Douglas E. Green

Comparison of temporal filtering methods for dynamic contrast MRI myocardial perfusion studies

Dynamic contrast myocardial perfusion studies may benefit from methods that speed up the acquisition. Unaliasing by Fourier encoding the overlaps using the temporal dimension (UNFOLD), and a similar linear interpolation method have been shown to be effective at reducing the number of phase encodes needed for cardiac wall motion studies by using interleaved sampling and temporal filtering. Here such methods are evaluated in cardiac dynamic contrast studies, with particular regard to the effects of the choice of filter and the interframe motion. Four different filters were evaluated using a motion‐free canine study. Full k‐space was acquired and then downsampled to allow for a measure of truth. The different filters gave nearly equivalent images and quantitative flow estimates compared to full k‐space. The effect of respiratory motion on these schemes was graphically depicted, and the performance of the four temporal filters was evaluated in seven human subjects with respiratory motion present. The four filters provided images of similar quality. However, none of the filters were effective at eliminating motion artifacts. Motion registration methods or motion‐free acquisitions may be necessary to make these reduced FOV approaches clinically useful. Magn Reson Med 49:895–902, 2003.

CTA and MRA: Visualization without catheterization

The ideal modality for vascular imaging would be noninvasive and inexpensive. A volumetric acquisition would permit visualization of vessels from arbitrary angles. High contrast between the vessel lumen and background tissue would be coupled with excellent spatial resolution allowing accurate depiction of small vessels. Characterization of the constituent components of the vessel wall would be possible. High temporal resolution would both freeze the motion of fast moving vessels and show the direction and speed of blood flow. Finally, the modality would expose the patient to a minimal amount of ionizing radiation or potentially toxic contrast agents. Diagnostic conventional catheter angiography offers unsurpassed spatial and temporal resolution. However, catheter angiography is an interventional procedure, exposes the patient to both ionizing radiation and iodinated contrast, and does not depict the vessel wall. Additionally, view angles are chosen before the administration of contrast and may not demonstrate certain lesions. These limitations have driven the development of both computed tomography angiography (CTA) and magnetic resonance angiography (MRA). Both of these modalities rapidly acquire volumetric data sets, which can then be evaluated slice by slice or by more advanced volumetric rendering techniques. CTA and MRA are minimally invasive and less costly than angiography. While CTA and MRA cannot compete with the spatial or temporal resolution of conventional angiography, present technology has proven clinical efficacy in a wide range of applications. The principles behind CTA and MRA and their comparative strengths and weaknesses will be discussed. The different volumetric rendering techniques will be reviewed. Finally, recent advances that will likely further improve these modalities will be summarized.

Can Big Data Lead Us to Big Savings

At the turn of the last century, department store owner John Wanamaker (1838–1922) lamented, “I know that half of my advertising doesn’t work. The problem is that I don’t know which half.” One hundred years later, Google has solved the “Wanamaker dilemma” (1). By tracking our online activities, Google can anticipate which advertisements will attract our attention. Advertisers then pay per click—for the advertising that works—rather than per impression. Tech oracle Tim O’Reilly asserts that medicine has a similar “Wanamaker problem” (1). Despite our best intentions, many medical interventions are ineffective. The problem is that we are not good at predicting our failures. Perhaps if we could learn how to analyze healthcare data in a fashion similar to how advertisers analyze consumer behavior, we might be able to realize efficiencies similar to those that resulted from the pay-per-click model, and thus bring down costs for healthcare consumers and improve our own profit margins in the process. Analysis of healthcare data presents more challenges than does analysis of consumer purchases, however, and significant obstacles must be overcome. First, before healthcare data can be leveraged meaningfully, the information must be aggregated. Because of the fractured nature of the U.S. healthcare system, data exist in myriad formats and databases, and this challenge is likely to only get worse over time. For years, academic researchers have enjoyed access to the claims data for Medicare beneficiaries (2). Now, the creation of the Health Data Initiative of the Department of Health and Human Services (HHS) has led to release of even more national health data (3). In the private sector, insurance companies have billing records, and pharmacies keep track of prescriptions. If your genome has been sequenced, it may be stored at 23andMe.com. The expanding use of electronic health records and consumergrade personal sensors (eg, sleep monitors and blood glucose sensors) will lead to proliferation of even more data. Second, the multitude of formats in which medical data are stored must be made meaningful. Some medical data (eg, laboratory data) are structured: Their composite parts can be fully parsed and stored in machine-readable matrices of columns and rows that can be easily processed by advanced algorithms. However, large portions of medical data are mostly unstructured (eg, radiologic images, clinic notes, operative reports, and pathologic slides). The informational content of these data cannot be accurately deciphered and codified without a human translator with medical expertise. Although there have been advances in converting unstructured data into structured data, meaningful information can be lost in the process. Douglas E. Green, MD • Elliot J. Rapp, MD Practice Corner