Breast cancer is the most common cancer among women worldwide and ranks second in terms of overall cancer deaths. One of the difficulties associated with treating breast cancer is that it is a heterogeneous disease with variations in benign and pathologic tissue composition, which contributes to disease development, progression, and treatment response. Many of these phenotypes are uncharacterized and their presence is difficult to detect, in part due to the sparsity of methods to correlate information between the cellular microscale and the whole-breast macroscale. Quantitative multiscale imaging of the breast is an emerging field concerned with the development of imaging technology that can characterize anatomic, functional, and molecular information across different resolutions and fields of view. It involves a diverse collection of imaging modalities, which touch large sections of the breast imaging research community. Prospective studies have shown promising results, but there are several challenges, ranging from basic physics and engineering to data processing and quantification, that must be met to bring the field to maturity. This paper presents some of the challenges that investigators face, reviews currently used multiscale imaging methods for preclinical imaging, and discusses the potential of these methods for clinical breast imaging.
During the last eight years our group has developed radial acquisitions with angular undersampling
factors of several hundred that accelerate MRI in selected applications. As with all previous
acceleration techniques, SNR typically falls as least as fast as the inverse square root of the
undersampling factor. This limits the SNR available to support the small voxels that these methods
can image over short time intervals in applications like time-resolved contrast-enhanced MR
angiography (CE-MRA). Instead of processing each time interval independently, we have developed
constrained reconstruction methods that exploit the significant correlation between temporal
sampling points. A broad class of methods, termed HighlY Constrained Back PRojection (HYPR),
generalizes this concept to other modalities and sampling dimensions.
While current MRI technology is adequate for imaging severe cartilage degeneration, significant increases in resolution
are necessary to image early changes and defects in cartilage. Though MRI advocates often tout its 3D capabilities, most
clinical scans consist of a series of 2D thick slices with gaps in between. Partial volume artifact can cause several low
grade lesions to be missed or incompletely characterized. Robust fat suppression is also necessary to provide high
contrast between bone and cartilage. Commonly available clinical 3D techniques are largely based on sequences which
spend considerable amounts of scan time suppressing fat instead of imaging.
We present a method that provides a comprehensive 3D evaluation of cartilage in the knee with isotropic resolution and
bright fluid through T2-like contrast. Termed VIPR-SSFP, the method separates fat and water and thus spends the entire
exam imaging cartilage and relevant joint tissues. A single VIPR-SSFP scan may be reformatted into multiple
orthogonal or oblique reformats where the variable thickness of the reformat allows a trade-off between SNR and partial
The radial trajectory in VIPR-SSFP is ideally suited to exploit larger coil arrays using the parallel imaging strategy
known as PILS. Relative to our previous work, we have reduced voxel volume by 100%, demonstrating 0.56 mm
isotropic resolution at 1.5T and 0.33 mm at 3.0T in a five minute scan, using a new eight channel coil. Improved
cartilage assessment is demonstrated in a study of nearly 100 patients through reduction in partial volume artifact.
It is becoming increasingly common to image time-resolved flow patterns through the vascular system in all three
spatial dimensions using non-invasive methods. The capability to generate four-dimensional (4D) (x, y, z and time)
vascular flow data is growing in several modalities. Vastly undersampled Isotropic PRojection (VIPR) is one such
method using high-resolution, fast Magnetic Resonance Imaging (MRI) of the vasculature system during intravenous
contrast injection. VIPR currently produces 4D data sets of twenty to forty frames of 2563 voxels each, and stronger
magnets will allow higher resolution time series that generate gigabytes of data. Real-time visualization and analysis of
4D data can quickly overwhelm the memory and processing capabilities of desktop workstations. 4D Cluster
Visualization (4DCV) offers a straightforward, scalable approach to interactively display and manipulate 4D,
reconstructed, VIPR data sets. 4DCV exploits the inherently parallel nature of 4D frame data to interactively manipulate
and render individual 3D data frames simultaneously across all nodes of a visualization cluster. An interactive
animation is produced in real-time by reading back the 2D rendered results to a central animation console where the
image sequence is assembled into a continuous stream for display. Basic 4DCV can be extended to allow rendering of
multiple frames on one node, compression of image streams for serving remote clinical workstations, and local archival
storage of 3D data frames at the cluster nodes for quick retrieval of medical exams. 4D Cluster Visualization concepts
can also be extended to distributed and Grid implementations.