High-resolution Diffusion-weighted Imaging Research Articles

A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI.

Over the past several years, significant efforts have been made to improve the spatial resolution of diffusion-weighted imaging (DWI), aiming at better detecting subtle lesions and more reliably resolving white-matter fiber tracts. A major concern with high-resolution DWI is the limited signal-to-noise ratio (SNR), which may significantly offset the advantages of high spatial resolution. Although the SNR of DWI data can be improved by denoising in post-processing, existing denoising procedures may potentially reduce the anatomic resolvability of high-resolution imaging data. Additionally, non-Gaussian noise induced signal bias in low-SNR DWI data may not always be corrected with existing denoising approaches. Here we report an improved denoising procedure, termed diffusion-matched principal component analysis (DM-PCA), which comprises 1) identifying a group of (not necessarily neighboring) voxels that demonstrate very similar magnitude signal variation patterns along the diffusion dimension, 2) correcting low-frequency phase variations in complex-valued DWI data, 3) performing PCA along the diffusion dimension for real- and imaginary-components (in two separate channels) of phase-corrected DWI voxels with matched diffusion properties, 4) suppressing the noisy PCA components in real- and imaginary-components, separately, of phase-corrected DWI data, and 5) combining real- and imaginary-components of denoised DWI data. Our data show that the new two-channel (i.e., for real- and imaginary-components) DM-PCA denoising procedure performs reliably without noticeably compromising anatomic resolvability. Non-Gaussian noise induced signal bias could also be reduced with the new denoising method. The DM-PCA based denoising procedure should prove highly valuable for high-resolution DWI studies in research and clinical uses.

In-plane "superresolution" MRI with phaseless sub-pixel encoding.

Acquisition of high-resolution imaging data using multiple excitations without the sensitivity to fluctuations of the transverse magnetization phase, which is a major problem of multi-shot MRI. The concept of superresolution MRI based on microscopic tagging is analyzed using an analogy with the optical method of structured illumination. Sinusoidal tagging is shown to provide subpixel resolution by mixing of neighboring spatial frequency (k-space) bands. It represents a phaseless modulation added on top of the standard Fourier encoding, which allows the phase fluctuations to be discarded at an intermediate reconstruction step. Improvements are proposed to correct for tag distortions due to magnetic field inhomogeneity and to avoid the propagation of Gibbs ringing from intermediate low-resolution images to the final image. The method was applied to diffusion-weighted EPI. Artifact-free superresolution images can be obtained despite a finite duration of the tagging sequence and related pattern distortions by a field map based phase correction of band-wise reconstructed images. The ringing effect present in the intermediate images can be suppressed by partial overlapping of the mixed k-space bands in combination with an adapted filter. High-resolution diffusion-weighted images of the human head were obtained with a three-shot EPI sequence despite motion-related phase fluctuations between the shots. Due to its phaseless character, tagging-based sub-pixel encoding is an alternative to k-space segmenting in the presence of unknown phase fluctuations, in particular those due to motion under strong diffusion gradients. Proposed improvements render the method practicable in realistic conditions.

Changes in structural network are associated with cortical demyelination in early multiple sclerosis.

The aim of this study was to investigate the interplay between structural connectivity and cortical demyelination in early multiple sclerosis. About 27 multiple sclerosis patients and 18 age-matched controls underwent two MRI scanning sessions. The first was done at 7T and involved acquiring quantitative T1 and T2 * high-resolution maps to estimate cortical myelination. The second was done on a Connectom scanner and consisted of acquiring high angular resolution diffusion-weighted images to compute white matter structural connectivity metrics: strength, clustering and local efficiency. To further investigate the interplay between structural connectivity and cortical demyelination, patients were divided into four groups according to disease-duration: 0-1 year, 1-2 years, 2-3 years, and >3 years. ANOVA and Spearman's correlations were used to highlight relations between metrics. ANOVA detected a significant effect between disease duration and both cortical myelin (p = 2 × 10-8 ) and connectivity metrics (p < 10-4 ). We observed significant cortical myelin loss in the shorter disease-duration cohorts (0-1 year, p = .0015), and an increase in connectivity in the longer disease-duration cohort (2-3 years, strength: p = .01, local efficiency: p = .002, clustering: p = .001). Moreover, significant covariations between myelin estimation and white matter connectivity metrics were observed: Spearman's Rho correlation coefficients of 0.52 (p = .0003), 0.55 (p = .0001), and 0.53 (p = .0001) for strength, local efficiency, and clustering, respectively. An association between cortical myelin loss and changes in white matter connectivity in early multiple sclerosis was detected. These changes in network organization might be the result of compensatory mechanisms in response to the ongoing cortical diffuse damage in the early stages of multiple sclerosis.

Left atrial appendage angiography is associated with the incidence and number of magnetic resonance imaging–detected brain lesions after percutaneous catheter-based left atrial appendage closure

Percutaneous catheter-based left atrial appendage closure (LAAC) is a procedure being increasingly performed in patients with atrial fibrillation and high bleeding risk. The purpose of this study was to evaluate the incidence of magnetic resonance imaging (MRI)-detected acute brain lesions (ABLs) as well as potential changes in neurocognitive function after percutaneous LAAC in patients with atrial fibrillation. Brain MRI at 3 T was performed within 24 hours before and after LAAC along with neurologic (National Institutes of Health Stroke Scale [NIHSS] score) and cognitive (Montreal Cognitive Assessment [MoCA] test) assessment. Acquired MRI sequences included high-resolution diffusion-weighted imaging as well as fluid-attenuated inversion recovery. Successful device implantation was achieved in all 23 patients (age 74.1 ± 10.5 years; 16 male) using the Amulet (n=18), Occlutech (n = 3), or LAmbre (n = 2) device. Thirty-seven ABLs were detected by MRI in 12 of 23 patients (52%) after LAAC. The number of periprocedural LAA angiographies was significantly higher in patients with ABL than in those without ABL (1.67 ± 0.65 vs 1.18 ± 0.41; P = .048) and was associated with a higher number of ABL (ρ = 0.615; P = .033). Compared to pre-LAAC assessment, post-LAAC MoCA and NIHSS scores revealed similar results. After LAAC, MoCA test (mean 24.1 ± 4.6 vs 23.2 ± 4.6; P=.09) and NIHSS score (mean 1.0 ± 1.7 vs 1.2 ± 1.8; P = .1) were similar between patients with and those without ABL, respectively. MRI-detected ABLs are commonly observed after percutaneous LAAC. The number of LAA angiographies is significantly associated with the number of ABLs; however, the clinical implications of ABL have yet to be determined.

Distinct contributions of the fornix and inferior longitudinal fasciculus to episodic and semantic autobiographical memory

Autobiographical memory (AM) is multifaceted, incorporating the vivid retrieval of contextual detail (episodic AM), together with semantic knowledge that infuses meaning and coherence into past events (semantic AM). While neuropsychological evidence highlights a role for the hippocampus and anterior temporal lobe (ATL) in episodic and semantic AM, respectively, it is unclear whether these constitute dissociable large-scale AM networks. We used high angular resolution diffusion-weighted imaging and constrained spherical deconvolution-based tractography to assess white matter microstructure in 27 healthy young adult participants who were asked to recall past experiences using word cues. Inter-individual variation in the microstructure of the fornix (the main hippocampal input/output pathway) related to the amount of episodic, but not semantic, detail in AMs – independent of memory age. Conversely, microstructure of the inferior longitudinal fasciculus, linking occipitotemporal regions with ATL, correlated with semantic, but not episodic, AMs. Further, these significant correlations remained when controlling for hippocampal and ATL grey matter volume, respectively. This striking correlational double dissociation supports the view that distinct, large-scale distributed brain circuits underpin context and concepts in AM.

Transcriptional signatures of connectomic subregions of the human striatum.

Functionally distinct regions of the brain are thought to possess a characteristic connectional fingerprint-a profile of incoming and outgoing connections that defines the function of that area. This observation has motivated efforts to subdivide brain areas using their connectivity patterns. However, it remains unclear whether these connectomically-defined subregions can be distinguished at the molecular level. Here, we combine high-resolution diffusion-weighted magnetic resonance imaging with transcriptomic data to show that connectomically-defined subregions of the striatum carry distinct transcriptional signatures. Using data-driven clustering of diffusion tractography, seeded from the striatum in 100 healthy individuals, we identify a tripartite organization of the caudate and putamen that comprises ventral, dorsal and caudal subregions. We then use microarray data of gene expression levels in 19 343 genes, taken from 98 tissue samples distributed throughout the striatum, to accurately discriminate the three connectomically-defined subregions with 80-90% classification accuracy using linear support vector machines. This classification accuracy was robust at the group and individual level and was superior for our parcellation of the striatum when compared with parcellations based on anatomical boundaries or other criteria. Genes contributing strongly to classification were enriched for gene ontology categories including dopamine signaling, glutamate secretion, response to amphetamine and metabolic pathways, and were implicated in risk for disorders such as schizophrenia, autism and Parkinson's disease. Our findings highlight a close link between regional variations in transcriptional activity and inter-regional connectivity in the brain, and suggest that there may be a strong genomic signature of connectomically-defined subregions of the brain.

High resolution diffusion-weighted imaging for characterising nasopharyngeal carcinoma

Objective: To evaluate the characteristics of high resolution diffusion-weighted imaging(DWI) using readout segmentation of long variable echo trains (RESOLVE ) for nasopharyngeal carcinoma (NPC). Methods: A total of 131 with newly diagnosed NPC patients from the 5th Affiliated Hospital of Sun Yat-sen University were included in this study from October 2013 to April 2016.DWI using RESOLVE technique was performed. The signal intensity (SI(lesion)), and mean(ADC(mean)), maximum(ADC(max)), minimum (ADC(min))ADCs of NPC were calculated. The signal intensity (SI(normal))and ADC (ADC(normal))of normal nasopharyngeal tissue were calculated. These quantitative parameters of NPC and normal nasopharyngeal tissue were compared.Statistical difference of ADC(mean), ADC(max) and ADC(min) between the clinical tumor stages were assessed. Results: On the DWI, all NPCs were clearly shown as high signal intensity relative to the surrounding normal nasopharyngeal structure(F=70.019, P=0.000). The ADC(mean)(F=20.442, P=0.000), ADC(max)(F=35.374, P=0.000), ADC(min)(F=61.534, P=0.000) in the carcinoma were significantly lower compared with that of normal nasopharyngeal structure. There was no statistically significant difference of ADC(mean), ADC(max) and ADC(min) (P>0.05)in different clinical stages of NPC. Conclusion: NPC can be clearly detected by RESOLVE-DWI, but the ADC(mean), ADC(max) and ADC(min) can not be used for differentiating the clinical stage of NPC.

Changes in Apparent Fiber Density and Track-Weighted Imaging Metrics in White Matter following Experimental Traumatic Brain Injury.

Traumatic brain injury (TBI) has been assessed with diffusion tensor imaging (DTI), a commonly used magnetic resonance imaging (MRI) marker for white matter integrity. However, given that the DTI model only fits a single fiber orientation, results can become confounded in regions of "crossing" white matter fibers. In contrast, constrained spherical deconvolution estimates a fiber orientation distribution directly from high angular resolution diffusion-weighted images. Consequently, constrained spherical deconvolution-based measures, such as apparent fiber density (AFD) and track-weighted imaging (TWI) metrics (including tract density imaging, average pathlength mapping, and mean curvature), may be more sensitive than DTI metrics to white matter injury post-TBI. As such, this study administered the lateral fluid percussion injury (FPI) model of TBI, assessed for changes in AFD and TWI metrics, and compared these results to the DTI metrics, fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). Rats received either an FPI (n = 11) or sham injury (n = 9) and after a recovery period of 12 weeks underwent MRI. AFD was calculated as described previously and statistical testing was performed using connectivity-based fixel enhancement. TWI and DTI metrics were assessed using voxel-wise nonparametric permutation testing. We found that rats given an FPI had significantly reduced AFD, tract density, average pathlength, and mean curvature when compared to sham-injured rats and significant changes in DTI metrics, including reduced FA and increased MD, RD, and AD. However, the latter DTI metrics identified fewer voxels affected by TBI. Additionally, analysis of AFD with connectivity-based fixel enhancement was the only method that identified damage within the corticospinal tract of rats given an FPI. These results support the use of constrained spherical deconvolution, in conjunction with DTI metrics, to better assess disease progression and treatment post-TBI.

Rotating single-shot acquisition (RoSA) with composite reconstruction for fast high-resolution diffusion imaging.

To accelerate high-resolution diffusion imaging, rotating single-shot acquisition (RoSA) with composite reconstruction is proposed. Acceleration was achieved by acquiring only one rotating single-shot blade per diffusion direction, and high-resolution diffusion-weighted (DW) images were reconstructed by using similarities of neighboring DW images. A parallel imaging technique was implemented in RoSA to further improve the image quality and acquisition speed. RoSA performance was evaluated by simulation and human experiments. A brain tensor phantom was developed to determine an optimal blade size and rotation angle by considering similarity in DW images, off-resonance effects, and k-space coverage. With the optimal parameters, RoSA MR pulse sequence and reconstruction algorithm were developed to acquire human brain data. For comparison, multishot echo planar imaging (EPI) and conventional single-shot EPI sequences were performed with matched scan time, resolution, field of view, and diffusion directions. The simulation indicated an optimal blade size of 48 × 256 and a 30 ° rotation angle. For 1 × 1 mm2 in-plane resolution, RoSA was 12 times faster than the multishot acquisition with comparable image quality. With the same acquisition time as SS-EPI, RoSA provided superior image quality and minimum geometric distortion. RoSA offers fast, high-quality, high-resolution diffusion images. The composite image reconstruction is model-free and compatible with various diffusion computation approaches including parametric and nonparametric analyses. Magn Reson Med 79:264-275, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Diffusion‐prepared stimulated‐echo turbo spin echo (DPsti‐TSE): An eddy current‐insensitive sequence for three‐dimensional high‐resolution and undistorted diffusion‐weighted imaging

In this study, we present a new three-dimensional (3D), diffusion-prepared turbo spin echo sequence based on a stimulated-echo read-out (DPsti-TSE) enabling high-resolution and undistorted diffusion-weighted imaging (DWI). A dephasing gradient in the diffusion preparation module and rephasing gradients in the turbo spin echo module create stimulated echoes, which prevent signal loss caused by eddy currents. Near to perfect agreement of apparent diffusion coefficient (ADC) values between DPsti-TSE and diffusion-weighted echo planar imaging (DW-EPI) was demonstrated in both phantom transient signal experiments and phantom imaging experiments. High-resolution and undistorted DPsti-TSE was demonstrated in vivo in prostate and carotid vessel wall. 3D whole-prostate DWI was achieved with four b values in only 6min. Undistorted ADC maps of the prostate peripheral zone were obtained at low and high imaging resolutions with no change in mean ADC values [(1.60±0.10)×10-3 versus (1.60±0.02)×10-3 mm2 /s]. High-resolution 3D DWI of the carotid vessel wall was achieved in 12min, with consistent ADC values [(1.40±0.23)×10-3 mm2 /s] across different subjects, as well as slice locations through the imaging volume. This study shows that DPsti-TSE can serve as a robust 3D diffusion-weighted sequence and is an attractive alternative to the traditional two-dimensional DW-EPI approaches.

High-resolution distortion-free diffusion imaging using hybrid spin-warp and echo-planar PSF-encoding approach

High-resolution diffusion-weighted imaging (DWI) has great potential to provide unique information about tissue microstructure in-vivo. Although single-shot echo-planar imaging (EPI) is a most popular tool for DWI, its application for high-resolution DWI is limited due to T2* blurring and susceptibility- and eddy-current-induced geometric distortions, especially at ultra-high field (UHF) such as 7T. In this study, we adapt a hybrid spin-warp and echo-planar encoding strategy inspired by point spread function (PSF) mapping and optimize it for high-resolution and distortion-free diffusion imaging applications. More specifically, a 2D navigator echo is added into the original sequence for shot-to-shot motion-induced phase error estimation and correction. The spatial encoding is shared between the PSF and the EPI phase encoding dimension allowing short echo trains to preserve the diffusion and navigator signals efficiently at UHF, where T2 decay is relatively fast. In addition, variable k-space spacing was applied in the PSF dimension and combined with parallel imaging in the EPI-PE dimension to further accelerate the PSF acquisition. The results demonstrate that this method can yield isotropic submillimeter resolution without T2* blurring and geometric distortions at 7T and enables a clear and detailed delineation of human brain structures in-vivo with the diffusion contrasts. In addition, results of the proposed approach for high-resolution diffusion imaging at 3T are presented.

High-Resolution Diffusion-Weighted Imaging Improves the Diagnostic Accuracy of Dynamic Contrast-Enhanced Sinonasal Magnetic Resonance Imaging.

The aim of the study was to evaluate the incremental value of high-resolution diffusion-weighted imaging and apparent diffusion coefficient (ADC) mapping compared with that of dynamic contrast-enhanced (DCE) sinonasal magnetic resonance imaging (MRI) in the characterization of benign versus malignant sinonasal tumors at 3.0T. Ninety-eight patients (61 females, 37 males; mean age, 48 years) with sinonasal lesions who underwent high-resolution DW-MRI and DCE-MRI were included in this study. The lesions were divided into malignant and benign groups on the basis of pathological examination. In total, 58 malignant tumors and 40 benign tumors were evaluated. Apparent diffusion coefficients were acquired with 0 and 1000 s/mm b values. Semiquantitative parameters (time-signal intensity curve [TIC] type, time of peak enhancement within the first 2 minutes [Tpeak], peak percentage enhancement within the first 2 minutes [PEpeak], the last time point percentage enhancement [PElast], and the washout percentage-enhancement difference [PEwashout]) were derived from DCE-MRI. The DCE-MRI parameters and ADCs were included in multivariate models to predict a diagnosis of a benign versus malignant lesion. The accuracy using semiquantitative DCE-MRI parameters alone was 70.4% (0.693 area under the ROC curve, 57.5% sensitivity, 79.3% specificity). Adding absolute ADCs to dynamic contrast-enhanced MR data showed the higher diagnostic accuracy of 85.7% (0.873 area under the ROC curve, 85.0% sensitivity, 86.2% specificity). Moreover, the absolute ADCs differed significantly between the benign (mean [SD] ADC, 1.211 [0.32] × 10 mm/s) and malignant (mean [SD] ADC, 0.702 [0.28] × 10 mm/s) sinonasal tumors (P < 0.001). In addition, a significant difference was found between the ADC values of malignant and benign lesions in washout enhancement TICs (t = 7.039, P < 0.001). High-resolution DWI with ADC mapping significantly improved the diagnostic accuracy of dynamic contrast-enhanced sinonasal MRI at 3.0T. In addition, ADC values could distinguish benign lesions from malignant tumors in washout enhancement TICs.

High angular resolution diffusion-weighted imaging in mild traumatic brain injury.

We sought to investigate white matter abnormalities in mild traumatic brain injury (mTBI) using diffusion-weighted magnetic resonance imaging (DW-MRI). We applied a global approach based on tract-based spatial statistics skeleton as well as constrained spherical deconvolution tractography.DW-MRI was performed on 102 patients with mTBI within two months post-injury and 30 control subjects.A robust global approach considering only the voxels with a single-fiber configuration was used in addition to global analysis of the tract skeleton and probabilistic whole-brain tractography. In addition, we assessed whether the microstructural parameters correlated with age, time from injury, patient's outcome and white matter MRI hyperintensities. We found that whole-brain global approach restricted to single-fiber voxels showed significantly decreased fractional anisotropy (FA) (p = 0.002) and increased radial diffusivity (p = 0.011) in patients with mTBI compared with controls. The results restricted to single-fiber voxels were more significant and reproducible than those with the complete tract skeleton or the whole-brain tractography. FA correlated with patient outcomes, white matter hyperintensities and age. No correlation was observed between FA and time of scan post-injury. In conclusion, the global approach could be a promising imaging biomarker to detect white matter abnormalities following traumatic brain injury.

Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic. Next, each of the patients was monitored carefully and survival time to death was recorded. Patients were labeled as short, medium or long survivors, based on their recorded time to death as measured from the time of disease onset. In the deep learning procedure, the total group of 135 patients was split into a training set for deep learning (n = 83 patients), a validation set (n = 20) and an independent evaluation set (n = 32) to evaluate the performance of the obtained deep learning networks. Deep learning based on clinical characteristics predicted survival category correctly in 68.8% of the cases. Deep learning based on MRI predicted 62.5% correctly using structural connectivity and 62.5% using brain morphology data. Notably, when we combined the three sources of information, deep learning prediction accuracy increased to 84.4%. Taken together, our findings show the added value of MRI with respect to predicting survival in ALS, demonstrating the advantage of deep learning in disease prognostication.

Two-dimensional single-shot diffusion-weighted stimulated EPI with reduced FOV for ultrahigh-b radial diffusion-weighted imaging of spinal cord.

High-resolution diffusion-weighted imaging (DWI) of the spinal cord (SC) is problematic because of the small cross-section of the SC and the large field inhomogeneity. Obtaining the ultrahigh-b DWI poses a further challenge. The purpose of the study was to design and validate two-dimensional (2D) single-shot diffusion-weighted stimulated echo planar imaging with reduced field of view (2D ss-DWSTEPI-rFOV) for ultrahigh-b radial DWI (UHB-rDWI) of the SC. A novel time-efficient 2D ss-DWSTEPI-rFOV sequence was developed based on the stimulated echo sequence. Reduced-phase field of view was obtained by using two slice-selective 90 ° radiofrequency pulses in the presence of the orthogonal slice selection gradients. The sequence was validated on a cylindrical phantom and demonstrated on SC imaging. Ultrahigh-b radial diffusion-weighted ( bmax = 7300 s/mm2) images of the SC with greatly reduced distortion were obtained. The exponential plus constant fitting of the diffusion-decay curve estimated the constant fraction (restricted water fraction) as 0.36 ± 0.05 in the SC white matter. A novel 2D ss-DWSTEPI-rFOV sequence has been designed and demonstrated for high-resolution UHB-rDWI of localized anatomic structures with significantly reduced distortion induced by nonlinear static field inhomogeneity. Magn Reson Med 77:2167-2173, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Joint correction of Nyquist artifact and minuscule motion-induced aliasing artifact in interleaved diffusion weighted EPI data using a composite two-dimensional phase correction procedure

Diffusion-weighted imaging (DWI) obtained with interleaved echo-planar imaging (EPI) pulse sequence has great potential of characterizing brain tissue properties at high spatial-resolution. However, interleaved EPI based DWI data may be corrupted by various types of aliasing artifacts. First, inconsistencies in k-space data obtained with opposite readout gradient polarities result in Nyquist artifact, which is usually reduced with 1D phase correction in post-processing. When there exist eddy current cross terms (e.g., in oblique-plane EPI), 2D phase correction is needed to effectively reduce Nyquist artifact. Second, minuscule motion induced phase inconsistencies in interleaved DWI scans result in image-domain aliasing artifact, which can be removed with reconstruction procedures that take shot-to-shot phase variations into consideration. In existing interleaved DWI reconstruction procedures, Nyquist artifact and minuscule motion-induced aliasing artifact are typically removed subsequently in two stages. Although the two-stage phase correction generally performs well for non-oblique plane EPI data obtained from well-calibrated system, the residual artifacts may still be pronounced in oblique-plane EPI data or when there exist eddy current cross terms. To address this challenge, here we report a new composite 2D phase correction procedure, which effective removes Nyquist artifact and minuscule motion induced aliasing artifact jointly in a single step. Our experimental results demonstrate that the new 2D phase correction method can much more effectively reduce artifacts in interleaved EPI based DWI data as compared with the existing two-stage artifact correction procedures. The new method robustly enables high-resolution DWI, and should prove highly valuable for clinical uses and research studies of DWI.

Simultaneous multislice accelerated interleaved EPI DWI using generalized blipped-CAIPI acquisition and 3D K-space reconstruction.

Simultaneous multislice (SMS) has been proved to be powerful for accelerating single-shot echo-planar imaging (ssh-EPI) based diffusion-weighted imaging (DWI), but there are some obstacles for applying SMS to interleaved echo-planar imaging (iEPI) DWI. The primary challenge is to effectively combine slice unfolding for SMS and intershot phase correction for multishot DWI. In this study, a novel acquisition and reconstruction method for SMS-accelerated high-resolution iEPI DWI is proposed. The traditional blipped-controlled aliasing in parallel imaging (blipped-CAIPI) for ssh-EPI is generalized for iEPI acquisitions. An SMS three-dimensional (3D) navigator acquisition is designed to record the intershot phase variations. Then, slice unfolding and intershot phase correction are performed simultaneously in an SMS 3D k-space. The performance of the proposed method is demonstrated in both four-shot and eight-shot iEPI DWI and compared with ssh-EPI and unaccelerated iEPI DWI. The proposed method successfully unfolded the simultaneously excited slices and effectively suppressed artifacts from intershot phase variations. The SMS-accelerated iEPI improved the imaging efficiency, while preserving comparable image quality as unaccelerated iEPI DWI. The proposed acquisition and reconstruction is an effective method for accelerating multishot high-resolution DWI, which may be valuable for both neuroscience research and clinical diagnosis. Magn Reson Med 77:1593-1605, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

High-Resolution Diffusion-weighted Imaging Increases Prostate Cancer Visibility?

Multiparametric MRI of the prostate has become a valuable imaging tool to detect clinically significant prostate cancer (Panebianco et al., 2015, Schoots et al., 2015). This tool consists of anatomical and functional imaging techniques. Currently, diffusion-weighted imaging is the most promising functional technique to detect and characterize prostate cancer. Despite its huge potential, it has some limitations that may hamper further implementation in the clinical practice. The signal-to-noise ratio is currently the major limitation. As a result, the highest in plane resolution, achievable with commercially available sequences, is in the range of 1.5 mm (Barentsz et al., 2012). In EBioMedicine, Sharif-Afshar et al. present an important step forward to overcome the current limitations of diffusion-weighted imaging (Sharif-Afshar et al., 2016). This prospective pilot study included 17 men scheduled for prostate biopsy for active surveillance. They underwent multiparametric MRI of the prostate prior to the prostate biopsy. Additionally, the men underwent a high resolution three-dimensional multi-shot diffusion-weighted imaging sequence, which has advantages over two-dimensional single-shot echo-planer imaging. These include markedly reduced geometric distortion, minimization of susceptibility-related artifacts, and higher achievable spatial resolution. This multishot technique allows differentiation of signal and noise, further improving resolution. The reported in plane resolution is 0.9 × 0.9 × 3.5 mm3, which is improved by a factor 4.4 to 5.4. This resulted in a significantly higher area under the ROC for the high resolution diffusion-weighted MRI compared to the standard clinical diffusion-weighted imaging. Subsequently, the new technique identified significantly more biopsy-proven lesions than standard diffusion MRI. The investigated functional imaging technique has great potentials. Diffusion-weighted MRI may provide an aid to establish the aggressiveness of prostate cancer and help predict those tumors most likely to progress rapidly. Prostate biopsies frequently underestimate the ‘true’ Gleason score of tumors found with transrectal ultrasound compared to radical prostatectomy (ranging from 30 to 50%, depending on initial Gleason score) (Pinthus et al., 2006, Corcoran et al., 2011). This high-resolution technique may further improve lesion characterization and ultimately be used to perform MRI diffusion-weighted imaging-targeted biopsy. A question that arises is whether this higher spatial resolution will improve patient selection for active surveillance. Particular in this patient group, it is difficult to find low-grade lesions with diffusion-weighted imaging. Contrast-enhanced MRI is more helpful in finding those lesions. On the other hand, the proposed technique may be used to exclude males from the active surveillance program. To conclude, high-resolution diffusion-weighted imaging of the prostate has the potential to further improve lesion characterization and visibility. It has paved the way for more accurate MRI-targeted biopsy.

Prospective Pilot Trial to Evaluate a High Resolution Diffusion-Weighted MRI in Prostate Cancer Patients

ObjectivesHigh-resolution prostate imaging may allow for detection of subtle changes in tumor size, decrease the reliance on biopsies, and help define tumor boundaries during ablation. This pilot clinical trial evaluates a novel high-resolution prostate MRI for detection of small, biopsy-proven prostate tumors. MethodsOur team developed a software that can be loaded on any modern MRI to generate high resolution diffusion-weighted imaging sequences (HR-DWI), which were compared to standard diffusion-weighted imaging sequence (S-DWI) in a prospective pilot trial in active surveillance patients. HR-DWI captures the entire volume of the prostate rather than sections, reducing streaking artifacts and geometric distortions. Multiple shots, rather than single shots, are used to differentiate signal and noise, enhancing resolution. All images were read by two radiologists. The primary outcome was the percent of biopsy-proven zones seen in 17 patients. The trial was powered to detect discordant proportions of 0.04 and 0.40 at one-sided alpha=0.05. ResultsThe resolution was defined using standard phantoms. HR-DWI produced a 5-fold improvement in spatial resolution when compared to S-DWI. Multiparametric (MP)-MRI incorporating S-DWI was useful for predicting biopsy results (AUC 0.72, Fisher's exact p<0.001); however, using HR-DWI allowed MP-MRI to be more highly predictive of biopsy results (AUC 0.88, Fisher's exact p<0.001). AUC for MP-MRI incorporating HR-DWI was significantly larger than MP-MRI incorporating S-DWI (p=0.002). MP-MRI with HR-DWI had a sensitivity of 95.7% and identified tumor in 22 of 23 zones proven to have cancer on biopsy. In contrast, MP-MRI with S-DWI had a sensitivity of 60.9% and only identified 14 of 23 biopsy-positive zones (p=0.004). ConclusionWe developed a novel DWI and evaluated its improved resolution in a clinical setting. This technology has many potential applications and should be evaluated in future clinical trials as a patient management tool.

PD06-08 PROSPECTIVE TRIAL TO EVALUATE A HIGH RESOLUTION DIFFUSION-WEIGHTED MRI IN PROSTATE CANCER PATIENTS

You have accessJournal of UrologyImaging/Radiology: Uroradiology II1 Apr 2016PD06-08 PROSPECTIVE TRIAL TO EVALUATE A HIGH RESOLUTION DIFFUSION-WEIGHTED MRI IN PROSTATE CANCER PATIENTS Ali Reza Sharif-Afshar, Christopher Nguyen, Tom Feng, Lucas Payor, Zhaoyang Fan, Rola Saouaf, Debiao Li, and Hyung Kim Ali Reza Sharif-AfsharAli Reza Sharif-Afshar More articles by this author , Christopher NguyenChristopher Nguyen More articles by this author , Tom FengTom Feng More articles by this author , Lucas PayorLucas Payor More articles by this author , Zhaoyang FanZhaoyang Fan More articles by this author , Rola SaouafRola Saouaf More articles by this author , Debiao LiDebiao Li More articles by this author , and Hyung KimHyung Kim More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.2628AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Prostate cancer active surveillance requires serial biopsies. However, high-resolution prostate imaging may allow for detection of subtle changes in tumor size, potentially decreasing the reliance on biopsies, and help define tumor boundaries during ablation. METHODS Our team developed a novel software that can be loaded on any modern MRI to generate high resolution diffusion-weighted imaging sequences (HR-DWI), which were compared to standard diffusion-weighted imaging sequence (S-DWI) in a prospective pilot trial in active surveillance patients. HR-DWI captures the entire volume of the prostate rather than sections, reducing streaking artifacts and geometric distortions. Multiple shots, rather than single shots, are used to differentiate signal and noise, enhancing resolution. All images were read by two radiologists. The primary outcome was the percent of biopsy-proven zones seen in 17 patients. The trial was powered to detect discordant proportions of 0.04 and 0.40 at one-sided alpha=0.05. RESULTS The resolution was defined using standard phantoms. HR-DWI produced a 5-fold improvement in spatial resolution when compared to S-DWI (Figures). Multiparametric (MP)-MRI incorporating S-DWI was useful for predicting biopsy results (AUC 0.72, Fisher’s exact p<0.001); however, using HR-DWI allowed MP-MRI to be more highly predictive of biopsy results (AUC 0.88, Fisher’s exact p<0.001). AUC for MP-MRI incorporating HR-DWI was significantly larger than MP-MRI incorporating S-DWI (p=0.002). MP-MRI with HR-DWI had a sensitivity of 95.7% and identified tumor in 22 of 23 zones proven to have cancer on biopsy. In contrast, MP-MRI with S-DWI had a sensitivity of 60.9% and only identified 14 of 23 biopsy-positive zones (p=0.004). CONCLUSIONS We developed a novel DWI sequence and evaluated its improved resolution in a clinical setting. This technology has many potential applications and should be evaluated in future clinical trials as a patient management tool. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e177-e178 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Ali Reza Sharif-Afshar More articles by this author Christopher Nguyen More articles by this author Tom Feng More articles by this author Lucas Payor More articles by this author Zhaoyang Fan More articles by this author Rola Saouaf More articles by this author Debiao Li More articles by this author Hyung Kim More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...