A Quantitative Analysis of Whole Body Outlines: Fourier Descriptors

Abstract Background: The whole brain is often covered in [18F]Fluorodeoxyglucose Positron emission tomography ([18F]FDG-PET) in oncology patients, but the covered brain abnormality is typically screened by visual interpretation without quantitative analysis in clinical practice. In this study, we aimed to develop a fully automated quantitative interpretation pipeline of brain volume from an oncology PET image.Method: We retrospectively collected five hundred oncologic [18F]FDG-PET scans for training and validation of the automated brain extractor. We trained the model for extracting brain volume with two manually drawn bounding boxes on maximal intensity projection (MIP) images. ResNet-50, a convolutional neural network (CNN) was used for the model training. The brain volume was automatically extracted using the CNN model and spatially normalized. As an application of this automated analytic method, we enrolled twenty-four subjects with small cell lung cancer (SCLC) and performed voxelwise two-sample T-test for automatic detection of metastatic lesions.Result: The deep learning-based brain extractor successfully identified the existence of whole-brain volume, with the accuracy of 98% for the validation set. The performance of extracting the brain measured by the intersection-over-union (IOU) of 3-D bounding boxes was 72.9±12.5% for the validation set. As an example of the application to automatically identify brain abnormality, this approach successfully identified the metastatic lesions in three of the four cases of SCLC patients with brain metastasis. Conclusion: Based on the deep-learning based model, the brain volume was successfully extracted from whole-body FDG PET. We suggest this fully automated approach could be used for the quantitative analysis of brain metabolic pattern to identify abnormality during clinical interpretation of oncologic PET studies.

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Quantification of myocardial 99mTc-labeled bisphosphonate uptake with cadmium zinc telluride camera in patients with transthyretin-related cardiac amyloidosis

EJNMMI Research ◽

10.1186/s13550-019-0584-8 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 2

Author(s):

Alain Manrique ◽

David Dudoignon ◽

Stéphanie Brun ◽

Catherine N’Ganoa ◽

Emmanuelle Cassol ◽

...

Keyword(s):

Quantitative Analysis ◽

Cardiac Amyloidosis ◽

Mean Value ◽

Whole Body ◽

Myocardial Uptake ◽

Whole Body Scan ◽

Body Scan ◽

Transthyretin Amyloidosis ◽

Septal Thickness ◽

Czt Spect

Abstract Purpose We aimed to compare different methods for semi-quantitative analysis of cardiac retention of bone tracers in patients with cardiac transthyretin amyloidosis (ATTR). Methods Data from 67 patients with ATTR who underwent both conventional whole-body scan and a CZT myocardial SPECT (DSPECT, Spectrum Dynamics) 3 h after injection of 99mTc-labeled bone tracer were analyzed. Visual scoring of cardiac retention was performed on whole-body scan according to Perugini 4-point grading system from 0 (no uptake) to 3 (strong cardiac uptake with mild/absent bone uptake). A planar heart-to-background (H:B) ratio was calculated using whole-body scan (wb-H:B). CZT SPECT was quantified using three methods: planar H:B ratio calculated from anterior reprojection (ant-H:B), left anterior oblique reprojection (LAO-H:B), and 3D-H:B ratio calculated from transaxial slices as mean counts in a VOI encompassing the heart divided by background VOI in the contralateral lung. Interventricular septal thickness was obtained using echocardiography. Results H:Bs obtained from planar and reprojected data were not statistically different (wb-H:B, 2.05 ± 0.64, ant-H:B, 1.97 ± 0.61, LAO-H:B, 2.06 ± 0.64, all p = ns). However, 3D-H:B was increased compared to planar H:Bs (3D-H:B, 4.06 ± 1.77, all p < 0.0001 vs. wb-H:B, ant-H:B, and LAO-H:B). Bland-Altman plots demonstrated that the difference between 3D and planar H:Bs increased with the mean value of myocardial uptake. 3D-H:B was best correlated to septal thickness (r = 0.45, p < 0.001). Finally, abnormal right ventricular uptake was associated with higher values of cardiac retention. Conclusion 3D semi-quantitative analysis of CZT SPECT optimized the assessment of 99mTc-labeled bone tracer myocardial uptake in patients with cardiac amyloidosis.

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Quantitative analysis of whole body autoradiogram using a computer-assisted image analyzer.

Drug Metabolism and Pharmacokinetics ◽

10.2133/dmpk.3.121 ◽

1988 ◽

Vol 3 (2) ◽

pp. 121-135 ◽

Cited By ~ 1

Author(s):

Shin-ichiro Nagatsuka ◽

Shin-ya Hanawa ◽

Takashi Honda ◽

Masaru Hasegawa

Keyword(s):

Quantitative Analysis ◽

Whole Body ◽

Computer Assisted ◽

Image Analyzer ◽

Computer Assisted Image

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Fully automated identification of brain abnormality from whole-body FDG-PET imaging using deep learning-based brain extraction and statistical parametric mapping

EJNMMI Physics ◽

10.1186/s40658-021-00424-0 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Wonseok Whi ◽

Hongyoon Choi ◽

Jin Chul Paeng ◽

Gi Jeong Cheon ◽

Keon Wook Kang ◽

...

Keyword(s):

Deep Learning ◽

Quantitative Analysis ◽

Fdg Pet ◽

Brain Volume ◽

Whole Body ◽

Brain Abnormality ◽

Metastatic Lesions ◽

Validation Set ◽

Bounding Boxes ◽

The Brain

Abstract Background The whole brain is often covered in [18F]Fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) in oncology patients, but the covered brain abnormality is typically screened by visual interpretation without quantitative analysis in clinical practice. In this study, we aimed to develop a fully automated quantitative interpretation pipeline of brain volume from an oncology PET image. Method We retrospectively collected 500 oncologic [18F]FDG-PET scans for training and validation of the automated brain extractor. We trained the model for extracting brain volume with two manually drawn bounding boxes on maximal intensity projection images. ResNet-50, a 2-D convolutional neural network (CNN), was used for the model training. The brain volume was automatically extracted using the CNN model and spatially normalized. For validation of the trained model and an application of this automated analytic method, we enrolled 24 subjects with small cell lung cancer (SCLC) and performed voxel-wise two-sample T test for automatic detection of metastatic lesions. Result The deep learning-based brain extractor successfully identified the existence of whole-brain volume, with an accuracy of 98% for the validation set. The performance of extracting the brain measured by the intersection-over-union of 3-D bounding boxes was 72.9 ± 12.5% for the validation set. As an example of the application to automatically identify brain abnormality, this approach successfully identified the metastatic lesions in three of the four cases of SCLC patients with brain metastasis. Conclusion Based on the deep learning-based model, extraction of the brain volume from whole-body PET was successfully performed. We suggest this fully automated approach could be used for the quantitative analysis of brain metabolic patterns to identify abnormalities during clinical interpretation of oncologic PET studies.

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Age and Sex Differences in Whole Body Shape: Fourier Descriptors

Biological Shape Analysis ◽

10.1142/9789813225701_0017 ◽

2017 ◽

Author(s):

ALBERT BODT ◽

PETE E. LESTREL ◽

CHARLES WOLFE

Keyword(s):

Sex Differences ◽

Body Shape ◽

Whole Body ◽

Fourier Descriptors ◽

Age And Sex

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Quantitative analysis of radioisotope cisternography in the diagnosis of intracranial hypotension

Journal of Neurosurgery ◽

10.3171/jns.2004.101.3.0421 ◽

2004 ◽

Vol 101 (3) ◽

pp. 421-426 ◽

Cited By ~ 48

Author(s):

Eiji Moriyama ◽

Tomoyuki Ogawa ◽

Ayumi Nishida ◽

Shinichi Ishikawa ◽

Hiroichi Beck

Keyword(s):

Quantitative Analysis ◽

Intracranial Hypotension ◽

Intrathecal Injection ◽

Whole Body ◽

Csf Leak ◽

Slow Phase ◽

Lumbosacral Region ◽

Rapid Phase ◽

Content Type ◽

Fine Print

Object. The authors attempted a quantitative analysis of conventional radioisotope cisternography for the purpose of more accurate diagnosis of intracranial hypotension. Methods. Fifty-seven patients suspected of having intracranial hypotension underwent radioisotope cisternography. Whole-body images were obtained 2.5, 6, and 24 hours after intrathecal injection of 111In—diethylenetriamine pentaacetic acid. Radioactivity in the cerebrospinal fluid (CSF) space was counted during scanning, and radioisotope clearance was studied. Direct signs of radioisotope leakage into the spinal epidural space were found in 25 patients. Most leaks were located in the lumbosacral region. Analysis of the radioisotope clearance curve revealed two different patterns. In patients without a radiographically demonstrated radioisotope leak, absolutely exponential curves were observed (R2 > 0.99). The activity of the radioisotope decreased at a rate of e−0.03 to e−0.107 (mean ± standard deviation, e−0.056 ± 0.018; 32 patients). Clearance in patients with an overt radioisotope leak was not a simple exponential curve; it could be divided into an early rapid phase and a late slow phase. The clearance rate during the first 6 hours was e−0.219 ± 0.127 (25 patients) and e−0.076 ± 0.021 thereafter. The authors speculated that the early escape of undiluted radioisotope solution through an aberrant CSF outlet, such as a traumatic spinal dural tear, was responsible for this phenomenon. Conclusions. The quantitative analysis featured in this study seems to be useful in the diagnosis of intracranial hypotension. A small CSF leak below the limit of radioisotope cisternography resolution might be detected using this technique.

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