This theme issue provides an overview on the basic quantitative methods, an in-depth discussion on the cutting-edge quantitative analysis approaches as well as their applications for both static and dynamic molecular diagnostic and therapeutic imaging

Li, Quanzheng

Theranostics

Harvard University - DASH 

 Quantitative Methods for Molecular Diagnostic and TherapeuticImaging  (Article begins on next page)The Harvard community has made this article openly available.Please share how this access benefits you. Your story matters.Citation Li, Quanzheng. 2013. “Quantitative Methods for MolecularDiagnostic and Therapeutic Imaging.” Theranostics 3 (10): 729-730. doi:10.7150/thno.7453. http://dx.doi.org/10.7150/thno.7453.Published Version doi:10.7150/thno.7453Accessed February 19, 2015 3:04:59 PM ESTCitable Link http://nrs.harvard.edu/urn-3:HUL.InstRepos:11879424Terms of Use This article was downloaded from Harvard University's DASHrepository, and is made available under the terms and conditionsapplicable to Other Posted Material, as set forth athttp://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAATheranostics 2013, Vol. 3, Issue 10   http://www.thno.org 729 Theranostics 2013; 3(10):729-730.  doi: 10.7150/thno.7453 Editorial Quantitative Methods for Molecular Diagnostic and Therapeutic Imaging Quanzheng Li Center for Advanced Medical Imaging Sciences, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA   Corresponding author: Quanzheng Li, 55 Fruit St, White 427, Boston MA 02114, USA. Tel: 1(617)643-9481, Fax: 1(617)726-6165, Email: Li.Quanzheng@mgh.harvard.edu © Ivyspring International Publisher. This is an open-access article distributed under the terms of the Creative Commons License (http://creativecommons.org/ licenses/by-nc-nd/3.0/). Reproduction is permitted for personal, noncommercial use, provided that the article is in whole, unmodified, and properly cited. Received: 2013.08.20; Accepted: 2013.08.20; Published: 2013.09.19 Abstract This theme issue provides an overview on the basic quantitative methods, an in-depth discussion on the cutting-edge quantitative analysis approaches as well as their applications for both static and dynamic molecular diagnostic and therapeutic imaging. Key words: tomography, image quality metrics, local impulse response, resolution, variance.  The field of molecular imaging has progressed from qualitative imaging to semi-quantitative and then quantitative imaging. Compared to qualitative imaging, quantitative imaging can provide not only more accurate and task-specific information for vari-ous diagnostic applications, but also more precise evaluation and more appropriate management of treatment. Furthermore, once a quantitative measure of diagnosis or treatment is available, one can opti-mize the performance of diagnosis or treatment by adapting the parameters in the mathematical descrip-tion of the quantitative measurement.   Because quantitative methods mathematically describe both diagnostic and therapeutic molecular imaging, they effectively provide a bridge to naturally merge these two components together into a new unified quantitative mathematical framework for the analysis and optimization of theranostics. We there-fore believe quantitative methods will play an im-portant role in the development of theranostics and take the initiative to publish this special issue on quantitative imaging, in which 6 groups of research-ers are invited to share their experience, expertise, ideas and results on quantitative molecular imaging.   The evolution of methodology for the analysis of Positron Emission Tomography (PET) data is a perfect example to demonstrate the transition from qualita-tive to quantitative imaging. Soon after people ap-plied a semi-quantitative standardized update value (SUV) to replace the visual evaluation of the image quality, they realized that SUV has a lot of disad-vantages (e.g. inconsistency). A number of modified SUV methods as well as quantitative analysis meth-ods are then developed to overcome the deficiencies of SUV. Bai et al. reviewed SUV and its derivatives and the factors that impede semi-quantitative as-sessment and limit its accuracy and reproducibility, with special emphasis on SUV analysis [1]. They also summarized the current efforts to improve the accu-racy of tumor uptake measurements, characterize overall metabolic tumor burden and heterogeneity of tumor uptake, and account for the effects of image noise.    In spite of the diversity of imaging modalities and applications, the underlying methodologies for quantitative imaging are the same. For static images, these include the analysis and optimization of image quality in terms of variance, resolution, contrast re-covery and lesion detectability. Dutta et al. review statistical analysis techniques that enable us to com- Ivyspring  International Publisher  Theranostics 2013, Vol. 3, Issue 10  http://www.thno.org 730 pute the properties of static molecular images [2]. In addition, these also allow us to control and enhance imaging performance. The practical applications where performance improvement is achieved by ap-plying these ideas to the contexts of both hardware and image reconstruction are also reviewed. While most of the discussed techniques were developed for emission tomography, the general methods are ex-tensible to other imaging modalities as well.  For a specific diagnostic or therapeutic task, human and computer observers are usually applied to study the sensitivity-specificity trade-off. He and Park thoroughly discuss the basic concepts of model ob-servers, which include the mathematical foundations and psychophysical considerations in designing both optimal observers for optimizing imaging systems and anthropomorphic observers for modeling human observers, as well as a few state-of-the-art computa-tional techniques for estimating model observers and the principles of implementing these techniques [3].  A few applications of model observers in medical imaging research are also reviewed.   The fundamental methodology used for dy-namic quantitative imaging is compartment model-ing. This special issue introduces two cutting-edge techniques in kinetic modeling. For a more basic in-troduction to kinetic analysis, we recommend several excellent reviews by other authors on this topic, such as [4-6]. Wang and Qi review recent progress on direct estimation of parametric images of both linear and nonlinear kinetic models using spatiotemporal in-formation in raw projection data [7]. These direct es-timation algorithms can include accurate noise mod-eling and therefore achieve better image quality than conventional indirect methods.  Different PET tracers can capture different aspects of the physiology of diseases; techniques that can rapidly image multiple tracers in a single scan could potentially provide ad-ditional critical information compared to traditional dynamic single tracer scans.   In [8], Kadrmas and Hoffman summarize past and ongoing work in mul-ti-tracer PET tumor imaging, and then organize and describe the main algorithmic approaches for achiev-ing multi-tracer PET signal-recovery. In these ap-proaches, dynamic imaging is generally used and signal-recovery processing is applied.   The above quantitative analysis methods have been applied to different preclinical and clinical ap-plications using different modalities, such as PET, Single Photon Emission Computed Tomography (SPECT) and optical imaging. As an example, an ap-plication of quantitative imaging in proton therapy is given in [9], in which Zhu et al. review the current status of proton therapy verification with PET imag-ing. Proton therapy is very sensitive to uncertainties introduced during treatment planning and dose de-livery. PET imaging can detect the distribution of proton-induced positron emitters and is the only practical approach for in vivo, in situ verification of proton therapy. The different data detecting systems (in-beam, in-room and off-line PET), calculation methods for the prediction of proton-induced PET activity distributions, and approaches for data evalu-ation are discussed in [9].  Quantitative analysis is now a very productive and active research front in molecular imaging.  We anticipate the introduction of quantitative methods to theranostics will produce an emerging, rapid growing and fruitful research field, which in turn will advance the development of theranostics. We hope this special issue can serve as an overview of the previous meth-ods as well as a starting point toward this new excit-ing direction.  Competing Interests The authors have declared that no competing interest exists. References 1. Bai B, Bading J, Conti PS. Tumor Quantification in Clinical Positron Emission Tomography. Theranostics 2013; 3(10):787-801.  2. Dutta J, Ahn S, Li Q. Quantitative Statistical Methods for Image Quality Assessment. Theranostics 2013; 3(10):741-756.  3. He X, Park S. Model Observers in Medical Imaging Re-search. Theranostics 2013; 3(10):774-786. 4. Carson RE. Tracer kinetic modeling in PET. In: Bailey DL,  Townsend DW, Valk PE, Maisey MN, eds. Positron Emission Tomography: Basic Sciences. Springer, 2005: 127-159. 5. Morris, ED, Christopher JE, Kathleen CS, Bradley TC, Raymond FMJr, and Ronald EF. Kinetic modeling in positron emission tomography. In: Wernick MN and Aarsvold JN, eds. Emission Tomography: The Fun-damentals of PET and SPECT. Academic Press, 2004: 499-540.  6. Zaidi H. Tracer kinetic modeling in PET. PET Clinics  2007;2: 267-77.  7. Wang G, Qi J. Direct Estimation of Kinetic Parametric Images for Dy-namic PET. Theranostics 2013; 3(10):802-815.  8. Kadrmas DJ, Hoffman JM. Methodology for Quantitative Rapid Mul-ti-Tracer PET Tumor Characterizations. Theranostics 2013; 3(10):757-773. 9. Zhu X, Fakhri GE. Proton Therapy Verification with PET Imag-ing. Theranostics 2013; 3(10):731-740.  

Quantitative Methods for Molecular Diagnostic and Therapeutic Imaging

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Quantitative Methods for Molecular Diagnostic and Therapeutic Imaging

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