3T multiparametric MRI of the prostate: Does intravoxel incoherent motion diffusion imaging have a role in the detection and stratification of prostate cancer in the peripheral zone?

Valerio, Mariacristina; Zini, Chiara; Fierro, Davide; Giura, Francesca; Colarieti, Anna; Giuliani, Alessandro; Laghi, Andréa; Catalano, Carlo; Panebianco, Valeria

doi:10.1016/j.ejrad.2016.01.006

Cited by 61 publications

(65 citation statements)

References 22 publications

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“…DWI is currently considered an important component of prostate mpMRI examinations, where it has been established as an important sequence for the detection of PCa [14]. However, as a possible improvement of DWI, it might be necessary to evaluate separately the two components of diffusion: the pure molecular diffusion and the perfusion-related diffusion (pseudo-diffusion) originating from capillary microcirculation.…”

Section: Discussionmentioning

confidence: 99%

“…However, because the monoexponential ADC calculated from DWI potentially mixes the diffusion of molecular and the perfusion of microcirculation blood in the capillaries, the Intravoxel Incoherent Motion (IVIM) model may better account for the pseudo-diffusion based on microvascular perfusion from tissue diffusion [11]. In recent years, several studies examined prostate IVIM in the comparison of cancerous regions and normal tissue [12], [13], [14] without partition of the prostate, but the comparison of DWI and IVIM for tumor in TZ has not been reported at length. In addition, the traditional manually selected regions of interest (ROIs) has been pointed out as a limitation in many studies in which the overlap of a single measurement, which may lead to interobserver variability in ROI placement [15], [16], [17].…”

Section: Introductionmentioning

confidence: 99%

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Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI

Bao

Wang

et al. 2017

European Journal of Radiology Open

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ObjectiveTo differentiate prostate cancer lesions in transition zone by diffusion-weighted-MRI (DW-MRI).MethodsData from a total of 63 patients who underwent preoperative DWI (b of 0–1000 s/mm2) were prospectively collected and processed by a monoexponential (DWI) model and compared with a biexponential (IVIM) model for quantitation of apparent diffusion coefficients (ADCs), perfusion fraction f, diffusivity D and pseudo-diffusivity D*. Histogram analyses were performed by outlining entire-tumor regions of interest (ROIs). These parameters (separately and combined in a logistic regression model) were used to differentiate lesions depending on histopathological analysis of Magnetic Resonance/transrectal Ultrasound (MR/TRUS) fusion-guided biopsy. The diagnostic ability of differentiate the PCa from BHP in TZ was analyzed by ROC regression. Histogram analysis of quantitative parameters and Gleason score were assessed with Spearman correlation.ResultsThirty (30 foci) cases of PCa in PZ and 33 (36 foci) cases of BPH were confirmed by pathology. Mean ADC, median ADC, 10th percentile ADC, 90th percentile ADC, kurtosis and skewness of ADC and mean D values, median D and 90th percentile D differed significantly between PCa and BHP in TZ. The highest classification accuracy was achieved by the mean ADC (0.841) and mean D (0.809). A logistic regression model based on mean ADC and mean D led to an AUC of 0.873, however, the difference is not significant. There were 7 Gleason 6 areas, 9 Gleason 7 areas, 8 Gleason 8 areas, 5 Gleason 9 areas and 2 Gleason 10 areas detected from the 31 prostate cancer areas, the mean Gleason value was(7.5 ± 1.2). The mean ADC and mean D had correlation with Gleason score(r = −0.522 and r = −0.407 respectively, P < 0.05).ConclusionThe diagnosis efficiency of IVIM parameters was not superior to ADC in the diagnosis of PCa in TZ. Moreover, the combination of mean ADC and mean D did not perform better than the parameters alone significantly; It is feasible to stratify the pathological grade of prostate cancer by mean ADC.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI

Bao

Wang

et al. 2017

European Journal of Radiology Open

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“…Perfusion has been proven an important surrogate marker of many physiologic or pathologic processes as well as a predictor of recovery with reperfusion in patients of acute ischemic stroke [12]. Also, perfusion measurement has demonstrated improved sensitivity and predictive value for tumor grading and prognosis [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Can the low and high b-value distribution influence the pseudodiffusion parameter derived from IVIM DWI in normal brain?

Yan

Han

et al. 2020

BMC Med Imaging

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Background: Our study aims to reveal whether the low b-values distribution, high b-values upper limit, and the number of excitation (NEX) influence the accuracy of the intravoxel incoherent motion (IVIM) parameter derived from multi-b-value diffusion-weighted imaging (DWI) in the brain. Methods: This prospective study was approved by the local Ethics Committee and informed consent was obtained from each participant. The five consecutive multi-b DWI with different b-value protocols (0-3500 s/mm 2 ) were performed in 22 male healthy volunteers on a 3.0-T MRI system. The IVIM parameters from normal white matter (WM) and gray matter (GM) including slow diffusion coefficient (D), fast perfusion coefficient (D*) and perfusion fraction (f) were compared for differences among defined groups with different IVIM protocols by one-way ANOVA. Results: The D* and f value of WM or GM in groups with less low b-values distribution (less than or equal to 5 b-values) were significantly lower than ones in any other group with more low b-values distribution (all P < 0.05), but no significant differences among groups with more low b-values distribution (P > 0.05). In addition, no significant differences in the D, D* and f value of WM or GM were found between group with one and more NEX of low b-values distribution (all P > 0.05). IVIM parameters in normal WM and GM strongly depended on the choice of the high b-value upper limit. Conclusions: Metrics of IVIM parameters can be affected by low and high b value distribution. Eight low b-values distribution with high b-value upper limit of 800-1000 s/mm 2 may be the relatively proper set when performing brain IVIM studies.

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“…IVIM was first investigated in the brain, but has recently seen growing interest in more highly perfused organs such as the liver, 3,4 pancreas, 5 kidney, 6 prostate, 7 and the heart. [8][9][10] The liver has a unique vasculature system due to 2 main blood supplies, namely, the hepatic artery and the portal vein.…”

Section: Introductionmentioning

confidence: 99%

Effect of flow‐encoding strength on intravoxel incoherent motion in the liver

Moulin

Aliotta

Ennis

2018

Magnetic Resonance in Med

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Purpose To study the impact of variable flow‐encoding strength on intravoxel incoherent motion (IVIM) liver imaging of diffusion and perfusion. Theory Signal attenuation in DWI arises from (1) intravoxel microvascular blood flow, which depends on the flow‐encoding strength α (first gradient moment) of the diffusion‐encoding waveform, and (2) intravoxel spin diffusion, which depends on the b‐value of the diffusion‐encoding gradient waveforms α and b‐value. Both are linked to the diffusion‐encoding gradient waveform and conventionally are not independently controlled. Methods In this work a convex optimization framework was used to generate gradient waveforms with independent α and b‐value. Thirty‐six unique α and b‐value sample points from 5 different gradient waveforms were used to reconstruct perfusion fraction (f), coefficient of diffusion (D), and blood velocity standard deviation (Vb) maps using a recently proposed IVIM model. Faster acquisition strategies were evaluated with 1000 random subsampling strategies of 16, 8, and 4 α and b‐value. Among the subsampled reconstructions, the sampling schemes that minimized the difference with the fully sampled reconstruction were reported. Results Healthy volunteers (N = 9) were imaged on a 3T scanner. Liver perfusion and diffusion estimates using the fully sampled IVIM method were f = 0.19 ± 0.06, D = 1.15 ± 0.15 × 10–3 mm2/s, and Vb = 5.22 ± 3.86 mm/s. No statistical differences were found between the fully sampled and 2‐times undersampled reconstruction (f = 0.2 ± 0.07, D = 1.19 ± 0.15 × 10–3 mm2/s, Vb = 5.79 ± 3.43 mm/s); 4‐times undersampled (f = 0.2 ± 0.06, D = 1.15 ± 0.17 × 10–3 mm2/s, Vb = 4.66 ± 3.61 mm/s), or 8‐times undersampled ( f = 0.2 ± 0.06, D = 1.23 ± 0.22 × 10–3 mm2/s, Vb = 4.99 ± 3.82 mm/s) approaches. Conclusion We demonstrate the IVIM signal’s dependence on the b‐value, the diffusion‐encoding time and the flow‐encoding strength and observe in vivo the ballistic regime signature of microperfusion in the liver. This work also demonstrates that using an IVIM model and sampling scheme matched to the ballistic regime, pixel‐wise IVIM parameter maps are possible when sampling as few as 4 IVIM signals.

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3T multiparametric MRI of the prostate: Does intravoxel incoherent motion diffusion imaging have a role in the detection and stratification of prostate cancer in the peripheral zone?

Cited by 61 publications

References 22 publications

Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI

Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI

Can the low and high b-value distribution influence the pseudodiffusion parameter derived from IVIM DWI in normal brain?

Effect of flow‐encoding strength on intravoxel incoherent motion in the liver

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