Background Region Of Interest Research Articles

Fully Automatic Quantitative Measurement of Equilibrium Radionuclide Angiocardiography Using a Convolutional Neural Network.

The aim of this study was to generate deep learning-based regions of interest (ROIs) from equilibrium radionuclide angiography datasets for left ventricular ejection fraction (LVEF) measurement. Manually drawn ROIs (mROIs) on end-systolic and end-diastolic images were extracted from reports in a Picture Archiving and Communications System. To reduce observer variability, preprocessed ROIs (pROIs) were delineated using a 41% threshold of the maximal pixel counts of the extracted mROIs and were labeled as ground-truth. Background ROIs were automatically created using an algorithm to identify areas with minimum counts within specified probability areas around the end-systolic ROI. A 2-dimensional U-Net convolutional neural network architecture was trained to generate deep learning-based ROIs (dlROIs) from pROIs. The model's performance was evaluated using Lin's concordance correlation coefficient (CCC). Bland-Altman plots were used to assess bias and 95% limits of agreement. A total of 41,462 scans (19,309 patients) were included. Strong concordance was found between LVEF measurements from dlROIs and pROIs (CCC = 85.6%; 95% confidence interval, 85.4%-85.9%), and between LVEF measurements from dlROIs and mROIs (CCC = 86.1%; 95% confidence interval, 85.8%-86.3%). In the Bland-Altman analysis, the mean differences and 95% limits of agreement of the LVEF measurements were -0.6% and -6.6% to 5.3%, respectively, for dlROIs and pROIs, and -0.4% and -6.3% to 5.4% for dlROIs and mROIs, respectively. In 37,537 scans (91%), the absolute LVEF difference between dlROIs and mROIs was <5%. Our 2-dimensional U-Net convolutional neural network architecture showed excellent performance in generating LV ROIs from equilibrium radionuclide angiography scans. It may enhance the convenience and reproducibility of LVEF measurements.

Semi-automatic standardized analysis method to objectively evaluate near-infrared fluorescent dyes in image-guided surgery.

Near-infrared fluorescence imaging still lacks a standardized, objective method to evaluate fluorescent dye efficacy in oncological surgical applications. This results in difficulties in translation between preclinical to clinical studies with fluorescent dyes and in the reproduction of results between studies, which in turn hampers further clinical translation of novel fluorescent dyes. Our aim is to develop and evaluate a semi-automatic standardized method to objectively assess fluorescent signals in resected tissue. A standardized imaging procedure was designed and quantitative analysis methods were developed to evaluate non-targeted and tumor-targeted fluorescent dyes. The developed analysis methods included manual selection of region of interest (ROI) on white light images, automated fluorescence signal ROI selection, and automatic quantitative image analysis. The proposed analysis method was then compared with a conventional analysis method, where fluorescence signal ROIs were manually selected on fluorescence images. Dice similarity coefficients and intraclass correlation coefficients were calculated to determine the inter- and intraobserver variabilities of the ROI selections and the determined signal- and tumor-to-background ratios. The proposed non-targeted fluorescent dyes analysis method showed statistically significantly improved variabilities after application on indocyanine green specimens. For specimens with the targeted dye SGM-101, the variability of the background ROI selection was statistically significantly improved by implementing the proposed method. Semi-automatic methods for standardized quantitative analysis of fluorescence images were successfully developed and showed promising results to further improve the reproducibility and standardization of clinical studies evaluating fluorescent dyes.

Spatial Coherence Approaches to Distinguish Suspicious Mass Contents in Fundamental and Harmonic Breast Ultrasound Images.

When compared to fundamental B-mode imaging, coherence-based beamforming, and harmonic imaging are independently known to reduce acoustic clutter, distinguish solid from fluid content in indeterminate breast masses, and thereby reduce unnecessary biopsies during a breast cancer diagnosis. However, a systematic investigation of independent and combined coherence beamforming and harmonic imaging approaches is necessary for the clinical deployment of the most optimal approach. Therefore, we compare the performance of fundamental and harmonic images created with short-lag spatial coherence (SLSC), M-weighted SLSC (M-SLSC), SLSC combined with robust principal component analysis with no M-weighting (r-SLSC), and r-SLSC with M-weighting (R-SLSC), relative to traditional fundamental and harmonic B-mode images, when distinguishing solid from fluid breast masses. Raw channel data acquired from 40 total breast masses (28 solid, 7 fluid, 5 mixed) were beamformed and analyzed. The contrast of fluid masses was better with fundamental rather than harmonic coherence imaging, due to the lower spatial coherence within the fluid masses in the fundamental coherence images. Relative to SLSC imaging, M-SLSC, r-SLSC, and R-SLSC imaging provided similar contrast across multiple masses (with the exception of clinically challenging complicated cysts) and minimized the range of generalized contrast-to-noise ratios (gCNRs) of fluid masses, yet required additional computational resources. Among the eight coherence imaging modes compared, fundamental SLSC imaging best identified fluid versus solid breast mass contents, outperforming fundamental and harmonic B-mode imaging. With fundamental SLSC images, the specificity and sensitivity to identify fluid masses using the reader-independent metrics of contrast difference, mean lag one coherence (LOC), and gCNR were 0.86 and 1, 1 and 0.89, and 1 and 1, respectively. Results demonstrate that fundamental SLSC imaging and gCNR (or LOC if no coherence image or background region of interest is introduced) have the greatest potential to impact clinical decisions and improve the diagnostic certainty of breast mass contents. These observations are additionally anticipated to extend to masses in other organs.

Gamma camera-specific reference standards for radioactive iodine uptake measurements

BackgroundCurrent guidelines of the radioiodine uptake (RAIU) test allow the use of different equipment, isotopes, activity and region-of-interest (ROI). We evaluated presence and extent of these differences in clinical practice and evaluated the effect of some of these variations on RAIU outcomes. Also, gamma camera-specific reference standards were calculated and retrospectively compared with measurements obtained during clinical RAIU tests.Materials and methodsFirst, questionnaires were sent to Dutch nuclear medicine departments requesting information about equipment usage, isotope, isotope formulation, activity and measurement techniques. Secondly, a neck phantom containing a range of activities in capsule or water-dissolved formulation was scanned. Counts were measured using automatic ROI, square box ROI or all counts in the image. Thirdly, clinical RAIU data were collected during 2015–2018 using three different gamma cameras. Reference standards for each scanner were calculated using regression analysis between reference activity and measured counts. Uptake measurements using this gamma camera-specific reference standard were compared with original measurements.ResultsThe survey demonstrated significant differences in isotope, isotope formulation, activity, use of neck phantoms, frequency and duration of reference measurements, distance to collimator, use of background measurements and ROI delineation. The phantom study demonstrated higher counts for the water-dissolved formulation than capsules using both automatic and square box ROI. Also, higher counts were found using a square box ROI than an automatic ROI. The retrospective study showed feasibility of RAIU calculations using camera-specific reference standards and good correlation with the original RAIU measurements.ConclusionsThis study demonstrated considerable technical variation in RAIU measurement in clinical practice. The phantom study demonstrated that these differences could result in differences in count measurements, potentially resulting in different dose calculations for radioactive iodine therapy. Retrospective data suggest that camera-specific reference standards may be used instead of individual reference measurements using separate activity sources, which may thus eliminate some sources of variation.

Accurate and efficient measurement of channelized Hotelling observer-based low-contrast detectability on the ACR CT accreditation phantom.

Current CT quality control (QC) for low-contrast detectability relies on visual inspection and measurement of contrast-to-noise ratio (CNR). However, CNR numbers become unreliable when it comes to nonlinear methods, such as iterative reconstruction (IR) and deep-learning-based techniques. Image quality metrics using channelized Hotelling observer (CHO) have been validated to be well correlated with human observer performance on phantom-based and patient-based tasks, but it has not been widely used in routine CT QC mainly because the CHO calculation typically requires a large number of repeated scans in order to provide accurate and precise estimate of index of detectability (d'). The main goal of this work is to optimize channel filters and other CHO parameters and accurately estimate the low-contrast detectability with minimum number of repeated scans for the widely used American College of Radiology (ACR) CT accreditation phantom so that it can become practically feasible for routine CT QC tests. To provide a converged d' value, an ACR phantom was repeatedly scanned 100 times at three dose levels (24, 12, and 6 mGy). Images were reconstructed with two kernels (FBP Br44 and IR Br44-3). d' as a function of number of repeated scans was determined for different number of background regions of interest (ROIs), different number of low-contrast objects, different number of slices per each object, and different channel filter options. A reference d' was established using the optimized CHO setting, and the bias of d' was quantified using the d' calculated from all 100 repeated scans. The variation of d' at each condition was estimated using a resampling method combining random subsampling among 100 repeated scans and bootstrapping of the ensembles of signal and background ROIs. Optimized parameters in CHO calculation were determined: two background ROIs per object, four objects per low-contrast object size, nine non-overlapping slices per object, and a 4-channel Gabor filter. The bias and uncertainty were estimated at different numbers of repeated scans using these parameters. When only one single scan was used in the CHO calculation, the bias of d' was below 6.2% and the uncertainty 15.6-19.6% for the 6, 5, and 4 mm objects, while with three repeated scans the bias was below 2.0% and uncertainty 8.7-10.9% for the three object sizes. With optimized parameter settings in CHO, efficient and accurate measurement of low-contrast detectability on the commonly used ACR phantom becomes feasible, which could potentially lead to adoption of CHO-based low-contrast evaluation in routine QC tests.

Methods of calculating 123I-β-methyl-P-iodophenyl-pentadecanoic acid washout rates in triglyceride deposit cardiomyovasculopathy

ObjectiveThis study aimed to optimize various methods of calculating washout rates (WRs) of 123I-β-methyl-p-iodophenyl-pentadecanoic (BMIPP), as they are essential to diagnose triglyceride deposit cardiomyovasculopathy (TGCV) which is a rare disease entity identified in Japan and has been encoded in Orphanet (ORPHA code 565612).MethodsWe calculated WRs of 123I-BMIPP from early (20 min) and delayed (200 min) images. We evaluated six methods of calculating WRs to discriminate TGVC patients (age, 56.8 ± 14.6 y; male, n = 13; female, n = 4) and 21 123I-BMIPP studies were involved including 4 follow-up studies. Washout rates were calculated by two planar methods using anterior images with cardiac and background regions of interest (ROIs) and by four SPECT methods using either array and polar plots or summed short-axis images. The final diagnoses of TGCV were confirmed according to the 2020 diagnostic criteria, and the diagnostic accuracy of WRs calculated using the six methods was analyzed using the area under receiver-operating characteristics curves (ROC-AUC). Multiple scatter-plot matrix methods were evaluated with correlations for comparison.ResultsAll six methods were useful for diagnosis and did not significantly differ. The four SPECT methods showed excellent diagnostic accuracy (AUC 1.0), whereas the planar methods with and without background correction could be acceptable (AUC 0.857 and 0.964, respectively). The WRs were relatively lower for patients with CAD and remarkable metabolic defects than for patients with TGCV but without defects.ConclusionsFor the diagnosis of TGCV, the WR cutoff of 10% of 123I-BMIPP functioned well in planar and SPECT discrimination based on computational methods as a classifier. However, calculation optimization should improve TGCV diagnoses.

Effects of Dietary Gingerol-Enriched Ginger Supplementation on Distribution of Colon Function Markers in Rats With Diabetic Neuropathic Pain

ObjectivesInflammation and leaky gut may contribute to the pathogenesis of diabetic neuropathic pain (DNP). Ginger has been used in the treatment of a variety of pain disorders for its anti-inflammatory, antioxidant, and analgesic effects. We previously reported that gingerol-enriched ginger (GEG) supplementation suppressed the DNP-induced mRNA expression levels of claudin-3 (tight junction protein), PINK1 (autophagy marker), DRP1 (mitochondrial fission protein), and GFAP (astrocyte marker) in colons. Thus, this study was to further investigate the effects of GEG supplementation on the distribution of function markers in the colon of DNP rats. MethodsMale Sprague-Dawley rats were assigned to three groups: low-fat diet (Control), high-fat diet (HFD)+ streptozotocin (DNP), and HFD + streptozotocin + 0.75% GEG w/w in diet (DNP + GEG). Colons were collected and preserved at -80 degrees. The localization and quantity of claudin-3, PINK1, DRP1, and GFAP in colons were determined by cryosectioning, immunohistochemistry and fluorescence confocal microscopy. The protein levels were quantified by the average fluorescence intensity in cross-sections of intestinal crypts within pre-determined regions of interest (ROIs), normalized by the background ROI. The comparisons between groups were made using an unpaired t-test. ResultsCompared to the Control group, the DNP group had higher expression levels of claudin-3 (Control, n = 6; DNP, n = 5; P < 0.005), PINK1 (Control, n = 7; DNP, n = 7; P < 0.05), and DRP1 (Control, n = 6; DNP, n = 7; P < 0.01) in colons of animals. GEG supplementation into diets significantly suppressed DNP-induced expression levels of claudin-3 (DNP, n = 7, DNP + GEG, N = 6; P < 0.05), PINK1 (DNP, n = 7, DNP + GEG, n = 5; P < 0.05), and DRP1 (DNP, n = 7, DNP + GEG, n = 6; P < 0.05) in colons of animals. There were no significant differences in PINK1 expression between the Control group and the DNP + GEG group (p > 0.05). Similar to PINK1 finding, we did not observe the difference in DRP1 expression between the Control and the DNP + GEG group. ConclusionsThese findings show that gingerol-enriched ginger reduces the physiological colonic disruption associated with diabetic neuropathy. Funding SourcesMedical Student Summer Research Program, Texas Tech University Health Sciences Center, Lubbock, TX.

A measurement of illumination variation-resistant noncontact heart rate based on the combination of singular spectrum analysis and sub-band method

Background and ObjectiveThe imaging photoplethysmography method is a non-contact and non-invasive measurement method that usually uses surrounding illumination as an illuminant, which can be easily influenced by the surrounding illumination variations. Thus, it has a practical value to develop an efficient method of heart rate measurement that can remove the interference of illumination variations robustly. MethodWe propose a novel framework of heart rate measurement that is robust to illumination variations by combining singular spectrum analysis and sub-band method. At first, we extract the blood volume pulse signal by applying the modified sub-band method to the raw facial RGB trace signals. Then the spectra for the interference of illumination variations are extracted from the raw signal obtained from facial regions of interest by using singular spectrum analysis. Finally, we estimate the more robust heart rate through comparison analysis between the spectra of the extracted blood volume pulse signal and the illumination variations. ResultsWe compared our method with several state-of-the-art methods through the analysis using the self-collected data and the UBFC-RPPG database. Bland-Altman plots and Pearson correlation coefficients pointed out that the proposed method could measure the heart rate more accurately than the state-of-the-art methods. For the self-collected data and the UBFC-RPPG database, Bland-Altman plots showed that proposed method caused better agreement with 95% limits from -4 bpm to 10 bpm and from -2 bpm to 4 bpm respectively than the other state-of-the-art methods. It also revealed that the highly linear relation was held between the estimated heart rate and ground truth with the correlation coefficients of 0.89 and 0.99, respectively. ConclusionBy extracting illumination variation directly from the facial region of interest rather than from the background region of interest, the proposed method demonstrates that it can overcome the drawbacks of the previous methods due to the illumination variation difference between the background and facial regions of interest. It can be found that the proposed method has a relatively good robustness regardless of whether illumination variation exists or not.

Region of interest extraction for image quality assessment

Print quality (PQ) is most important in the printing industry. To detect and analyze print defects is an effective solution to improve print quality. As the different types of print defects appear in different regions of interest (ROI) in the digital image of a scanned page, extracting the different ROIs helps to detect and analyze the printer defect. This paper proposes a method to extract different ROIs based on the digital image object map [1], which includes three different labels: raster (images or pictures), vector (background and smooth gradient color areas), and symbol (symbols and texts). Our ROI extraction method will extract four kinds of ROIs based on these three labeled objects. So we need to distinguish the background area and smooth gradient color area (color vector) from other vector objects. The process of the ROI extraction method includes four parts; and each part will extract one kind of ROI. For the color vector and background ROI extraction part, we develop two approaches: one is to obtain the maximum area rectangular ROI; and the other approach is to extract the deepest rectangular ROI. With both of these two methods, we use a greedy algorithm to gather additional useful ROIs. In the final result of the ROI extraction process, we only save the left top and right bottom positions for each ROI. In the end, we design a Matlab GUI Tool and label the ROI ground truth manually. We calculate the intersection over union (IoU)) between the ROI extraction result and the ROI manually labeled ground truth to evaluate our ROI extraction algorithm, and check whether it is good enough to crop different ROIs from the image of the scanned page to detect and analyze print defects.

Assessment of PET and SPECT phantom image quality through automated binary classification of cold rod arrays.

Evaluation of positron emission tomography (PET) or single photon emission computed tomography (SPECT) performance often involves qualitative assessment of phantom images, namely the visibility of hot or cold structures in a warm background. Structure-to-background contrast is a quantitative measure of scanner performance; however, contrast measurements do not account for image noise and its effect on task performance. Although task-based performance could be evaluated over an ensemble of phantom scans, a more practical approach is desired. Repeated structures, such as the cold rod arrays of the American College of Radiography (ACR) PET and SPECT phantoms, offer an opportunity to evaluate image quality based on statistical decision theory. Images are co-registered to a digital template for placement of numerous regions of interest (ROIs) in multiple thick slices centered on each cold rod and on each midpoint between rods. The assumption is made that each ROI corresponds to a statistically independent measurement, known to be of either a cold rod or the background. A receiver operating characteristic (ROC) curve then is generated for each cold rod sector of the phantom. The area under the ROC curve (AUC) provides a quantitative measure of cold rod visibility. In addition, signal-to-noise ratio (SNR) is calculated, under the assumption that the rod and background ROI measurements are distributed normally. Using this approach, data from PET and SPECT phantom studies from prior annual physics surveys were analyzed retrospectively. Rod and background ROI measurements had nearly normal (PET) or approximately normal (SPECT) distributions. Resultant ROC curves illustrated the varying degrees of overlap between rod and background histograms vs cold rod diameter. Both AUC and SNR correlated well with visual image assessment and had high consistency between phantom studies, demonstrating the quantitative accuracy of the automated analysis. AUC reliably quantified cases where cold rods were partially or mostly resolved, while SNR provided further characterization when cold rods were completely resolved. In comparison, contrast measurements often mirrored AUC and SNR but were inconsistent in cases of varying noise. Automated analysis of phantom cold rod arrays using binary classification provides informative quantitative measures of image quality and is practical for routine use. This approach avoids observer dependencies associated with visual assessment and potentially can benefit quality assurance, physics surveys, laboratory accreditation, and harmonization of multicenter image quality.

Correlation between quantitative assessment of contrast enhancement in contrast-enhanced spectral mammography (CESM) and histopathology\u2014preliminary results

ObjectivesContrast-enhanced spectral mammography (CESM) is a novel method for breast cancer detection. The aim of this study is to check if there is a possibility of quantitative assessment of contrast enhancement in CESM and if there is any correlation between quantitative assessment of contrast enhancement in CESM and histopathology.MethodsA total of 167 female patients underwent CESM. All subjects previously had suspicious lesions found on mammography, breast ultrasound, or both. After imaging, the following parameters were evaluated: number of enhancing lesions in each breast and size and degree of enhancement of each lesion. Based on the collected data, the percentage signal difference between enhancing lesion and background (%RS) and signal-difference-to-noise ratio (SDNR) were measured for each lesion.ResultsThe number of lesions detected in the study population was 195. Among all diagnosed lesions, 120 (62%) were assessed to be infiltrating cancers, 16 (8%) non-infiltrating cancers, and 59 (30%) were benign. Thirteen (7%) lesions did not enhance in CESM; all non-enhancing lesions were confirmed to be benign under histopathological examination. Analysis of enhancement indices showed that signal values within lesions and signal values within background ROIs (regions of interest) were similar in CC (craniocaudal) and MLO (mediolateral) projections. Mean %RS values were correlated with the type of enhancing lesion, infiltrating cancers having the highest values, benign lesions the lowest.ConclusionsThis work has demonstrated a significant correlation between the degree of lesion enhancement in CESM and malignancy. Quantitative analysis of enhancement levels in CESM can distinguish between invasive cancers and benign or in situ lesions.Key Points• There is a possibility of quantitative assessment of contrast enhancement in CESM.• Correlation between quantitative assessment of contrast enhancement in CESM and histopathology was observed.

Utility of 123I-MIBG Standardized Uptake Value in Patients with Refractory Pheochromocytoma and Paraganglioma.

Objective(s):Single-photon emission computed tomography (SPECT) using metaiodobenzylguanidine (MIBG) is an important diagnostic tool for the treatment of refractory pheochromocytoma and paraganglioma (PPGL). Owing to the difficulty of SPECT quantification, the tumour-to-background ratio (TBR) is used to assess disease activity. However, the utility of TBR is limited owing to the background setting. A quantification technique of SPECT/computed tomography (CT) would facilitate image interpretation. This study aimed to assess the relationship between 123I-MIBG maximum standardized uptake value (SUVmax) and TBR and levels of urinary catecholamines and metabolites in patients with refractory PPGL. Methods:This study included 15 patients with refractory PPGL who underwent 131I-MIBG therapy. Overall, 27 123I-MIBG SPECT/CT images were acquired before and after the therapy. Lesions observed on whole-body images were analysed; the maximum number of lesions per scan was 10. 123I-MIBG SUVmax was semi-automatically calculated using Q. Metrix package (GE Healthcare). TBR was manually calculated according to the following formula: (max count in lesion − max count in background)/max count in background. Background was set in the contralateral area. When a background region of interest could not be set in the area, it was set in the thigh area. Urine was sampled for 24 h to measure catecholamine and metabolite levels. Increases of ≥3-fold were considered abnormal. TBR, 123I-MIBG SUVmax and urinary catecholamine and metabolite levels were compared using linear regression analysis.Results:All patients had MIBG-avid lesions, as seen on 123I-MIBG SPECT/CT. A significant relationship between 123I-MIBG SUVmax and TBR was observed (correlation coefficient [r] =0.84, P < 0.0001). In 27 SPECT/CT examinations, normetanephrine (NMN) level was abnormally increased in 51% (14/27), but other catecholamine and other metabolites were abnormally increased in < 26% (7/27). 123I-MIBG SUVmax strongly correlated with NMN (r=0.76, P < 0.01) and log NMN (r=0.74, P < 0.01).Conclusion: 123I-MIBG SUVmax demonstrated similar trends as TBR and reflected urinary NMN in patients with refractory PPGL. Semi-automatic quantification of SPECT/CT could be a useful tool for the evaluation of disease activity.

Hepatic selective internal radiation therapy: how well does pretreatment 99mTc-macroaggregated albumin activity distribution and quantification agree with post-therapy bremsstrahlung imaging?

The aim of this study was to examine the agreement of pretreatment Tc-macroaggregated albumin imaging performed for selective internal radiation therapy (SIRT) workup with Y percentage lung shunt (PLS) and regional hepatic distribution in subsequent post-therapy bremsstrahlung imaging. Planar images were used to calculate PLS. The significant Y bremsstrahlung scatter required background correction. Results using both Y lung background regions of interest (ROI) reported in previous studies and extended ROIs (reflecting lung background variation) were compared with Tc-MAA PLS. Lesion and healthy liver volumes were outlined on diagnostic computed tomography scans and registered to Tc-MAA and Y single-photon emission computed tomography/computed tomographydata. Single-photon emission computed tomography voxel values were normalized to injected Y activity. Volume mean activities were calculated, and converted into the mean absorbed dose. Agreement was quantified using Bland-Altman analysis. PLS: The bias using previous studies' lung background ROIs was -10.71%, with a 95% confidence interval (CI) of -18.79 to -2.64%. The extended ROI yielded a bias of 0.77% (95% CI: -2.23 to 3.70%). Liver: The healthy liver bias was 0.01 MBq/ml (0.17 Gy), with a -0.05 to 0.06 MBq/ml (95% CI:0.80 -1.93 Gy). The lesion mean activity/ml bias was -0.02 MBq/ml (3.71 Gy), with a -0.81 to 0.76 MBq/ml (95% CI: -35.49 to 28.07 Gy). The PLS agreement was sensitive to the Y lung background correction ROI, potentially explaining a previously published controversy. The mean activity and absorbed dose agreement for the metastatic lesions was poorer than the healthy liver volumes studied here.

OA043] A new signal-to-noise ratio (SNR) measurement method based on low-pass filtering for magnetic resonance imaging (MRI) quality control (QC) purposes

Purposes Signal-to-noise ratio (SNR) is a prominent metric to assess scanners performances. SNR measurement in MRI is challenging because of the difficulty to quantify noise level from images. A gold-standard consist in pixel-by-pixel variance calculation on several images (>100) scanned in identical conditions. This method is accurate but cannot be practically implemented because of time constraints. Another gold-standard consists in scanning images without radiofrequency excitation hence collecting noise-only. This latter is also unpractical because it requires a specific and generally not-granted access to the hardware. Consequently, there is a need for designing alternative methods for accurate and precise noise quantification. This work aims to introduce a new noise level quantification method for SNR measurement. Methods Our method (Noisefilt) quantifies noise level by combining these operations: (1) subtraction of two images subsequently scanned in identical conditions, (2) Low-pass filtering to extract and suppress low-frequency remaining information, (3) (Noisefilt = standard deviation of pixels. Using a dataset of 30 exams of a head phantom scanned on a 3T MR unit with an 8-channel phased-array brain coil, we compared our method with three others from the literature: noise level quantified using one image-background region-of-interest (ROI) (Noisebgd), several background ROIs (Noisebgd+), and from the subtraction of two images (Noisediff) [6]. By means of Bland–Altman analysis, we assessed the agreement between all these methods and the noise-only standard method (NoisenoRF). We also assessed their correlation with NoisenoRF (Pearson’s correlation coefficient r, p Results Bland–Altman analysis showed that Noisefilt agrees more with the standard than the three other methods. Correlation of our Noisefilt method with the gold-standard (rfilt =0.80, p Conclusion We demonstrated that our method produces accurate measurement, is simple to implement, and is usable for instance for quality control (QC) purposes. This new metric is being tested with additional scans in QC monitoring of different MR scanners. Download : Download high-res image (87KB) Download : Download full-size image Download : Download high-res image (159KB) Download : Download full-size image

Automated Region of Interest Detection Method in Scintigraphic Glomerular Filtration Rate Estimation.

The glomerular filtration rate (GFR) is a crucial index to measure renal function. In daily clinical practice, the GFR can be estimated using the Gates method, which requires the clinicians to define the region of interest (ROI) for the kidney and the corresponding background in dynamic renal scintigraphy. The manual placement of ROIs to estimate the GFR is subjective and labor-intensive, however, making it an undesirable and unreliable process. This work presents a fully automated ROI detection method to achieve accurate and robust GFR estimations. After image preprocessing, the ROI for each kidney was delineated using a shape prior constrained level set (spLS) algorithm and then the corresponding background ROIs were obtained according to the defined kidney ROIs. In computer simulations, the spLS method had the best performance in kidney ROI detection compared with the previous threshold method (threshold) and the Chan-Vese level set (cvLS) method. In further clinical applications, 223 sets of 99mTc-diethylenetriaminepentaacetic acid renal scintigraphic images from patients with abnormal renal function were reviewed. Compared with the former ROI detection methods (threshold and cvLS), the GFR estimations based on the ROIs derived by the spLS method had the highest consistency and correlations (r = 0.98, p < 0.001) with the reference estimated by experienced physicians. The results indicate that the proposed automated ROI detection method has great potential in automated ROI detection for accurate and robust GFR estimation in dynamic renal scintigraphy.

A computer tomography assisted method for the automatic detection of region of interest in dynamic kidney images

Glomerular filtration rate (GFR), which can be estimated by Gates method with dynamic kidney single photon emission computed tomography (SPECT) imaging, is a key indicator of renal function. In this paper, an automatic computer tomography (CT)-assisted detection method of kidney region of interest (ROI) is proposed to achieve the objective and accurate GFR calculation. In this method, the CT coronal projection image and the enhanced SPECT synthetic image are firstly generated and registered together. Then, the kidney ROIs are delineated using a modified level set algorithm. Meanwhile, the background ROIs are also obtained based on the kidney ROIs. Finally, the value of GFR is calculated via Gates method. Comparing with the clinical data, the GFR values estimated by the proposed method were consistent with the clinical reports. This automatic method can improve the accuracy and stability of kidney ROI detection for GFR calculation, especially when the kidney function has been severely damaged.

Predictive value of scintigraphic (semi-)quantitative thyroid parameters on radioiodine therapy outcome in hyperthyroid cats.

Objectives The outcome of radioiodine therapy in hyperthyroid cats is suspected to be influenced by multiple factors. The degree of activity of the thyroid gland, represented by uptake of sodium pertechnetate or tracer activities of radioiodine by the thyroid gland on thyroid scintigraphy, has been suggested in the literature as one of those. Thyroid gland pertechnetate uptake can be represented by (semi-)quantitative factors such as the thyroid to salivary gland (T/S) ratio, the thyroid to background (T/B) ratio and the percentage technetium uptake by the thyroid glands (%TcU). The aim of this study was to investigate a possible relationship between these thyroid scan parameters and radioiodine therapy outcome. Methods Sodium pertechnetate thyroid scans of 75 hyperthyroid cats were retrospectively evaluated and statistical analysis was performed with and without correction for injected radioiodine activity. Three different background regions of interest (ROIs) were used to calculate the T/B ratio and %TcU: 'neck', 'circle' and 'copy ROI'. Results Higher T/S ratios were found to be significantly related to a persistent hyperthyroid outcome in both analyses. For the T/S ratio, a threshold value of 5.4 was determined, with a sensitivity of 73% and a specificity of 59%. An increased risk for persistent hyperthyroidism compared with a final euthyroid outcome with an increased T/Bcircle ratio was only found to be significant without correction for the activity of radioiodine administered. For the %TcU no statistical significance was reached. Regarding a low total thyroxine outcome, no significant relationships with any of the investigated parameters were found. Conclusions and relevance The findings of this study suggest that semi-quantification of thyroid gland uptake is best performed using the T/S ratio. A T/S ratio ⩾5.4 is a possible indicator for an increased risk of persistent hyperthyroidism.

Illumination variation interference suppression in remote PPG using PLS and MEMD

A novel framework based on partial least squares (PLS) and multivariate empirical mode decomposition (MEMD) is proposed to effectively evaluate heart rate from webcam videos captured during illumination changing conditions. The framework takes the assumption that both facial region of interest (ROI) and background ROI have the similar illumination variations and the background ROI can be treated as the denoising reference by using PLS to extract the underlying common illumination variation sources existing in both ROIs. Then, a number of intrinsic mode functions are decomposed by applying MEMD to the illumination-variation-suppressed facial ROI and the HR is evaluated. Compared to the experimental results obtained by the recently proposed independent component analysis and the ensemble empirical mode decomposition methods, the proposed method led to a better agreement with HR ground truth (the mean bias was 3.4 bpm with 95% limits of agreement ranging from −13.2 to 19.9 bpm), indicating a promising solution for the realistic HR estimation remotely.

Illumination Variation-Resistant Video-Based Heart Rate Measurement Using Joint Blind Source Separation and Ensemble Empirical Mode Decomposition.

Recent studies have demonstrated that heart rate (HR) could be estimated using video data [e.g., exploring human facial regions of interest (ROIs)] under well-controlled conditions. However, in practice, the pulse signals may be contaminated by motions and illumination variations. In this paper, tackling the illumination variation challenge, we propose an illumination-robust framework using joint blind source separation (JBSS) and ensemble empirical mode decomposition (EEMD) to effectively evaluate HR from webcam videos. The framework takes the hypotheses that both facial ROI and background ROI have similar illumination variations. The background ROI is then considered as a noise reference sensor to denoise the facial signals by using the JBSS technique to extract the underlying illumination variation sources. Further, the reconstructed illumination-resisted green channel of the facial ROI is detrended and decomposed into a number of intrinsic mode functions using EEMD to estimate the HR. Experimental results demonstrated that the proposed framework could estimate HR more accurately than the state-of-the-art methods. The Bland-Altman plots showed that it led to better agreement with HR ground truth with the mean bias 1.15 beats/min (bpm), with 95% limits from -15.43 to 17.73 bpm, and the correlation coefficient 0.53. This study provides a promising solution for realistic noncontact and robust HR measurement applications.

IMPROVED PLANAR KIDNEY ACTIVITY CONCENTRATION ESTIMATE BY THE POSTERIOR VIEW METHOD IN 177LU-DOTATATE TREATMENTS.

The aims of this study were to determine how different background regions of interest (ROIs) around the kidney represent true background activity in over- and underlying tissues in 177Lu-DOTA-octreatate (177Lu-DOTATATE) treatments and to determine the influence of the background positions on the kidney activity concentration estimates by the conjugate view (ConjV) and posterior view (PostV) methods. The analysis was performed in single-photon emission computed tomography (SPECT) images of 20 patients, acquired 24 h post injection of a 177Lu-DOTATATE treatment, by a computer algorithm that created planar images from the SPECT data. The ratio between the activity concentration in the background and the true background varied from 0.36 to 2.08 [coefficient of variation (CV) = 25–181 %] and from 0.44 to 1.52 (CV = 16–70 %) for the right and left kidneys, respectively. The activity concentration estimate in the kidneys was most accurate with the PostV method using a background ROI surrounding the whole kidney, and this combination might be an alternative planar method for improved kidney dosimetry in the 177Lu-DOTATATE treatments.