Error model for reduction of cardiac and respiratory motion effects in quantitative liver DW‐MRI

Abstract

Diffusion-weighted images of the liver exhibit signal dropout from cardiac and respiratory motion, particularly in the left lobe. These artifacts cause bias and variance in derived parameters that quantify intra-voxel incoherent motion (IVIM). Many models of diffusion have been proposed, but few separate attenuation from diffusion or perfusion from that of bulk motion. The error model proposed here (Beta*LogNormal) is intended to accomplish that separation by modeling stochastic attenuation from bulk motion as multiplication by a Beta-distributed random variate. Maximum likelihood estimation with this error model can be used to derive IVIM parameters separate from signal dropout, and does not require a priori specification of parameters to do so. Liver IVIM parameters were derived for six healthy subjects under this error model and compared with least-squares estimates. Least-squares estimates exhibited bias due to cardiac and respiratory gating and due to location within the liver. Bias from these factors was significantly reduced under the Beta*LogNormal model, as was within-organ parameter variance. Similar effects were appreciable in diffusivity maps in two patients with focal liver lesions. These results suggest that, relative to least-squares estimation, the Beta*LogNormal model accomplishes the intended reduction of bias and variance from bulk motion in liver diffusion imaging.

Liver apparent diffusion coefficient repeatability with individually predetermined optimal cardiac timing and artifact elimination by signal filtering

“…Several correction methods have been proposed previously and similar to ours they succeeded in mitigating the signal attenuation related to bulk motion, without penalizing unaffected regions in the right liver by a large bias or too much noise . The p‐mean method involved 25 signal samples for each b‐value acquired in breath‐hold without cardiac triggering .…”

“…The p‐mean method involved 25 signal samples for each b‐value acquired in breath‐hold without cardiac triggering . In another attempt without any triggering, signal attenuation from diffusion and from bulk motion were separated by modeling the bulk motion stochastic attenuation with a beta distribution . Unlike these studies, our correction method was applicable to sequences both with and without cardiac triggering.…”

Liver histology and diffusion‐weighted MRI in children with nonalcoholic fatty liver disease: A MAGNET study

“…Image reconstruction was performed using a recently-described method based on the Beta*LogNormal (BLN) distribution [30]. The BLN method reduces bias from bulk motion in liver DWI [30] by modeling it separately from variance, effectively weighting artifact-free images more heavily.…”

Associations between histologic features of nonalcoholic fatty liver disease (NAFLD) and quantitative diffusion‐weighted MRI measurements in adults

“…Under the assumption that signal from the pseudo‐diffusion compartment is negligible at b = 100 s/mm 2 (which holds if D* ≥ 70 × 10 −3 mm 2 /s as shown in prior studies ), diffusivity (D) and perfusion fraction (F) were calculated directly according to the following equations: Thus, three DWI parameters (ADC, D, and F) were calculated using two different image reconstruction methods (CMA and BLN). DWI parameter maps were generated for each slice for every patient and were subjectively assessed for consistency with prior studies .…”

“…Using CMA and BLN reconstructions, respectively, the means (and ranges) were 1.7 (1.1–3.5) and 1.4 (1.0–3.2) × 10 –3 mm 2 /s for ADC, 1.1 (0.84–1.4) and 0.84 (0.53–1.1) × 10 –3 mm 2 /s for D, and 17 and 18 (2.3–35)% for F. For both reconstruction methods, D decreased with steatosis and F decreased with fibrosis ( P < 0.05). ADC was not independently associated with any histologic feature.…”