Optimization of b-value distribution for biexponential diffusion-weighted MR imaging of normal prostate

Jambor, Ivan; Merisaari, Harri; Aronen, Hannu J.; Järvinen, Jukka; Saunavaara, Jani; Kauko, Tommi; Borra, Ronald; Pesola, M.

doi:10.1002/jmri.24271

Cited by 38 publications

(61 citation statements)

References 26 publications

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“…Therefore, there are a number of studies investigating the optimum b-value for different DWI applications (29)(30)(31). However, the reported optimum b-values in the literature are quite diverse even for the same anatomical regions and the differences in the range of reported optimum b-values are expected due to the dependencies shown in this study.…”

Section: Discussionmentioning

confidence: 78%

Effect of imaging parameters on the accuracy of apparent diffusion coefficient and optimization strategies

Çelik¹

2015

Diagn Interv Radiol

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D iffusion-weighted imaging (DWI) measures the degree of water mobility, i.e., random Brownian motion, in vivo and is a noninvasive tool (1-3). DWI has been used mainly in cranial magnetic resonance imaging (MRI) applications to visualize stroke, neoplasms, intracranial infections, traumatic brain injury, and demyelinating processes since early 1990s (4-8). However, in recent years, DWI applications has been extended to breast, musculoskeletal, liver, prostate, pelvis, and general whole body imaging with the development of multichannel coils, parallel imaging, faster gradients, and MRI hardware (9)(10)(11)(12)(13)(14). DWI can provide a quantitative map of water diffusion coefficient. Water diffusion coefficient can be calculated from diffusion-weighted images using at least two different DWI values. DWI is achieved by applying diffusion gradients and is called the b-value. Water diffusion coefficient in the tissue is called apparent diffusion coefficient (ADC) and can be calculated from diffusion-weighted images using a linear regression analysis. The term "apparent" is used for diffusion coefficient to differentiate from true diffusion coefficient since the measured water diffusion coefficient in the tissue is influenced by a number of other factors such as capillary network orientation and gross motion in addition to random Brownian motion. ADC measurements are considered to be of greater importance in differential diagnosis of various pathological conditions and its accurate measurement is of great importance (12,13,(15)(16)(17)(18).In the past, the magnetic resonance gradients were much slower and repetition time (TR) and echo-time (TE) were quite long. Thanks to the fast pace of advancement in MRI, the imaging parameters were shortened significantly. Therefore, TR and TE could be reduced in such a way that they could be comparable to tissue relaxation times (T1 and T2) in order to reduce susceptibility artifacts and the total scan time for various DWI applications. As a 101 GE Healthcare ( Azim.celik@ge.com), Antalya, Turkey. PURPOSEWe aimed to investigate the effect of key imaging parameters on the accuracy of apparent diffusion coefficient (ADC) maps using a phantom model combined with ADC calculation simulation and propose strategies to improve the accuracy of ADC quantification. METHODSDiffusion-weighted imaging (DWI) sequences were acquired on a phantom model using single-shot echo-planar imaging DWI at 1.5 T scanner by varying key imaging parameters including number of averages (NEX), repetition time (TR), echo time (TE), and diffusion preparation pulses. DWI signal simulations were performed for varying TR and TE. RESULTSMagnetic resonance diffusion signal and ADC maps were dependent on TR and TE imaging parameters as well as number of diffusion preparation pulses, but not on the NEX. However, the choice of a long TR and short TE could be used to minimize their effects on the resulting DWI sequences and ADC maps. CONCLUSIONThis study shows that TR and TE imaging parameters affect the diffusion ima...

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Section: Discussionmentioning

confidence: 78%

Effect of imaging parameters on the accuracy of apparent diffusion coefficient and optimization strategies

Çelik¹

2015

Diagn Interv Radiol

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“…Although this study was also performed in the liver, the tissue properties (perfusion and diffusion) in tumors are different compared with that in (malignant) liver lesions. Jambor et al 13 Zhang et al 12 optimized the b-value sampling for a biexponential DWI model of the kidney, and Lemke et al 15 optimized the b-value distributions in tissue for low (brain), medium (kidney), and high (liver) IVIM perfusion. The studies by Jambor et al, Zhang et al, and Lemke et al were, however, validated in healthy volunteers.…”

Section: Discussionmentioning

confidence: 99%

“…These findings are similar to previous work by Lemke et al and Jambor et al in healthy liver, kidney, and spleen. 13,15 Dyvorne et al 14 on the other hand recommended a minimum of 4 b-values for their liver fibrosis study. Their D* reproducibility was, however, significantly worse compared with that of other studies.…”

Section: Discussionmentioning

confidence: 99%

“…8,9 Previously, other studies have investigated potentially optimal b-value distributions. [10][11][12][13][14] Lemke et al, 15 for example, performed Monte Carlo simulations to find the optimal b-value distributions in tissue with low (brain), medium (kidney), and high (liver) IVIM perfusion. However, in clinical cases, the chosen optimal b-value distributions may not perform as one would expect due to, for example, different IVIM parameter ranges in healthy and inhomogeneous tumor tissue.…”

Section: Intravoxel Incoherent Motion Protocol Evaluation and Data Qumentioning

confidence: 99%

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Intravoxel Incoherent Motion Protocol Evaluation and Data Quality in Normal and Malignant Liver Tissue and Comparison to the Literature

Voert¹,

Delso²,

Porto

et al. 2016

Investigative Radiology

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OBJECTIVES: Although intravoxel incoherent motion (IVIM) becomes more and more popular, there is currently no clear consensus on the number and distribution of b-values to use. In this work, we (1) tested and evaluated the data quality of a 25-b-value IVIM protocol in patients with malignant liver lesions and normal liver tissue as a standard of reference, (2) calculated an optimal b-value distribution and compared with the standard of reference, and (3) compared the 25-b-value protocol with other proposed protocols in the literature. MATERIALS AND METHODS: Intravoxel incoherent motion imaging with 25 b-values was performed at 3 T in a total of 15 patients with malignant liver lesions. Reference IVIM parameter maps were calculated in tumor and normal liver tissue. With these parameters, optimal IVIM protocols with reduced numbers of b-values were calculated. These optimal IVIM protocols were again applied to calculate new IVIM parameter maps that were compared with the reference parameter maps by calculating mean relative errors. In addition, 35 other IVIM protocols, as found in literature, were compared in a similar way with the 25-b-value protocol serving as a standard of reference. RESULTS: The mean relative error depends on the number of b-values and their distribution. In tumor tissue, the error is higher and more variable than in normal-appearing liver tissue. The largest errors occur in tumor tissue and in the protocols having low numbers of b-values in the IVIM protocols. In the calculated optimal IVIM protocols, the mean relative errors decreased by 40% or more when the number of b-values included increased from 4 to 16. The mean relative errors in the protocols adapted from the literature vary substantially between the various b-value distributions. One optimized 16-b-value protocol, which was found in literature, reduced the average relative error by 80% when compared with 4-and 5-b-value protocols listed in literature. CONCLUSIONS: Including more b-values and applying an optimized b-value distribution significantly reduces errors in the IVIM parameter estimates, thereby increasing its accuracy.This effect is even more pronounced in inhomogeneous tumor compared with that in normal liver tissue. However, when restrictions in acquisition time or patient-related factors apply, a minimum of 16 b-values should be considered for reliable results.

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“…Significant inter-histology ADC differences are represented by an asterisk ( * ) (p < 0.025) and between b-value sets by a hat symbol (^) (p < 0.025). [26,27]. We measured a linear character to our data and a tendency for equivalent ADC between b-value sets (Tables 1 and 2, Supplemental content), similar to Hoogendam et al, [28], which may have reflected ROI placement remote from the tumor periphery.…”

Section: Discussionmentioning

confidence: 96%

Variation in apparent diffusion coefficient measurements among women with locally advanced cervical cancer

Gladwish

Han

Foltz

2015

Radiotherapy and Oncology

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Optimization of b-value distribution for biexponential diffusion-weighted MR imaging of normal prostate

Abstract: The optimal b-value distribution was found to be a clustered distribution with b-values concentrated in the low, mid, and high ranges and was shown to improve the estimation quality of biexponential DWI parameters of in vivo experiments.

Cited by 38 publications

References 26 publications

Effect of imaging parameters on the accuracy of apparent diffusion coefficient and optimization strategies

Effect of imaging parameters on the accuracy of apparent diffusion coefficient and optimization strategies

Intravoxel Incoherent Motion Protocol Evaluation and Data Quality in Normal and Malignant Liver Tissue and Comparison to the Literature

Variation in apparent diffusion coefficient measurements among women with locally advanced cervical cancer

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