Clinical applications for advanced body diffusion imaging: challenges and opportunities
Dariya Malyarenko1

1Radiology, University of Michigan Health System, Ann Arbor, MI, United States

Synopsis

Notwithstanding technological advances in body DWI acquisition and analysis, their clinical applications remain relatively sparse. Quantitative (q)DWI protocols enable evaluation of sophisticated tissue diffusivity models to derive Gaussian and non-Gaussian parameters (ADC, IVIM, kurtosis) and/or texture-based features (histogram moments and gray-level) of high potential relevance to pathology. Clinically viable qDWI metrics should reflect target pathology using practical acquisition protocol and analysis observing relevant biophysical constraints. By reviewing examples of successful qDWI implementations in clinical oncology studies (for prostate, liver, breast, and whole body metastasis), this lecture will highlight venues to balance existing disparities between research and unmet clinical need.

Target Audience

Scientists and radiologists seeking implementation of advanced DWI techniques for clinical applications

Objective

Learn how to effectively tailor advanced body DWI techniques for specific clinical targets

Motivation

Recent advances in DWI acquisition and analysis techniques allow probing quantitative spatial-functional information on tissue microstructure using clinically viable scan protocols (1-4). The derived quantitative diffusion metrics hold high potential, particularly for oncology applications (e.g., radiation-free screening, contrast-free staging, biopsy-free surveillance, and therapy response). However, their current clinical implementations are limited largely due to the lack of demonstrated repeatability and robust relation to pathology. To meet the clinical need, intertwined optimization of qDWI acquisition and analysis for specific pathology target is required.

Methods

Current qDWI analysis lags behind most acquisition advances and is predominantly performed retrospectively for SS-EPI magnitude images with moderate (mm) resolution (5-11). The routinely derived apparent diffusion coefficient (ADC) metrics is confounded both by acquisition artefacts (SNR/CNR, EPI distortions, b-range and uniformity) and tissue-specific deviations from Gaussian diffusion model (perfusion and restricted components).

More comprehensive quantitative description of heterogeneous diffusion properties of imaged tissue requires wide-range multi-b acquisition and application of non-Gaussian multi-parameter qDWI models interrogating perfusion and kurtosis (1, 12). Alternative analysis methods, shift model complexity to multi-feature image texture analysis (histogram moments and gray-level) of a single (or multiple) parametric maps (9, 13).

For both approaches, the acquisition-dependent biophysical model constraints need to be uniformly implemented to provide meaningful quantitative parameters with confidence intervals (including measurement error and bias (14)). The practical clinical implementation favors acquisition and analysis standardization for target pathology with minimization of acquisition time, qDWI model complexity (number of parameters/features), and bias and error.

Clinical Application Examples

Prostate:

  • #1 clinical DWI application (PI-RADS based on high-b tumor contrast) with most rich evidence base for advanced qDWI analysis for prostate cancer (PCA)(9, 15).
  • Highly heterogeneous tissue anatomy (16, 17) and low SNR of benign versus malignant tissue presents a challenge for optimal b-range and causes relatively low-ADC repeatability (15, 18, 19), but also provides opportunities for automated DWI-based tumor segmentation.
  • Presence of perfused tissue/image “zones” is challenging for uniform qDWI model and necessitates multi-b acquisition and application of complex (multi-parameter) or pixel-specific models (12) with finite redundancy among model parameters (20, 21).
  • The most promising future clinical uses of advanced qDWI metrics include biopsy-free pathology grading (e.g., Gleason score) that reflect higher tumor density (e.g., apparent diffusion/kurtosis combination correlated to stromal fraction (20, 21)) or disordered tissue structures (e.g., ADC texture analysis (9, 13) and histogram moments (6)).

Liver:

  • Current clinical DWI used by LI-RADS to aid diagnosis of hepatocellular carcinoma (HCC).
  • Main qDWI acquisition and analysis challenge is IVIM pulsatility (b-range and CNR) (4, 10)).
  • The performance of multi-parameter non-Gaussian model (22) has been found similar to single parameter (ADC) Gaussian diffusion model.
  • The promising future qDWI applications include HCC versus intrahepatic cholangiocarcinoma (ICC) differentiation by histogram moments (7) and tumor shape (e.g., concentric HCC pattern) for texture analysis methods.

Breast:

  • qDWI has potential for biopsy-free surveillance and radiation-free screening of high-risk breast-cancer (BCA) population (23, 24) with main acquisition challenge related to effective fat suppression.
  • qDWI metrics is evaluated for therapy response prediction in multi-center trials, using functional ADC maps (23, 24), parametric response maps (25), and low ADC histogram portions (11).
  • Finite redundancy has been observed for multi-parameter model metrics (e.g., fractional anisotropy and perfusion fraction (23)).

Whole-body:

  • Inverted color high-b DWIBS are used clinically for metastatic disease and multiple-myeloma diagnosis and therapy response monitoring (2, 26, 27) . These applications could benefit from reliable auto-segmentation of multiple lesions (e.g., based on gray-level DWIBS features).
  • Multi-station large-FOV acquisition presents a challenge for b-uniformity (28) causing system-dependent bias in qDWI ADC (e.g., evaluated for therapy response prediction (5, 27))

Multi-center Clinical Trials: Standardization Efforts

Recent multi-center, multi-vendor standardization efforts will be briefly introduced as venues for clinical translation and validation of advanced qDWI techniques. Through scan protocol and analysis standardization, these efforts aim at achieving repeatable qDWI measurements for whole body and organ-specific qDWI protocols (3-5, 10).

Relevant resources and workflows for evaluation and elimination of technical bias based on physical and digital phantoms (29, 30) will be reviewed with several examples from recent multicenter clinical trials utilizing qDWI metrics (8, 24, 31). The generated test-retest BCA and PCA qDWI data (18, 31) could be used for assessment of new proposed qDWI metrics.

Summary: Take-Home Points

  • To fully realize advanced qDWI potential for outstanding clinical needs, the viable workflow should define target pathology and appropriately optimize acquisition protocol and analysis to derive candidate quantitative metrics.
  • The diagnostic value of qDWI metrics is established with respect to technical bias, redundancy and confidence intervals followed by validation through a prospective clinical trial.
  • Quantitative model constraints are tied to acquisition protocols and derived quantitative parameters require error estimates.
  • Clinically viable qDWI metrics should be non-redundant with diagnostic change exceeding technical bias and repeatability errors.

Acknowledgements

Slide contributors: Thomas Chenevert (UMHS), Amita Shukla-Dave (MSKCC), Bachir Taouli (MtSinai), Savannah Partridge (SCCA), Peter LaViolette (MCW)); and National Institutes of Health Grants: U01CA166104, R01CA190299 and P01CA085878.

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Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)