Dataset Information

Diffusion weighted MRI by spatiotemporal encoding: analytical description and in vivo validations.

ABSTRACT: Diffusion-weighted (DW) MRI is a powerful modality for studying microstructure in normal and pathological tissues. The accuracy derived from DW MRI depends on the acquisition of quality images, and on a precise assessment of the b-values involved. Conventional DW MRI tends to be of limited use in regions suffering from large magnetic field or chemical shift heterogeneities, which severely distort the MR images. In this study we propose novel sequences based on SPatio-temporal ENcoding (SPEN), which overcome such shortcomings owing to SPEN's inherent robustness to offsets. SPEN, however, relies on the simultaneous application of gradients and radiofrequency-swept pulses, which may impart different diffusion weightings along the spatial axes. These will be further complicated in DW measurements by the diffusion-sensitizing gradients, and will in general lead to complex, spatially-dependent b-values. This study presents a formalism for analyzing these diffusion-weighted SPEN (dSPEN) data, which takes into account the concomitant effects of adiabatic pulses, of the imaging as well as diffusion gradients, and of the cross-terms between them. These analytical b-values derivations are subject to experimental validations in phantom systems and ex vivo spinal cords. Excellent agreement is found between the theoretical predictions and these dSPEN experiments. The ensuing methodology is then demonstrated by in vivo mapping of diffusion in human breast - organs where conventional k-space DW acquisition methods are challenged by both field and chemical shift heterogeneities. These studies demonstrate the increased robustness of dSPEN vis-à-vis comparable DW echo planar imaging, and demonstrate the value of this new methodology for medium- or high-field diffusion measurements in heterogeneous systems.

SUBMITTER: Solomon E

PROVIDER: S-EPMC5040484 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Diffusion weighted MRI by spatiotemporal encoding: analytical description and in vivo validations.

Solomon Eddy E Shemesh Noam N Frydman Lucio L

Journal of magnetic resonance (San Diego, Calif. : 1997) 20130314

Diffusion-weighted (DW) MRI is a powerful modality for studying microstructure in normal and pathological tissues. The accuracy derived from DW MRI depends on the acquisition of quality images, and on a precise assessment of the b-values involved. Conventional DW MRI tends to be of limited use in regions suffering from large magnetic field or chemical shift heterogeneities, which severely distort the MR images. In this study we propose novel sequences based on SPatio-temporal ENcoding (SPEN), wh ...[more]

PMID: 23562003

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Project description:Diffusion magnetic resonance images may suffer from geometric distortions due to susceptibility induced off resonance fields, which cause geometric mismatch with anatomical images and ultimately affect subsequent quantification of microstructural or connectivity indices. State-of-the art diffusion distortion correction methods typically require data acquired with reverse phase encoding directions, resulting in varying magnitudes and orientations of distortion, which allow estimation of an undistorted volume. Alternatively, additional field maps acquisitions can be used along with sequence information to determine warping fields. However, not all imaging protocols include these additional scans and cannot take advantage of state-of-the art distortion correction. To avoid additional acquisitions, structural MRI (undistorted scans) can be used as registration targets for intensity driven correction. In this study, we aim to (1) enable susceptibility distortion correction with historical and/or limited diffusion datasets that do not include specific sequences for distortion correction and (2) avoid the computationally intensive registration procedure typically required for distortion correction using structural scans. To achieve these aims, we use deep learning (3D U-nets) to synthesize an undistorted b0 image that matches geometry of structural T1w images and intensity contrasts from diffusion images. Importantly, the training dataset is heterogenous, consisting of varying acquisitions of both structural and diffusion. We apply our approach to a withheld test set and show that distortions are successfully corrected after processing. We quantitatively evaluate the proposed distortion correction and intensity-based registration against state-of-the-art distortion correction (FSL topup). The results illustrate that the proposed pipeline results in b0 images that are geometrically similar to non-distorted structural images, and more closely match state-of-the-art correction with additional acquisitions. In addition, we show generalizability of the proposed approach to datasets that were not in the original training / validation / testing datasets. These datasets included varying populations, contrasts, resolutions, and magnitudes and orientations of distortion and show efficacious distortion correction. The method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation.

Project description:Diffusion weighted imaging (DWI) constitutes a major functional parameter performed in Magnetic Resonance Imaging (MRI). The DW sequence is performed by acquiring a set of native images described by their b-values, each b-value representing the strength of the diffusion MR gradients specific to that sequence. By fitting the data with models describing the motion of water in tissue, an apparent diffusion coefficient (ADC) map is built and allows the assessment of water mobility inside the tissue. The high cellularity of tumors restricts the water diffusion and decreases the value of ADC within tumors, which makes them appear hypointense on ADC maps. The role of this sequence now largely exceeds its first clinical apparitions in neuroimaging, whereby the method helped diagnose the early phases of cerebral ischemic stroke. The applications extend to whole-body imaging for both neoplastic and non-neoplastic diseases. This review emphasizes the integration of DWI in the genitourinary system imaging by outlining the sequence's usage in female pelvis, prostate, bladder, penis, testis and kidney MRI. In gynecologic imaging, DWI is an essential sequence for the characterization of cervix tumors and endometrial carcinomas, as well as to differentiate between leiomyosarcoma and benign leiomyoma of the uterus. In ovarian epithelial neoplasms, DWI provides key information for the characterization of solid components in heterogeneous complex ovarian masses. In prostate imaging, DWI became an essential part of multi-parametric Magnetic Resonance Imaging (mpMRI) to detect prostate cancer. The Prostate Imaging-Reporting and Data System (PI-RADS) scoring the probability of significant prostate tumors has significantly contributed to this success. Its contribution has established mpMRI as a mandatory examination for the planning of prostate biopsies and radical prostatectomy. Following a similar approach, DWI was included in multiparametric protocols for the bladder and the testis. In renal imaging, DWI is not able to robustly differentiate between malignant and benign renal tumors but may be helpful to characterize tumor subtypes, including clear-cell and non-clear-cell renal carcinomas or low-fat angiomyolipomas. One of the most promising developments of renal DWI is the estimation of renal fibrosis in chronic kidney disease (CKD) patients. In conclusion, DWI constitutes a major advancement in genitourinary imaging with a central role in decision algorithms in the female pelvis and prostate cancer, now allowing promising applications in renal imaging or in the bladder and testicular mpMRI.

Dataset Information

Diffusion weighted MRI by spatiotemporal encoding: analytical description and in vivo validations.

Publications

Diffusion weighted MRI by spatiotemporal encoding: analytical description and in vivo validations.

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