3D Amide Proton Transfer Weighted Brain Tumor Imaging With Compressed SENSE: Effects of Different Acceleration Factors

Evaluation of Amide Proton Transfer Imaging Combined With Serum Squamous Cell Carcinoma Antigen for Grading Cervical Cancer

OBJECTIVE

The aim of the study is to investigate the efficacy of amide proton transfer-weighted (APT) imaging combined with serum squamous cell carcinoma antigen (SCC-Ag) in grading cervical cancer.

METHODS

Sixty-three patients with surgically confirmed cervical SCC were enrolled and categorized into 3 groups: highly differentiated (G1), moderately differentiated (G2), and poorly differentiated (G3). The diagnostic efficacies of APT imaging and serum SCC-Ag, alone or in combination, for grading cervical SCC were compared.

RESULTS

The APT values measured by the 2 observers were in excellent agreement (intraclass correlation coefficient >0.75). Mean (± standard deviation) APT values for the high, moderate, and poor differentiation groups were 2.542 ± 0.215% (95% confidence interval [CI]: 2.423-2.677), 2.784 ± 0.175% (95% CI: 2.701-2.856), and 3.120 ± 0.221% (95% CI: 2.950-3.250), respectively. APT values for groups G2 and G3 were significantly higher than those for G1 ( P  < 0.05). APT values for identifying cervical SCC in groups G1 and G2, G2 and G3, and G1 and G3, had areas under the receiver operating characteristic curve, sensitivities, and specificities of 0.815 (95% confidence interval [CI]: 0.674-0.914), 82.1%, and 72.2%, 0.882 (95% CI: 0.751-0.959), 70.6%, and 92.7%, and 0.961 (95% CI: 0.835-0.998), 94.1%, and 94.4%, respectively. APT values were significantly and positively correlated with the histological grade of cervical SCC (Spearman's correlation [ rs ] = 0.731, P  < 0.01). Serum SCC-Ag levels for the high, moderate, and poor differentiation groups were 1.60 (0.88-4.63) ng/mL, 4.10 (1.85-6.98) ng/mL, and 26.10 (9.65-70.00) ng/mL, respectively. The differences were statistically significant only between groups G1 and G3 and G2 and G3 ( P  < 0.05), whereas the differences between groups G1 and G2 were not statistically significant ( P  > 0.05). Spearman's analysis revealed a positive correlation between SCC-Ag levels and the histological grade of cervical SCC ( rs  = 0.573, P  < 0.01). The diagnostic efficacy of APT imaging for the histological grading of cervical SCC was better than that of serum SCC-Ag, and the discriminatory efficacy of the combination of the 2 parameters was better than that of either alone.

CONCLUSIONS

The diagnostic efficacy of APT imaging was better than that of serum SCC-Ag, and the combined diagnostic utility of APT and SCC-Ag was better than that of the individual parameters.

Parameter map guided explainable segmentation framework for breast cancer using amide proton transfer weighted imaging

BACKGROUND

Amide proton transfer weighted (APTw) imaging has demonstrated extensive clinical applications in diagnosing, treating evaluating, and prognosis prediction of breast cancer. There is a pressing need to automatically segment breast lesions on APTw original images to facilitate downstream quantification, which is however challenging.

PURPOSE

To build a segmentation model on the original images of APTw imaging sequence by leveraging the varying contrasts between breast lesions and their surrounding glandular and fat tissues displayed on the original images of APTw imaging at different frequency offsets.

METHODS

This paper proposes a network with multiple tasks, including a breast lesion segmentation model (task I) incorporating multiple images at different frequencies with different contrasts between tumor and surrounding tissues, an automatic classification of pathological task (task II), and an APTw parameter map fitting (task III).

RESULTS

Compared with these advanced segmentation methods such as U-Net, segment anything model (SAM), segment anything in medical images (Med-SAM), and transfomer for MRI brain tumor segmentation (TransBTS), our method achieves higher accuracy (ACC). Furthermore, the model's interpretability facilitates the evaluation of how maps with varying gray contrasts contribute to the segmentation. Moreover, improving the ACC of segmentation can be accomplished through tasks such as pathological classification and parametric map fitting.

CONCLUSIONS

The pathological classification task and parameter fitting task could improve the ACC of segmentation.

Three-dimensional turbo-spin-echo amide proton transfer-weighted and intravoxel incoherent motion imaging mri for triple-negative breast cancer: a comparison with molecular subtypes and histological grades

OBJECTIVE

To investigate associations between breast cancer molecular subtype and intravoxel incoherent motion imaging (IVIM) and amide proton transfer-weighted (APTw).

METHODS

This prospective study involved 264 patients with suspected breast tumors who underwent both breast APTw and IVIM MRI. The maximum diameter of the tumor (Dmax), APT value, apparent diffusion coefficient (ADC), diffusion coefficient (D), pseudo diffusion coefficient (D*), and perfusion fraction (f) values along with histological subtype, grade, and prognostic factors (Ki-67, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2), were compared. APT values about biological subtypes, Ki-67 labeling index, and nuclear grades (NGs) were further analyzed.

RESULTS

A total of 205 participants (mean age, 53 years, range 29-80) were included in the evaluation. The triple-negative breast cancer (TNBC) cancers showed significantly higher D* values than the Luminal B cancers (P = 0.002), while there was no difference in Dmax, ADC, D, and APT (P = 0.068,0.318,0.432,0.089). The TN-type cancers showed significantly higher APT values than the HER2-type cancers (P = 0.002). The area under the curve (AUC) obtained from APTw, IVIM, and Dmax was 0.874. The APT had a moderate positive correlation with the unclear grade (r = 0.473, P < 0.001), and the D* had a weak positive correlation with the Ki-67 labeling index(r = 0.160, P = 0.022).

CONCLUSION

The TN subtype of breast cancer is associated with APT value and D* from IVIM. The APTw may be a promising method for predicting TNBC molecular subtypes.

Acceleration of uterine 3D T2-weighted imaging by compressed SENSE-a multicentre study

OBJECTIVES

To find the optimal acceleration factor (AF) of the compressed SENSE (CS) technique for uterine isotropic high-resolution 3D T2-weighted imaging (3D-ISO-T2WI).

METHODS

A total of 91 female volunteers from the First Affiliated Hospital of Dalian Medical University, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, and The Fourth Hospital of Harbin were recruited. A total of 44 volunteers received uterus sagittal 3D-ISO-T2WI scans on 3.0T MRI device with different CS AFs (including SENSE3, CS3, CS4, CS5, CS6, and CS7), 51 received 3D-ISO-T2WI scans with different degrees of fat suppression (none, light, moderate, and severe), while 4 volunteers received both series of scans. Image quality was subjectively evaluated with a 3-point scoring system. Junction zone signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and myometrial SNR were also calculated. Intraclass correlation coefficients were used to analyse the consistency of the measurement results by 2 observers. Analysis of variance test or Friedman rank sum test was used to compare the differences in subjective scores, SNR, and CNR under different AFs/different degrees of fat suppression.

RESULTS

Images by AFs of CS3, CS4, and CS5 had the highest SNR and CNR. Among them, CS5 had the shortest scan time. CS5 also had one of the highest subjective scores. There was no significant difference in SNR and CNR among images acquired with different degrees of fat suppression. Also, images with moderate fat suppression had the highest subjective scores.

CONCLUSION

The CS5 combined with moderate fat suppression is recommended for routine female pelvic 3D-ISO-T2WI scan.

ADVANCES IN KNOWLEDGE

The CS5 has the highest image quality and has the shortest scan time, which is the best AF. Moderate fat suppression has the highest subjective scores. The CS5 and moderate fat suppression are the best combination for a female pelvic 3D-ISO-T2WI scan.

Compressed Sensitivity Encoding Artificial Intelligence Accelerates Brain Metastasis Imaging by Optimizing Image Quality and Reducing Scan Time

BACKGROUND AND PURPOSE

Accelerating the image acquisition speed of MR imaging without compromising the image quality is challenging. This study aimed to evaluate the feasibility of contrast-enhanced (CE) 3D T1WI and CE 3D-FLAIR sequences reconstructed with compressed sensitivity encoding artificial intelligence (CS-AI) for detecting brain metastases (BM) and explore the optimal acceleration factor (AF) for clinical BM imaging.

MATERIALS AND METHODS

Fifty-one patients with cancer with suspected BM were included. Fifty participants underwent different customized CE 3D-T1WI or CE 3D-FLAIR sequence scans. Compressed SENSE encoding acceleration 6 (CS6), a commercially available standard sequence, was used as the reference standard. Quantitative and qualitative methods were used to evaluate image quality. The SNR and contrast-to-noise ratio (CNR) were calculated, and qualitative evaluations were independently conducted by 2 neuroradiologists. After exploring the optimal AF, sample images were obtained from 1 patient by using both optimized sequences.

RESULTS

Quantitatively, the CNR of the CS-AI protocol for CE 3D-T1WI and CE 3D-FLAIR sequences was superior to that of the CS protocol under the same AF (P < .05). Compared with reference CS6, the CS-AI groups had higher CNR values (all P < .05), with the CS-AI10 scan having the highest value. The SNR of the CS-AI group was better than that of the reference for both CE 3D-T1WI and CE 3D-FLAIR sequences (all P < .05). Qualitatively, the CS-AI protocol produced higher image quality scores than did the CS protocol with the same AF (all P < .05). In contrast to the reference CS6, the CS-AI group showed good image quality scores until an AF of up to 10 (all P < .05). The CS-AI10 scan provided the optimal images, improving the delineation of normal gray-white matter boundaries and lesion areas (P < .05). Compared with the reference, CS-AI10 showed reductions in scan time of 39.25% and 39.93% for CE 3D-T1WI and CE 3D-FLAIR sequences, respectively.

CONCLUSIONS

CE 3D-T1WI and CE 3D-FLAIR sequences reconstructed with CS-AI for the detection of BM may provide a more effective alternative reconstruction approach than CS. CS-AI10 is suitable for clinical applications, providing optimal image quality and a shortened scan time.

Radiomic feature reliability of amide proton transfer-weighted MR images acquired with compressed sensing at 3T

Compressed sensing (CS) is a novel technique for MRI acceleration. The purpose of this paper was to assess the effects of CS on the radiomic features extracted from amide proton transfer-weighted (APTw) images. Brain tumor MRI data of 40 scans were studied. Standard images using sensitivity encoding (SENSE) with an acceleration factor (AF) of 2 were used as the gold standard, and APTw images using SENSE with CS (CS-SENSE) with an AF of 4 were assessed. Regions of interest (ROIs), including normal tissue, edema, liquefactive necrosis, and tumor, were manually drawn, and the effects of CS-SENSE on radiomics were assessed for each ROI category. An intraclass correlation coefficient (ICC) was first calculated for each feature extracted from APTw images with SENSE and CS-SENSE for all ROIs. Different filters were applied to the original images, and the effects of these filters on the ICCs were further compared between APTw images with SENSE and CS-SENSE. Feature deviations were also provided for a more comprehensive evaluation of the effects of CS-SENSE on radiomic features. The ROI-based comparison showed that most radiomic features extracted from CS-SENSE-APTw images and SENSE-APTw images had moderate or greater reliabilities (ICC ≥ 0.5) for all four ROIs and all eight image sets with different filters. Tumor showed significantly higher ICCs than normal tissue, edema, and liquefactive necrosis. Compared to the original images, filters (such as Exponential or Square) may improve the reliability of radiomic features extracted from CS-SENSE-APTw and SENSE-APTw images.