High-resolution Diffusion-weighted Imaging Research Articles

Best MRI predictors of complete response to neoadjuvant chemoradiation in locally advanced rectal cancer.

To identify the MRI parameters which best predict complete response (CR) to neoadjuvant chemoradiotherapy (CRT) in patients with locally advanced rectal cancer (LARC) and to assess their diagnostic performance. This was a prospective study of pre- and post-CRT MRI and diffusion-weighted imaging (DWI) of 64 patients with LARC who underwent neoadjuvant CRT and subsequent surgery. Histopathological tumour regression grade was the reference standard. Multivariate regression analysis was performed to identify the best MRI predictors of CR to neoadjuvant CRT, and their diagnostic performance was assessed. The study cohort comprised 48 males and 16 females (n = 64), with mean age of 49.48 ± 14.3 years, range of 23-74 years. 11 patients had pathological complete response. The following factors predicted CR on univariate analysis: low initial (pre-CRT) tumour volume on T2 weighted high-resolution (HR) images and DWI, tumour volume-reduction rate (TVRR) of >95% on DWI and CR on post-CRT DWI (ydwiT0) as assessed by the radiologist. However, the best MRI predictors of CR on multivariate regression analysis were CR on post-CRT DWI (ydwiT0) as assessed by the radiologist and TVRR of >95% on DWI, and these parameters had an area under the curve (95% confidence interval) of 0.881 (0.74-1.0) and 0.843 (0.7-0.98), respectively. The sensitivity, specificity, positive-predictive value, negative-predictive value and accuracy of DWI in predicting CR was 81.8%, 94.3%, 75%, 96.1% and 76%; the sensitivity, specificity and accuracy of TVRR of >95% as a predictor of CR was 80%, 84.1% and 64.1%, respectively; however, this difference was not statistically significant. The interobserver agreement was substantial for ydwiT0. Visual assessment of CR on post-CRT DWI and TVRR of >95% on DWI were the best predictors of CR after neoadjuvant CRT in patients with LARC, and the former being more practical can be used in daily practice. In rectal cancer, ydwiT0 as assessed by the radiologist was the best and most practical imaging predictor of CR and scores over standard T2W HR images.

Myelination of the right parahippocampal cingulum is associated with physical activity in young healthy adults.

Recent evidence suggests that individual differences in physical activity (PA) may be associated with individual differences in white matter microstructure and with grey matter volume of the hippocampus. Therefore, this study investigated the association between PA and white matter microstructure of pathways connecting to the hippocampus. A total of 33 young, healthy adults underwent magnetic resonance imaging (MRI). High angular resolution diffusion-weighted imaging and multi-component relaxometry MRI scans (multi-component driven equilibrium pulse observation of T1 and T2) were acquired for each participant. Activity levels (AL) of participants were calculated from 72-h actigraphy recordings. Tractography using the damped Richardson Lucy algorithm was used to reconstruct the fornix and bilateral parahippocampal cinguli (PHC). The mean fractional anisotropy (FA) and the myelin water fraction (MWF), a putative marker of myelination, were determined for each pathway. A positive correlation between both AL and FA and between AL and MWF were hypothesized for the three pathways. There was a selective positive correlation between AL and MWF in the right PHC (r = 0.482, p = 0.007). Thus, our results provide initial in vivo evidence for an association between myelination of the right PHC and PA in young healthy adults. Our results suggest that MWF may not only be more specific, but also more sensitive than FA to detect white matter microstructural alterations. If PA was to induce structural plasticity of the right PHC this may contribute to reverse structural alterations of the right PHC in neuropsychiatric disorder with hippocampal pathologies.

High-resolution diffusion-weighted imaging of the breast with multiband 2D radiofrequency pulses and a generalized parallel imaging reconstruction.

To develop a technique for high-resolution diffusion-weighted imaging (DWI) and to compare it with standard DWI methods. Multiple in-plane bands of magnetization were simultaneously excited by identically phase modulating each subpulse of a two-dimensional (2D) RF pulse. Several excitations with the same multiband pattern progressively shifted in the phase-encode direction were used to cover the prescribed field of view (FOV). The phase-encoded FOV was limited to the width of a single band to reduce off-resonance-induced distortion and blurring. Parallel imaging (PI) techniques were used to resolve aliasing from the other bands and to combine the different excitations. Following validation in phantoms and healthy volunteers, a preliminary study in breast cancer patients (N=14) was performed to compare the proposed method to conventional DWI with PI and to reduced-FOV DWI. The proposed method gave high-resolution diffusion-weighted images with minimal artifacts at the band intersections. Compared to PI alone, higher phase-encoded FOV-reduction factors and reduced noise amplification were obtained, which translated to higher resolution images than conventional (non-multiband) DWI. The same resolution and image quality achievable over targeted regions using existing reduced-FOV methods was obtained, but the proposed method also enables complete bilateral coverage. We developed an in-plane multiband technique for high-resolution DWI and compared its performance with other standard DWI methods. Magn Reson Med 77:209-220, 2017. © 2016 Wiley Periodicals, Inc.

Super-Resolution Reconstruction of Diffusion-Weighted Images using 4D Low-Rank and Total Variation

Diffusion-weighted imaging (DWI) provides invaluable information in white matter microstructure and is widely applied in neurological applications. However, DWI is largely limited by its relatively low spatial resolution. In this paper, we propose an image post-processing method, referred to as super-resolution reconstruction, to estimate a high spatial resolution DWI from the input low-resolution DWI, e.g., at a factor of 2. Instead of requiring specially designed DWI acquisition of multiple shifted or orthogonal scans, our method needs only a single DWI scan. To do that, we propose to model both the blurring and downsampling effects in the image degradation process where the low-resolution image is observed from the latent high-resolution image, and recover the latent high-resolution image with the help of two regularizations. The first regularization is 4-dimensional (4D) low-rank, proposed to gather self-similarity information from both the spatial domain and the diffusion domain of 4D DWI. The second regularization is total variation, proposed to depress noise and preserve local structures such as edges in the image recovery process. Extensive experiments were performed on 20 subjects, and results show that the proposed method is able to recover the fine details of white matter structures, and outperform other approaches such as interpolation methods, non-local means based upsampling, and total variation based upsampling.

A combined Compressed Sensing Super-Resolution Diffusion and gSlider-SMS acquisition/reconstruction for rapid sub-millimeter whole-brain diffusion imaging.

Event Abstract Back to Event A combined Compressed Sensing Super-Resolution Diffusion and gSlider-SMS acquisition/reconstruction for rapid sub-millimeter whole-brain diffusion imaging. Kawin Setsompop1, Lipeng Ning2 and Yogesh Rathi2* 1 Harvard Medical School, Massachusetts General Hospital, United States 2 Harvard Medical School, Brigham and Women’s Hospital, United States Purpose: Submillimeter isotropic diffusion imaging (DI) is made difficult by long acquisition time and low SNR. Slider-SMS acquisition (1) has recently been proposed to provide large improvement to SNR efficiency for DI, by allowing a large number of imaging slices to be acquire simultaneously through the combined use of SMS parallel imaging and super-resolution approaches. Further improvement to this acquisition with a new generalized Slider approach (gSlider) is also being proposed (2), which utilizes RF encoding to improve the orthogonality of the slice-encoding basis for super-resolution, to enable high quality 5 simultaneous slice super-resolution, which when combines with SMS parallel imaging can achieve highly efficient 10 simultaneous slices DI (gSlider x MB = 5x2). In this work, we provide further improvement to this acquisition by incorporating the framework of Compressed Sensing for Super-Resolution Diffusion (CS-RSD) proposed in (3) to gSlider-SMS. This enables acquisition acceleration through undersampling of the gSlider slice-encoded super-resolution acquisitions needed for super-resolution reconstruction of each diffusion direction (with different random undersampling for different diffusion directions). Such acquisition undersampling is made possible through sparsity enforce reconstruction where the diffusion signal in each voxel of the final high-resolution image is enforced to be sparse in the basis of spherical ridgelets. We demonstrate that the incorporation of the CS-RSD approach enables a further 3x? efficiency gain to an already highly efficient 10 simultaneous slice gSlider-SMS acquisition. This permits, for the first time, high quality 860um whole-brain DI at high b-value to be perform in a relevant clinical timeframe of 8 minutes. Method: gSlider-SMS data with 10 simultaneous slice acquisition (gSlider×MB = 5×2) were acquired in a healthy volunteer on the 3T CONNECTOM system using custom-built 64-channel array. Imaging parameters were: 860μm iso; FOV 220×163.4×130 mm3; p.f. 6/8; TE = 64ms and TR per thick-slice volume = 4.2s; effective echo spacing = 0.32ms, 64 directions at b=2000 s/mm2 with interspersed b0 every 10 volumes, where for each diffusion direction, imaging were performed 5 times for different RF gSlider encoding, total scan-time ~25 min. Background phase removal was performed using real value diffusion algorithm (6). Reconstruction for full sampled Slider encoding data were performed using Tikhonov regularized super-resolution reconstruction to represent fullysampled acquisition for comparison. For incorporation of CS-RSD, the 5 different gSlider slice-encode imaging per diffusion direction were randomly undersampled to provide three times acceleration to result in an effective acquisition time of ~8 min. This accelerated acquisition is reconstructed using algorithm proposed in (3) and compared with the fully sampled reconstruction. Results: Figure (1) shows the acquired thick-slice images and the reconstructed high-resolution diffusion-weighted images obtained using the Tikhonov regularization and the compressed-sensing approach, respectively. The high-resolution image obtained using the compressed-sensing approach has the lowest noise level due to the sparse representation using spherical ridgelets. Figure (2) shows the corresponding colored-coded diffusion tensor images for the three diffusion MRI volumes, respectively.

Diffusion-Weighted Images Superresolution Using High-Order SVD.

The spatial resolution of diffusion-weighted imaging (DWI) is limited by several physical and clinical considerations, such as practical scanning times. Interpolation methods, which are widely used to enhance resolution, often result in blurred edges. Advanced superresolution scanning acquires images with specific protocols and long acquisition times. In this paper, we propose a novel single image superresolution (SR) method which introduces high-order SVD (HOSVD) to regularize the patch-based SR framework on DWI datasets. The proposed method was implemented on an adaptive basis which ensured a more accurate reconstruction of high-resolution DWI datasets. Meanwhile, the intrinsic dimensional decreasing property of HOSVD is also beneficial for reducing the computational burden. Experimental results from both synthetic and real DWI datasets demonstrate that the proposed method enhances the details in reconstructed high-resolution DWI datasets and outperforms conventional techniques such as interpolation methods and nonlocal upsampling.

Identifying preoperative language tracts and predicting postoperative functional recovery using HARDI q-ball fiber tractography in patients with gliomas.

OBJECT Diffusion MRI has uniquely enabled in vivo delineation of white matter tracts, which has been applied to the segmentation of eloquent pathways for intraoperative mapping. The last decade has also seen the development from earlier diffusion tensor models to higher-order models, which take advantage of high angular resolution diffusion-weighted imaging (HARDI) techniques. However, these advanced methods have not been widely implemented for routine preoperative and intraoperative mapping. The authors report on the application of residual bootstrap q-ball fiber tracking for routine mapping of potentially functional language pathways, the development of a system for rating tract injury to evaluate the impact on clinically assessed language function, and initial results predicting long-term language deficits following glioma resection. METHODS The authors have developed methods for the segmentation of 8 putative language pathways including dorsal phonological pathways and ventral semantic streams using residual bootstrap q-ball fiber tracking. Furthermore, they have implemented clinically feasible preoperative acquisition and processing of HARDI data to delineate these pathways for neurosurgical application. They have also developed a rating scale based on the altered fiber tract density to estimate the degree of pathway injury, applying these ratings to a subset of 35 patients with pre- and postoperative fiber tracking. The relationships between specific pathways and clinical language deficits were assessed to determine which pathways are predictive of long-term language deficits following surgery. RESULTS This tracking methodology has been routinely implemented for preoperative mapping in patients with brain gliomas who have undergone awake brain tumor resection at the University of California, San Francisco (more than 300 patients to date). In this particular study the authors investigated the white matter structure status and language correlation in a subcohort of 35 subjects both pre- and postsurgery. The rating scales developed for fiber pathway damage were found to be highly reproducible and provided significant correlations with language performance. Preservation of the left arcuate fasciculus (AF) and the temporoparietal component of the superior longitudinal fasciculus (SLF-tp) was consistent in all patients without language deficits (p < 0.001) at the long-term follow-up. Furthermore, in patients with short-term language deficits, the AF and/or SLF-tp were affected, and damage to these 2 pathways was predictive of a long-term language deficit (p = 0.005). CONCLUSIONS The authors demonstrated the successful application of q-ball tracking in presurgical planning for language pathways in brain tumor patients and in assessing white matter tract integrity postoperatively to predict long-term language dysfunction. These initial results predicting long-term language deficits following tumor resection indicate that postoperative injury to dorsal language pathways may be prognostic for long-term clinical language deficits. Study results suggest the importance of dorsal stream tract preservation to reduce language deficits in patients undergoing glioma resection, as well as the potential prognostic value of assessing postoperative injury to dorsal language pathways to predict long-term clinical language deficits.

Improved Framework for Tractography Reconstruction of the Optic Radiation.

The optic radiation (OR) is one of the major components of the visual system and a key structure at risk in white matter diseases such as multiple sclerosis (MS). However, it is challenging to perform track reconstruction of the OR using diffusion MRI due to a sharp change of direction in the Meyer’s loop and the presence of kissing and crossing fibers along the pathway. As such, we aimed to provide a highly precise and reproducible framework for tracking the OR from thalamic and visual cortex masks. The framework combined the generation of probabilistic streamlines by high order fiber orientation distributions estimated with constrained spherical deconvolution and an automatic post-processing based on anatomical exclusion criteria (AEC) to compensate for the presence of anatomically implausible streamlines. Specifically, those ending in the contralateral hemisphere, cerebrospinal fluid or grey matter outside the visual cortex were automatically excluded. We applied the framework to two distinct high angular resolution diffusion-weighted imaging (HARDI) acquisition protocols on one cohort, comprised of ten healthy volunteers and five MS patients. The OR was successfully delineated in both HARDI acquisitions in the healthy volunteers and MS patients. Quantitative evaluation of the OR position was done by comparing the results with histological reference data. Compared with histological mask, the OR reconstruction into a template (OR-TCT) was highly precise (percentage of voxels within the OR-TCT correctly defined as OR), ranging from 0.71 to 0.83. The sensitivity (percentage of voxels in histological reference mask correctly defined as OR in OR-TCT) ranged from 0.65 to 0.81 and the accuracy (measured by F1 score) was 0.73 to 0.77 in healthy volunteers. When AEC was not applied the precision and accuracy decreased. The absolute agreement between both HARDI datasets measured by the intraclass correlation coefficient was 0.73. This improved framework allowed us to reconstruct the OR with high reliability and accuracy independently of the acquisition parameters. Moreover, the reconstruction was possible even in the presence of tissue damage due to MS. This framework could also be applied to other tracts with complex configuration.

High and ultra-high b-value diffusion-weighted imaging in prostate cancer: a quantitative analysis.

Diffusion-weighted imaging (DWI) is routinely used in magnetic resonance imaging (MRI) of prostate cancer. However, the routine use of b values higher than 1000 s/mm(2) is not clear up to present. Moreover, the complex diffusion behavior of malignant and benign prostate tissues hampers precise predictions of contrast in DWI images and apparent diffusion coefficient (ADC) maps. To quantitatively analyze DWI with different b values in prostate cancer and to identify b values best suitable for cancer detection. Forty-one patients with histologically proven prostate cancer were examined with high resolution T2-weighted imaging and DWI at 3 Tesla. Five different b values (0, 800, 1000, 1500, 2000 s/mm(2)) were applied. ADC values of tumors and reference areas were measured on ADC maps derived from different pairs of b values. Furthermore, signal intensities of DW images of tumors and reference areas were measured. For analysis, contrast ratios of ADC values and signal intensities of DW images were calculated and compared. No significant differences were found between contrast ratios measured on ADC maps of all analyzed b value pairs (P = 0.43). Contrast ratios calculated from signal intensities of DW images were highest at b values of 1500 and 2000 s/mm(2) and differed significantly from contrast ratios at b values of 800 and 1000 s/mm(2) (P < 0.01). Whereas contrast in ADC maps does not significantly change with different b values, contrast ratios of DW images are significantly higher at b-values of 1500 and 2000 s/mm(2) in comparison to b values of 800 and 1000 s/mm(2). Therefore, diagnostic performance of DWI in prostate cancer might be increased by application of b values higher than 1000 s/mm(2).

Callosal Function in Pediatric Traumatic Brain Injury Linked to Disrupted White Matter Integrity.

Traumatic brain injury (TBI) is the primary cause of death and disability in children and adolescents. There is considerable heterogeneity in postinjury outcome, which is only partially explained by injury severity. Imaging biomarkers may help explain some of this variance, as diffusion weighted imaging is sensitive to the white matter disruption that is common after injury. The corpus callosum (CC) is one of the most commonly reported areas of disruption. In this multimodal study, we discovered a divergence within our pediatric moderate-to-severe TBI sample 1-5 months postinjury. A subset of the TBI sample showed significant impairment in CC function, which is supported by additional results showing deficits in CC structural integrity. This subset also had poorer neurocognitive functioning. Our research sheds light on postinjury heterogeneity.

EP-1236: Multiparametric MRI appearance of prostate after androgen deprivation therapy ñ preliminary results

Purpose/Objective: Globally, prostate cancer is responsible for around 250000 deaths annually. Radiotherapy is an effective definitive treatment and, for more aggressive disease, is commonly preceded by neo-adjuvant androgen deprivation therapy (ADT). It has been demonstrated that dose escalated radiotherapy improves biochemical free survival but not overall survival. Further dose escalation is limited by OAR tolerances. Giving a further focal boost to the dominant intra-prostatic lesion (DIPL) appears to be feasible. The DIPL is effectively identified using multi-parametric (MP) MR imaging, however, ADT results in a reduction in prostate and tumour volume and also a loss of contrast on T2w MR images, making lesions less conspicuous. The DIPL is therefore conventionally identified on pre-hormone imaging but it is known that response to therapy is variable and directing a DIPL boost to disease that is resistant to ADT may be most likely to improve clinical outcome. The aim of this work was to compare pre and post treatment imaging to evaluate the feasibility of using post treatment MP-MRI to locate the DIPL. Materials and Methods: Six patients with prostate cancer stage T2b or greater were recruited. All underwent local pre ADT DWI. Patients were imaged post ADT at 1.5 T (Achieva, Philips Medical Systems, Best, The Netherlands) using the cardiac coil. The MR examination included high resolution T2w images, DCE-MRI and diffusion-weighted imaging (DWI). ADC and DCE-MRI parameter maps were calculated on a voxel-by-voxel basis for the whole prostate. Results: Four patients had a clear DIPL pre ADT. This remained largely unchanged in appearance in three cases post ADT, as shown in the example case in the top row of figure 1, where arrows indicate the high flow (left, Fp in ml/ml tissue/min overlayed on T2w imaging), low ADC (right, overlayed on T2w imaging) regions matching those reported as tumour on pre-treatment imaging. In one case the pre ADT DIPL in the right peripheral zone was hard to identify post ADT on T2w imaging (black arrow bottom row, figure 1). However a further lesion in the left anterior gland (white arrow), which was equivocal in appearance pre-treatment, was now conspicuous in both Fp (left) and ADC maps (right).

Comparison of field-of-view (FOV) optimized and constrained undistorted single shot (FOCUS) with conventional DWI for the evaluation of prostate cancer

ObjectivesQualitative and quantitative comparison of the application value of diffusion-weighted imaging (DWI) magnetic resonance imaging based on field-of-view (FOV) optimized and constrained undistorted single shot (FOCUS) with the standard single-shot echo-planar imaging (ss-EPI) in patients with prostate cancer. MethodsA total of 28 patients with 33 prostate cancers underwent ss-EPI and FOCUS DWI. Two independent readers assessed image studies. ResultsImage qualities of FOCUS were rated superior to ss-EPI (P<.001). The mean apparent diffusion coefficient value of 33 cancers was 974.030×10−6mm2/s and 941.727×10−6mm2/s, respectively, and the difference was statistically significant (t=2.579, P=.015). ConclusionsFOCUS as a high-resolution DWI technique has potential clinical utility.

Prostate cancer: the correlation between apparent diffusion coefficient values obtained from high resolution diffusion-weighted imaging and Gleason scores

Objective To compare ADC values obtained from readout-segmented echo-planar imaging (RS-EPI) and single-shot echo-planar imaging (SS-EPI) techniques of prostate cancer and to investigate the correlation between RS-EPI ADC values and Gleason scores. Methods A retrospective analysis of RS-EPI and SS-EPI DWI were performed in 23 patients with pathologically confirmed prostate cancer. Routine MRI, RS-EPI DWI and SS-EPI DWI were obtained prior to ultrasound guided systemic biopsy in all cases. According to the location of lesion from biopsy report, a ROI was drawn on ADC map obtained from RS-EPI DWI and SS-EPI DWI, respectively, at cancerous foci with low T2WI and high DWI signal intensity. Foci were classified into well differentiated group (Gleason score≤6) and poorly and moderately differentiated group (Gleason score≥7). ADC values between RS-EPI and SS-EPI were compared by using t test. The relationship between ADC values and Gleason scores was assessed using Spearman test. The sensitivity and specificity of RS-EPI ADC value for differential diagnosis of two groups and the best cutoff were evaluated from ROC. Results Twenty nine well differentiated foci and 126 poorly and moderately differentiated foci were identified. The mean RS-EPI ADC value and the mean SS-EPI ADC value of well differentiated group and poorly and moderately differentiated group were (1.136 ± 0.136) × 10-3mm2/s, (1.125±0.198)×10-3mm2/s; (0.891±0.161)×10-3mm2/s, (0.875 ±0.184)×10-3mm2/s, respectively. Neither well differentiated group nor poorly and moderately differentiated group had a significant difference between the mean ADC value of two techniques (t values were 0.359 and 1.727, P values were 0.722 and 0.087, respectively). Either mean RS-EPI ADC value or mean SS-EPI ADC value between two groups had a significant difference (t values were 7.572 and 6.499, P<0.01, respectively). There was an inverse relationship between ADC values obtained from RS-EPI and SS-EPI and Gleason scores (r values were -0.638 and -0.565, P<0.01, respectively). When the cutoff value was 1.041×10-3mm2/s, the sensitivity and specificity of RS-EPI ADC value to differentiate two groups were 85.7% (108/126), 86.2% (25/29), respectively, and the area under ROC curve was 0.882. Conclusions There were no significant difference between ADC values obtained from RS-EPI and SS-EPI. ADC values of both techniques had positive correlation with Gleason scores. RS-EPI ADC values may have potential to allow noninvasive assessment of prostate cancer aggressiveness, which could help in the stratification of patients for appropriate therapy. Key words: Prostate neoplasms; Magnetic resonance imaging; Pathology

Atlasing the frontal lobe connections and their variability due to age and education: a spherical deconvolution tractography study.

In neuroscience, there is a growing consensus that higher cognitive functions may be supported by distributed networks involving different cerebral regions, rather than by single brain areas. Communication within these networks is mediated by white matter tracts and is particularly prominent in the frontal lobes for the control and integration of information. However, the detailed mapping of frontal connections remains incomplete, albeit crucial to an increased understanding of these cognitive functions. Based on 47 high-resolution diffusion-weighted imaging datasets (age range 22-71 years), we built a statistical normative atlas of the frontal lobe connections in stereotaxic space, using state-of-the-art spherical deconvolution tractography. We dissected 55 tracts including U-shaped fibers. We further characterized these tracts by measuring their correlation with age and education level. We reported age-related differences in the microstructural organization of several, specific frontal fiber tracts, but found no correlation with education level. Future voxel-based analyses, such as voxel-based morphometry or tract-based spatial statistics studies, may benefit from our atlas by identifying the tracts and networks involved in frontal functions. Our atlas will also build the capacity of clinicians to further understand the mechanisms involved in brain recovery and plasticity, as well as assist clinicians in the diagnosis of disconnection or abnormality within specific tracts of individual patients with various brain diseases.

Application of brain diffusion-weighted imaging performed using readout segmentation of long variable echo trains

We report the preliminary use of the readout segmentation of long variable echo trains(RESOLVE)sequence, a novel magnetic resonance(MR)scanning technique based on a readout segmented echo planar imaging(EPI)strategy. RESOLVE enables high-resolution diffusion-weighted imaging(DWI)by minimizing susceptibility distortions and T2* blurring. The software for this sequence was provided by Siemens AG, Germany. Previously, we determined appropriate sequence parameters to obtain sufficiently high-resolution images through phantom studies. Then, we applied the sequence to some clinical cases with neurological disorders and analyzed the RESOLVE-DWI data with diffusion tensor imaging(DTI)techniques. In this article, we report clinical application of the RESOLVE sequence in two cases, one with cerebellar infarction and one with an intracranial epidermoid cyst. In both cases, RESOLVE-DWI clearly exposed structures that were obscured or severely distorted by artifacts on usual single-shot EPI-DWI. DTI analyses for RESOLVE-DWI data provided detailed information about fiber tracts and cranial nerves.

Automated tract extraction via atlas based Adaptive Clustering

Advancements in imaging protocols such as the high angular resolution diffusion-weighted imaging (HARDI) and in tractography techniques are expected to cause an increase in the tract-based analyses. Statistical analyses over white matter tracts can contribute greatly towards understanding structural mechanisms of the brain since tracts are representative of connectivity pathways. The main challenge with tract-based studies is the extraction of the tracts of interest in a consistent and comparable manner over a large group of individuals without drawing the inclusion and exclusion regions of interest. In this work, we design a framework for automated extraction of white matter tracts. The framework introduces three main components, namely a connectivity based fiber representation, a fiber bundle atlas, and a clustering approach called Adaptive Clustering. The fiber representation relies on the connectivity signatures of fibers to establish an easy correspondence between different subjects. A group-wise clustering of these fibers that are represented by the connectivity signatures is then used to generate a fiber bundle atlas. Finally, Adaptive Clustering incorporates the previously generated clustering atlas as a prior, to cluster the fibers of a new subject automatically. Experiments on the HARDI scans of healthy individuals acquired repeatedly, demonstrate the applicability, reliability and the repeatability of our approach in extracting white matter tracts. By alleviating the seed region selection and the inclusion/exclusion ROI drawing requirements that are usually handled by trained radiologists, the proposed framework expands the range of possible clinical applications and establishes the ability to perform tract-based analyses with large samples.

Hedonic tone is associated with left supero-lateral medial forebrain bundle microstructure.

The medial forebrain bundle (MFB) is an important pathway of the reward system. Two branches have been described using diffusion magnetic resonance imaging (MRI)-based tractography: the infero-medial MFB (imMFB) and the supero-lateral MFB (slMFB). Previous studies point to white-matter microstructural alterations of the slMFB in major depressive disorder (MDD) during acute episodes. To extend this finding, this study investigates whether white-matter microstructure is also altered in MDD patients that are in remission. Further, we explore associations between diffusion MRI-based metrics of white-matter microstructure of imMFB, slMFB and hedonic tone, the ability to derive pleasure. Eighteen remitted depressed (RD) and 22 never depressed (ND) participants underwent high angular resolution diffusion-weighted imaging (HARDI) scans. To reconstruct the two pathways of the MFB (imMFB and slMFB) we used the damped Richardson-Lucy (dRL) algorithm. Mean fractional anisotropy (FA) was sampled along the tracts. Mean FA of imMFB, slMFB and a comparison tract (the middle cerebellar peduncle) did not differ between ND and RD participants. Hedonic capacity correlated negatively with mean FA of the left slMFB, explaining 21% of the variance. Diffusion MRI-based metrics of white-matter microstructure of the MFB in RD do not differ from ND. Hedonic capacity is associated with altered white-matter microstructure of the slMFB.

Frontal white matter tracts sustaining speech production in primary progressive aphasia.

In primary progressive aphasia (PPA), speech and language difficulties are caused by neurodegeneration of specific brain networks. In the nonfluent/agrammatic variant (nfvPPA), motor speech and grammatical deficits are associated with atrophy in a left fronto-insular-striatal network previously implicated in speech production. In vivo dissection of the crossing white matter (WM) tracts within this "speech production network" is complex and has rarely been performed in health or in PPA. We hypothesized that damage to these tracts would be specific to nfvPPA and would correlate with differential aspects of the patients' fluency abilities. We prospectively studied 25 PPA and 21 healthy individuals who underwent extensive cognitive testing and 3 T MRI. Using residual bootstrap Q-ball probabilistic tractography on high angular resolution diffusion-weighted imaging (HARDI), we reconstructed pathways connecting posterior inferior frontal, inferior premotor, insula, supplementary motor area (SMA) complex, striatum, and standard ventral and dorsal language pathways. We extracted tract-specific diffusion tensor imaging (DTI) metrics to assess changes across PPA variants and perform brain-behavioral correlations. Significant WM changes in the left intrafrontal and frontostriatal pathways were found in nfvPPA, but not in the semantic or logopenic variants. Correlations between tract-specific DTI metrics with cognitive scores confirmed the specific involvement of this anterior-dorsal network in fluency and suggested a preferential role of a posterior premotor-SMA pathway in motor speech. This study shows that left WM pathways connecting the speech production network are selectively damaged in nfvPPA and suggests that different tracts within this system are involved in subcomponents of fluency. These findings emphasize the emerging role of diffusion imaging in the differential diagnosis of neurodegenerative diseases.

High-resolution diffusion-weighted imaging of the prostate.

The purpose of this study was to evaluate the effect of increasing the spatial resolution of the prostate DWI protocol on image quality and lesion conspicuity. Twenty-nine patients with biopsy-proven prostate cancer undergoing MRI examinations were imaged with two diffusion-weighted imaging (DWI) protocols: current standard clinical protocol (6.7 mm(3) voxels) and a new high-resolution protocol (3.1 mm(3) voxels). Diffusion-weighted images were independently and subjectively scored on lesion conspicuity, internal architecture definition, and overall image quality by two radiologists. Average apparent diffusion coefficient (ADC) values were measured in normal tissue and cancerous lesions on both sequences. Reader scores and ADC and contrast values were compared between the two protocols. Cancer ADC values were correlated with Gleason scores. The signal-to-noise ratio of the new high-resolution DWI protocol was 40% lower than that of the standard protocol. The reader scores were higher by 0.73 (range, 0.29-1.16) grades, or 19% (range, 7-32%), on average, for the new protocol, indicating better image quality. The average ADC values were 8% higher with the new protocol, with ADC contrast values between cancer and normal prostate unchanged. There was marginally significant correlation of cancer ADC values with Gleason scores (p = 0.05, r ≈ -0.36). We showed that for DWI of the prostate at 3-7 mm(3) voxel sizes the benefits of higher spatial resolution outweigh the effects of reduced signal-to-noise and contrast-to-noise ratios, potentially improving the sensitivity to small or sparse prostate cancers. Radiologists can consider using higher-spatial-resolution DWI sequences in their practices.

Interleaved diffusion-weighted improved by adaptive partial-Fourier and multiband multiplexed sensitivity-encoding reconstruction.

We report a series of techniques to reliably eliminate artifacts in interleaved echo-planar imaging (EPI) based diffusion-weighted imaging (DWI). First, we integrate the previously reported multiplexed sensitivity encoding (MUSE) algorithm with a new adaptive Homodyne partial-Fourier reconstruction algorithm, so that images reconstructed from interleaved partial-Fourier DWI data are free from artifacts even in the presence of either (a) motion-induced k-space energy peak displacement, or (b) susceptibility field gradient induced fast phase changes. Second, we generalize the previously reported single-band MUSE framework to multiband MUSE, so that both through-plane and in-plane aliasing artifacts in multiband multishot interleaved DWI data can be effectively eliminated. The new adaptive Homodyne-MUSE reconstruction algorithm reliably produces high-quality and high-resolution DWI, eliminating residual artifacts in images reconstructed with previously reported methods. Furthermore, the generalized MUSE algorithm is compatible with multiband and high-throughput DWI. The integration of the multiband and adaptive Homodyne-MUSE algorithms significantly improves the spatial-resolution, image quality, and scan throughput of interleaved DWI. We expect that the reported reconstruction framework will play an important role in enabling high-resolution DWI for both neuroscience research and clinical uses.