Liver apparent diffusion coefficient repeatability with individually predetermined optimal cardiac timing and artifact elimination by signal filtering

Pulsation artifact reduction using compressed sensitivity encoding in Gd-EOB-DTPA contrast-enhanced magnetic resonance imaging

In Gd-EOB-DTPA-enhanced MRI, cardiac pulsation artifacts in the left lobe often hinder diagnosis, the image quality need to improve. This study aimed to reduce cardiac pulsation artifacts in Gd-EOB-DTPA-enhanced three-dimensional (3D) T1-weighted turbo-field echo (3D-T1TFE) using compressed sensitivity encoding (CS).For phantom evaluation, the cardiac phantom was manually operated using a metronome-synchronized apparatus, comprising a bag-valve mask, a breathing circuit, and a Jackson-Rees system. Transverse images of a liver phantom were acquired using enhanced T1 high-resolution isotropic volumetric excitation with CS (CS-eTHRIVE) and sensitivity encoding (S-eTHRIVE). For evaluation, images obtained during cardiac phantom operation were subtracted from those obtained when the phantom was stationary. Standard deviation (SD) of the difference images was used as the evaluation metric, and assessments were conducted based on changes in heart rate and TFE factor. For clinical image evaluation, artifacts in hepatobiliary phase images acquired 15 min after Gd-EOB-DTPA injection in the order of S-eTHRIVE and CS-eTHRIVE were visually evaluated at four levels. In heart-rate evaluation (40, 60, and 80 beats/min), CS-eTHRIVE revealed significantly lower SD values compared to S-eTHRIVE across all heart rates (P < 0.01), with no significant differences between heart rates. For TFE factor evaluation, CS-eTHRIVE with a factor of 35 exhibited the lowest SD, which was significantly different from all other groups (P < 0.01). In clinical image evaluation, CS-eTHRIVE achieved higher visual scores (mean ± SD: 3.72 ± 0.46) compared with S-eTHRIVE (2.72 ± 0.98, P < 0.01).CS reduced pulsation artifacts in Gd-EOB-DTPA-enhanced 3D-T1TFE.

Relevance of lesion size in navigator-triggered and free-breathing diffusion-weighted liver MRI

OBJECTIVES

The purpose of this study was to investigate the relevance of focal liver lesions (FLL) size for lesion detection comparing navigator triggering (TRIG) to free breathing (FB) liver Diffusion-weighted magnetic resonance imaging (DWI).

MATERIALS AND METHOD

Patients with known or suspected FLL were prospectively (registry number 276_19 B) included from October to December 2019 in this study, out of which 32 had liver lesions. Echo planar spin-echo DWI data both with TRIG and FB were with approximately constant acquisition times acquired at 1.5 T. Lesions were segmented in the b = 800 s/mm² images in both the TRIG and FB images. The lesion size, location (liver segment), liver lesion visibility, as well as contrast-to-noise ratio (CNR) were recorded. The CNR was assessed with the Wilcoxon-Mann-Whitney test and the number of visible lesions with the Fisher test.

RESULTS

Data from 43 patients (22 female) were analyzed. The mean patient age was 58 ± 14 years. A total of 885 FLL (Ntotal) were segmented. Among these, 811 lesions (Nboth) were detected with TRIG and FB, 65 lesions exclusively with TRIG (NTRIG_Only), and nine exclusively in FB (NFB_Only). The largest additional lesion in TRIG/FB had a diameter of 10.4 mm/7.6 mm. The number of additional lesions detected with TRIG decreased with size. Among all lesions ≤ 4.7 mm, the relative number of additional lesions was 15.6%. Additional lesions were found in all liver segments with TRIG. In the left liver lobe, the relative proportion was 9.2%, and in the right liver lobe 5.4%. CNR and visibility were significantly higher in TRIG than in FB (p < 0.001). In relation to size, the difference is significant in terms of visibility and CNR for lesion diameters ≤ 8 mm.

CONCLUSION

Respiration triggering can improve the detection of small liver lesions with diameters up to approx. 1 cm in the whole liver.

KEY POINTS

Question Can respiration triggering (TRIG) improve the detection of small FLL compared to FB diffusion-weighted imaging? Findings Among 885 segmented FLL, TRIG was superior to FB for lesions smaller than 8 mm and had improved CNR and visibility. Clinical relevance Diffusion-weighted magnetic resonance imaging is used for the detection of focal liver lesions and image quality is influenced by breathing motion. Navigator triggering becomes more important for smaller lesions, and seems recommendable for the detection of small focal liver lesions.

Feature-guided deep learning reduces signal loss and increases lesion CNR in diffusion-weighted imaging of the liver

PURPOSE

This research aims to develop a feature-guided deep learning approach and compare it with an optimized conventional post-processing algorithm in order to enhance the image quality of diffusion-weighted liver images and, in particular, to reduce the pulsation-induced signal loss occurring predominantly in the left liver lobe.

METHODS

Data from 40 patients with liver lesions were used. For the conventional approach, the best-suited out of five examined algorithms was chosen. For the deep learning approach, a U-Net was trained. Instead of learning "gold-standard" target images, the network was trained to optimize four image features (lesion CNR, vessel darkness, data consistency, and pulsation artifact reduction), which could be assessed quantitatively using manually drawn ROIs. A quality score was calculated from these four features. As an additional quality assessment, three radiologists rated different features of the resulting images.

RESULTS

The conventional approach could substantially increase the lesion CNR and reduce the pulsation-induced signal loss. However, the vessel darkness was reduced. The deep learning approach increased the lesion CNR and reduced the signal loss to a slightly lower extent, but it could additionally increase the vessel darkness. According to the image quality score, the quality of the deep-learning images was higher than that of the images obtained using the conventional approach. The radiologist ratings were mostly consistent with the quantitative scores, but the overall quality ratings differed among the readers.

CONCLUSION

Unlike the conventional algorithm, the deep-learning algorithm increased the vessel darkness. Therefore, it may be a viable alternative to conventional algorithms.

Evaluation of simultaneous multi-slice acquisition with advanced processing for free-breathing diffusion-weighted imaging in patients with liver metastasis

OBJECTIVES

Diffusion-weighted imaging (DWI) with simultaneous multi-slice (SMS) acquisition and advanced processing can accelerate acquisition time and improve MR image quality. This study evaluated the image quality and apparent diffusion coefficient (ADC) measurements of free-breathing DWI acquired from patients with liver metastases using a prototype SMS-DWI acquisition (with/without an advanced processing option) and conventional DWI.

METHODS

Four DWI schemes were compared in a pilot 5-patient cohort; three DWI schemes were further assessed in a 24-patient cohort. Two readers scored image quality of all b-value images and ADC maps across the three methods. ADC measurements were performed, for all three methods, in left and right liver parenchyma, spleen, and liver metastases. The Friedman non-parametric test (post-hoc Wilcoxon test with Bonferroni correction) was used to compare image quality scoring; t-test was used for ADC comparisons.

RESULTS

SMS-DWI was faster (by 24%) than conventional DWI. Both readers scored the SMS-DWI with advanced processing as having the best image quality for highest b-value images (b750) and ADC maps; Cohen's kappa inter-reader agreement was 0.6 for b750 image and 0.56 for ADC maps. The prototype SMS-DWI sequence with advanced processing allowed a better visualization of the left lobe of the liver. ADC measured in liver parenchyma, spleen, and liver metastases using the SMS-DWI with advanced processing option showed lower values than those derived from the SMS-DWI method alone (t-test, p < 0.0001; p < 0.0001; p = 0.002).

CONCLUSIONS

Free-breathing SMS-DWI with advanced processing was faster and demonstrated better image quality versus a conventional DWI protocol in liver patients.

CLINICAL RELEVANCE STATEMENT

Free-breathing simultaneous multi-slice- diffusion-weighted imaging (DWI) with advanced processing was faster and demonstrated better image quality versus a conventional DWI protocol in liver patients.

KEY POINTS

• Diffusion-weighted imaging (DWI) with simultaneous multi-slice (SMS) can accelerate acquisition time and improve image quality. • Apparent diffusion coefficients (ADC) measured in liver parenchyma, spleen, and liver metastases using the simultaneous multi-slice DWI with advanced processing were significantly lower than those derived from the simultaneous multi-slice DWI method alone. • Simultaneous multi-slice DWI sequence with inline advanced processing was faster and demonstrated better image quality in liver patients.

Impact of velocity- and acceleration-compensated encodings on signal dropout and black-blood state in diffusion-weighted magnetic resonance liver imaging at clinical TEs

PURPOSE

The study aims to develop easy-to-implement concomitant field-compensated gradient waveforms with varying velocity-weighting (M1) and acceleration-weighting (M2) levels and to evaluate their efficacy in correcting signal dropouts and preserving the black-blood state in liver diffusion-weighted imaging. Additionally, we seek to determine an optimal degree of compensation that minimizes signal dropouts while maintaining blood signal suppression.

METHODS

Numerically optimized gradient waveforms were adapted using a novel method that allows for the simultaneous tuning of M1- and M2-weighting by changing only one timing variable. Seven healthy volunteers underwent diffusion-weighted magnetic resonance imaging (DWI) with five diffusion encoding schemes (monopolar, velocity-compensated (M1 = 0), acceleration-compensated (M1 = M2 = 0), 84%-M1-M2-compensated, 67%-M1-M2-compensated) at b-values of 50 and 800 s/mm2 at a constant echo time of 70 ms. Signal dropout correction and apparent diffusion coefficients (ADCs) were quantified using regions of interest in the left and right liver lobe. The blood appearance was evaluated using two five-point Likert scales.

RESULTS

Signal dropout was more pronounced in the left lobe (19%-42% less signal than in the right lobe with monopolar scheme) and best corrected by acceleration-compensation (8%-10% less signal than in the right lobe). The black-blood state was best with monopolar encodings and decreased significantly (p < 0.001) with velocity- and/or acceleration-compensation. The partially M1-M2-compensated encoding schemes could restore the black-blood state again. Strongest ADC bias occurred for monopolar encodings (difference between left/right lobe of 0.41 μm2/ms for monopolar vs. < 0.12 μm2/ms for the other encodings).

CONCLUSION

All of the diffusion encodings used in this study demonstrated suitability for routine DWI application. The results indicate that a perfect value for the level of M1-M2-compensation does not exist. However, among the examined encodings, the 84%-M1-M2-compensated encodings provided a suitable tradeoff.

Technical Advancements in Abdominal Diffusion-weighted Imaging

Since its first observation in the 18th century, the diffusion phenomenon has been actively studied by many researchers. Diffusion-weighted imaging (DWI) is a technique to probe the diffusion of water molecules and create a MR image with contrast based on the local diffusion properties. The DWI pixel intensity is modulated by the hindrance the diffusing water molecules experience. This hindrance is caused by structures in the tissue and reflects the state of the tissue. This characteristic makes DWI a unique and effective tool to gain more insight into the tissue's pathophysiological condition. In the past decades, DWI has made dramatic technical progress, leading to greater acceptance in clinical practice. In the abdominal region, however, acquiring DWI with good quality is challenging because of several reasons, such as large imaging volume, respiratory and other types of motion, and difficulty in achieving homogeneous fat suppression. In this review, we discuss technical advancements from the past decades that help mitigate these problems common in abdominal imaging. We describe the use of scan acceleration techniques such as parallel imaging and compressed sensing to reduce image distortion in echo planar imaging. Then we compare techniques developed to mitigate issues due to respiratory motion, such as free-breathing, respiratory-triggering, and navigator-based approaches. Commonly used fat suppression techniques are also introduced, and their effectiveness is discussed. Additionally, the influence of the abovementioned techniques on image quality is demonstrated. Finally, we discuss the current and future clinical applications of abdominal DWI, such as whole-body DWI, simultaneous multiple-slice excitation, intravoxel incoherent motion, and the use of artificial intelligence. Abdominal DWI has the potential to develop further in the future, thanks to scan acceleration and image quality improvement driven by technological advancements. The accumulation of clinical proof will further drive clinical acceptance.

Directional and inter-acquisition variability in diffusion-weighted imaging and editing for restricted diffusion

PURPOSE

To evaluate and quantify inter-directional and inter-acquisition variation in diffusion-weighted imaging (DWI) and emphasize signals that report restricted diffusion to enhance cancer conspicuity, while reducing the effects of local microscopic motion and magnetic field fluctuations.

METHODS

Ten patients with biopsy-proven prostate cancer were studied under an Institutional Review Board-approved protocol. Individual acquisitions of DWI signal intensities were reconstructed to calculate inter-acquisition distributions and their statistics, which were compared for healthy versus cancer tissue. A method was proposed to detect and filter the acquisitions affected by motion-induced signal loss. First, signals that reflect restricted diffusion were separated from the acquisitions that suffer from signal loss, likely due to microscopic motion, by imposing a cutoff value. Furthermore, corrected apparent diffusion coefficient maps were calculated by employing a weighted sum of the multiple acquisitions, instead of conventional averaging. These weights were calculated by applying a soft-max function to the set of acquisitions per-voxel, making the analysis immune to acquisitions with significant signal loss, even if the number of such acquisitions is high.

RESULTS

Inter-acquisition variation is much larger than the Rician noise variance, local spatial variations, and the estimates of diffusion anisotropy based on the current data, as well as the published values of anisotropy. The proposed method increases the contrast for cancers and yields a sensitivity of98 . 8 % $$ 98.8\% $$ with a false positive rate of3 . 9 % $$ 3.9\% $$ .

CONCLUSION

Motion-induced signal loss makes conventional signal-averaging suboptimal and can obscure signals from areas with restricted diffusion. Filtering or weighting individual acquisitions prior to image analysis can overcome this problem.

Motion compensated renal diffusion weighted imaging

PURPOSE

To assess the effect of respiratory motion and cardiac driven pulsation in renal DWI and to examine asymmetrical velocity-compensated diffusion encoding waveforms for robust ADC mapping in the kidneys.

METHODS

The standard monopolar Stejskal-Tanner pulsed gradient spin echo (pgse) and the asymmetric bipolar velocity-compensated (asym-vc) diffusion encoding waveforms were used for coronal renal DWI at 3T. The robustness of the ADC quantification in the kidneys was tested with the aforementioned waveforms in respiratory-triggered and breath-held cardiac-triggered scans at different trigger delays in 10 healthy subjects.

RESULTS

The pgse waveform showed higher ADC values in the right kidney at short trigger delays in comparison to longer trigger delays in the respiratory triggered scans when the diffusion gradient was applied in the feet-head (FH) direction. The coefficient of variation over all respiratory trigger delays, averaged over all subjects was 0.15 for the pgse waveform in the right kidney when diffusion was measured in the FH direction; the corresponding coefficient of variation for the asym-vc waveform was 0.06. The effect of cardiac driven pulsation was found to be small in comparison to the effect of respiratory motion.

CONCLUSION

Short trigger delays in respiratory-triggered scans can cause higher ADC values in comparison to longer trigger delays in renal DWI, especially in the right kidney when diffusion is measured in the FH direction. The asym-vc waveform can reduce ADC variation due to respiratory motion in respiratory-triggered scans at the cost of reduced SNR compared to the pgse waveform.

Reduction of the cardiac pulsation artifact and improvement of lesion conspicuity in flow‐compensated diffusion images in the liver—A quantitative evaluation of postprocessing algorithms

PURPOSE

To enhance image quality of flow-compensated diffusion-weighted liver MRI data by increasing the lesion conspicuity and reducing the cardiac pulsation artifact using postprocessing algorithms.

METHODS

Diffusion-weighted image data of 40 patients with liver lesions had been acquired at 1.5 T. These data were postprocessed with 5 different algorithms (weighted averaging, p-mean, percentile, outlier exclusion, and exception set). Four image properties of the postprocessed data were evaluated for optimizing the algorithm parameters. These properties were the lesion to tissue contrast-to-noise ratio (CNR), the reduction of the cardiac pulsation artifact, the data consistency, and the vessel darkness. They were combined into a total quality score ( Q total , $$ {Q}_{\mathrm{total}}, $$ set to 1 for the trace-weighted reference image), which was used to rate the image quality objectively.

RESULTS

The weighted averaging algorithm performed best according to the total quality score ( Q total = 1.111 ± 0.067 $$ {Q}_{\mathrm{total}}=1.111\pm 0.067 $$ ). The further ranking was outlier exclusion algorithm ( Q total = 1.086 ± 0.061 $$ {Q}_{\mathrm{total}}=1.086\pm 0.061 $$ ), p-mean algorithm ( Q total = 1.045 ± 0.049 $$ {Q}_{\mathrm{total}}=1.045\pm 0.049 $$ ), percentile algorithm ( Q total = 1.012 ± 0.049 $$ {Q}_{\mathrm{total}}=1.012\pm 0.049 $$ ), and exception set algorithm ( Q total = 0.957 ± 0.027 $$ {Q}_{\mathrm{total}}=0.957\pm 0.027 $$ ). All optimized algorithms except for the exception set algorithm corrected the pulsation artifact and increased the lesion CNR. Changes in Q total $$ {Q}_{\mathrm{total}} $$ were significant for all optimized algorithms except for the percentile algorithm. Liver ADC was significantly reduced (except for the exception set algorithm), particularly in the left lobe.

CONCLUSION

Postprocessing algorithms should be used for flow-compensated liver DWI. The proposed weighted averaging algorithm seems to be suited best to increase the image quality of artifact-corrupted flow-compensated diffusion-weighted liver data.

b value and first-order motion moment optimized data acquisition for repeatable quantitative intravoxel incoherent motion DWI

PURPOSE

To design a b value and first-order motion moment (M1 ) optimized data acquisition for repeatable intravoxel incoherent motion (IVIM) quantification in the liver.

METHODS

Cramer-Rao lower bound optimization was performed to determine optimal monopolar and optimal 2D samplings of the b-M1 space based on noise performance. Monte Carlo simulations were used to evaluate the bias and variability in estimates obtained using the proposed optimal samplings and conventional monopolar sampling. Diffusion MRI of the liver was performed in 10 volunteers using 3 IVIM acquisitions: conventional monopolar, optimized monopolar, and b-M1 -optimized gradient waveforms (designed based on the optimal 2D sampling). IVIM parameter maps of diffusion coefficient, perfusion fraction, and blood velocity SD were obtained using nonlinear least squares fitting. Noise performance (SDs), stability (outlier percentage), and test-retest or scan-rescan repeatability (intraclass correlation coefficients) were evaluated and compared across acquisitions.

RESULTS

Cramer-Rao lower bound and Monte Carlo simulations demonstrated improved noise performance of the optimal 2D sampling in comparison to monopolar samplings. Evaluating the designed b-M1 -optimized waveforms in healthy volunteers, significant decreases (p < 0.05) in the SDs and outlier percentages were observed for measurements of diffusion coefficient, perfusion fraction, and blood velocity SD in comparison to measurements obtained using monopolar samplings. Good-to-excellent repeatability (intraclass correlation coefficients ≥ 0.77) was observed for all 3 parameters in both the right and left liver lobes using the b-M1 -optimized waveforms.

CONCLUSIONS

2D b-M1 -optimized data acquisition enables repeatable IVIM quantification with improved noise performance. 2D acquisitions may advance the establishment of IVIM quantitative biomarkers for liver diseases.

Flow-compensated diffusion encoding in MRI for improved liver metastasis detection

Magnetic resonance (MR) diffusion-weighted imaging (DWI) is often used to detect focal liver lesions (FLLs), though DWI image quality can be limited in the left liver lobe owing to the pulsatile motion of the nearby heart. Flow-compensated (FloCo) diffusion encoding has been shown to reduce this pulsation artifact. The purpose of this prospective study was to intra-individually compare DWI of the liver acquired with conventional monopolar and FloCo diffusion encoding for assessing metastatic FLLs in non-cirrhotic patients. Forty patients with known or suspected multiple metastatic FLLs were included and measured at 1.5 T field strength with a conventional (monopolar) and a FloCo diffusion encoding EPI sequence (single refocused; b-values, 50 and 800 s/mm²). Two board-certified radiologists analyzed the DWI images independently. They issued Likert-scale ratings (1 = worst, 5 = best) for pulsation artifact severity and counted the difference of lesions visible at b = 800 s/mm² separately for small and large FLLs (i.e., < 1 cm or > 1 cm) and separately for left and right liver lobe. Differences between the two diffusion encodings were assessed with the Wilcoxon signed-rank test. Both readers found a reduction in pulsation artifact in the liver with FloCo encoding (p < 0.001 for both liver lobes). More small lesions were detected with FloCo diffusion encoding in both liver lobes (left lobe: six and seven additional lesions by readers 1 and 2, respectively; right lobe: five and seven additional lesions for readers 1 and 2, respectively). Both readers found one additional large lesion in the left liver lobe. Thus, flow-compensated diffusion encoding appears more effective than monopolar diffusion encoding for the detection of liver metastases.

Gradient nonlinearity correction in liver DWI using motion-compensated diffusion encoding waveforms

OBJECTIVE : To experimentally characterize the effectiveness of a gradient nonlinearity correction method in removing ADC bias for different motion-compensated diffusion encoding waveforms.

METHODS

The diffusion encoding waveforms used were the standard monopolar Stejskal-Tanner pulsed gradient spin echo (pgse) waveform, the symmetric bipolar velocity-compensated waveform (sym-vc), the asymmetric bipolar velocity-compensated waveform (asym-vc) and the asymmetric bipolar partial velocity-compensated waveform (asym-pvc). The effectiveness of the gradient nonlinearity correction method using the spherical harmonic expansion of the gradient coil field was tested with the aforementioned waveforms in a phantom and in four healthy subjects.

RESULTS

The gradient nonlinearity correction method reduced the ADC bias in the phantom experiments for all used waveforms. The range of the ADC values over a distance of ± 67.2 mm from isocenter reduced from 1.29 × 10^-4 to 0.32 × 10^-4 mm²/s for pgse, 1.04 × 10^-4 to 0.22 × 10^-4 mm²/s for sym-vc, 1.22 × 10^-4 to 0.24 × 10^-4 mm²/s for asym-vc and 1.07 × 10^-4 to 0.11 × 10^-4 mm²/s for asym-pvc. The in vivo results showed that ADC overestimation due to motion or bright vessels can be increased even further by the gradient nonlinearity correction.

CONCLUSION

The investigated gradient nonlinearity correction method can be used effectively with various motion-compensated diffusion encoding waveforms. In coronal liver DWI, ADC errors caused by motion and residual vessel signal can be increased even further by the gradient nonlinearity correction.

Whole tumor volumetric ADC analysis: relationships with histopathological differentiation of hepatocellular carcinoma

PURPOSE

The aim of this study was to investigate the relationships between values obtained from whole tumor volumetric apparent diffusion coefficient (ADC) measurements and histopathological grade in patients with hepatocellular carcinoma (HCC).

METHODS

Fifty-one naïve patients with HCC were included in the study. The tumors were classified according to the Edmondson-Steiner grade and separated as well-differentiated and non-well-differentiated (moderately and poorly differentiated). The ADC parameters of groups were compared by applying Mann-Whitney U test. The correlation between tumors' histopathological stage and whole tumor ADC parameters was investigated using Spearman's Rank Correlation Coefficient. The receiver operating characteristic curve analysis (ROC) was applied to calculate the area under curve (AUC) with intersection point of ADC parameters and curve.

RESULTS

Mean and percentile ADC values of well-differentiated tumors were significantly higher than those of non-well-differentiated tumors (p < 0.05). The strongest correlation between histopathological grade and ADC parameters was 75th percentile ADC (r = - 0.501), 50th percentile ADC (r = - 0.476) and mean ADC (r = - 0.465). Mean, 75th and 50th percentile ADC values used for the distinction of groups gave the highest AUC at ROC analysis (0.781, 0.781, 0.767, respectively). When threshold values of mean, 75th and 50th percentile ADC values were applied (1516 mm²/s, 1194 mm²/s, and 1035 mm²/s) sensitivity was calculated as 0.73, 0.91, 0.83, respectively, and specificity was calculated as 0.82, 0.61, and 0.68, respectively.

CONCLUSIONS

A correlation between whole tumor volumetric ADC values and HCCs' histopathological grade was detected in this study. 75th percentile, 50th percentile and mean ADC values are determined as highly sensitive and specific tests when the threshold values are applied for distinguishing between well-differentiated tumors and moderately/poorly differentiated tumors. When all these findings are evaluated together, HCCs' volumetric ADC values might be a useful noninvasive predictive parameters for histopathological grade in patients with HCC.

Repeatability and reproducibility of ADC measurements: a prospective multicenter whole-body-MRI study

OBJECTIVES

Multicenter oncology trials increasingly include MRI examinations with apparent diffusion coefficient (ADC) quantification for lesion characterization and follow-up. However, the repeatability and reproducibility (R&R) limits above which a true change in ADC can be considered relevant are poorly defined. This study assessed these limits in a standardized whole-body (WB)-MRI protocol.

METHODS

A prospective, multicenter study was performed at three centers equipped with the same 3.0-T scanners to test a WB-MRI protocol including diffusion-weighted imaging (DWI). Eight healthy volunteers per center were enrolled to undergo test and retest examinations in the same center and a third examination in another center. ADC variability was assessed in multiple organs by two readers using two-way mixed ANOVA, Bland-Altman plots, coefficient of variation (CoV), and the upper limit of the 95% CI on repeatability (RC) and reproducibility (RDC) coefficients.

RESULTS

CoV of ADC was not influenced by other factors (center, reader) than the organ. Based on the upper limit of the 95% CI on RC and RDC (from both readers), a change in ADC in an individual patient must be superior to 12% (cerebrum white matter), 16% (paraspinal muscle), 22% (renal cortex), 26% (central and peripheral zones of the prostate), 29% (renal medulla), 35% (liver), 45% (spleen), 50% (posterior iliac crest), 66% (L5 vertebra), 68% (femur), and 94% (acetabulum) to be significant.

CONCLUSIONS

This study proposes R&R limits above which ADC changes can be considered as a reliable quantitative endpoint to assess disease or treatment-related changes in the tissue microstructure in the setting of multicenter WB-MRI trials.

KEY POINTS

• The present study showed the range of R&R of ADC in WB-MRI that may be achieved in a multicenter framework when a standardized protocol is deployed. • R&R was not influenced by the site of acquisition of DW images. • Clinically significant changes in ADC measured in a multicenter WB-MRI protocol performed with the same type of MRI scanner must be superior to 12% (cerebrum white matter), 16% (paraspinal muscle), 22% (renal cortex), 26% (central zone and peripheral zone of prostate), 29% (renal medulla), 35% (liver), 45% (spleen), 50% (posterior iliac crest), 66% (L5 vertebra), 68% (femur), and 94% (acetabulum) to be detected with a 95% confidence level.

Diffusion-weighted imaging and texture analysis: current role for diffuse liver disease

Multiparametric MRI represents the primary imaging modality to assess diffuse liver disease, both in a qualitative and in a quantitative manner. Diffusion-weighted imaging (DWI) is among the imaging techniques that can be used to assess fibrosis due to its unique capability to assess microstructural changes at the tissue level. DWI is based on water mobility patterns and has the potential to become a non-invasive and non-destructive virtual biopsy to assess diffuse liver disease, overcoming sampling bias errors due to its three-dimensional imaging capabilities. Parallel to DWI, another quantitative method called texture analysis may be used to assess early and advanced diffused liver disease through quantifying spatial relationships in a global and local level, applying to any type of digital imaging technique like MRI or CT. Initial results using texture analysis hold great promise. In the current paper, we will review the role of DWI and texture analysis using MR images in assessing diffuse liver disease.

A mixed waveform protocol for reduction of the cardiac motion artifact in black-blood diffusion-weighted imaging of the liver

OBJECTIVE

Diffusion-weighted imaging (DWI) in the liver suffers from signal loss due to the cardiac motion artifact, especially in the left liver lobe. The purpose of this work was to improve the image quality of liver DWI in terms of cardiac motion artifact reduction and achievement of black-blood images in low b-value images.

MATERIAL AND METHODS

Ten healthy volunteers (age 20-31 years) underwent MRI examinations at 1.5 T with a prototype DWI sequence provided by the vendor. Two diffusion encodings (i.e. waveforms), monopolar and flow-compensated, and the b-values 0, 20, 50, 100, 150, 600 and 800 s/mm² were used. Two Likert scales describing the severity of the pulsation artifact and the quality of the black-blood state were defined and evaluated by two experienced radiologists. Regions of interest (ROIs) were manually drawn in the right and left liver lobe in each slice and combined to a volume of interest (VOI). The mean and coefficient of variation were calculated for each normalized VOI-averaged signal to assess the severity of the cardiac motion artifact. The ADC was calculated using two b-values once for the monopolar data and once with mixed data, using the monopolar data for the small and the flow-compensated data for the high b-value. A Wilcoxon rank sum test was used to compare the Likert scores obtained for monopolar and flow-compensated data.

RESULTS

At b-values from 20 to 150 s/mm², unlike the flow-compensated diffusion encoding, the monopolar encoding yielded black blood in all images with a negligible signal loss due to the cardiac motion artifact. At the b-values 600 and 800 s/mm², the flow-compensated encoding resulted in a significantly reduced cardiac motion artifact, especially in the left liver lobe, and in a black-blood state. The ADC calculated with monopolar data was significantly higher in the left than in the right liver lobe.

CONCLUSION

It is recommendable to use the following mixed waveform protocol: Monopolar diffusion encodings at small b-values and flow-compensated diffusion encodings at high b-values.

Whole-lesion histogram analysis metrics of the apparent diffusion coefficient: a correlation study with histological grade of hepatocellular carcinoma

PURPOSE

The study evaluated the relationship between the histological grade of hepatocellular carcinoma (HCC) and the histogram-derived parameters of apparent diffusion coefficient (ADC) obtained from the whole-lesion assessment of diffusion-weighted magnetic resonance (MR) imaging in the liver.

METHODS

A total of 51 patients were included. The parameters were correlated with the Edmondson-Steiner grades by using the Spearman correlation coefficient (ρ). The differences of ADC parameters between different tumor histological grades were compared using the Mann-Whitney U test. The extent to which each parameter aided in differentiating tumors with poor performance (III, IV) and fair performance (I, II) was assessed by using the area under the receiver operating characteristic curve (Az).

RESULTS

The 25th percentile ADC exhibits the most negative correlation with histological grade (ρ = - 0.397), followed by the 30th percentile ADC (ρ = - 0.395), the minimum ADC value (ρ = - 0.390) and the 20th percentile ADC (ρ = - 0.385), whereas the minimum ADC value yielded the highest Az (0.763) in the discrimination of tumor foci with poor differentiation from fairly differentiated HCCs. The minimum ADC of 4.15 × 10^-3 mm²/s or lower was considered to indicate poorly differentiated performance, and the corresponding sensitivity and specificity were 66.7 and 90.9%, respectively.

CONCLUSION

The 25th percentile ADC showed a stronger correlation with the histological grade of HCC than other ADC parameters, and the minimum ADC value might be an optimal metric for determining poor and fair differentiations of HCC in DWI.

Assessment of cardiac-driven liver movements with filtered harmonic phase image representation, optical flow quantification, and motion amplification

PURPOSE

To characterize cardiac-driven liver movements using a harmonic phase image representation (HARP) with an optical flow quantification and motion amplification method. The method was applied to define the cardiac trigger delay providing minimal signal losses in liver DWI images.

METHODS

The 16-s breath-hold balanced-SSFP time resolved 20 images/s were acquired at 3T in coronal and sagittal orientations. A peripheral pulse unit signal was recorded. Cardiac-triggered DWI images were acquired after different peripheral pulse unit delays. A steerable pyramid decomposition with multiple orientations and spatial frequencies was applied. The liver motion field-map was derived from temporal variations of the HARP representation filtered around the cardiac frequency. Liver displacements were quantified with an optical flow method; moreover the right liver motion was amplified.

RESULTS

The largest displacements were observed in the left liver (feet-head:3.70 ± 1.06 mm; anterior-posterior: 2.35 ± 0.51 mm). Displacements were statistically significantly weaker in the middle right liver (0.47 ± 0.11 mm; P = 0.0156). The average error was 0.013 ± 0.022 mm (coronal plane) and 0.021 ± 0.041 mm (sagittal plane). The velocity field demonstrated opposing movements of the right liver extremities during the cardiac cycle. DWI signal loss was minimized in regions and instants of smallest amplitude of both velocity and velocity gradient.

CONCLUSION

Cardiac-driven liver movements were quantified with combined cardiac frequency-filtered HARP and optical flow methods. A motion phase opposition between right liver extremities was demonstrated. Displacement amplitude and velocity were larger in the left liver especially along the vertical direction. Motion amplification visually emphasized cardiac-driven right liver displacements. The optimal cardiac timing minimizing signal loss in liver DWI images was derived.

Assessment of different mathematical models for diffusion-weighted imaging as quantitative biomarkers for differentiating benign from malignant solid hepatic lesions

To quantitatively compare the monoexponential, biexponential, and stretched‐exponential diffusion‐weighted imaging (DWI) models in differentiating benign from malignant solid hepatic lesions. The institutional review board approved this retrospective study and waived the informed consent requirement. A total of 188 patients with 288 hepatic lesions included 202 malignant lesions and 86 benign lesions were assessed (confirmed by pathology or clinical follow‐up for 6 months). All patients underwent hepatic 3.0‐T MRI, including multi‐b DWI that used 12 b values. The ADC, D_p, D_t, perfusion fraction (f_p), α, and DDC values for normal liver, benign liver lesions, and malignant liver lesions were calculated. Independent sample t tests were used for comparisons. The diagnostic performance of the parameters was evaluated using ROC analysis. The AUC value for each model was also calculated. The value of D_p was significantly lower in benign lesions than in normal hepatic parenchyma while others were significantly higher (P < .001). Whereas Values of D_t and α in malignant hepatic lesions were significantly higher than in normal hepatic parenchyma (P < .001), and the D_p value was significantly lower (P < .001). Values of ADC, f_p, DDC, and α for malignant hepatic lesions were significantly lower than those for benign hepatic lesions (P < .001). ROC analysis showed that the diagnostic value of the biexponential model of normal hepatic parenchyma vs benign hepatic lesions and normal hepatic parenchyma vs malignant hepatic lesions was high (0.946 and 0.876, respectively). In the differential diagnosis of benign and malignant hepatic lesions, DDC had the highest AUC value (0.819). The biexponential and stretched‐exponential DWI may provide additional information and improve the differential diagnosis of benign and malignant hepatic lesions compared with the monoexponential DWI.

Implementing diffusion-weighted MRI for body imaging in prospective multicentre trials: current considerations and future perspectives

For body imaging, diffusion-weighted MRI may be used for tumour detection, staging, prognostic information, assessing response and follow-up. Disease detection and staging involve qualitative, subjective assessment of images, whereas for prognosis, progression or response, quantitative evaluation of the apparent diffusion coefficient (ADC) is required. Validation and qualification of ADC in multicentre trials involves examination of i) technical performance to determine biomarker bias and reproducibility and ii) biological performance to interrogate a specific aspect of biology or to forecast outcome. Unfortunately, the variety of acquisition and analysis methodologies employed at different centres make ADC values non-comparable between them. This invalidates implementation in multicentre trials and limits utility of ADC as a biomarker. This article reviews the factors contributing to ADC variability in terms of data acquisition and analysis. Hardware and software considerations are discussed when implementing standardised protocols across multi-vendor platforms together with methods for quality assurance and quality control. Processes of data collection, archiving, curation, analysis, central reading and handling incidental findings are considered in the conduct of multicentre trials. Data protection and good clinical practice are essential prerequisites. Developing international consensus of procedures is critical to successful validation if ADC is to become a useful biomarker in oncology.

KEY POINTS

• Standardised acquisition/analysis allows quantification of imaging biomarkers in multicentre trials. • Establishing "precision" of the measurement in the multicentre context is essential. • A repository with traceable data of known provenance promotes further research.

Intravoxel incoherent motion diffusion-weighted MR imaging of the liver using respiratory-cardiac double triggering

To investigate the influence of respiratory-cardiac double triggering (RCT) on intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for the liver, twelve healthy volunteers underwent liver DWI twice respectively with respiratory triggering (RT) and RCT schemes. Signal-to-noise ratios (SNRs) of the images, values, repeatability (evaluating with within-subject coefficient of variation), and variability of quantitative parameters, including apparent diffusion coefficient (ADC), pure diffusion coefficient (D), perfusion fraction (f), and perfusion-related diffusion coefficient (D*), were evaluated for each DWI sequence. Results showed that the use of RCT scheme significantly enhanced SNRs (P < 0.001), improved the measurement precision (P ≤ 0.023) and repeatability (P ≤ 0.009) of ADC, D, and f values, decreased the variability of ADC and D values (P ≤ 0.015). Furthermore, this improvement was not completely confined to the left liver lobe, but also observed for the right liver lobe. Moreover, the precision of D* values in the right lobe (P < 0.001) and its repeatability in the left lobe (P = 0.002) were also significantly improved. Thus, our findings suggest that RCT is a more effective physiological scheme for improving SNRs, the precision, repeatability, and variability of quantitative parameters than RT for IVIM-DWI in the liver.

DCE-MRI, DW-MRI, and MRS in Cancer: Challenges and Advantages of Implementing Qualitative and Quantitative Multi-parametric Imaging in the Clinic

Multi-parametric magnetic resonance imaging (mpMRI) offers a unique insight into tumor biology by combining functional MRI techniques that inform on cellularity (diffusion-weighted MRI), vascular properties (dynamic contrast-enhanced MRI), and metabolites (magnetic resonance spectroscopy) and has scope to provide valuable information for prognostication and response assessment. Challenges in the application of mpMRI in the clinic include the technical considerations in acquiring good quality functional MRI data, development of robust techniques for analysis, and clinical interpretation of the results. This article summarizes the technical challenges in acquisition and analysis of multi-parametric MRI data before reviewing the key applications of multi-parametric MRI in clinical research and practice.

3D analysis of apparent diffusion coefficient histograms in hepatocellular carcinoma: correlation with histological grade

Background

To evaluate the usefulness of differentiation of histological grade in hepatocellular carcinoma (HCC) using three-dimensional (3D) analysis of apparent diffusion coefficient (ADC) histograms retrospectively.

Methods

The subjects consisted of 53 patients with 56 HCCs. The subjects included 12 well-differentiated, 35 moderately differentiated, and nine poorly differentiated HCCs. Diffusion-weighted imaging (b-values of 100 and 800 s/mm²) were obtained within 3 months before surgery. Regions of interest (ROIs) covered the entire tumor. The data acquired from each slice were summated to derive voxel-by-voxel ADCs for the entire tumor. The following parameters were derived from the ADC histogram: mean, standard deviation, minimum, maximum, mode, percentiles (5th, 10th, 25th, 50th, 75th, and 90th), skew, and kurtosis. These parameters were analyzed according to histological grade. After eliminating steatosis lesions, these parameters were re-analyzed.

Results

A weak correlation was observed in minimum ADC and 5th percentile for each histological grade (r = –0.340 and r = –0.268, respectively). The minimum ADCs of well, moderately, and poorly differentiated HCC were 585 ± 388, 411 ± 278, and 235 ± 102 × 10⁻⁶ mm²/s, respectively. Minimum ADC showed significant differences among tumor histological grades (P = 0.009). The minimum ADC of poorly differentiated HCC and that of combined well and moderately differentiated HCC were 236 ± 102 and 437 ± 299 × 10⁻⁶ mm²/s. The minimum ADC of poorly differentiated HCC was significantly lower than that of combined well and moderately differentiated HCC (P = 0.001). The sensitivity and specificity, when a minimum ADC of 400 × 10⁻⁶ mm²/s or lower was considered to be poorly differentiated HCC, were 100 and 54%, respectively. After exclusion of the effect of steatosis, the sensitivity and specificity did not change, although the statistical differences became strong (P < 0.0001).

Conclusion

Minimum ADC was most useful to differentiate poorly differentiated HCC in 3D analysis of ADC histograms.

Diffusion-weighted imaging of the liver: Current applications

Diffusion-weighted imaging (DWI) of the liver can be performed using most commercially available machines and is currently accepted in routine sequence. This sequence has some potential as an imaging biomarker for fibrosis, tumor detection/characterization, and following/predicting therapy. To improve reliability including accuracy and reproducibility, researchers have validated this new technique in terms of image acquisition, data sampling, and analysis. The added value of DWI in contrast-enhanced magnetic resonance imaging was established in the detection of malignant liver lesions. However, some limitations remain in terms of lesion characterization and fibrosis detection. Furthermore, the methodologies of image acquisition and data analysis have been inconsistent. Therefore, researchers should make every effort to not only improve accuracy and reproducibility but also standardize imaging parameters.