Diffusion-weighted MR imaging of non-complicated hepatic cysts: Value of 3T computed diffusion-weighted imaging

Nakamura, Yuko; Higaki, Toru; Akiyama, Yuji; Fukumoto, Wataru; Kono, Kenji; Kaichi, Yoko; Honda, Yukiko; Komoto, Daisuke; Tatsugami, Fuminari; Iida, Michihisa; T, Ohmoto; Date, Shuji; Awai, Kazuo

doi:10.1016/j.ejro.2016.07.001

Cited by 7 publications

(13 citation statements)

References 29 publications

(43 reference statements)

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“…would be observed at other arbitrary b values. This is the fundamental theory behind computed DW imaging (8,9,12,13,16,20). Figure 3 shows a flow diagram illustrating the computed DW imaging generation process.…”

Section: Teaching Pointsmentioning

confidence: 99%

“…The optimal b value is dependent on the organ being imaged (8,9). In many cases, a value that is slightly higher than that used during the routine examination is selected (11,13,16,20).…”

Section: Target Bmentioning

confidence: 99%

“…As described previously, signal intensity decreases as the TE increases, causing the SNR on DW images to drop. In addition, extending TE elicits T2 shine-through effects, potentially causing false-positive high-intensity lesions to be diagnosed (16). TE elongation is associated with the long duration of the MPG pulse required to obtain a high b-value image.…”

Section: Clinical Advantages Of Computed Dw Imagingmentioning

confidence: 99%

“…By generating a computed DW image with a b value of 1500 sec/mm 2 , the lesions can be correctly evaluated without referring to the ADC map. Nakamura et al (16) reported the usefulness of computed DW imaging for imaging noncomplicated hepatic cysts.…”

Section: Eliminating T2 Shine-through Effectmentioning

confidence: 99%

“…Furthermore, the superimposed T2-weighted contrast material contaminates the "pure" diffusion signal because the T2 relaxation progresses as the TE is extended. This phenomenon is called the T2 shine-through effect, and it requires special attention because it may produce false-positive highintensity areas (ie, a high diffusion signal that is unrelated to diffusion restriction) (1,12,16,17). While a short TE is ideal, the duration of the MPG pulse required to obtain a high b value causes it to be prolonged.…”

Section: Introductionmentioning

confidence: 99%

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Introduction to the Technical Aspects of Computed Diffusion-weighted Imaging for Radiologists

et al. 2018

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Diffusion-weighted (DW) imaging is a magnetic resonance (MR) imaging method. It is an indispensable sequence for the diagnosis of acute cerebral infarction and is recognized as a standard tool in oncologic imaging. Computed DW imaging refers to the synthesizing of arbitrary b-value DW images from a set of measured b-value images by voxelwise fitting. Computed DW imaging is advantageous because it generates DW images with a higher diffusion effect than that achievable by using the MR imaging units in use today. Additionally, computed DW imaging can reduce imaging time while producing images characterized by a higher signal-to-noise ratio than what the acquired DW images would display at the corresponding b values. By fitting input images acquired at a lower b value and correspondingly a shorter echo time, the signal intensity of the resulting computed DW image is closer to the ideal case. Computed DW images are generated by employing mathematical models that use mono-, bi-, or triexponential equations. To generate accurate computed DW images, the appropriate model must be selected, and the image parameters for the input data must be chosen accordingly. In addition, to reduce artifacts on computed DW images, the misalignment of input data must be corrected with the aid of image registration techniques. RSNA, 2018.

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Section: Teaching Pointsmentioning

confidence: 99%

“…The optimal b value is dependent on the organ being imaged (8,9). In many cases, a value that is slightly higher than that used during the routine examination is selected (11,13,16,20).…”

Section: Target Bmentioning

confidence: 99%

Section: Clinical Advantages Of Computed Dw Imagingmentioning

confidence: 99%

Section: Eliminating T2 Shine-through Effectmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Introduction to the Technical Aspects of Computed Diffusion-weighted Imaging for Radiologists

et al. 2018

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Repeatability and reproducibility of apparent exchange rate measurements in yeast cell phantoms using filter-exchange imaging

Schillmaier

Kaika

Topping

et al. 2023

Magn Reson Mater Phy

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Objectives Development of a protocol for validation and quality assurance of filter-exchange imaging (FEXI) pulse sequences with well-defined and reproducible phantoms. Materials and methods A FEXI pulse sequence was implemented on a 7 T preclinical MRI scanner. Six experiments in three different test categories were established for sequence validation, demonstration of the reproducibility of phantoms and the measurement of induced changes in the apparent exchange rate (AXR). First, an ice–water phantom was used to investigate the consistency of apparent diffusion coefficient (ADC) measurements with different diffusion filters. Second, yeast cell phantoms were utilized to validate the determination of the AXR in terms of repeatability (same phantom and session), reproducibility (separate but comparable phantoms in different sessions) and directionality of diffusion encodings. Third, the yeast cell phantoms were, furthermore, used to assess potential AXR bias because of altered cell density and temperature. In addition, a treatment experiment with aquaporin inhibitors was performed to evaluate the influence of these compounds on the cell membrane permeability in yeast cells. Results FEXI-based ADC measurements of an ice–water phantom were performed for three different filter strengths, showed good agreement with the literature value of 1.099 × 10–3 mm2/s and had a maximum coefficient of variation (CV) of 0.55% within the individual filter strengths. AXR estimation in a single yeast cell phantom and imaging session with five repetitions resulted in an overall mean value of (1.49 ± 0.05) s−1 and a CV of 3.4% between the chosen regions of interest. For three separately prepared phantoms, AXR measurements resulted in a mean value of (1.50 ± 0.04) s−1 and a CV of 2.7% across the three phantoms, demonstrating high reproducibility. Across three orthogonal diffusion directions, a mean value of (1.57 ± 0.03) s−1 with a CV of 1.9% was detected, consistent with isotropy of AXR in yeast cells. Temperature and AXR were linearly correlated (R2 = 0.99) and an activation energy EA of 37.7 kJ/mol was determined by Arrhenius plot. Furthermore, a negative correlation was found between cell density (as determined by the reference ADC/fe) and AXR (R2 = 0.95). The treatment experiment resulted in significantly decreased AXR values at different temperatures in the treated sample compared to the untreated control indicating an inhibiting effect. Conclusions Using ice–water and yeast cell-based phantoms, a protocol for the validation of FEXI pulse sequences was established for the assessment of stability, repeatability, reproducibility and directionality. In addition, a strong dependence of AXR on cell density and temperature was shown. As AXR is an emerging novel imaging biomarker, the suggested protocol will be useful for quality assurance of AXR measurements within a study and potentially across multiple sites.

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Diffusion-weighted MRI and FLAIR sequence for differentiation of hydatid cysts and simple cysts in the liver

Yalçınöz

İkizceli

Kahveci

et al. 2021

European Journal of Radiology Open

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Diffusion-weighted MR imaging of non-complicated hepatic cysts: Value of 3T computed diffusion-weighted imaging

Cited by 7 publications

References 29 publications

Introduction to the Technical Aspects of Computed Diffusion-weighted Imaging for Radiologists

Introduction to the Technical Aspects of Computed Diffusion-weighted Imaging for Radiologists

Repeatability and reproducibility of apparent exchange rate measurements in yeast cell phantoms using filter-exchange imaging

Diffusion-weighted MRI and FLAIR sequence for differentiation of hydatid cysts and simple cysts in the liver

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