The Quantitative Imaging Network: NCI's Historical Perspective and Planned Goals

Clarke, Laurence P.; Nordstrom, Robert J.; Zhang, Huiming; Tandon, Pushpa; Zhang, Yantian; Redmond, George; Farahani, Keyvan; Kelloff, Gary J.; Henderson, Lori A.; Shankar, Lalitha; Deye, James A.; Capala, Jacek; Jacobs, Paula

doi:10.1593/tlo.13832

Cited by 79 publications

(71 citation statements)

References 4 publications

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“…Computerized analysis has, and continues to aid in their efforts as it allows for reproducibility and efficiency, while still allowing for personalized medicine. 67 But the advantages of computerized imaging analysis extend far beyond cancer evaluation and treatment, to aiding in our understanding of many different pathological conditions with imaging correlates, not the least of which is brain pathology. In fact, in recent years computerized MR and CT volumetric analysis employing a variety of image analysis software has been used to investigate a gamut of neurological conditions ranging from Alzheimer's, [68][69][70] Schizophrenia, 38,71 Huntington's disease, Tourette's syndrome, 38 Brain tumor, [72][73][74] HIV-related CNS disease 44 and Epilepsy.…”

Section: Potential Sources Of Errormentioning

confidence: 99%

A Methodology for Systematic Volumetric Analysis of Perioperative Cranial Imaging in Neurosurgical Patients

Atsina¹,

Gorniak²,

Sharan³

et al. 2016

JHN Journal

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Section: Potential Sources Of Errormentioning

confidence: 99%

A Methodology for Systematic Volumetric Analysis of Perioperative Cranial Imaging in Neurosurgical Patients

Atsina¹,

Gorniak²,

Sharan³

et al. 2016

JHN Journal

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“…This can be seen from the variety of coauthors on selected publications. 14,15 He had a focused interest in quantitative imaging, but he shared it with a wide range of people. In addition, he carried his interest in quantitative imaging to the United Kingdom, South Korea, and other countries where the value of quantitative imaging could be discussed.…”

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confidence: 99%

Special Section Guest Editorial: Quantitative Imaging and the Pioneering Efforts of Laurence P. Clarke

Nordstrom¹

2017

J. Med. Imag

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“…We delineate acquisition data as proprietary, which subsequently impedes the academic and commercial exploration that traditionally propels potentially impactful ideas beyond the labs they are created in. For example, third party commercial or open-source standardized PET image reconstruction techniques could be developed in a clinically usable form and enable new levels of image quantification standardization across PET machines and centers and boost statistical power in research and multicenter clinical trials (5). Standardization can also make a large impact in the newly emerging fields of big data (6), machine learning (7), and radiomics (8).…”

mentioning

confidence: 99%

Small Data: A Ubiquitous, Yet Untapped, Resource for Low-Cost Imaging Innovation

Kesner

2016

J Nucl Med

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PET, conventional nuclear imaging, and most contemporary medical imaging modalities are inherently digital technologies. Over the last several decades, there has been a transformative evolution of the digital computing landscape with respect to speed, cost of storage, infrastructure, and available expertise. However, our use of data, and in fact our whole understanding of the role of data in relation to emission imaging, has remained relatively unchanged. If we take a moment to reflect on this resource, generated ubiquitously in our daily imaging procedures, we can recognize that we have the capacity to support information use beyond the present convention and that the raw data provided by nuclear imaging studies can be tapped to fuel innovation.Our general understanding of image data is that it exists in DICOM-format images, essentially analogous to film and representing a quantity of source signal distributed in space. However, the signals and information used to create these images in nuclear medicine originate in a much denser form; our imaging machines capture highly detailed time, location, and energy information for individual decay events. The current practice in PET, for example, is to truncate this information using assumptions and reconstruction techniques so as to provide a representation of tracer emissions distributed in recognizable Cartesian space. This process of biodistribution-representative image generation has essentially defined nuclear imaging for half a century. The procedure of truncating (unused) information is heavily ingrained in our practice likely because, for most of the field's existence, it has been expensive and impractical to save raw acquisition data.The costs associated with saving data have never been a static consideration. In 1980, a gigabyte of data cost $600,000 (1) (approximate value, inflation-adjusted), in 1990 that cost went down to $15,000, in 2016 it went down to $0.02, and we can confidently project continuation of this financial trend. Retaining a 2-Gb raw PET acquisition file now represents approximately 0.001% of the market cost of a scan. Both the cost and the capacity of digital imaging have undergone a slow but, in aggregate, very large shift. Each year, data-driven solutions become more practical and more relevant than in the year before, as shown in Figure 1. In the 1990s, we passed a milestone when digital storage became more cost-effective than paper storage (2). It is possible that we have now passed a new barrier in that we can say the cost of saving raw imaging acquisition data is negligible relative to the cost of generating the data. Furthermore, with ionizing radiation imaging, the cost-of-data paradigm does not include only an economic cost. Because patients are being exposed to radiation to generate these data, and at a risk to their health, it is prudent for us to periodically reconsider whether our practices are making optimal use of it.One reason to support changing our data-saving practice toward more robust access and archiving comes from th...

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The Quantitative Imaging Network: NCI's Historical Perspective and Planned Goals

Cited by 79 publications

References 4 publications

A Methodology for Systematic Volumetric Analysis of Perioperative Cranial Imaging in Neurosurgical Patients

A Methodology for Systematic Volumetric Analysis of Perioperative Cranial Imaging in Neurosurgical Patients

Special Section Guest Editorial: Quantitative Imaging and the Pioneering Efforts of Laurence P. Clarke

Small Data: A Ubiquitous, Yet Untapped, Resource for Low-Cost Imaging Innovation

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