Magnetic resonance imaging of mean cell size in human breast tumors

Abstract

Purpose Cell size is a fundamental characteristic of all tissues, and changes in cell size in cancer reflect tumor status and response to treatments, such as apoptosis and cell‐cycle arrest. Unfortunately, cell size can currently be obtained only by pathological evaluation of tumor tissue samples obtained invasively. Previous imaging approaches are limited to preclinical MRI scanners or require relatively long acquisition times that are impractical for clinical imaging. There is a need to develop cell‐size imaging for clinical applications. Methods We propose a clinically feasible IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) approach that can characterize mean cell sizes in solid tumors. We report the use of a combination of pulse sequences, using different gradient waveforms implemented on clinical MRI scanners and analytical equations based on these waveforms to analyze diffusion‐weighted MRI signals and derive specific microstructural parameters such as cell size. We also describe comprehensive validations of this approach using computer simulations, cell experiments in vitro, and animal experiments in vivo and demonstrate applications in preoperative breast cancer patients. Results With fast acquisitions (~7 minutes), IMPULSED can provide high‐resolution (1.3 mm in‐plane) mapping of mean cell size of human tumors in vivo on clinical 3T MRI scanners. All validations suggest that IMPULSED provides accurate and reliable measurements of mean cell size. Conclusion The proposed IMPULSED method can assess cell‐size variations in tumors of breast cancer patients, which may have the potential to assess early response to neoadjuvant therapy.

Higher‐order diffusion MRI characterization of mesorectal lymph nodes in rectal cancer

“…50 Another assumption is having the same diffusivity inside and outside the sphere, with time-dependence originating only from restricted diffusion, which might once again lead to bias. 46,64 Nevertheless, this assumption has been successfully applied in the past to model various tumor types, 33,37,40 with fitted parameters showing a good correlation with histology features. 33,41 It is important to stress that, even if the estimated model parameters do not naturally correspond to features visible to histology, the modeling approach successfully captured the higher-order effects in the signal decay and recovered parameters, which were useful for lymph node classification, whereas the ADC approach was much less sensitive.…”

“…To further enhance the specificity of dMRI-derived parameters to the underlying microstructure, recently proposed higher-order dMRI techniques [33][34][35][36][37][38][39][40] employ biophysical compartment models of various (assumed) tissue features to fit the dMRI measurements, usually acquired at multiple b-values and diffusion times. Such approaches showed enhanced ability over ADC to explain the diffusion measurements and differentiate between benign and malignant tumors in various cancer types such as xenograft colorectal tumors, 33 prostate cancer, 37,41-44 breast cancer, 40 and gliomas. 45,46 Moreover, the estimated dMRI parameters for restriction size and volume fraction correlated well with histology measurements.…”

MRI‐cytometry: Mapping nonparametric cell size distributions using diffusion MRI

“…Similar to previous studies, 12,14,16,23 MRI‐cytometry assumes diffusion MRI signals arise from 2 compartments, that is, intra‐ and extracellular spaces without transcytolemmal water exchange. The following assumptions were made.…”

“… is cell volume; is the normalized distribution function of the number of cells with a diameter d and an intracellular diffusivity ; is the normalized distribution function of the number of spin packets with and ; and are the T 2 ‐weighted intra‐ and extracellular diffusion MRI signals per unit volume, respectively; and is intracellular signal attenuation of an impermeable spherical cell. The analytical equations for linking geometric features ( d and ) to diffusion MRI signals have been reported for sine‐ and cosine‐modulated gradient waveforms 27 and cosine‐modulated trapezoidal OGSE waveforms 23 . Unlike previous studies that enforced a known distribution function, 28 Equation () does not assume any specific distribution function for any microstructural parameters.…”

Diffusion‐prepared 3D gradient spin‐echo sequence for improved oscillating gradient diffusion MRI

“…This leads to a limited capacity to access shorter diffusion times, [24][25][26][27] as the effective diffusion time is approximately 1/4f. 8 Nevertheless, recent studies have investigated the feasibility and utility of OG-dMRI on clinical systems, [24][25][26][27][28][29][30][31][32][33][34][35] given the limited oscillating frequencies and b-values. For example, Xu et al 32 demonstrated that on a 3T clinical scanner, cell size could be accurately estimated in breast cancer with a simplified PG-dMRI and OG-dMRI protocol and an advanced biophysical model.…”