VERDICT Prostate Parameter Estimation with AMICO

Bonet‐Carne, Elisenda; Daducci, Alessandro; Johnston, Edward; Jacobs, Joseph G.; Freeman, Alex; Atkinson, David; Hawkes, David J.; Punwani, Shonit; Alexander, Daniel C.; Panagiotaki, Eleftheria

doi:10.1007/978-3-319-73839-0_18

Cited by 2 publications

(7 citation statements)

References 12 publications

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“…Here, we use both fitting methods (original non‐linear and VERDICT‐AMICO) to estimate the extra parameter ( d EES ) in two datasets, and we examine the run‐time and goodness of fit. The VERDICT‐AMICO dictionary with unfixed d EES is N r = 13 different radii (linearly spaced from 0.01 μm to 15.1 μm) with fixed d IC = 2 × 10 −9 m 2 /s. N e = 5 diffusion coefficients for EES: d EES = 1.1 × 10 −9 , 1.6 × 10 −9 , 2.1 × 10 −9 , 2.6 × 10 −9 and 3.1 × 10 −9 m 2 /s. N v = 1, P = 8 × 10 −9 m 2 /s. Details about the rationale and the discretization of d EES can be found in Reference .…”

Section: Methodsmentioning

confidence: 99%

“…The dictionary for VERDICT‐AMICO

boldΦ = ({}, ϕ_{italicij}) \in ℝ_{+}^{N_{d} \times N_{k}}

is partitioned into three sub‐matrices, corresponding to the VERDICT compartments:

boldΦ = ([]||, {boldΦ}^{normalr}, {boldΦ}^{normale}, {boldΦ}^{normalv})

where

Φ^{r} \in ℝ^{N_{d} \times N_{r}}

Φ^{e} \in ℝ^{N_{d} \times N_{e}}

and

Φ^{v} \in ℝ^{N_{d} \times N_{v}}

each model the IC, EES and vascular contributions of the diffusion signal in the voxel with N k = N r + N e + N v . The regularization function used is the basic Tikhonov regularization with the same λ value ( λ = 0.001) as used in Reference .…”

Section: Theorymentioning

confidence: 99%

“…We hypothesize that VERDICT‐AMICO can estimate a diffusivity parameter without compromising the overall parameter estimation. We previously tested different AMICO dictionaries and observed that different d IC values have a small impact on the other parameter estimates with fixed perfusion . Thus, we unfixed d EES and kept d IC and P fixed with their original values, d IC = 2 × 10 −9 m 2 /s and P = 8 × 10 −9 m 2 /s …”

Section: Theorymentioning

confidence: 99%

“…The AMICO framework can be adapted to linearize the estimation of the VERDICT parameters (VERDICT-AMICO), allowing a fast-fitting approach. 30 Here, we present VERDICT-AMICO and demonstrate its benefits over the original VERDICT implementation. We utilize AMICO to fit the VERDICT model for prostate tissue 13 to demonstrate reduction in computation time and robust parameter estimation.…”

Section: Introductionmentioning

confidence: 96%

“…The AMICO framework can be adapted to linearize the estimation of the VERDICT parameters (VERDICT‐AMICO), allowing a fast‐fitting approach . Here, we present VERDICT‐AMICO and demonstrate its benefits over the original VERDICT implementation.…”

Section: Introductionmentioning

confidence: 99%

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VERDICT‐AMICO: Ultrafast fitting algorithm for non‐invasive prostate microstructure characterization

Bonet‐Carne¹,

Johnston²,

Daducci

et al. 2018

NMR in Biomedicine

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VERDICT (vascular, extracellular and restricted diffusion for cytometry in tumours) estimates and maps microstructural features of cancerous tissue non‐invasively using diffusion MRI. The main purpose of this study is to address the high computational time of microstructural model fitting for prostate diagnosis, while retaining utility in terms of tumour conspicuity and repeatability. In this work, we adapt the accelerated microstructure imaging via convex optimization (AMICO) framework to linearize the estimation of VERDICT parameters for the prostate gland. We compare the original non‐linear fitting of VERDICT with the linear fitting, quantifying accuracy with synthetic data, and computational time and reliability (performance and precision) in eight patients. We also assess the repeatability (scan‐rescan) of the parameters. Comparison of the original VERDICT fitting versus VERDICT‐AMICO showed that the linearized fitting (1) is more accurate in simulation for a signal‐to‐noise ratio of 20 dB; (2) reduces the processing time by three orders of magnitude, from 6.55 seconds/voxel to 1.78 milliseconds/voxel; (3) estimates parameters more precisely; (4) produces similar parametric maps and (5) produces similar estimated parameters with a high Pearson correlation between implementations, r 2 > 0.7. The VERDICT‐AMICO estimates also show high levels of repeatability. Finally, we demonstrate that VERDICT‐AMICO can estimate an extra diffusivity parameter without losing tumour conspicuity and retains the fitting advantages. VERDICT‐AMICO provides microstructural maps for prostate cancer characterization in seconds.

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Section: Methodsmentioning

confidence: 99%

“…The dictionary for VERDICT‐AMICO

boldΦ = ({}, ϕ_{italicij}) \in ℝ_{+}^{N_{d} \times N_{k}}

is partitioned into three sub‐matrices, corresponding to the VERDICT compartments:

boldΦ = ([]||, {boldΦ}^{normalr}, {boldΦ}^{normale}, {boldΦ}^{normalv})

where

Φ^{r} \in ℝ^{N_{d} \times N_{r}}

Φ^{e} \in ℝ^{N_{d} \times N_{e}}

and

Φ^{v} \in ℝ^{N_{d} \times N_{v}}

Section: Theorymentioning

confidence: 99%

Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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VERDICT‐AMICO: Ultrafast fitting algorithm for non‐invasive prostate microstructure characterization

Bonet‐Carne¹,

Johnston²,

Daducci

et al. 2018

NMR in Biomedicine

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Diffusion MRI analysis

Harms¹

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VERDICT Prostate Parameter Estimation with AMICO

Cited by 2 publications

References 12 publications

VERDICT‐AMICO: Ultrafast fitting algorithm for non‐invasive prostate microstructure characterization

VERDICT‐AMICO: Ultrafast fitting algorithm for non‐invasive prostate microstructure characterization

Diffusion MRI analysis

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