Principles and applications of diffusion-weighted imaging in cancer detection, staging, and treatment follow-up

Abstract

Diffusion-weighted imaging relies on the detection of the random microscopic motion of free water molecules known as Brownian movement. With the development of new magnetic resonance (MR) imaging technologies and stronger diffusion gradients, recent applications of diffusion-weighted imaging in whole-body imaging have attracted considerable attention, especially in the field of oncology. Diffusion-weighted imaging is being established as a pivotal aspect of MR imaging in the evaluation of specific organs, including the breast, liver, kidney, and those in the pelvis. When used in conjunction with apparent diffusion coefficient mapping, diffusion-weighted imaging provides information about the functional environment of water in tissues, thereby augmenting the morphologic information provided by conventional MR imaging. Detected changes include shifts of water from extracellular to intracellular spaces, restriction of cellular membrane permeability, increased cellular density, and disruption of cellular membrane depolarization. These findings are commonly associated with malignancies; therefore, diffusion-weighted imaging has many applications in oncologic imaging and can aid in tumor detection and characterization and in the prediction and assessment of response to therapy.

Figure 1a

Figure 1b

Figure 2

Pulse sequence diagrams illustrate how a diffusion-weighted sequence incorporates two symmetric motion-probing gradient pulses into a single-shot SE T2-weighted sequence, one on either side of the 180° refocusing pulse. Restricted diffusion (top) manifests as retained signal, whereas free diffusion (bottom) translates into signal loss. The sensitivity of diffusion-weighted imaging to diffusion can be incrementally increased by increasing the amplitude, duration, and temporal spacing of the two motion-probing gradients. RF = radiofrequency.

Figure 3a

Figure 3b

Figure 3c

Figure 4

Graph illustrates the logarithm of relative signal intensity (SI) (y-axis) versus b value (in this case, 0 and 500 sec/mm²) (x-axis) for tumor and normal tissue. The slope of the “tumor line” is less than that of the line representing normal tissue, which translates into lower signal on the ADC map.

Figure 5a

Figure 5b

Figure 5c

Figure 6a

Figure 6b

Figure 6c

Figure 7a

Figure 7b

Figure 7c

Figure 8a

Figure 8b

Figure 8c

Figure 8d

Figure 8e

Figure 8f

Figure 9a

Figure 9b

Figure 9c

Figure 9d

Figure 10a

Figure 10b

Figure 10c

Figure 11a

Figure 11b

Figure 11c

Figure 12a

Figure 12b

Figure 13a

Figure 13b

Figure 13c

Figure 14a

Figure 14b

Figure 14c