Ultrasound Lesion Detectability as a Distance Between Probability Measures

Hyun, Dongwoon; Kim, Gene B; Bottenus, Nick; Dahl, Jeremy J.

doi:10.1109/tuffc.2021.3138058

Cited by 12 publications

(5 citation statements)

References 38 publications

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“…7(b) ], whereas consistently negative contrast differences were obtained for solid masses. Note that these consistencies were achieved with direct quantitative contrast comparison across image domains without implementing histogram matching recommendations [ 44 ], [ 55 ], which are not always feasible to implement [ 56 ]. Aside from the generally low sensitivity reported in Table II (with the exception of fundamental R-SLSC imaging), one drawback of the contrast difference metric is the requirement to beamform both B-mode and coherence images, followed by the required selection of both mass and background ROIs for analysis of both the B-mode and coherence images (whereas LOC only requires a coherence computation and a single ROI selection within an indeterminate mass and gCNR only requires analysis of the coherence image).…”

Section: Discussionmentioning

confidence: 99%

“…The gCNR of breast masses relative to surrounding tissue is measured as follows:

1

where

bins centered at

were defined to derive histograms

and

of the beamformed signals (after envelope-detection for B-mode images; prior to normalization and log compression for B-mode and coherence images) of signals within the mass and the surrounding breast tissue ROIs, respectively, and

is the index of the bin. Although a fixed or data-driven approach to selecting

can be used to compute the histograms [ 42 ], [ 43 ], [ 44 ], we relied on a previously reported approach [ 45 ] (validated in the Appendix of [ 42 ]) that uses the data-based method described by Wand [ 46 ] (resulting in 19 ≤

≤ 304).…”

Section: Methodsmentioning

confidence: 99%

“…where N bins centered at {x 1 , x 2 ...x N } were defined to derive histograms h mass and h tissue of the beamformed signals (after envelope-detection for B-mode images; prior to normalization and log compression for B-mode and coherence images) from the mass and the surrounding breast tissue, respectively, and j is the index of the bin. Although a fixed or data-driven approach to selecting N can be used to compute the histograms [42]- [44], we relied on a previously reported approach [45] (validated in the Appendix of [42]) that uses the data-based method described by Wand [46] (resulting in 19 ≤ N ≤ 304). 5) Statistical analysis: Sensitivity and specificity are two common statistical metrics to evaluate and compare the performance of a classifier.…”

Section: B Data Acquisitionmentioning

confidence: 99%

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

Sharma,

Oluyemi,

Myers

et al. 2024

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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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.

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Section: Discussionmentioning

confidence: 99%

“…The gCNR of breast masses relative to surrounding tissue is measured as follows:

1

where

bins centered at

were defined to derive histograms

and

is the index of the bin. Although a fixed or data-driven approach to selecting

≤ 304).…”

Section: Methodsmentioning

confidence: 99%

Section: B Data Acquisitionmentioning

confidence: 99%

See 1 more Smart Citation

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

Sharma,

Oluyemi,

Myers

et al. 2024

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

View full text Add to dashboard Cite

show abstract

“…The traditional quantitative metrics of image quality, such as SNR and CNR, were not included in this study as recent studies have shown that these measurements could be adjusted by dynamic range alterations [34][35][36] [37], and they are highly dependent on the ROI selection. To provide a fairer comparison, the images were rendered with adaptive DR, which is determined by the statistics of the log compressed power Doppler images.…”

Section: Discussionmentioning

confidence: 99%

High-resolution Power Doppler Using Null Subtraction Imaging

Kou,

Lowerison,

You

et al. 2024

IEEE Trans. Med. Imaging

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To improve the spatial resolution of power Doppler (PD) imaging, we explored null subtraction imaging (NSI) as an alternative beamforming technique to delay-and-sum (DAS). NSI is a nonlinear beamforming approach that uses three different apodizations on receive and incoherently sums the beamformed envelopes. NSI uses a null in the beam pattern to improve the lateral resolution, which we apply here for improving PD spatial resolution both with and without contrast microbubbles. In this study, we used NSI with three types of singular value decomposition (SVD)-based clutter filters and noise equalization to generate high-resolution PD images. An element sensitivity correction scheme was also proposed as a crucial component of NSI-based PD imaging. First, a microbubble trace experiment was performed to evaluate the resolution improvement of NSI-based PD over traditional DAS-based PD. Then, both contrast-enhanced and contrast free ultrasound PD images were generated from the scan of a rat brain. The cross-sectional profile of the microbubble traces and microvessels were plotted. FWHM was also estimated to provide a quantitative metric. Furthermore, iso-frequency curves were calculated to provide a resolution evaluation metric over the global field of view. Up to six-fold resolution improvement was demonstrated by the FWHM estimate and four-fold resolution improvement was demonstrated by the iso-frequency curve from the NSI-based PD microvessel images compared to microvessel images generated by traditional DAS-based beamforming. A resolvability of 39 µm was measured from the NSI-based PD microvessel image. The computational cost of NSI-based PD was only increased by 40 percent over the DAS-based PD.

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“…where h L and h H represent the histogram of the voxel values of the low-and high-intensity regions, respectively, and N is the number of bins in the histogram. Hyun et al (2022) reported that it is important to choose a bin size that is not too fine or too coarse to obtain a valid estimate of the GCNR. We have chosen the number of bins as the square root of the number of pixels in the low-(or high-) intensity region and examined the histograms to check that the sampling was appropriate.…”

Section: Evaluation Criteriamentioning

confidence: 99%