Correlated MR spectroscopic imaging of breast cancer to investigate metabolites and lipids: acceleration and compressed sensing reconstruction

Joy, Ajin; Saucedo, Andres; Joines, Melissa; Lee‐Felker, Stephanie; Kumar, Sandeep; Sarma, Manoj K.; Sayre, James W.; DiNome, Maggie L.; Thomas, M. Albert

doi:10.1259/bjro.20220009

Cited by 4 publications

(8 citation statements)

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“…Ideally, the ratios should be similar on either side of the diagonal, but the lower resolution along F 1 , and t 1 ridging effects could influence these measures. Earlier studies have reported this measure from the cross peaks either above or below the diagonal or an average of the peaks on either side 16 , 32 . Statistically significant difference among IP, CT and FFT were observed in the cross peaks CP2, CP3, CP4 and CP5, especially in healthy controls.…”

Section: Discussionmentioning

confidence: 99%

“…2 , 3 , 4 ). This is especially important considering the ability of 5D EP-COSI in vivo detection and quantitation of metabolites like mI and Gly, in addition to measuring lipid-based biomarkers 16 . Reports from ex vivo breast cancer tissues have also shown the role of these metabolites including mI, Gly and Cho in identifying malignancy 5 , 33 .…”

Section: Discussionmentioning

confidence: 99%

“…The dominant lipid peak around 1.3 ppm was zeroed in the Fourier transform of the non-water suppressed signal to obtain a water-dominant time domain signal for the eddy current phase correction and coil combination. The spectral peak volume integrals were computed as described in 16 . The quantified proton resonances along the diagonal (F 1 -F 2 ), and off-diagonal are listed in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

“…It was later extended to MR spectroscopic imaging (MRSI) sequences that record 2D COSY spectra in multiple locations at clinically feasible times, with the help of non-uniform sampling (NUS) and Compressed Sensing (CS) reconstruction 13 , 14 . Recently, a five-dimensional (5D) echo-planar correlated spectroscopic imaging (EP-COSI) technique combining 2 spectral and 3 spatial dimensions further increased the coverage area by measuring 2D spectra from multiple voxels within multiple slices, during a single scan session, and therefore helps to better localize the malignant tissues across the breast 15 , 16 .…”

Section: Introductionmentioning

confidence: 99%

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Enhanced spectral resolution for correlated spectroscopic imaging using inner-product and covariance transform: a pilot analysis of metabolites and lipids in breast cancer in vivo

Joy,

Thomas

2023

Sci Rep

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Acquisition duration of correlated spectroscopy in vivo can be longer due to a large number of t1 increments along the indirect (F1) dimension. Limited number of t1 increments on the other hand leads to poor spectral resolution along F1. Covariance transformation (CT) instead of Fourier transform along t1 is an alternative way of increasing the resolution of the 2D COSY spectrum. Prospectively undersampled five-dimensional echo-planar correlated spectroscopic imaging (EP-COSI) data from ten malignant patients and ten healthy women were acquired and reconstructed using compressed sensing. The COSY spectrum at each voxel location was then generated using FFT, CT and a variant of CT called Inner Product (IP). Metabolite and lipid ratios were computed with respect to water from unsuppressed one-dimensional spectrum. The effects of t1-ridging artifacts commonly seen with FFT were not observed with CT/IP. Statistically significant differences were observed in the fat cross peaks measured with CT/IP/FFT. Spectral resolution was increased ~ 8.5 times (~ 19.53 Hz in FFT, ~ 2.32 Hz in CT/IP) without affecting the spectral width along F1 was possible with CT/IP. CT and IP enabled substantially increased F1 resolution effectively with significant gain in scan time and reliable measure of unsaturation index as a biomarker for malignant breast cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced spectral resolution for correlated spectroscopic imaging using inner-product and covariance transform: a pilot analysis of metabolites and lipids in breast cancer in vivo

Joy,

Thomas

2023

Sci Rep

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“…While single-voxel spectroscopy has a reported 64–82% sensitivity and 85–91% specificity [ 41 ], the multi-voxel technique of MRSI can cover a larger area of the breast with a relatively higher spatial resolution. Advanced MRSI techniques like five-dimensional (5D) echo-planar correlated spectroscopic imaging (EP-COSI) can record two-dimensional (2D) correlated spectroscopy (COSY) from multiple regions in three-dimensional (3D) space [ 42 ]. Achieving high specificity is also challenging in MRSI due to overlapping patterns of the measures between benign and malignant lesions.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning for Breast Cancer Lesion Classification: A Pilot Validation Using Correlated Spectroscopic Imaging and Diffusion-Weighted Imaging

et al. 2023

Self Cite

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The main objective of this work was to evaluate the application of individual and ensemble machine learning models to classify malignant and benign breast masses using features from two-dimensional (2D) correlated spectroscopy spectra extracted from five-dimensional echo-planar correlated spectroscopic imaging (5D EP-COSI) and diffusion-weighted imaging (DWI). Twenty-four different metabolite and lipid ratios with respect to diagonal fat peaks (1.4 ppm, 5.4 ppm) from 2D spectra, and water and fat peaks (4.7 ppm, 1.4 ppm) from one-dimensional non-water-suppressed (NWS) spectra were used as the features. Additionally, water fraction, fat fraction and water-to-fat ratios from NWS spectra and apparent diffusion coefficients (ADC) from DWI were included. The nine most important features were identified using recursive feature elimination, sequential forward selection and correlation analysis. XGBoost (AUC: 93.0%, Accuracy: 85.7%, F1-score: 88.9%, Precision: 88.2%, Sensitivity: 90.4%, Specificity: 84.6%) and GradientBoost (AUC: 94.3%, Accuracy: 89.3%, F1-score: 90.7%, Precision: 87.9%, Sensitivity: 94.2%, Specificity: 83.4%) were the best-performing models. Conventional biomarkers like choline, myo-Inositol, and glycine were statistically significant predictors. Key features contributing to the classification were ADC, 2D diagonal peaks at 0.9 ppm, 2.1 ppm, 3.5 ppm, and 5.4 ppm, cross peaks between 1.4 and 0.9 ppm, 4.3 and 4.1 ppm, 2.3 and 1.6 ppm, and the triglyceryl–fat cross peak. The results highlight the contribution of the 2D spectral peaks to the model, and they demonstrate the potential of 5D EP-COSI for early breast cancer detection.

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A non-axisymmetric temperature field reconstruction method based on the compressive sensing background oriented Schlieren method

Wu,

Pan,

Zhang

et al. 2024

Physics of Fluids

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Measurement of the combustion temperature field is an extremely important issue in industrial production. Temperature is one of the key parameters in combustion studies. With the temperature field distribution of the combustion field obtained, heat transfer, heat convection, and heat radiation can be calculated directly and efficiently. Traditional background oriented Schlieren (BOS) is an effective method for non-axisymmetric temperature field measurements, but it requires simultaneous Schlieren imaging at multiple angles for tomographic reconstruction, which will greatly limit its application. In this paper, the compressive sensing algorithm is introduced into the temperature field reconstruction, which establishes the system of equations between the deflection angle and the refractive index gradient. Then, the reconstruction of the non-axisymmetric temperature field is realized by solving the underdetermined system of equations by the method of solving the sparse solution through the compressive sensing. First, light offsets across the non-axisymmetric temperature field are measured by the under-angled BOS system and image processing method. Second, the spatial refractive index field is reconstructed by the compressive sensing BOS method proposed in this paper. Finally, the spatial temperature field is obtained. The experimental results show that by comparing the iRadon reconstruction algorithm and the compressive sensing reconstruction algorithm, the temperature field reconstructed by the compressive sensing under the condition of the under-angled sampling of projection data had a higher accuracy than that reconstructed by the tomographic reconstruction algorithm under the same condition. The average error of the temperature field was reduced from 34.6 to 29.7 K under the same measurement conditions; however, the accuracy is better maintained by using the compressive sensing algorithm under the condition of undersampling projection.

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Correlated MR spectroscopic imaging of breast cancer to investigate metabolites and lipids: acceleration and compressed sensing reconstruction

Cited by 4 publications

References 57 publications

Enhanced spectral resolution for correlated spectroscopic imaging using inner-product and covariance transform: a pilot analysis of metabolites and lipids in breast cancer in vivo

Enhanced spectral resolution for correlated spectroscopic imaging using inner-product and covariance transform: a pilot analysis of metabolites and lipids in breast cancer in vivo

Ensemble Learning for Breast Cancer Lesion Classification: A Pilot Validation Using Correlated Spectroscopic Imaging and Diffusion-Weighted Imaging

A non-axisymmetric temperature field reconstruction method based on the compressive sensing background oriented Schlieren method

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