Use of fuzzy edge single-photon emission computed tomography analysis in definite Alzheimer's disease - a retrospective study

Rusina, Robert; Kukal, Jaromír; Bělíček, T.; Buncová, Marie; Matěj, Radoslav

doi:10.1186/1471-2342-10-20

Cited by 8 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When differentiating AD from healthy elderly controls, studies using relatively low-resolution, single-headed gamma cameras yielded an overall sensitivity of 84% and an overall specificity of 83% ( 187 , 200 , 202 – 211 ). Studies utilizing multi-headed gamma cameras and often quantitative analysis ( 91 , 186 , 212 – 231 ) yielded a modest, but clinically significant, increase in overall sensitivity to 89% and overall specificity to 89%. Figure 9 illustrates the advancements in the images produced by perfusion SPECT imaging including statistical parametric analysis to an age-matched normative database as depicted for AD.…”

Section: Assessment Of the State Of The Art—spect In Brain Disordersmentioning

confidence: 99%

The Legacy of the TTASAAN Report—Premature Conclusions and Forgotten Promises: A Review of Policy and Practice Part I

2022

View full text Add to dashboard Cite

Brain perfusion single photon emission computed tomography (SPECT) scans were initially developed in 1970's. A key radiopharmaceutical, hexamethylpropyleneamine oxime (HMPAO), was originally approved in 1988, but was unstable. As a result, the quality of SPECT images varied greatly based on technique until 1993, when a method of stabilizing HMPAO was developed. In addition, most SPECT perfusion studies pre-1996 were performed on single-head gamma cameras. In 1996, the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology (TTASAAN) issued a report regarding the use of SPECT in the evaluation of neurological disorders. Although the TTASAAN report was published in January 1996, it was approved for publication in October 1994. Consequently, the reported brain SPECT studies relied upon to derive the conclusions of the TTASAAN report largely pre-date the introduction of stabilized HMPAO. While only 12% of the studies on traumatic brain injury (TBI) in the TTASAAN report utilized stable tracers and multi-head cameras, 69 subsequent studies with more than 23,000 subjects describe the utility of perfusion SPECT scans in the evaluation of TBI. Similarly, dementia SPECT imaging has improved. Modern SPECT utilizing multi-headed gamma cameras and quantitative analysis has a sensitivity of 86% and a specificity of 89% for the diagnosis of mild to moderate Alzheimer's disease—comparable to fluorodeoxyglucose positron emission tomography. Advances also have occurred in seizure neuroimaging. Lastly, developments in SPECT imaging of neurotoxicity and neuropsychiatric disorders have been striking. At the 25-year anniversary of the publication of the TTASAAN report, it is time to re-examine the utility of perfusion SPECT brain imaging. Herein, we review studies cited by the TTASAAN report vs. current brain SPECT imaging research literature for the major indications addressed in the report, as well as for emerging indications. In Part II, we elaborate technical aspects of SPECT neuroimaging and discuss scan interpretation for the clinician.

show abstract

Section: Assessment Of the State Of The Art—spect In Brain Disordersmentioning

confidence: 99%

The Legacy of the TTASAAN Report—Premature Conclusions and Forgotten Promises: A Review of Policy and Practice Part I

2022

View full text Add to dashboard Cite

show abstract

“…When differentiating AD from healthy elderly controls, studies using relatively low-resolution, single-headed gamma cameras yielded an overall sensitivity of 84% and an overall specificity of 83% 25 , 174 , 207 , 213 – 221 . Studies utilizing multiheaded gamma cameras and often quantitative analysis 8 , 109 , 174 , 217 , 221 – 241 yielded a modest, but clinically significant, increase in overall sensitivity from 84–89% and in overall specificity from 83–89%.…”

Section: Neuroimaging and The Diagnosis Of Dementias And MCImentioning

confidence: 99%

The diagnosis and evaluation of dementia and mild cognitive impairment with emphasis on SPECT perfusion neuroimaging

Henderson¹

2012

CNS Spectr.

View full text Add to dashboard Cite

As the world population ages, the incidence of dementing illnesses will dramatically increase. The number of people afflicted with dementia is expected to quadruple in the next 50 years. Since the neuropathology of the dementias precedes clinical symptoms often by several years, earlier detection and intervention could be key steps to mitigating the progression and burden of these diseases. This review will explore methods of evaluating, differentiating, and diagnosing the multiple forms of dementia. Particular emphasis will be placed on the diagnosis of mild cognitive impairment-the precursor to dementia. Anatomical imaging; cerebrospinal fluid markers; functional neuroimaging, such as positron emission tomography and single photon emission tomography; and molecular imaging, such as amyloid marker imaging, will be assessed in terms of sensitivity and specificity. Cost will also be a consideration, as the growing population afflicted with dementia represents an increasingly large financial encumbrance to the healthcare systems of every nation. In the face of expensive new markers and limited availability of cyclotrons, single photon emission computer tomography (SPECT) provides relatively high sensitivity and specificity at a comparatively low overall cost.

show abstract

“…In the fuzzy clustering methods an object can belong to more than one cluster with a degree of membership continuously variable between 0 and 1. For ordinary clusters, the degree of membership for an object x is 1 if it belongs to the cluster and 0 if it does not; for the fuzzy clusters, larger is the membership degree to the clusters, greater will be the confidence level that the object belongs to that cluster [ 30 - 33 ]. The output of a fuzzy algorithm not only includes a partition but also additional information in the form of membership value.…”

Section: The Fuzzy C-means Implemented With Features (Fcm-wf)mentioning

confidence: 99%

Fuzzy technique for microcalcifications clustering in digital mammograms

et al. 2014

View full text Add to dashboard Cite

BackgroundMammography has established itself as the most efficient technique for the identification of the pathological breast lesions. Among the various types of lesions, microcalcifications are the most difficult to identify since they are quite small (0.1-1.0 mm) and often poorly contrasted against an images background. Within this context, the Computer Aided Detection (CAD) systems could turn out to be very useful in breast cancer control.MethodsIn this paper we present a potentially powerful microcalcifications cluster enhancement method applicable to digital mammograms. The segmentation phase employs a form filter, obtained from LoG filter, to overcome the dependence from target dimensions and to optimize the recognition efficiency. A clustering method, based on a Fuzzy C-means (FCM), has been developed. The described method, Fuzzy C-means with Features (FCM-WF), was tested on simulated clusters of microcalcifications, implying that the location of the cluster within the breast and the exact number of microcalcifications are known.The proposed method has been also tested on a set of images from the mini-Mammographic database provided by Mammographic Image Analysis Society (MIAS) publicly available.ResultsThe comparison between FCM-WF and standard FCM algorithms, applied on both databases, shows that the former produces better microcalcifications associations for clustering than the latter: with respect to the private and the public database we had a performance improvement of 10% and 5% with regard to the Merit Figure and a 22% and a 10% of reduction of false positives potentially identified in the images, both to the benefit of the FCM-WF. The method was also evaluated in terms of Sensitivity (93% and 82%), Accuracy (95% and 94%), FP/image (4% for both database) and Precision (62% and 65%).ConclusionsThanks to the private database and to the informations contained in it regarding every single microcalcification, we tested the developed clustering method with great accuracy. In particular we verified that 70% of the injected clusters of the private database remained unaffected if the reconstruction is performed with the FCM-WF. Testing the method on the MIAS databases allowed also to verify the segmentation properties of the algorithm, showing that 80% of pathological clusters remained unaffected.

show abstract

Use of fuzzy edge single-photon emission computed tomography analysis in definite Alzheimer's disease - a retrospective study

Cited by 8 publications

References 16 publications

The Legacy of the TTASAAN Report—Premature Conclusions and Forgotten Promises: A Review of Policy and Practice Part I

The Legacy of the TTASAAN Report—Premature Conclusions and Forgotten Promises: A Review of Policy and Practice Part I

The diagnosis and evaluation of dementia and mild cognitive impairment with emphasis on SPECT perfusion neuroimaging

Fuzzy technique for microcalcifications clustering in digital mammograms

Contact Info

Product

Resources

About