: Stage 2.

Motion-robust, blood-suppressed, reduced-distortion diffusion MRI of the liver.

To evaluate feasibility and reproducibility of liver diffusion-weighted (DW) MRI using cardiac-motion-robust, blood-suppressed, reduced-distortion techniques. DW-MRI data were acquired at 3T in an anatomically accurate liver phantom including controlled pulsatile motion, in eight healthy volunteers and four patients with known or suspected liver metastases. Standard monopolar and motion-robust (M1-nulled, and M1-optimized) DW gradient waveforms were each acquired with single-shot echo-planar imaging (ssEPI) and multishot EPI (msEPI). In the motion phantom, apparent diffusion coefficient (ADC) was measured in the motion-affected volume. In healthy volunteers, ADC was measured in the left and right liver lobes separately to evaluate ADC reproducibility between the two lobes. Image distortions were quantified using the normalized cross-correlation coefficient, with an undistorted T2-weighted reference. In the motion phantom, ADC mean and SD in motion-affected volumes substantially increased with increasing motion for monopolar waveforms. ADC remained stable in the presence of increasing motion when using motion-robust waveforms. M1-optimized waveforms suppressed slow flow signal present with M1-nulled waveforms. In healthy volunteers, monopolar waveforms generated significantly different ADC measurements between left and right liver lobes ( , reproducibility coefficients (RPC)= mm /s for monopolar-msEPI), while M1-optimized waveforms showed more reproducible ADC values ( , mm /s for M1-optimized-msEPI). In phantom and healthy volunteer studies, motion-robust acquisitions with msEPI showed significantly reduced image distortion ( ) compared to ssEPI. Patient scans showed reduction of wormhole artifacts when combining M1-optimized waveforms with msEPI. Synergistic effects of combined M1-optimized diffusion waveforms and msEPI acquisitions enable reproducible liver DWI with motion robustness, blood signal suppression, and reduced distortion.

Fast T2-Weighted Imaging With Deep Learning-Based Reconstruction: Evaluation of Image Quality and Diagnostic Performance in Patients Undergoing Radical Prostatectomy.

Deep learning-based reconstruction (DLR) can potentially improve image quality by reduction of noise, thereby enabling fast acquisition of magnetic resonance imaging (MRI). However, a systematic evaluation of image quality and diagnostic performance of MRI using short acquisition time with DLR has rarely been investigated in men with prostate cancer. To assess the image quality and diagnostic performance of MRI using short acquisition time with DLR for the evaluation of extraprostatic extension (EPE). Retrospective. One hundred and nine men. 3 T; turbo spin echo T2-weighted images (T2WI), echo-planar diffusion-weighted, and spoiled gradient echo dynamic contrast-enhanced images. To compare image quality, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and subjective analysis using Likert scales on three T2WIs (MRI using conventional acquisition time, MRI using short acquisition time [fast MRI], and fast MRI with DLR) were performed. The diagnostic performance for EPE was evaluated by three independent readers. SNR, CNR, and image quality scores across the three imaging protocols were compared using Friedman tests. The diagnostic performance for EPE was assessed using the area under receiver operating characteristic curves (AUCs). P < 0.05 was considered statistically significant. Fast MRI with DLR demonstrated significantly higher SNR (mean ± SD, 14.7 ± 6.8 vs. 8.8 ± 4.9) and CNR (mean ± SD, 6.5 ± 6.3 vs. 3.4 ± 3.6) values and higher image quality scores (median, 4.0 vs. 3.0 for three readers) than fast MRI. The AUCs for EPE were significantly higher with the use of DLR (0.86 vs. 0.75 for reader 2 and 0.82 vs. 0.73 for reader 3) compared with fast MRI, whereas differences were not significant for reader 1 (0.81 vs. 0.74; P=0.09). DLR may be useful in reducing the acquisition time of prostate MRI without compromising image quality or diagnostic performance. 4 TECHNICAL EFFICACY: Stage 3.

Design of a high-performance non-linear gradient coil for diffusion weighted MRI of the breast

Diffusion-weighted imaging (DWI) in the female breast is a magnetic resonance imaging (MRI) technique that complements clinical routine protocols, and that might provide an independent diagnostic value for specific clinical tasks in breast imaging. To further improve specificity of DWI in the breast, stronger and faster diffusion weighting is advantageous. Here, a dedicated gradient coil is designed, targeted at diffusion weighting in the female breast, with the peak gradient magnitude exceeding that of the current clinical MR scanners by an order of a magnitude.Design of application-tailored gradient coils in MRI has recently attracted increased attention. With the target application in mind, the gradient coil is designed on an irregularly shaped semi-open current-carrying surface. Due to the coil former closely fitting the non-spherical target region, non-linear encoding fields become particularly advantageous for achieving locally exceptionally high gradient strengths. As breast tissue has a predominantly isotropic cellular microstructure, the direction of the diffusion-weighting gradient may be allowed to vary within the target volume. However, due to the quadratic dependency of the b-factor on the gradient strength, variation of the gradient magnitude should be carefully controlled.To achieve the above design goals the corresponding multi-objective optimization problem is reformulated as a constrained optimization, allowing for flexible and precise control of the coil properties. A novel constraint is proposed, limiting gradient magnitude variation within every slice while allowing for variations in both the direction of the gradient within the slice and the magnitude across the slices. These innovations enable the design of a unilateral coil for diffusion weighting in the female breast with local gradient strengths exceeding 1 T/m with highly homogeneous diffusion weighting for imaging in the coronal slice orientation.

Motion-insensitive diffusion imaging of the brain using optical tracking and dynamic sequence updates.

Diffusion-weighted imaging (DWI) is sensitive to head movements, which may cause signal losses because of motion-induced gradient imbalances. Prospective motion correction using fast optical tracking can attenuate these artifacts. Approaches include quasicontinuous updates of gradients and radiofrequency (RF) pulses or dynamically applying a rebalancing gradient to restore the gradient balance, but these prior methods used bipolar diffusion gradients. The goal of this project was to develop and evaluate a motion-insensitive implementation for the more common monopolar diffusion sequence. A monopolar diffusion sequence was developed with motion updates before each RF pulse and each diffusion-weighting gradient. The sequence was tested in a phantom and human brain at b=1000s/mm2 and rotational velocities up to 20°/s. Motion sensitivity, signal losses, and in vivo image profiles were compared between scans with and without intrasequence motion updates. With typical motion parameters, intrasequence motion updates with optimal parameters reduced the motion sensitivity of DWI (motion-induced gradient moment imbalance) sevenfold. Optimal results were achieved by matching the echo time of the pulse sequence to an even multiple of the tracking system frame-to-frame period. Average signal losses and the frequency of signal dropouts in phantom and in vivo measurements were reduced when intrasequence updates were enabled, and quality measures of DTI analyses were improved. A correction scheme for the monopolar DWI sequence can reduce the motion sensitivity of brain DWI up to sevenfold compared with an implementation without intrasequence updates.

Ancillary imaging and clinical features for the characterization of prostate lesions: A proposed approach to reduce false positives.

The relatively low specificity and positive predictive value of the Prostate Imaging-Reporting and Data System (PI-RADS) can lead to considerable false-positive results and unnecessary biopsies. The aim of this study was to propose ancillary features (AFs) indicating clinically significant prostate cancer (csPCa) or benign tissues in PI-RADS category ≥3 lesions and determine the usefulness of these AFs in reducing false-positive assessments of suspicious lesions in men at csPCa risk. This was a retrospective study, which included 199 men. A 3T, including turbo spin echo T2 -weighted, echo-planar diffusion-weighted, and spoiled gradient echo dynamic contrast-enhanced (DCE) images, was used. Five AFs (prostate-specific antigen density ≥0.15 ng/mL2 ; size ≥10 mm; heterogeneous T2 signal intensity; circumscribed nodule in the junction of peripheral and transition zone; and DCE time curves) indicating csPCa or non-csPCa were evaluated by three independent readers. The sensitivity and specificity of each AF were calculated. Inter-reader agreement was evaluated using κ statistics. Univariate and multivariate logistic regression analyses were conducted to determine significant AFs. The reduction in positive call rates and csPCa detection rates with combined AF use were calculated and compared with the findings obtained with PI-RADS use alone. The sensitivities and specificities of the AFs indicating csPCa were 72.1%-96.5% and 27.4%-75.2% for reader 1, 66.3%-96.5% and 23.9%-62.0% for reader 2, and 67.4%-96.5% and 34.5%-78.8% for reader 3, with moderate to substantial inter-reader agreement (Fleiss κ, 0.551-0.643). The combined use of two or more AFs for assessing PI-RADS ≥3 lesions resulted in a 19.6%-30.7% reduction in positive calls (p < .05) compared to PI-RADS use alone while preserving the csPCa detection rates (p ≥ .06) for three readers. The use of AFs in combination with PI-RADS can reduce positive calls and false positives without csPCa under-detection.

Advances in Diffusion and Perfusion MRI for Quantitative Cancer Imaging.

This article is to review recent technical developments and their clinical applications in cancer imaging quantitative measurement of cellular and vascular properties of the tumors. Rapid development of fast Magnetic Resonance Imaging (MRI) technologies over last decade brought new opportunities in quantitative MRI methods to measure both cellular and vascular properties of tumors simultaneously. Diffusion MRI (dMRI) and dynamic contrast enhanced (DCE)-MRI have become widely used to assess the tissue structural and vascular properties, respectively. However, the ultimate potential of these advanced imaging modalities has not been fully exploited. The dependency of dMRI on the diffusion weighting gradient strength and diffusion time can be utilized to measure tumor perfusion, cellular structure, and cellular membrane permeability. Similarly, DCE-MRI can be used to measure vascular and cellular membrane permeability along with cellular compartment volume fractions. To facilitate the understanding of these potentially important methods for quantitative cancer imaging, we discuss the basic concepts and recent developments, as well as future directions for further development.

Diffusion Tensor Model links to Neurite Orientation Dispersion and Density Imaging at high b-value in Cerebral Cortical Gray Matter

Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) are widely used models to infer microstructural features in the brain from diffusion-weighted MRI. Several studies have recently applied both models to increase sensitivity to biological changes, however, it remains uncertain how these measures are associated. Here we show that cortical distributions of DTI and NODDI are associated depending on the choice of b-value, a factor reflecting strength of diffusion weighting gradient. We analyzed a combination of high, intermediate and low b-value data of multi-shell diffusion-weighted MRI (dMRI) in healthy 456 subjects of the Human Connectome Project using NODDI, DTI and a mathematical conversion from DTI to NODDI. Cortical distributions of DTI and DTI-derived NODDI metrics were remarkably associated with those in NODDI, particularly when applied highly diffusion-weighted data (b-value = 3000 sec/mm2). This was supported by simulation analysis, which revealed that DTI-derived parameters with lower b-value datasets suffered from errors due to heterogeneity of cerebrospinal fluid fraction and partial volume. These findings suggest that high b-value DTI redundantly parallels with NODDI-based cortical neurite measures, but the conventional low b-value DTI is hard to reasonably characterize cortical microarchitecture.

Feasibility of fast non-local means noise reduction algorithm in magnetic resonance imaging using 1.5 and 3.0 T with diffusion-weighted image technique

Magnetic resonance imaging (MRI) has many advantages and has developed various pulse sequences. In particular, the diffusion weighted image (DWI) technique is widely used because it can acquire images quickly during examination of stroke, through a proper adjustment of the diffusion-weighted gradient b-value. However, a setting with inappropriate b-value causes loss of image signal that increases the influence of noise. Therefore, in this study, we quantitatively evaluated image quality after applying a variety of algorithms to the image acquired by changing the b-value and the main magnetic field in the MRI device. To acquire the image, the phantom was self-produced with an acrylic panel and chicken breast. Wiener filter, total variation (TV), and our proposed fast non-local means (FNLM) noise reduction algorithms were applied to the image. Consequently, the signal intensity at a 3.0 T magnetic field increased by a factor 4.8 compared to a 1.5 T magnetic field. Moreover, the signal-to-noise ratio and contrast-to-noise ratio were highest with the FNLM algorithm, and the values increased by factors of 9.5 and 9.9 with a 1.5 T magnetic field and by factors of 9.9 and 5.0 with a 3.0 T magnetic field compared to the noise image, respectively. The result of time resolution, the Wiener filter appeared the finest value, but had no significant difference compared to FNLM algorithm. In conclusion, our results confirmed that the proposed FNLM noise reduction algorithm can acquire both improved image quality and high processing time in MRI imaging with the DWI technique.

Optimized Diffusion-Weighting Gradient Waveform Design (ODGD) formulation for motion compensation and concomitant gradient nulling.

To present a novel Optimized Diffusion-weighting Gradient waveform Design (ODGD) method for the design of minimum echo time (TE), bulk motion-compensated, and concomitant gradient (CG)-nulling waveforms for diffusion MRI. ODGD motion-compensated waveforms were designed for various moment-nullings Mn (n=0,1,2), for a range of b-values, and spatial resolutions, both without (ODGD-Mn ) and with CG-nulling (ODGD-Mn -CG). Phantom and in-vivo (brain and liver) experiments were conducted with various ODGD waveforms to compare motion robustness, signal-to-noise ratio (SNR), and apparent diffusion coefficient (ADC) maps with state-of-the-art waveforms. ODGD-Mn and ODGD-Mn -CG waveforms reduced the TE of state-of-the-art waveforms. This TE reduction resulted in significantly higher SNR (P< 0.05) in both phantom and in-vivo experiments. ODGD-M1 improved the SNR of BIPOLAR (42.8±5.3 vs. 32.9±3.3) in the brain, and ODGD-M2 the SNR of motion-compensated (MOCO) and Convex Optimized Diffusion Encoding-M2 (CODE-M2 ) (12.3±3.6 vs. 9.7±2.9 and 10.2±3.4, respectively) in the liver. Further, ODGD-M2 also showed excellent motion robustness in the liver. ODGD-Mn -CG waveforms reduced the CG-related dephasing effects of non CG-nulling waveforms in phantom and in-vivo experiments, resulting in accurate ADC maps. ODGD waveforms enable motion-robust diffusion MRI with reduced TEs, increased SNR, and reduced ADC bias compared to state-of-the-art waveforms in theoretical results, simulations, phantoms and in-vivo experiments.

Multiparametric MRI Characterization of Funaki Types of Uterine Fibroids Considered for MR-Guided High-Intensity Focused Ultrasound (MR-HIFU) Therapy

To compare quantitative multiparametric magnetic resonance imaging (mpMRI) data of symptomatic uterine fibroids being considered for MR-guided high-intensity focused ultrasound ablation with fibroid characterization based on the Funaki Classification scheme. This was a prospective, Institutional Review Board -approved, Health Insurance Portability, and Accountability Act-compliant study. Informed consent was obtained. From December 2013 to April 2015, 48 women with symptomatic fibroids underwent screening with mpMRI protocol including sagittal/axial T2-weighted fast spin-echo, sagittal diffusion-weighted, and sagittal dynamic contrast-enhanced 3D T1-weighted gradient echo imaging on a 3T magnet. All fibroids were assigned Funaki type 1, 2, or 3 based on T2-weighted imaging. Differences in size, perfusion, and diffusion/intravoxel incoherent motion parameters among the three Funaki types were determined using linear mixed model. A logistic regression analysis was performed to select the best model in predicting type 3 fibroids. A total of 100 fibroids were assessed (20 type 1, 66 type 2, and 14 type 3). Apparent diffusion coefficient and D of type 3 fibroids were significantly higher than those of type 1 (P < 0.0001, P < 0.0001) and 2 fibroids (P = 0.004, P < 0.0001) respectively. Transfer constant of type 3 fibroids was significantly higher than type 1 (P = 0.0357), but not than type 2 (P = 0.0752). A cutoff value of D = 1 × 10-3 mm2/s offers an accuracy, sensitivity, and specificity of 76%, 71%, and 77%, respectively, for the diagnosis of Funaki 3 fibroids. mpMRI-derived quantitative parameters may enable a more objective selection of patients prior to MR-guided high-intensity focused ultrasound therapy.

Optimization of diffusion-weighted single-refocused spin-echo EPI by reducing eddy-current artifacts and shortening the echo time.

The purpose of this work was to optimize the acquisition of diffusion-weighted (DW) single-refocused spin-echo (srSE) data without intrinsic eddy-current compensation (ECC) for an improved performance of ECC postprocessing. The rationale is that srSE sequences without ECC may yield shorter echo times (TE) and thus higher signal-to-noise ratios (SNR) than srSE or twice-refocused spin-echo (trSE) schemes with intrinsic ECC. The proposed method employs dummy scans with DW gradients to drive eddy currents into a steady state before data acquisition. Parameters of the ECC postprocessing algorithm were also optimized. Simulations were performed to obtain minimum TE values for the proposed sequence and sequences with intrinsic ECC. Experimentally, the proposed method was compared with standard DW-trSE imaging, both in vitro and in vivo. Simulations showed substantially shorter TE for the proposed method than for methods with intrinsic ECC when using shortened echo readouts. Data of the proposed method showed a marked increase in SNR. A dummy scan duration of at least 1.5s improved performance of the ECC postprocessing algorithm. Changes proposed for the DW-srSE sequence and for theparameter setting of the postprocessing ECC algorithm considerably reduced eddy-current artifacts and provided a higher SNR.