Tumor Contrast-to-noise Ratio Research Articles

Achieving sub-millisievert CT colonography for accurate colorectal tumor detection using smart examination protocols: a prospective self-controlled study.

To assess the feasibility of combining Auto-kVp selection technique, higher preset ASIR-V and noise index (NI) to realize individualized sub-mSv CT colonography (CTC) for accurate colorectal tumor detection and localization. Ninety patients with suspected colorectal cancer (CRC) were prospectively enrolled to undergo standard dose CTC (SDCTC) in the prone and ultra-low dose CTC (ULDCTC) in the supine position. SDCTC used 120 kVp, preset ASIR-V of 30%, SmartmA for a NI of 13; ULDCTC used Auto-kVp selection technique with 80 or 100 kVp, preset ASIR-V of 60%, SmartmA for a NI of 13 for 80 kVp, and NI of 15 for 100 kVp. The effective dose (ED), image quality [signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of colorectal neoplasms] between the two protocols were compared and the accuracies of tumor locations were evaluated for CTC in comparison with the surgery results. The mean ED of the ULDCTC-80 kVp subgroup was 0.70 mSv, 71.43% lower than the 2.45mSv for the 120 kVp group, while that of the ULDCTC-100 kVp subgroup was 0.98mSv, 73.00% lower than the 3.63mSv for the 120 kVp group (P < 0.001). The tumor SNR and CNR of the ULDCTC were higher than those of SDCTC (P < 0.05), while there was no difference in the subjective image quality between them with good inter-observer agreement (Kappa: 0.805-0.923). Both SDCTC and ULDCTC groups had high detection rate of colorectal tumors, along with good consistency in determining tumor location compared with surgery reports (Kappa: 0.718-0.989). The combination of Auto-kVp selection, higher preset ASIR-V and NI achieves individualized sub-mSv CTC with good performance in detecting and locating CRC with surgery and consistent results between SDCTC and ULDCTC.

Comparison of HASTE versus EPI-Based DWI for Retinoblastoma and Correlation with Prognostic Histopathologic Parameters.

Non-EPI-based DWI has shown better performance in head and neck pathologies owing to lesser susceptibility artifacts compared with EPI-DWI. However, only sporadic studies have investigated the feasibility of non-EPI-based DWI in retinoblastoma (RB). We qualitatively and quantitively compared EPI-DWI and HASTE-DWI in RB and correlated the tumor ADC values obtained from these 2 techniques with histopathologic markers. Twenty-one treatment-naive patients with RB underwent 1.5T orbital MR imaging. EPI-DWI and HASTE-DWI were acquired at 3 b-values (0, 500, and 1000 s/mm2). All patients subsequently underwent surgical enucleation. For qualitative image assessment, scoring of overall image quality, artifacts, tumor sharpness, and tumor conspicuity was done by using a 5-point Likert scale. Quantitative assessment included calculations of SNR, contrast-to-noise ratio (CNR), geometric distortion, and ADC. Qualitative scores were compared by using the Wilcoxon signed-rank test, and quantitative parameters were analyzed with a t test. All 21 patients had unilateral RB; 15 were male and 6 were female with a median age of 36 months (range, 9-72 months). On histopathology, patients had either poorly differentiated (n = 13/21) or moderately differentiated (n = 8/21) RB. Other poor prognostic markers evaluated were optic nerve invasion (n = 10/21), choroidal invasion (n = 12/21), and anterior eye segment enhancement on MRI (n = 6/21). HASTE-DWI demonstrated higher image quality scores than EPI-DWI (P < .01), except for tumor conspicuity score, which was higher for EPI-DWI (P < .001). HASTE-DWI showed lower SNR, CNR, and geometric distortion than EPI-DWI (P < .001). The average acquisition times of EPI-DWI and HASTE-DWI were ∼1 and 14 minutes, respectively. The mean tumor ADC value on EPI-DWI was 0.62 ± 0.14 × 10-3 mm2/s and on HASTE-DWI was 0.83 ± 0.17 × 10-3 mm2/s. A significant correlation between EPI-DWI and HASTE-DWI ADC values (r = 0.8; P = .01) was found. Lower ADC values were found in tumors with poor prognostic markers, but none reached a statistically significant difference. HASTE-DWI shows improved overall image quality; however, it lacks in terms of tumor conspicuity, SNR, CNR, and longer acquisition time compared with EPI-DWI. ADC values derived from HASTE-DWI show no advantage over EPI-DWI in correlation with histopathologic prognostic markers.

Iterative Metal Artifact Reduction in Head and Neck CT Facilitates Tumor Visualization of Oral and Oropharyngeal Cancer Obscured by Artifacts From Dental Hardware

The purpose of this study was to evaluate the diagnostic utility of iterative metal artifact reduction (iMAR) in computed tomography (CT)-imaging of oral and oropharyngeal cancers when obscured by dental hardware artifacts and to determine the most appropriate iMAR settings for this purpose. The study retrospectively enrolled 27 patients (8 female, 19 male; mean age 64±12.7years) with histologically confirmed oral or oropharyngeal cancer obscured by dental artifacts in contrast-enhanced CT. Raw CT data were reconstructed with ascending iMAR strengths (levels 1/2/3/4/5) and one reconstruction without iMAR (level 0). For subjective analysis, two blinded radiologists rated tumor visualization and artifact severity on a five-point Likert scale. For objective analysis, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artifact index (AI) were determined. iMAR reconstructions improved the subjective image quality of tumor edge and contrast, and the objective parameters of tumor SNR and CNR, reaching their optimum at iMAR levels 4 and 5 (P<.001). AI decreased with iMAR reconstructions reaching its minimum at iMAR level 5 (P<.001). Tumor detection rates increased 2.4-fold with iMAR 5, 2.1-fold with iMAR 4, and 1.9-fold with iMAR 3 compared to reconstructions without iMAR. Disadvantages such as algorithm-induced artifacts increased significantly with higher iMAR strengths (P<.05), reaching a maximum with iMAR 5. iMAR significantly improves CT imaging of oral and oropharyngeal cancers, as confirmed by both subjective and objective measures, with best results at highest iMAR strengths.

Dual-energy CT with virtual monoenergetic images and iodine maps improves tumor conspicuity in patients with pancreatic ductal adenocarcinoma

ObjectivesTo evaluate the value of monoenergetic images (MEI [+]) and iodine maps in dual-source dual-energy computed tomography (DECT) for assessing pancreatic ductal adenocarcinoma (PDAC), including the visually isoattenuating PDAC.Materials and methodsThis retrospective study included 75 PDAC patients, who underwent contrast-enhanced DECT examinations. Conventional polyenergetic image (PEI) and 40–80 keV MEI (+) (10-keV increments) were reconstructed. The tumor contrast, contrast-to-noise ratio (CNR) of the tumor and peripancreatic vessels, the signal-to-noise ratio (SNR) of the pancreas and tumor, and the tumor diameters were quantified. On iodine maps, the normalized iodine concentration (NIC) in the tumor and parenchyma was compared. For subjective analysis, two radiologists independently evaluated images on a 5-point scale.ResultsAll the quantitative parameters were maximized at 40-keV MEI (+) and decreased gradually with increasing energy. The tumor contrast, SNR of pancreas and CNRs in 40–60 keV MEI (+) were significantly higher than those in PEI (p < 0.05). For visually isoattenuating PDAC, 40–50 keV MEI (+) provided significantly higher tumor CNR compared to PEI (p < 0.05). The reproducibility in tumor measurements was highest in 40-keV MEI (+) between the two radiologists. The tumor and parenchyma NIC were 1.28 ± 0.65 and 3.38 ± 0.72 mg/mL, respectively (p < 0.001). 40–50 keV MEI (+) provided the highest subjective scores, compared to PEI (p < 0.001).ConclusionsLow-keV MEI (+) of DECT substantially improves the subjective and objective image quality and consistency of tumor measurements in patients with PDAC. Combining the low-keV MEI (+) and iodine maps may yield diagnostically adequate tumor conspicuity in visually isoattenuating PDAC.

Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric MRI fusion.

Multiparametric MRI contains rich and complementary anatomical and functional information, which is often utilized separately. This study aims to propose an adaptive multiparametric MRI (mpMRI) fusion method and examine its capability in improving tumor contrast and synthesizing novel tissue contrasts among liver cancer patients. An adaptive mpMRI fusion method was developed with five components: image pre-processing, fusion algorithm, database, adaptation rules, and fused MRI. Linear-weighted summation algorithm was used for fusion. Weight-driven and feature-driven adaptations were designed for different applications. A clinical-friendly graphic-user-interface (GUI) was developed in Matlab and used for mpMRI fusion. Twelve liver cancer patients and a digital human phantom were included in the study. Synthesis of novel image contrast and enhancement of image signal and contrast were examined in patient cases. Tumor contrast-to-noise ratio (CNR) and liver signal-to-noise ratio (SNR) were evaluated and compared before and after mpMRI fusion. The fusion platform was applicable in both XCAT phantom and patient cases. Novel image contrasts, including enhancement of soft-tissue boundary, vertebral body, tumor, and composition of multiple image features in a single image were achieved. Tumor CNR improved from -1.70 ± 2.57 to 4.88 ± 2.28 (p < 0.0001) for T1-w, from 3.39 ± 1.89 to 7.87 ± 3.47 (p < 0.01) for T2-w, and from 1.42 ± 1.66 to 7.69 ± 3.54 (p < 0.001) for T2/T1-w MRI. Liver SNR improved from 2.92 ± 2.39 to 9.96 ± 8.60 (p < 0.05) for DWI. The coefficient of variation (CV) of tumor CNR lowered from 1.57, 0.56, and 1.17 to 0.47, 0.44, and 0.46 for T1-w, T2-w and T2/T1-w MRI, respectively. A multiparametric MRI fusion method was proposed and a prototype was developed. The method showed potential in improving clinically relevant features such as tumor contrast and liver signal. Synthesis of novel image contrasts including the composition of multiple image features into single image set was achieved.

Evaluation of Multisource Adaptive MRI Fusion for Gross Tumor Volume Delineation of Hepatocellular Carcinoma.

PurposeTumor delineation plays a critical role in radiotherapy for hepatocellular carcinoma (HCC) patients. The incorporation of MRI might improve the ability to correctly identify tumor boundaries and delineation consistency. In this study, we evaluated a novel Multisource Adaptive MRI Fusion (MAMF) method in HCC patients for tumor delineation.MethodsTen patients with HCC were included in this study retrospectively. Contrast-enhanced T1-weighted MRI at portal-venous phase (T1WPP), contrast-enhanced T1-weighted MRI at 19-min delayed phase (T1WDP), T2-weighted (T2W), and diffusion-weighted MRI (DWI) were acquired on a 3T MRI scanner and imported to in-house-developed MAMF software to generate synthetic MR fusion images. The original multi-contrast MR image sets were registered to planning CT by deformable image registration (DIR) using MIM. Four observers independently delineated gross tumor volumes (GTVs) on the planning CT, four original MR image sets, and the fused MRI for all patients. Tumor contrast-to-noise ratio (CNR) and Dice similarity coefficient (DSC) of the GTVs between each observer and a reference observer were measured on the six image sets. Inter-observer and inter-patient mean, SD, and coefficient of variation (CV) of the DSC were evaluated.ResultsFused MRI showed the highest tumor CNR compared to planning CT and original MR sets in the ten patients. The mean ± SD tumor CNR was 0.72 ± 0.73, 3.66 ± 2.96, 4.13 ± 3.98, 4.10 ± 3.17, 5.25 ± 2.44, and 9.82 ± 4.19 for CT, T1WPP, T2W, DWI, T1WDP, and fused MRI, respectively. Fused MRI has the minimum inter-observer and inter-patient variations as compared to original MR sets and planning CT sets. GTV delineation inter-observer mean DSC across the ten patients was 0.81 ± 0.09, 0.85 ± 0.08, 0.88 ± 0.04, 0.89 ± 0.08, 0.90 ± 0.04, and 0.95 ± 0.02 for planning CT, T1WPP, T2W, DWI, T1WDP, and fused MRI, respectively. The patient mean inter-observer CV of DSC was 3.3%, 3.2%, 1.7%, 2.6%, 1.5%, and 0.9% for planning CT, T1WPP, T2W, DWI, T1WDP, and fused MRI, respectively.ConclusionThe results demonstrated that the fused MRI generated using the MAMF method can enhance tumor CNR and improve inter-observer consistency of GTV delineation in HCC as compared to planning CT and four commonly used MR image sets (T1WPP, T1WDP, T2W, and DWI). The MAMF method holds great promise in MRI applications in HCC radiotherapy treatment planning.

Accelerated Breast Diffusion-weighted Imaging Using Multiband Sensitivity Encoding with the CAIPIRINHA Method: Clinical Experience at 3 T

Purpose To examine the clinical value of multiband (MB) sensitivity encoding (SENSE)-accelerated diffusion-weighted imaging (DWI) for breast imaging by performing quantitative and qualitative comparisons with conventional diffusion-weighted echo-planar imaging, or conventional DWI (cDWI). Materials and Methods In this prospective study (ClinicalTrials.gov identifier NCT03607552), women with breast cancer were recruited from July 2018 to July 2019 to undergo additional MB SENSE DWI during clinical 3-T breast MRI examinations. The cDWI and MB SENSE DWI acquisitions were assessed both quantitatively and qualitatively. Regions of interest were defined for tumorous and normal tissue, and the tumor apparent diffusion coefficient (ADC), contrast-to-noise ratio (CNR), and signal index (SI) were calculated for both DWI methods. Three readers independently reviewed the two acquisitions side by side and provided relative image quality scores. Tumor ADC, CNR, and SI measures were compared between cDWI and MB SENSE DWI acquisitions by using a paired t test, and reader preferences were evaluated by using the sign test. Results The study included 38 women (median age, 48 years; range, 28-83 years). Overall agreement was good between cDWI and MB SENSE DWI tumor ADC measures (intraclass correlation coefficient, 0.87 [95% CI: 0.75, 0.94]), and no differences were evident in the ADC (median, 0.93 × 10-3 mm2/sec vs 0.87 ×10-3 mm2/sec; P = .50), CNR (2.2 vs 2.3; P = .17), or SI (9.2 vs 9.2; P = .23) measurements. The image quality of cDWI and MB SENSE DWI acquisitions were considered equal for 51% of images (58 of 114), whereas MB SENSE DWI was preferred more often than cDWI (37% [42 of 114] vs 12% [14 of 114]; P < .001). The preference for MB SENSE DWI was most often attributed to better fat suppression. Conclusion MB SENSE can be used to accelerate breast DWI acquisition times without compromising the image quality or the fidelity of quantitative ADC measurements. Keywords: MR-Diffusion-weighted Imaging, Breast, Comparative Studies, Technology Assessment Clinical trial registration no. NCT03607552 © RSNA, 2022.

Multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI): Development and initial evaluation in liver tumor patients.

To develop a novel multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI) technique that expands single image contrast 4D-MRI to a spectrum of native and synthetic image contrasts and to evaluate its feasibility in liver tumor patients. The MC-4D-MRI technique integrates multi-parametric MRI fusion, 4D-MRI, and deformable image registration (DIR) techniques. The fusion technique consists of native MRI as input, image pre-processing, fusion algorithm, adaptation, and fused multi-contrast MRI as output. Four-dimensional deformation vector fields (4D-DVF) were generated from an original T2/T1-w 4D-MRI by deforming end-of-inhalation (EOI) to nine other phase volumes via DIR. The 4D-DVF were applied to multi-contrast MRI to generate a spectrum of 4D-MRI in different image contrasts. The MC-4D-MRI technique was evaluated in five liver tumor patients on tumor contrast-to-noise ratio (CNR), internal target volume (ITV) contouring consistency, diaphragm motion range, and tumor motion trajectory; and in digital anthropomorphic phantoms on 4D-DIR introduced errors in tumor motion range, centroid location, extent, and volume. MC-4D-MRI consisting of 4D-MRIs in native image contrasts (T1-w, T2-w, and T2/T1-w) and synthetic image contrasts, such as tumor-enhanced contrast (TEC) were generated in five liver tumor patients. Patient tumor CNR increased from 2.6±1.8 in the T2/T1-w MRI, to -4.4±2.4, 6.6±3.0, and 9.6±3.9 in the T1-w, T2-w, and TEC MRI, respectively. Patient ITV inter-observer mean Dice similarity coefficient (mDSC) increased from 0.65±0.10 in the original T2/T1-w 4D-MRI, to 0.76±0.14, 0.77±0.12, and 0.86±0.05 in the T1-w, T2-w, and TEC 4D-MRI, respectively. Patient diaphragm motion range absolute differences between the three new 4D-MRIs and original T2/T1-w 4D-MRI were 1.2±1.3, 0.3±0.7, and 0.5±0.5mm, respectively. Patient tumor displacement phase-averaged absolute differences between the three 4D-MRIs and the original 4D-MRI were 0.72±0.33, 0.62±0.54, and 0.74±0.43mm in the superior-inferior (SI) direction, and 0.59±0.36, 0.51±0.30, and 0.50±0.24mm in the anterior-posterior (AP) direction, respectively. In the digital phantoms, phase-averaged absolute tumor centroid shift caused by the 4D-DIR were at or below 0.5mm in SI, AP, and left-right (LR) directions. We developed an MC-4D-MRI technique capable of expanding single image contrast 4D-MRI along a new dimension of image contrast. Initial evaluations in liver tumor patients showed enhancements in image contrast variety, tumor contrast, and ITV contouring consistencies using MC-4D-MRI. The technique might offer new perspectives on the image contrast of MRI and 4D-MRI in MR-guided radiotherapy.

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models.

Multicompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer. To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo. Retrospective. Forty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer. 3T multishell diffusion-weighted sequence. Multicompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps. Analysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models. The lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm2 /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment-1 signal maps than conventional DWI (P < 0.05) and increased with model order. The 4-compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer. Level of Evidence 3 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:628-639.

A Multi-Source Adaptive MR Image Fusion Technique for MR-Guided Radiation Therapy

Current radiation oncology practice to capture clinical indications like tumor from MR images is to review multiple types of MRI separately, which can be inefficient and tumor contrast is limited by existing MRI. The purpose of this study is to develop an integrated MR image fusion technique to adaptively fuse multi-source standard MRI for improved MR-guided radiation therapy applications. The hypothesis is that by integrating multi-source MR images, tumor contrast can be enhanced and the efficiency of using multiple MR images can be improved. The multi-source adaptive MRI fusion technique is designed with five key components: input multiple MRI, image pre-processing, fusion algorithm, adaptation methods, and output fused MRI. T1-weighted (T1-w), T2-weighted (T2-w), cine and diffusion-weighted MRI (DWI) were used as source images. Deformable image registration is applied to align all images to T2-w MRI. A linear-weighting algorithm Yi = ∑wik⋅Xk is used for fusion, where Xk is the kth source image, wik is the weighting factor of the kth source image in the ith fused image Yi, w∈[-1,1] with an interval of 0.2. Features of fused images are extracted, including signal-to-noise-ratio (SNR) and contrast-to-noise-ratio (CNR) of tumor and major organs. The weighting factors and image features are pre-calculated and saved in a database for fast adaptation operation. Weighting-factor driven and image-feature driven approaches are developed for MRI fusion adaptation. Digital human phantom (XCAT) and seven liver cancer patients were evaluated. Multiple new organ contrasts were explored and tumor CNR were quantified and compared before and after adaptive fusion using Student’s t-test. Hundreds of fused MR images with versatile image contrasts were generated in both XCAT phantom and liver cancer patients. In XCAT phantom, versatile new organ contrasts were achieved, including tumor, vertebral body, and blood vessel enhancement, respectively. In the patient study, tumor contrasts were significantly improved (mean: 14.83 ± 7.72 vs. 1.5 ± 0.65, 2.54 ± 1.31, 2.24 ± 2.23, 3.72 ± 2.45 and 0.74 ± 0.66 in T1-w, T2-w, cine, DWI, and CT, respectively) (p-values: 0.003∼0.010). Multiple new contrast MR images were efficiently generated, including liver, vertebral body, and cerebrospinal fluid enhancement, respectively. A novel multi-source adaptive image fusion technique capable of generating versatile contrast MRI from a limited number of standard MR images was developed. This method has the potential to improve tumor contrast, as well as the effectiveness and efficiency of multiple MR applications in radiation therapy.

Investigating a novel split-filter dual-energy CT technique for improving pancreas tumor visibility for radiation therapy.

PurposeTumor delineation using conventional CT images can be a challenge for pancreatic adenocarcinoma where contrast between the tumor and surrounding healthy tissue is low. This work investigates the ability of a split‐filter dual‐energy CT (DECT) system to improve pancreatic tumor contrast and contrast‐to‐noise ratio (CNR) for radiation therapy treatment planning.Materials and methodsMultiphasic scans of 20 pancreatic tumors were acquired using a split‐filter DECT technique with iodinated contrast medium, OMNIPAQUE TM. Analysis was performed on the pancreatic and portal venous phases for several types of DECT images. Pancreatic gross target volume (GTV) contrast and CNR were calculated and analyzed from mixed 120 kVp‐equivalent images and virtual monoenergetic images (VMI) at 57 and 40 keV. The role of iterative reconstruction on DECT images was also investigated. Paired t‐tests were used to assess the difference in GTV contrast and CNR among the different images.ResultsThe VMIs at 40 keV had a 110% greater image noise compared to the mixed 120 kVp‐equivalent images (P < 0.0001). VMIs at 40 keV increased GTV contrast from 15.9 ± 19.9 HU to 93.7 ± 49.6 HU and CNR from 1.37 ± 2.05 to 3.86 ± 2.78 in comparison to the mixed 120 kVp‐equivalent images. The iterative reconstruction algorithm investigated decreased noise in the VMIs by about 20% and improved CNR by about 30%.ConclusionsPancreatic tumor contrast and CNR were significantly improved using VMIs reconstructed from the split‐filter DECT technique, and the use of iterative reconstruction further improved CNR. This gain in tumor contrast may lead to more accurate tumor delineation for radiation therapy treatment planning.

Conspicuity of Malignant Liver Tumors on Diffusion-Weighted Imaging With Short tau Inversion Recovery After Gadolinium Ethoxybenzyl Diethylenetriaminepentaacetic Acid Administration.

Diffusion-weighted imaging (DWI) has been used for the detection and characterization of liver tumors because it has excellent contrast resolution. DWI using short tau inversion recovery (STIR) can improve tumor-to-liver contrast after gadolinium ethoxybenzyl diethylenetriaminepentaacetic acid (Gd-EOB-DTPA) administration that shortens the T1 relaxation of liver parenchyma. To quantitatively and qualitatively compare the conspicuity of malignant liver tumors on DWI after Gd-EOB-DTPA administration between STIR and chemical shift selective (CHESS) sequences. Single-institution retrospective study. Fifty-seven patients with histologically confirmed malignant liver tumors were evaluated. Low b-value DWIs with STIR and CHESS sequences 18-20 minutes after Gd-EOB-DTPA administration were acquired at 1.5T. Tumor contrast-to-noise ratio (CNR) and visual grade of tumor conspicuity on DWI between STIR and CHESS sequences were compared. Paired Student's t-test and the Wilcoxon signed rank-test were applied. P < 0.05 was considered statistically significant. The mean tumor CNR and visual grade of tumor conspicuity on DWI were significantly higher for STIR than for CHESS (both P < 0.001). Regardless of the presence of chronic liver disease, the mean CNR (normal liver 33.5 ± 19.8 vs. 15.7 ± 12.2, P < 0.001; chronic liver disease 19.6 ± 11.0 vs. 9.2 ± 7.8, P < 0.001) and the visual conspicuity grade (normal liver 3.36 ± 0.64 vs. 2.56 ± 0.77, P < 0.001; chronic liver disease 2.94 ± 0.80 vs. 2.25 ± 0.84, P = 0.001) were significantly higher for STIR than for CHESS. Mean CNR and the visual conspicuity grade were also significantly higher for STIR than for CHESS in patients with hepatocellular carcinomas (CNR 18.1 ± 10.5 vs. 8.8 ± 7.2, P < 0.001; visual grade 2.88 ± 0.83 vs. 2.22 ± 0.87, P = 0.001) or metastases (CNR 35.0 ± 19.3 vs. 16.2 ± 13.1, P < 0.001; visual grade 3.45 ± 0.51 vs. 2.59 ± 0.73, P < 0.001). DWI using STIR may be more helpful for depicting malignant liver tumors after Gd-EOB-DTPA administration compared with DWI using CHESS. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:565-573.

Development and validation of a new MRI simulation technique that can reliably estimate optimal in vivo scanning parameters in a glioblastoma murine model.

BackgroundMagnetic Resonance Imaging (MRI) relies on optimal scanning parameters to achieve maximal signal-to-noise ratio (SNR) and high contrast-to-noise ratio (CNR) between tissues resulting in high quality images. The optimization of such parameters is often laborious, time consuming, and user-dependent, making harmonization of imaging parameters a difficult task. In this report, we aim to develop and validate a computer simulation technique that can reliably provide “optimal in vivo scanning parameters” ready to be used for in vivo evaluation of disease models.MethodsA glioblastoma murine model was investigated using several MRI imaging methods. Such MRI methods underwent a simulated and an in vivo scanning parameter optimization in pre- and post-contrast conditions that involved the investigation of tumor, brain parenchyma and cerebrospinal fluid (CSF) CNR values in addition to the time relaxation values of the related tissues. The CNR tissues information were analyzed and the derived scanning parameters compared in order to validate the simulated methodology as a reliable technique for “optimal in vivo scanning parameters” estimation.ResultsThe CNRs and the related scanning parameters were better correlated when spin-echo-based sequences were used rather than the gradient-echo-based sequences due to augmented inhomogeneity artifacts affecting the latter methods. “Optimal in vivo scanning parameters” were generated successfully by the simulations after initial scanning parameter adjustments that conformed to some of the parameters derived from the in vivo experiment.ConclusionScanning parameter optimization using the computer simulation was shown to be a valid surrogate to the in vivo approach in a glioblastoma murine model yielding in a better delineation and differentiation of the tumor from the contralateral hemisphere. In addition to drastically reducing the time invested in choosing optimal scanning parameters when compared to an in vivo approach, this simulation program could also be used to harmonize MRI acquisition parameters across scanners from different vendors.

Split-bolus pancreas CTA protocol for local staging of pancreatic cancer and detection and characterization of liver lesions.

To validate the use of a split-bolus pancreas CTA protocol for local staging of pancreatic cancer and to evaluate its ability to detect and characterize liver lesions. Consecutive patients with pancreatic cancer who underwent split-bolus pancreas CTA between 12/2015 and 12/2016 were included in this IRB-approved HIPAA-compliant retrospective study. Objective evaluation of the abdominal vessels, the pancreas, the liver, and lesions, if present, was performed with attenuation measurements and tumor conspicuity and contrast-to-noise ratio (CNR) calculations. An abdominal radiologist with 20years of experience performed subjective evaluation of image quality and blindly detected and characterized liver lesions. Any inconclusive findings or grading scores were evaluated in consensus with another abdominal radiologist with 7years of experience. Liver findings were validated using a composite reference standard to assess accuracy. There were 82 pancreatic cancer patients with a total of 91 liver findings. Tumor conspicuity and CNR were 60.8±35.1 HU and 8.0±5.8 for the pancreatic lesions and 58±34.7 HU and 9.7±6.3 for the liver lesions, respectively. The accuracy, sensitivity, and specificity of the split-bolus protocol for the hepatic findings were correspondingly 89/91 (97.8%, 95% CI 92.3-99.4), 58/60 (96.7%, 95% CI 88.6-99.1), and 33/33 (100%, 95% CI 89.6-100). The subjective image quality ratings were optimal in more than 89% of the cases for various structures, with no non-diagnostic ratings. Split-bolus pancreas CTA protocol allows for detection and staging of pancreatic cancer, both for the primary tumor and detection and characterization of liver lesions.

SU‐F‐J‐103: Assessment of Liver Tumor Contrast for Radiation Therapy: Inter‐Patient and Inter‐Sequence Variability

Purpose:To determine the variation in tumor contrast between different MRI sequences and between patients for the purpose of MRI‐based treatment planning.Methods:Multiple MRI scans of 11 patients with cancer(s) in the liver were included in this IRB‐approved study. Imaging sequences consisted of T1W MRI, Contrast‐Enhanced T1W MRI, T2W MRI, and T2*/T1W MRI. MRI images were acquired on a 1.5T GE Signa scanner with a four‐channel torso coil. We calculated the tumor‐to‐tissue contrast to noise ratio (CNR) for each MR sequence by contouring the tumor and a region of interest (ROI) in a homogeneous region of the liver using the Eclipse treatment planning software. CNR was calculated (I_Tum‐I_ROI)/SD_ROI, where I_Tum and I_ROI are the mean values of the tumor and the ROI respectively, and SD_ROI is the standard deviation of the ROI. The same tumor and ROI structures were used in all measurements for different MR sequences. Inter‐patient Coefficient of variation (CV), and inter‐sequence CV was determined. In addition, mean and standard deviation of CNR were calculated and compared between different MR sequences.Results:Our preliminary results showed large inter‐patient CV (range: 37.7% to 88%) and inter‐sequence CV (range 5.3% to 104.9%) of liver tumor CNR, indicating great variations in tumor CNR between MR sequences and between patients. Tumor CNR was found to be largest in CE‐T1W (8.5±7.5), followed by T2W (4.2±2.4), T1W (3.4±2.2), and T2*/T1W (1.7±0.6) MR scans. The inter‐patient CV of tumor CNR was also the largest in CE‐T1W (88%), followed by T1W (64.3%), T1W (56.2%), and T2*/T1W (37.7) MR scans.Conclusion:Large inter‐sequence and inter‐patient variations were observed in liver tumor CNR. CE‐T1W MR images on average provided the best tumor CNR. Efforts are needed to optimize tumor contrast and its consistency for MRI‐based treatment planning of cancer in the liver.This project is supported by NIH grant: 1R21CA165384

SU-G-JeP2-07: Fusion Optimization of Multi-Contrast MRI Scans for MR-Based Treatment Planning

Purpose: To develop an image fusion method using multiple contrast MRI scans for MR-based treatment planning. Methods: T1 weighted (T1-w), T2 weighted (T2-w) and diffusion weighted images (DWI) were acquired from liver cancer patient with breath-holding. Image fade correction and deformable image registration were performed using VelocityAI (Varian Medical Systems, CA). Registered images were normalized to mean voxel intensity for each image dataset. Contrast to noise ratio (CNR) between tumor and liver was quantified. Tumor area was defined as the GTV contoured by physicians. Normal liver area with equivalent dimension was used as background. Noise was defined by the standard deviation of voxel intensities in the same liver area. Linear weightings were applied to T1-w, T2-w and DWI images to generate composite image and CNR was calculated for each composite image. Optimization process were performed to achieve different clinical goals. Results: With a goal of maximizing tumor contrast, the composite image achieved a 7–12 fold increase in tumor CNR (142.8 vs. −2.3, 11.4 and 20.6 for T1-w, T2-w and DWI only, respectively), while anatomical details were largely invisible. With a weighting combination of 100%, −10% and −10%, respectively, tumor contrast was enhanced from −2.3 to −5.4, while the anatomical details were clear. With a weighting combination of 25%, 20% and 55%, balanced tumor contrast and anatomy was achieved. Conclusion: We have investigated the feasibility of performing image fusion optimization on multiple contrast MRI images. This mechanism could help utilize multiple contrast MRI scans to potentially facilitate future MR-based treatment planning.

Gastric Cancer: Preoperative TNM Staging With Individually Adjusted Computed Tomography Scanning Phase.

The aim of this study was to evaluate test bolus scan technology on preoperative diagnostic performance, vascular enhancement, and artery visualization for gastric cancer. The institutional review board approved this study. Fifty-four patients in protocol 1 were resigned to a fixed delay time scan method, and their images were obtained in the late arterial phase (AP) and portal venous phase (PP), with start delays of 40 and 70 seconds, respectively. Fifty-six patients in protocol 2 had undergone the test bolus method first and received the time to peak enhancement of the aorta. Their images were obtained in the AP and PP with start delays in the time to peak enhancement and 20 seconds after the AP, respectively. Two radiologists performed consensus interpretation of the preoperative TNM staging, vascular enhancement, tumor contrast-to-noise ratio (CNR) and artery visualization between the 2 protocols. There is no significant difference in the T, N, and M staging diagnostic accuracy between the protocols (P = 0.41, P > 0.99, and P = 0.34, respectively). For serosa-negative (T1, T2, and T3) tumors, the diagnostic accuracy obtained with protocol 2 was superior to that obtained with protocol 1 (P = 0.04). Protocol 2 was superior for perigastric vessel enhancement (left gastric artery, right gastroepiploic artery, and splenic artery; P < 0.001, P < 0.001, and P = 0.001, respectively). The stomach-to-tumor CNR during the PP of protocol 2 was significantly higher than that during either the AP or PP of protocol 1 (P = 0.004 and P = 0.001, respectively). The mean rankings of the artery visualization were significantly higher with protocol 2 than with protocol 1 (P < 0.001). The dual-phase scan with test bolus technology could improve the tumor CNR and had high staging accuracy for serosa-negative tumors as well as high perigastric artery enhancement, yielding satisfactory artery visualization for diagnosis.

Noise-optimized advanced image-based virtual monoenergetic imaging for improved visualization of lung cancer: Comparison with traditional virtual monoenergetic imaging

PurposeTo assess the effect of a noise-optimized image-based virtual monoenergetic imaging (VMI+) algorithm in direct comparison with the traditional VMI technique and standard linearly-blended images emulating 120-kVp acquisition (M_0.3) on image quality at dual-energy CT in patients with lung cancer. Materials and MethodsDual-source dual-energy CT examinations of 48 patients with biopsy-proven primary (n=31) or recurrent (n=20) lung cancer were evaluated. Images were reconstructed as M_0.3, and VMI+ and traditional VMI series at 40, 55, and 70keV. Attenuation of tumor, descending aorta, pulmonary trunk, latissimus muscle, and noise were measured. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Five-point scales were used by three observers to subjectively evaluate general image impression, tumor delineation, image sharpness, and image noise. ResultsBackground noise was consistently lower with VMI+ compared to VMI at all keV levels (all p<0.0001) and M_0.3 (all p≤0.0004). Tumor SNR and CNR peaked in the 40keV VMI+ series, significantly higher compared to all VMI and M_0.3 series (all p<0.0008). Observers preferred the 55keV VMI+ series regarding general image impression and tumor delineation compared to all other series (all p<0.0001). Image sharpness and image noise ratings were highest in the 55keV VMI+ and 70keV VMI and VMI+ reconstructions. ConclusionsTumor CNR peaked at 40keV VMI+ while observers preferred 55keV VMI+ series overall other series for dual-energy CT of lung cancer. The noise-optimized VMI+ technique showed significantly lower background noise and higher SNR and CNR compared to the traditional VMI technique at matching keV levels.

Objective and subjective image quality of primary and recurrent squamous cell carcinoma on head and neck low-tube-voltage 80-kVp computed tomography.

To investigate low-tube-voltage 80-kVp computed tomography (CT) of head and neck primary and recurrent squamous cell carcinoma (SCC) regarding objective and subjective image quality. We retrospectively evaluated 65 patients (47 male, 18 female; mean age: 62.1 years) who underwent head and neck dual-energy CT (DECT) due to biopsy-proven primary (n = 50) or recurrent (n = 15) SCC. Eighty peak kilovoltage and standard blended 120-kVp images were compared. Attenuation and noise of malignancy and various soft tissue structures were measured. Tumor signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Subjective image quality was rated by three reviewers using 5-point grading scales regarding overall image quality, lesion delineation, image sharpness, and image noise. Radiation dose was assessed as CT dose index volume (CTDIvol). Interobserver agreement was calculated using intraclass correlation coefficient (ICC). Mean tumor attenuation (153.8 Hounsfield unit (HU) vs. 97.1 HU), SNR (10.7 vs. 8.3), CNR (8.1 vs. 4.8), and subjective tumor delineation (score, 4.46 vs. 4.13) were significantly increased (all P < 0.001) with 80-kVp acquisition compared to standard blended 120-kVp images. Noise of all measured structures was increased in 80-kVp acquisition (P < 0.001). Overall interobserver agreement was good (ICC, 0.86; 95 % confidence intervals: 0.82-0.89). CTDIvol was reduced by 48.7 % with 80-kVp acquisition compared to standard DECT (4.85 ± 0.51 vs. 9.94 ± 0.81 mGy cm, P < 0.001). Head and neck CT with low-tube-voltage 80-kVp acquisition provides increased tumor delineation, SNR, and CNR for CT imaging of primary and recurrent SCC compared to standard 120-kVp acquisition with an accompanying significant reduction of radiation exposure.

Preliminary study of improved quality of computed tomographic angiography on tumor blood supply arteries of gastrointestinal tract malignant tumors with low tube voltage and low concentration contrast medium

To assess the image quality of gastrointestinal tract malignant tumors arteries using low tube voltage, low contrast medium concentration and 50% adaptive statistical iterative reconstruction (ASiR) algorithm. A total of 58 patients with pathologically confirmed gastrointestinal tract malignant tumors and body mass index (BMI) under 22 kg/m² undergoing computed tomography (CT) scanning during March-December 2013. They were randomly divided into group A (270 mgI/ml of contrast medium, 80 kVp tube voltage scanning and 50% ASiR; 21 males and 11 females with an age range of 40-90 years) and group B (350 mgI/ml of contrast medium, 120 kVp routine tube voltage; 22 males and 4 females with an age range of 40-76 years). The inter-group differences of age and gender ratio were compared with Mann-Whitney U and Fisher exact tests respectively. Two independent radiologists reviewed reconstructions and separated the reconstructed image into 5 points according to image quality. The CT value of tumor blood supplying artery (CT1 value) and fat in anterior abdominal wall (SD value) were measured and image noise, contrast-to-noise ratio (CNR) and figure of merit (FOM) calculated. The interobserver variation was estimated by weighted kappa statistics and intra-class correlation coefficients (ICC) test. The point of image quality of two groups was compared by Mann-Whitney U test. The paired Student t test was used to compare the inter-group differences in CT1, SD, CNR, FOM and CT dose index (CTDIvol). No inter-group difference existed in patient age or gender ratio. The consistency of two radiologists was excellent (kappa value > 0.80; ICC value > 0.75). The subjective image quality scores of tumor blood supplying arteries showed no inter-group difference (P = 0.09). The tumor blood supply arteries CT value, CNR and FOM of group A (459 ± 69 HU, 20.2 ± 3.3 and 85 ± 37) were significantly higher than those of group B (250 ± 42 HU, 9.3 ± 1.9 and 9 ± 4) (both P < 0.01). The SD value of group A (20.0 ± 1.7) was obviously lower than that of group B (22.4 ± 3.2) (P < 0.01). And the CTDIvol of group A (5.2 ± 1.2 mGy) was obviously lower than that of group B (13.5 ± 4.7 mGy) (P < 0.01). For patients of BMI ≤ 22 kg/m², low tube voltage and low contrast medium concentration scan condition with 50% ASiR algorithm is feasible for acquiring better quality image and ensuring significant reduction in effective dose.