Diffusion‐weighted signal models in healthy and cancerous peripheral prostate tissues: Comparison of outcomes obtained at different b‐values

Mazzoni, Lorenzo Nicola; Lucarini, Silvia; Chiti, Stefano; Busoni, S.; Gori, C.; Menchi, Ilario

doi:10.1002/jmri.24184

Cited by 33 publications

(42 citation statements)

References 28 publications

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“…Although there has been a shift towards more quantitative analyses in the prostate imaging community, with an emphasis on ADC values [25] and pharmacokinetic parameters, quantitative T 2 analyses has remained largely within the realm of research [2]. It is perhaps somewhat surprising that quantitative T 2 analysis is not more prevalent considering that conventional T 2 -weighted imaging is very much in the front-line for visualization of prostate anatomy and identification of suspected regions of cancer.…”

Section: Discussionmentioning

confidence: 99%

Prostate cancer discrimination in the peripheral zone with a reduced field-of-view T2-mapping MRI sequence

Yamauchi

Penzkofer

Fedorov

et al. 2015

Magnetic Resonance Imaging

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Objectives To evaluate the performance of T2 mapping in discriminating prostate cancer from normal prostate tissue in the peripheral zone using a practical reduced field-of-view MRI sequence requiring less than 3 minutes of scan time. Materials and Methods Thirty-six patients with biopsy-proven peripheral zone prostate cancer without prior treatment underwent routine multiparametric MRI at 3.0T with an endorectal coil. An Inner-Volume Carr-Purcell-Meiboom-Gill imaging sequence that required 2.8 minutes to obtain data for quantitative T2 mapping covering the entire prostate gland was added to the routine multiparametric protocol. Suspected cancer (SC) and suspected healthy (SH) tissue in the peripheral zone were identified in consensus by three radiologists and were correlated with available biopsy results. Differences in mean T2 values in SC and SH ROIs were tested for significance using unpaired Student’s two-tailed t test. The area under the receiver operating characteristic curve was used to assess the optimal threshold T2 value for cancer discrimination. Results ROI analyses revealed significantly (p<0.0001) shorter T2 values in SC (85.4 ± 12.3 ms) compared to SH (169.6 ± 38.7 ms). An estimated T2 threshold of 99 ms yielded a sensitivity of 92% and a specificity of 97% for prostate cancer discrimination. Conclusions Quantitative values derived from this clinically practical T2-mapping sequence allow high precision discrimination between healthy and cancerous peripheral zone in the prostate.

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Section: Discussionmentioning

confidence: 99%

Prostate cancer discrimination in the peripheral zone with a reduced field-of-view T2-mapping MRI sequence

Yamauchi

Penzkofer

Fedorov

et al. 2015

Magnetic Resonance Imaging

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“…It should be noted that in most previous body DKI studies, K and D have been estimated by fitting the DKI model to TW images, without assessing the appropriateness and reliability of this empirical approach. The use of TW images, which has no impact on data acquisition time and quality in terms of artifacts reduction, is suggested in order to improve the signal‐to‐noise ratio .…”

Section: Discussionmentioning

confidence: 99%

“…Diffusional kurtosis imaging (DKI) is a non‐Gaussian technique with the potential to improve the diagnostic capability of diffusion‐MRI . DKI has been shown to be useful in the study of various disease‐related alterations of the brain, and to have oncological applications in several extra‐cranial regions, such as the head and neck (HN), prostate, breast, and rectum . DKI is based on a mathematical model of diffusion that does not require biophysical priors.…”

Section: Introductionmentioning

confidence: 99%

“…9 DKI has been shown to be useful in the study of various disease-related alterations of the brain, 10 and to have oncological applications in several extra-cranial regions, such as the head and neck (HN), prostate, breast, and rectum. [11][12][13][14][15][16][17][18][19][20] DKI is based on a mathematical model of diffusion that does not require biophysical priors. In particular, DKI extends conventional Gaussian diffusion-MRI techniques by estimating the non-Gaussian component of water diffusion.…”

Section: Introductionmentioning

confidence: 99%

“…23,24 However, it should be noted that there is a growing tendency to perform only a single fit of the DKI model to the geometric means of acquired diffusion-weighted images along (x, y, z), referred to as trace-weighted (TW) images. [11][12][13][14]17,[25][26][27][28] A previous simulation study 23 has shown that this empirical and straightforward approach (with no advantage in terms of acquisition time) can potentially affect the estimation of K. In particular, for a true K of approximately 1 and a low diffusion anisotropy degree, typically observed in various body regions, the absolute value of the percentage error in the estimated K, introduced by fitting the DKI model to TW images, can exceed 40%.…”

Section: Introductionmentioning

confidence: 99%

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Diffusional kurtosis imaging in head and neck cancer: On the use of trace‐weighted images to estimate indices of non‐Gaussian water diffusion

et al. 2018

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Purpose While previous studies have demonstrated the feasibility and potential usefulness of quantitative non‐Gaussian diffusional kurtosis imaging (DKI) of the brain, more recent research has focused on oncological application of DKI in various body regions such as prostate, breast, and head and neck (HN). Given the need to minimize scan time during most routine magnetic resonance imaging (MRI) acquisitions of body regions, diffusion‐weighted imaging (DWI) with only three orthogonal diffusion weighting directions (x, y, z) is usually performed. Moreover, as water diffusion within malignant tumors is generically thought to be almost isotropic, DWI with only three diffusion weighting directions is considered sufficient for oncological application and it represents the de facto standard in body DKI. In this context, since the kurtosis tensor and diffusion tensor cannot be obtained, the averages of the three directional (Kx, Ky, Kz) and (Dx, Dy, Dz) — namely K and D, respectively — represent the best‐possible surrogates of directionless DKI‐derived indices of kurtosis and diffusivity, respectively. This would require fitting the DKI model to the diffusion‐weighted images acquired along each direction (x, y, z) prior to averaging. However, there is a growing tendency to perform only a single fit of the DKI model to the geometric means of the images acquired with diffusion‐sensitizing gradient along (x, y, z), referred to as trace‐weighted (TW) images. To the best of our knowledge, no in vivo studies have evaluated how TW images affect estimates of DKI‐derived indices of K and D. Thus, the aim of this study was to assess the potential bias and error introduced in estimated K and D by fitting the DKI model to the TW images in HN cancer patients. Methods Eighteen patients with histologically proven malignant tumors of the HN were enrolled in the study. They underwent pretreatment 3 T MRI, including DWI (b‐values: 0, 500, 1000, 1500, 2000 s/mm2). Some patients had multiple lesions, and thus a total of 34 lesions were analyzed. DKI‐derived indices were estimated, voxel‐by‐voxel, using single diffusion‐weighted images along (x, y, z) as well as TW images. A comparison between the two estimation methods was performed by calculating the percentage error in D (Derr) and K (Kerr). Also, diffusivity anisotropy (Danis) and diffusional kurtosis anisotropy (Kanis) were estimated. Agreements between the two estimation methods were assessed by Bland–Altman plots. The Spearman rank correlation test was used to study the correlations between Kerr/Derr and Danis/Kanis. Results The median (95% confidence interval) Kerr and Derr were 5.1% (0.8%, 32.6%) and 1.7% (−2.5%, 5.3%), respectively. A significant relationship was observed between Kerr and Danis (correlation coefficient R = 0.694, P < 0.0001), as well as between Kerr and Kanis (R = 0.848, P < 0.0001). Conclusions In HN cancer, the fit of the DKI model to TW images can introduce bias and error in the estimation of K and D, which may be non‐negligible for single lesions, and should h...

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Quality assurance multicenter comparison of different MR scanners for quantitative diffusion-weighted imaging

Belli

Busoni

Ciccarone

et al. 2015

J. Magn. Reson. Imaging

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Diffusion‐weighted signal models in healthy and cancerous peripheral prostate tissues: Comparison of outcomes obtained at different b‐values

Abstract: A significant dependence on the adopted b-values of DWI parameters is shown. The best performance in distinguishing HP from PCa tissues was obtained by K, calculated using a high b-value sequence.

Cited by 33 publications

References 28 publications

Prostate cancer discrimination in the peripheral zone with a reduced field-of-view T2-mapping MRI sequence

Prostate cancer discrimination in the peripheral zone with a reduced field-of-view T2-mapping MRI sequence

Diffusional kurtosis imaging in head and neck cancer: On the use of trace‐weighted images to estimate indices of non‐Gaussian water diffusion

Quality assurance multicenter comparison of different MR scanners for quantitative diffusion-weighted imaging

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