Diffusion-weighted Imaging with Navigated Interleaved Echo-planar Imaging and a Conventional Gradient System

Bammer, Roland; Stollberger, Rudolf; Augustin, Michael; Simbrunner, J.; Offenbacher, H.; Kooijman, Hendrik; Ropele, Stefan; Kapeller, Peter; Wach, Paul; Ebner, Franz; Fazekas, Franz

doi:10.1148/radiology.211.3.r99jn15799

Cited by 88 publications

(74 citation statements)

References 29 publications

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“…The navigated EPI sequence we used in this study is superior to other DWI sequences because of its shorter acquisition time and, consequently, fewer artifacts. 21 EPI methods have the disadvantage of sensitivity to resonance offsets, such as tissue susceptibility differences, magnetic field inhomogeneities, and chemical shift artifacts, as well as susceptibility to motion artifacts. The application of navigator echoes and the adaptation of the acquisition parameters can overcome these disadvantages, and sufficient image quality can be achieved.…”

Section: Discussionmentioning

confidence: 99%

“…For DWI, a navigated interleaved echo-planar imaging (EPI) sequence with fat saturation (TR/TE, 2500/102 ms; 13 gradient echoes per shot; 2 signals acquired; FOV, 250 ϫ 250 mm; section thickness, 6 mm; intersection gap, 1.0 mm; and matrix, 128 ϫ 128 pixels, which was zero-filled and reconstructed to 256 ϫ 256 pixels) was performed. 21 Diffusion-weighted gradients were applied in 3 orthogonal directions. The signal-intensity-to-noise ratio (SNR) was increased by obtaining isotropic DWIs from the geometric average derived from these images.…”

Section: Mr Imagingmentioning

confidence: 99%

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Can Quantitative Diffusion-Weighted MR Imaging Differentiate Benign and Malignant Cold Thyroid Nodules? Initial Results in 25 Patients

Schueller-Weidekamm¹,

Kaserer²,

Schueller³

et al. 2008

AJNR Am J Neuroradiol

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Section: Discussionmentioning

confidence: 99%

Section: Mr Imagingmentioning

confidence: 99%

Can Quantitative Diffusion-Weighted MR Imaging Differentiate Benign and Malignant Cold Thyroid Nodules? Initial Results in 25 Patients

Schueller-Weidekamm¹,

Kaserer²,

Schueller³

et al. 2008

AJNR Am J Neuroradiol

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“…[7]) onto an arbitrary k-space trajectory given by k δ,ζ,κ . Therefore, this DFT operation can be sped up significantly by approximating it by an inverse FFT followed by an inverse gridding operation: (10) Here, GRID −1 δ,ζ denotes the inverse gridding operation of Cartesian DWI data onto the ζ th interleaf of the δ th diffusion-weighting direction. To be consistent with the notation in Ref.…”

Section: Nonlinear Conjugate Gradient Methods For Direct Tensor Estimamentioning

confidence: 99%

“…Each interleaf accrues a different (typically nonlinear) phase in the image domain. Navigator-based methods that utilize 1D navigators (5,(8)(9)(10)(11)(12)(13), 2D navigators (14)(15)(16)(17), or self-navigating trajectories (18)(19)(20) have demonstrated variable efficacy to eliminate these motion artifacts.…”

mentioning

confidence: 99%

Single‐step nonlinear diffusion tensor estimation in the presence of microscopic and macroscopic motion

Aksoy

Liu

Moseley

et al. 2008

Magnetic Resonance in Med

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Patient motion can cause serious artifacts in diffusion tensor imaging (DTI), diminishing the reliability of the estimated diffusion tensor information. Studies in this field have so far been limited mainly to the correction of miniscule physiological motion. In order to correct for gross patient motion it is not sufficient to correct for misregistration between successive shots; the change in the diffusion-encoding direction must also be accounted for. This becomes particularly important for multishot sequences, whereby-in the presence of motion-each shot is encoded with a different diffusion weighting. In this study a general mathematical framework to correct for gross patient motion present in a multishot and multicoil DTI scan is presented. A signal model is presented that includes the effect of rotational and translational motion in the patient frame of reference. This model was used to create a nonlinear least-squares formulation, from which the diffusion tensors were obtained using a nonlinear conjugate gradient algorithm. Applications to both phantom simulations and in vivo studies showed that in the case of gross motion the proposed algorithm performs superiorly compared to conventional methods used for tensor estimation. Keywordsdiffusion; DTI; motion; parallel imaging Diffusion-weighted imaging (DWI) has been known for its ability to provide a unique tissue contrast that can be used for early detection of ischemic stroke (1). Diffusion tensor imaging (DTI) is an extension of DWI whereby multiple diffusion-weighted measurements are applied to obtain a diffusion tensor for each pixel (2,3). Because of prolonged scan times and motion sensitivity of diffusion-weighting gradients, countermeasures to prevent motioninduced artifacts in DTI are important for the success of a study.One way to eliminate the effects of motion is to "freeze" the anatomy being imaged by using snapshot imaging techniques-the most popular of which is single-shot EPI (sshEPI). However, susceptibility artifacts due to the very low bandwidth per pixel in the phaseencode direction and T 2 *-induced blurring caused by the long EPI readout substantially limit the applicability of this method. To address the limitations of sshEPI, the use of parallel imaging has been suggested (4). Nevertheless, the maximum speedup in the phase encode direction that can be achieved by parallel imaging is limited and, thus, the degree of distortion reduction. Another method employed frequently is to use interleaved k-space While the phase-navigation used in DTI is usually focused on minuscule motion (i.e., motion in a range that affects image phase but not pixel position), gross patient motion becomes a concern, especially in the case of uncooperative patients such as children and patients that suffer from a specific medical condition (e.g., stroke) that keeps them from staying stationary. Mere misregistration can be corrected for by advanced registration methods. However, motion exposes the object to a different diffusion-encoding gradient than the...

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“…As ADC standard phantom materials, gelatinous substances such as agar (8), agarose (9) and polyacrylamide (10), and liquid solution materials such as ethanol (11), acetone (12)(13)(14)(15), Gd-DTPA solution (16) and cupric sulfate solution (17) have been used previously. However, these reports have provided only a limited number of ADC values for certain concentrations of these materials at certain measurement temperatures.…”

Section: Introductionmentioning

confidence: 99%

A new phantom using polyethylene glycol as an apparent diffusion coefficient standard for MR imaging

Kuroda¹

2009

Int J Oncol

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Abstract. In recent years, magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) has seen wide clinical use, such as for early detection of cerebrovascular diseases and whole body screening for tumors. The apparent diffusion coefficient (ADC) standard phantom, which mimics the ADC values of several lesions in the body, is indispensable for the development of new pulse sequences for DWI, such as diffusion-weighted whole-body imaging with background body-signal suppression (DWIBS). However, information on the ADC values of the previously reported ADC standard phantoms is limited, because these phantoms were made using only a few different materials at a limited range of concentrations, and the ADC values were measured only at certain temperatures. It has been considered difficult, if not impossible, to create a phantom that provides arbitrary ADC values, because it is difficult to calculate the concentrations of the materials and the temperature at ADC measurement. In this study, we used polyethylene glycol (PEG) as a phantom material, and developed an empirical formula to calculate the PEG concentration at any measurement temperature to obtain arbitrary ADC values of the phantom. DWI images of phantoms made using seven different PEG concentrations were taken under heating from 17 to 46˚C at 1˚C intervals. Using ADC values calculated from these DWI images, we developed two empirical formulas: i) an empirical formula to calculate the ADC values of phantoms made using any PEG concentration at any measurement temperature; and ii) an empirical formula to calculate PEG concentrations to obtain arbitrary ADC values at any measurement temperature. We inspected the accuracy of these empirical formulas by newly made PEG phantoms. A comparison between the ADC values calculated with the empirical formulas and the measured ADC values confirmed the high accuracy of these formulas. PEG phantoms are safe, inexpensive and easy to make, compared with the previously reported ADC standard phantoms. Our empirical formulas enable us to calculate PEG concentrations that provide arbitrary ADC values at any measurement temperature. The empirical formulas could be used within a range of ADC values from 0.37x10 -3 to 3.67x10 -3 mm 2 /s, PEG concentrations from 0 to 120 mM, and measurement temperatures from 18 to 45˚C. Using these formulas, it would be possible to make standard phantoms that mimic the ADC values of any clinical lesions. The PEG phantom might thus be an excellent new ADC standard phantom for MRI with DWI. IntroductionMagnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) is useful for early diagnosis of cerebrovascular diseases and whole body screening for tumors. The apparent diffusion coefficient (ADC) map, which is calculated from DWI images and reflects the diffusion of water molecules (1), is available for diagnosis and follow-up after treatment of cerebral infarction (1) or brain tumors (2-4) and for evaluation of the degree of tumor differentiation (1,5). New pulse sequences, such as d...

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Diffusion-weighted Imaging with Navigated Interleaved Echo-planar Imaging and a Conventional Gradient System

Abstract: DW-interleaved echo-planar imaging with phase navigation and cardiac triggering is robust, reliable, and fast. With high sensitivity for detection of early ischemic infarction, it is useful for examining stroke patients by using MR systems with conventional gradient hardware.

Cited by 88 publications

References 29 publications

Can Quantitative Diffusion-Weighted MR Imaging Differentiate Benign and Malignant Cold Thyroid Nodules? Initial Results in 25 Patients

Can Quantitative Diffusion-Weighted MR Imaging Differentiate Benign and Malignant Cold Thyroid Nodules? Initial Results in 25 Patients

Single‐step nonlinear diffusion tensor estimation in the presence of microscopic and macroscopic motion

A new phantom using polyethylene glycol as an apparent diffusion coefficient standard for MR imaging

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