SOURCES SOUGHT
Q -- SOURCES SOUGHT: Development of harmonic analysis and machine learning-based approaches for magnetic resonance parameter estimation and for data fusion in MRI
- Notice Date
- 12/13/2021 5:44:39 AM
- Notice Type
- Sources Sought
- NAICS
- 541380
— Testing Laboratories
- Contracting Office
- NATIONAL INSTITUTES OF HEALTH NIDA Bethesda MD 20892 USA
- ZIP Code
- 20892
- Solicitation Number
- 75N95022Q00054
- Response Due
- 12/27/2021 12:00:00 PM
- Point of Contact
- Rashiid Cummins
- E-Mail Address
-
rashiid.cummins@nih.gov
(rashiid.cummins@nih.gov)
- Description
- This is a Small Business Sources Sought notice. This is NOT a solicitation for proposals, proposal abstracts, or quotations. The purpose of this notice is to obtain information regarding: (1) the availability and capability of qualified small business sources; (2) whether they are small businesses; HUBZone small businesses; service-disabled, veteran-owned small businesses; 8(a) small businesses; veteran-owned small businesses; woman-owned small businesses; or small disadvantaged businesses; and (3) their size classification relative to the North American Industry Classification System (NAICS) code for the proposed acquisition. Your responses to the information requested will assist the Government in determining the appropriate acquisition method, including whether a set-aside is possible. An organization that is not considered a small business under the applicable NAICS code should not submit a response to this notice. This notice is issued to help determine the availability of qualified companies technically capable of meeting the Government requirement and to determine the method of acquisition.� It is not to be construed as a commitment by the Government to issue a solicitation or ultimately award a contract.� Responses will not be considered as proposals or quotes.� No award will be made as a result of this notice.� The Government will NOT be responsible for any costs incurred by the respondents to this notice.� This notice is strictly for research and information purposes only. Statement of Need and Purpose:� The MRI Section of the NIA/IRP (National Institute on Aging/Intramural Research Program) specializes in studies of tissue response to aging, and age-related pathology. As part of our program in brain mapping in particular, NIA has a need to work with emerging methods in harmonic analysis, machine learning, and data fusion. These will become central elements in our work on non-invasive diagnosis of brain tissue pathology and understanding microstructural changes that occur with aging. Background Information and Objective: One of the major open questions in aging research is how the brain and brain stem change with age, and what differentiates between healthy and non-healthy aging.� This incorporates the development of age-related pathology and disease, including Alzheimer�s disease.� The MRI Section has made major advances over the past several years using data stabilization methods.� However, new, even more specialized approaches are being developed in the applied mathematics area for signal analysis.� These highly mathematical methods are in the realm of novel neural network architectures and implementations and what may be called �data un-compression�, along with data fusion. We wish to apply these emerging methods to our brain MRI work at the NIA IRP after testing on simulated data. Project Requirements/Salient Characteristics: Develop mathematical models of how neural network architecture and hyperparameter settings contribute to the efficiency of input-layer-regularized neural networks. Based on (1), develop self-regularizing networks for parameter estimation in MR relaxometry and MRI data fusion. Construct graph-matching schemes for heterogeneous data fusion in Magnetic Resonance Imaging and other NMR applications, taking advantage of Laplacian embeddings as efficient feature extractors. Apply the machine learning methods developed in (1-3) to simulated MR relaxometry and imaging data based on input parameters from published brain, muscle and cartilage studies to compare the accuracy and precision of these methods with the state of the art. Anticipated Period of Performance: 8 months after contract award. Other Considerations: Key Personnel- (2) Mathematics graduate students with experience in advanced machine learning and their application to image processing problems. Capability Statement/Information Sought: Respondents must provide, as part of their responses, a capability statement clearly identifying their ability to provide the requested service. The respondent must also provide their DUNS number, organization name, address, point of contact, and size and type of business (e.g., 8(a), HubZone, etc., pursuant to the applicable NAICS code and any other information that may be helpful in developing or finalizing the acquisition requirements. One (1) copy of the response is required and must be in Microsoft Word or Adobe PDF format using 11-point or 12-point font, 8-1/2� x 11� paper size, with 1� top, bottom, left and right margins, and with single or double spacing. The information submitted must be in an outline format that addresses each of the elements of the project requirement and in the capability statement /information sought paragraphs stated herein.� A cover page and an executive summary may be included but is not required. The response is limited to ten (10) page limit.� The 10-page limit does not include the cover page, executive summary, or references, if requested. The response must include the respondents� technical and administrative points of contact, including names, titles, addresses, telephone and fax numbers, and e-mail addresses. All responses to this notice must be submitted electronically to the Contract Specialist.� Facsimile responses are NOT accepted. The response must be submitted to Rashiid Cummins (Contract Specialist) at e-mail address Rashiid.Cummins@nih.gov. The response must be received on or before December 27th, 2021, 3:00pm, Eastern Time. �Disclaimer and Important Notes:� This notice does not obligate the Government to award a contract or otherwise pay for the information provided in response. The Government reserves the right to use information provided by respondents for any purpose deemed necessary and legally appropriate. Any organization responding to this notice should ensure that its response is complete and sufficiently detailed to allow the Government to determine the organization�s qualifications to perform the work. Respondents are advised that the Government is under no obligation to acknowledge receipt of the information received or provide feedback to respondents with respect to any information submitted. After a review of the responses received, a presolicitation synopsis and solicitation may be published in Federal Business Opportunities. However, responses to this notice will not be considered adequate responses to a solicitation. Confidentiality: No proprietary, classified, confidential, or sensitive information should be included in your response. The Government reserves the right to use any non-proprietary technical information in any resultant solicitation(s).�
- Web Link
-
SAM.gov Permalink
(https://beta.sam.gov/opp/554e75322f354761974745359c1e5a1b/view)
- Place of Performance
- Address: Baltimore, MD, USA
- Country: USA
- Country: USA
- Record
- SN06196713-F 20211215/211213230115 (samdaily.us)
- Source
-
SAM.gov Link to This Notice
(may not be valid after Archive Date)
| FSG Index | This Issue's Index | Today's SAM Daily Index Page |