Intravoxel Incoherent Motion Imaging Research Articles

The value of multiple diffusion metrics based on whole-lesion histogram analysis in evaluating the subtypes and proliferation status of non-small cell lung cancer.

Limited studies have explored the utility of whole-lesion histogram analysis in discerning the subtypes and proliferation status of non-small cell lung cancer (NSCLC), despite its potential to provide comprehensive tissue assessment through the computation of additional quantitative metrics. This study sought to assess the significance of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram parameters in discriminating between squamous cell carcinoma (SCC) and adenocarcinoma (AC), and to examine the correlation of each parameter with the proliferative marker Ki-67. Patients with space-occupying lesions detected by chest CT examination and with further routine MRI, DKI and IVIM functional sequence scans were enrolled. Based on the pathological results, seventy patients with NSCLC were selected and divided into AC and SCC groups. Histogram parameters of IVIM (D, D*, f) and DKI (Dapp, Kapp) were calculated, and the Mann-Whitney U test or independent samples t test was used to analyze the differences in each histogram parameter of the SCC and AC groups. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the histogram parameters. The correlation coefficient between histogram parameters and Ki-67 was calculated using Spearman's or Pearson's methods. The D 10th percentile, D 90th percentile, D mean, D median, Dapp 10th percentile, Dapp 90th percentile, Dapp mean, Dapp median, Dapp skewness, Dapp SD of the AC groups were significantly higher than those of the SCC groups, while the Kapp entropy and Kapp SD of the SCC groups were significantly higher than those of the AC groups. All the above differences were statistically significant (all P < 0.05). ROC curve analysis revealed that Dapp mean showed the best performance for differentiating AC from SCC lesions, with an area under the ROC curve of 0.832 (95% confidence interval [CI]: 0.707-0.919). But there was no statistically significant difference in diagnostic efficacy compared to other histogram parameters (all P>0.05). Dapp 90thpercentile, Dapp mean, Kapp skewnes showed a slight negative correlation with Ki-67 expression (r value -0.340, -0.287, -0.344, respectively; P< 0.05), while the other histogram parameters showed no significant correlation with Ki-67 (all P > 0.05). Our study demonstrates the utility of IVIM and DKI histogram analyses in differentiating NSCLC subtypes, particularly AC and SCC. Correlations with the Ki-67 index suggest that Dapp mean, Dapp 90th percentile, and Kapp skewness may serve as markers of tumor aggressiveness, supporting their use in NSCLC diagnosis and treatment planning.

Advanced quantitative magnetic resonance imaging of lower extremity muscle microtrauma after marathon: a mini review.

This article reviews the existing literature and outlines recent advances in quantitative Magnetic Resonance Imaging (MRI) techniques for the assessment of lower extremity muscle microtrauma following a marathon. Single-modality quantitative MRI techniques include T2 mapping to assess the dynamics of muscle inflammatory edema and variability at the site of injury, Diffusion Tensor Imaging (DTI) to detect subclinical changes in muscle injury, Intravoxel Incoherent Motion (IVIM) imaging to provide simultaneous information on perfusion and diffusion in muscle tissue without the need for intravenous contrast, and Magnetic Resonance Spectroscopy (MRS) to noninvasively detect intramyocellular lipid (IMCL)content in muscle before and after marathon exercise to explain the use of fatty acids as an energy source in skeletal muscle during long-distance running. As well as Chemical Exchange Saturation Transfer (CEST) is particularly suitable for detecting changes in free creatine, pH values and lactate concentrations in muscles before and after exercise, providing a more detailed picture of muscle physiology and chemistry. These metabolic MRI methods enhance the understanding of biochemical alterations occurring in muscles pre- and post-exercise. Multimodal techniques combine different modalities to provide a comprehensive evaluation of muscle structural and functional changes. These advanced techniques aim to better assess microtrauma and guide clinical treatment, though further validation with larger studies is needed to establish their potential over traditional qualitative methods.

SpinDoctor-IVIM: A virtual imaging framework for intravoxel incoherent motion MRI

Intravoxel incoherent motion (IVIM) imaging is increasingly recognised as an important tool in clinical MRI, where tissue perfusion and diffusion information can aid disease diagnosis, monitoring of patient recovery, and treatment outcome assessment. Currently, the discovery of biomarkers based on IVIM imaging, similar to other medical imaging modalities, is dependent on long preclinical and clinical validation pathways to link observable markers derived from images with the underlying pathophysiological mechanisms. To speed up this process, virtual IVIM imaging is proposed. This approach provides an efficient virtual imaging tool to design, evaluate, and optimise novel approaches for IVIM imaging. In this work, virtual IVIM imaging is developed through a new finite element solver, SpinDoctor-IVIM, which extends SpinDoctor, a diffusion MRI simulation toolbox. SpinDoctor-IVIM simulates IVIM imaging signals by solving the generalised Bloch-Torrey partial differential equation. The input velocity to SpinDoctor-IVIM is computed using HemeLB, an established Lattice Boltzmann blood flow simulator. Contrary to previous approaches, SpinDoctor-IVIM accounts for volumetric microvasculature during blood flow simulations, incorporates diffusion phenomena in the intravascular space, and accounts for the permeability between the intravascular and extravascular spaces. The above-mentioned features of the proposed framework are illustrated with simulations on a realistic microvasculature model.

Cerebral infarcts, edema, hypoperfusion and vasospasm in preeclampsia and eclampsia

Eclampsia, a serious pregnancy complication, is associated with cerebral edema and infarctions but the underlying pathophysiology remains largely unexplored. To assess the pathophysiology of eclampsia using specialized magnetic resonance imaging that measures diffusion, perfusion, and vasospasm. This was a cross-sectional study recruiting consecutive pregnant women between April 2018 to November 2021 at Tygerberg Hospital, Cape Town, South Africa. We recruited women with eclampsia, preeclampsia, and normotensive pregnancies who underwent magnetic resonance imaging after birth. Main outcome measures were cerebral infarcts, edema, and perfusion using intravoxel incoherent motion imaging and vasospasm using magnetic resonance imaging angiography. The imaging protocol was established before inclusion. Forty-nine women with eclampsia, 20 with preeclampsia and 10 normotensive women were included. Cerebral infarcts were identified in 34% of eclamptic, 5% of preeclamptic (risk difference (RD) 0.29; 95% confidence interval (CI) 0.06 to 0.52, p=0.012) and in no normotensive controls. Eclamptic women were more likely to have vasogenic cerebral edema compared to preeclamptic (80% vs 20%, RD 0.60; CI 0.34 to 0.85, p<.001) and normotensive women (RD 0.80; CI 0.47 to 1.00, p<.001). Diffusion was increased in eclampsia in the parietooccipital white matter (mean difference (MD) 0.02 x10-3 mm2/s, CI 0.00 to 0.05, p=0.045) and the caudate nucleus (MD 0.02 x10-3 mm2/s, CI 0.00 to 0.04, p=0.033) when compared to preeclamptic women. Diffusion was also increased in eclamptic women in the frontal (MD 0.07 x10-3 mm2/s, CI 0.02 to 0.12, p=0.012) and parietooccipital white matter (MD 0.05 x10-3 mm2/s, CI 0.02 to 0.07, p=0.03) and the caudate nucleus (MD 0.04 x10-3 mm2/s, CI 0.00 to 0.07, p=0.028) when compared to normotensive women. Perfusion was decreased in edematous regions. Hypoperfusion was present in the caudate nucleus in eclampsia (MD -0.17 x10-3 mm2/s, CI -0.27 to -0.06, p=0.003) when compared to preeclampsia. There were no signs of hyperperfusion. Vasospasm was present in 18% of eclamptic, 6% of preeclamptic and none of the controls. Eclampsia is associated with cerebral infarcts, vasogenic cerebral edema, vasospasm and decreased perfusion, all not usually evident on standard clinical imaging. This may explain why some have cerebral symptoms and signs despite having normal conventional imaging.

Added value of histogram analysis of intravoxel incoherent motion and diffusion kurtosis imaging for the evaluation of complete response to neoadjuvant therapy in locally advanced rectal cancer

Abstract Objective To evaluate how intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram analysis contribute to assessing complete response (CR) to neoadjuvant therapy (NAT) in locally advanced rectal cancer (LARC). Material and methods In this prospective study, participants with LARC, who underwent NAT and subsequent surgery, with adequate MR image quality, were enrolled from November 2021 to March 2023. Conventional MRI (T2WI and DWI), IVIM, and DKI were performed before NAT (pre-NAT) and within two weeks before surgery (post-NAT). Image evaluation was independently performed by two experienced radiologists. Pathological complete response (pCR) was used as the reference standard. An IVIM–DKI-added model (a combination of IVIM and DKI histogram parameters with T2WI and DWI) was constructed. Receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic performance of conventional MRI and the IVIM–DKI-added model. Results A total of 59 participants (median age: 58.00 years [IQR: 52.00, 62.00]; 38 [64%] men) were evaluated, including 21 pCR and 38 non-pCR cases. The histogram parameters of DKI, including skewness of kurtosis post-NAT (post-KSkewness) and root mean squared of change ratio of diffusivity (Δ%DDKI-root mean squared), were entered into the IVIM–DKI-added model. The area under the ROC curve (AUC) of the IVIM–DKI-added model for assessing CR to NAT was significantly higher than that of conventional MRI (0.855 [95% CI: 0.749–0.960] vs 0.685 [95% CI: 0.565–0.806], p &lt; 0.001). Conclusion IVIM and DKI provide added value in the evaluation of CR to NAT in LARC. Key Points QuestionThe current conventional imaging evaluation system lacks adequacy for assessing CR to NAT in LARC. FindingsSignificantly improved diagnostic performance was observed with the histogram analysis of IVIM and DKI in conjunction with conventional MRI. Clinical relevanceIVIM and DKI provide significant value in evaluating CR to NAT in LARC, which bears significant implications for reducing surgical complications and facilitating organ preservation.

Imaging of Ulcerative Colitis: The Role of Diffusion-Weighted Magnetic Resonance Imaging.

Magnetic resonance imaging (MRI) has emerged as a promising and appealing alternative to endoscopy in the objective assessment of patients with inflammatory bowel disease (IBD). Diffusion-weighted imaging (DWI) is a specialized imaging technique that enables the mapping of water molecule diffusion within biological tissues, eliminating the need for intravenous gadolinium contrast injection. It is expanding the capability of traditional MRI sequences in Ulcerative Colitis (UC). Recently, there has been growing interest in the application of intravoxel incoherent motion (IVIM) imaging in the field of IBD. This technique combines diffusion and perfusion information, making it a valuable tool for assessing IBD treatment response. Previous studies have extensively studied the use of DWI techniques for evaluating the severity of activity in IBD. However, the majority of these studies have primarily focused on Crohn's disease (CD), with only a limited number of reports specifically examining UC. Therefore, this review briefly introduces the basics of DWI and IVIM imaging and conducts a review of relevant studies that have investigated its application in UC to show whether these techniques are useful techniques for evaluating patients with UC in terms of detection, characterization, and quantification of disease activity. Through the extensive literature survey, most of these studies indicate that DWI proves valuable in the differential diagnosis of UC and could be used as an effective modality for staging UC.

Role of Intravoxel Incoherent Motion and Dynamic-Contrast-Enhanced Perfusion in Response Prediction of Patients with Colorectal Cancer after Neoadjuvant Chemotherapy.

Colorectal cancer (CRC) is a complex malignancy requiring multimodal treatment strategies, including neoadjuvant chemoradiation therapy (Neo-CRT), to improve patient outcomes. However, the response to Neo-CRT varies among individuals, which necessitates the development of reliable predictors of treatment response. The present study aimed to investigate the role of intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) perfusion in predicting treatment response in CRC patients after Neo-CRT. This study was conducted on patients diagnosed with locally advanced CRC who received Neo-CRT. IVIM and DCE perfusion imaging were performed before and after CRT. Quantitative parameters, including perfusion fraction (f), diffusion coefficient (D), and transfer constant (Ktrans), were calculated from the imaging data. Treatment response was assessed based on the pathological response after surgery. Statistical data were analyzed in SPSS v. 26 using the t-test and the chi-square method. A total of 51 patients (female: male = 22:29, mean age = 58.14±3.49) participated in the study. Among all the patients, 15 (29.4%) cases had good responses, while 36 (70.58%) cases did not respond to treatment. All DCE parameters showed higher sensitivity and specificity than IVIM D*. Ve, Kep, and DCE Ktrans indicated significant predictive power for treatment response. Ktrans was the most accurate parameter for predicting response to treatment. Overall sensitivity and specificity of DCE were 88.8% [95% CI: 80-95.6], and 80 % [95% CI: 65-90], and those of IVIM were 65.5% and 81%, respectively. Sensitivity and specificity for DCE + IVIM were 79.5% and 93.5%, and those of DCE + IVIM + standard magnetic resonance imaging were 80.2% and 86%, respectively. IVIM and DCE perfusion imaging could serve as promising tools for predicting treatment response in CRC patients after Neo-CRT.

Detection of synovial inflammation in the sacroiliac joint space through intravoxel incoherent motion imaging: an alternative to contrast agents.

We investigated the diagnostic accuracy of simplified intravoxel incoherent motion (IVIM) imaging for detecting synovial inflammation in the sacroiliac joint (SIJ) in a population with active sacroiliitis. In accordance with the Assessment of Spondyloarthritis International Society criteria, 86 SIJs of 46 patients with active sacroiliitis were included in this retrospective study conducted between November 2020 and January 2022. Based on T1-weighted post-gadolinium images, the SIJs were divided into two groups: synovial inflammation positive (SIP) (n = 28) and synovial inflammation negative (SIN) (n = 58). Synovial areas in the SIJ space were independently and blindly reviewed for the presence of inflammation by two radiologists with differing levels of expertise in radiology. Using four b values, apparent diffusion coefficient (ADC)= ADC (0, 800) and the simplified 3T IVIM method parameters true diffusion coefficient (D1)= ADC (50, 800), D= ADC (400, 800), f1= f (0, 50, 800), f2= f (0, 400, 800), pseudodiffusion coefficient (D*)= D* (0, 50, 400, 800), ADClow = ADC (0, 50), and ADCdiff= ADClow - D were generated voxel by voxel for each patient. The IVIM and ADC parameters at the SIN and SIP joints were compared. The D parameter was significantly increased in SIP areas (1.23 ± 0.34 × 10-3 mm2/s) compared with SIN areas (1.02 ± 0.16 × 10-3 mm2/s) (P = 0.004). Conversely, the D* parameter was significantly decreased in SIP areas (21.78 ± 3.77 × 10-3 mm2/s) compared with SIN areas (16.19 ± 4.58 × 10-3 mm2/s) (P < 0.001). When the optimal cut-off value of 1.11 × 10-3 mm2/s was selected, the sensitivity for the D value was 71% and the specificity was 72% [area under the curve (AUC): 0.716)]. When the optimal cut-off value of 21.06 × 10-3 mm2/s was selected, the sensitivity for the D* value was 78.6%, and the specificity was 79.3% (AUC: 0.829). The interclass correlation coefficient was excellent for f1, f2 D*, D, and ADCdiff, good for ADClow and D1, but reasonable for ADC. The presence of synovial inflammation in the SIJ can be evaluated with high sensitivity and specificity using only four b values through the simplified IVIM method without the need for a contrast agent. IVIM imaging is a technique that allows us to gain insights into tissue perfusion without the administration of contrast agents, utilizing diffusion-weighted images. In this study, for the first time, we demonstrated the potential of detecting synovial inflammation in the SIJ using IVIM, specifically through the pseudodiffusion (D*) parameter, without the need for contrast agents.

Preliminary investigations into human neurofluid transport using multiple novel non-contrast MRI methods.

We discuss two potential non-invasive MRI methods to study phenomena related to subarachnoid cerebrospinal fluid (CSF) motion and perivascular fluid transport, and their association with sleep and aging. We apply diffusion-based intravoxel incoherent motion (IVIM) imaging to evaluate pseudodiffusion coefficient, D*, or CSF movement across large spaces like the subarachnoid space (SAS). We also performed perfusion-based multi-echo, Hadamard encoded arterial spin labeling (ASL) to evaluate whole brain cortical cerebral blood flow (CBF) and trans-endothelial exchange (Tex) of water from the vasculature into the perivascular space and parenchyma. Both methods were used in young adults (N = 9, 6 F, 23 ± 3 years old) in the setting of sleep and sleep deprivation. To study aging, 10 older adults (6 F, 67 ± 3 years old) were imaged after a night of normal sleep and compared with the young adults. D* in SAS was significantly (p < 0.05) reduced with sleep deprivation (0.016 ± 0.001 mm2/s) compared to normal sleep (0.018 ± 0.001 mm2/s) and marginally reduced with aging (0.017 ± 0.001 mm2/s, p = 0.029). Cortical CBF and Tex were unchanged with sleep deprivation but significantly lower in older adults (37 ± 3 ml/100 g/min, 578 ± 61 ms) than in young adults (42 ± 2 ml/100 g/min, 696 ± 62 ms). IVIM was sensitive to sleep physiology and aging, and multi-echo, multi-delay ASL was sensitive to aging.

Early and late assessment of renal allograft dysfunction using intravoxel incoherent motion (IVIM) and diffusion-weighted imaging (DWI): a prospective study.

To evaluate the ability of the Intravoxel Incoherent Motion (IVIM) and monoexponentially ADC in renal allograft function in the early and late phases of transplantation, and to predict their effectiveness in discrimination of the graft pathology. This is a prospective study included participants scanned with quantitative diffusion and perfusion sequences on a 3-T MR scanner (Philips, Ingenia); the ADC and IVIM parameters; were calculated. Correlations and regression analysis with the eGFR, transplantation periods, and pathology were assessed. This study included 105 renal allograft recipients (85 males, and 20 females with mean age = 32.4 ± 11.9years and age range = 22-61years). There was a significant positive correlation between the whole parameters of the ADC and IVIM with eGFR however, the cortical parameters showed higher significant correlation coefficients (p < 0.001). Regression analysis revealed the most significant model can predict eGFR groups included cortical pseudo diffusion (D*) and cortical ADC (p < 0.001). In graft dysfunction eGFR was 61.5ml/min and normal graft was 64ml/min. This model demonstrates a high performance of an AUC 96% [0.93-0.97]. In the late transplantation, there is a higher correlation with D* compared to ADC, p-values = 0.001. IVIM and ADC Values are significant biomarkers for renal allograft function assessment, cortical ADC, and D* had the highest performance even in situations with mild impairment that is not affect the eGFR yet as cases of proteinuria with normal eGFR. Furthermore, D* is superior to ADC in the late assessment of the renal transplant.

Precise Prostate Cancer Assessment Using IVIM-Based Parametric Estimation of Blood Diffusion from DW-MRI.

Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays a crucial role in improving patient outcomes. This study introduces a non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the detection and diagnosis of prostate cancer (PCa). IVIM imaging enables the differentiation of water molecule diffusion within capillaries and outside vessels, offering valuable insights into tumor characteristics. The proposed approach utilizes a two-step segmentation approach through the use of three U-Net architectures for extracting tumor-containing regions of interest (ROIs) from the segmented images. The performance of the CAD system is thoroughly evaluated, considering the optimal classifier and IVIM parameters for differentiation and comparing the diagnostic value of IVIM parameters with the commonly used apparent diffusion coefficient (ADC). The results demonstrate that the combination of central zone (CZ) and peripheral zone (PZ) features with the Random Forest Classifier (RFC) yields the best performance. The CAD system achieves an accuracy of 84.08% and a balanced accuracy of 82.60%. This combination showcases high sensitivity (93.24%) and reasonable specificity (71.96%), along with good precision (81.48%) and F1 score (86.96%). These findings highlight the effectiveness of the proposed CAD system in accurately segmenting and diagnosing PCa. This study represents a significant advancement in non-invasive methods for early detection and diagnosis of PCa, showcasing the potential of IVIM parameters in combination with machine learning techniques. This developed solution has the potential to revolutionize PCa diagnosis, leading to improved patient outcomes and reduced healthcare costs.

Histogram analysis of multiple diffusion models for predicting advanced non-small cell lung cancer response to chemoimmunotherapy

BackgroundThere is an urgent need to find a reliable and effective imaging method to evaluate the therapeutic efficacy of immunochemotherapy in advanced non-small cell lung cancer (NSCLC). This study aimed to investigate the capability of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram analysis based on different region of interest (ROI) selection methods for predicting treatment response to chemoimmunotherapy in advanced NSCLC.MethodsSeventy-two stage III or IV NSCLC patients who received chemoimmunotherapy were enrolled in this study. IVIM and DKI were performed before treatment. The patients were classified as responders group and non-responders group according to the Response Evaluation Criteria in Solid Tumors 1.1. The histogram parameters of ADC, Dslow, Dfast, f, Dk and K were measured using whole tumor volume ROI and single slice ROI analysis methods. Variables with statistical differences would be included in stepwise logistic regression analysis to determine independent parameters, by which the combined model was also established. And the receiver operating characteristic curve (ROC) were used to evaluate the prediction performance of histogram parameters and the combined model.ResultsADC, Dslow, Dk histogram metrics were significantly lower in the responders group than in the non-responders group, while the histogram parameters of f were significantly higher in the responders group than in the non-responders group (all P < 0.05). The mean value of each parameter was better than or equivalent to other histogram metrics, where the mean value of f obtained from whole tumor and single slice both had the highest AUC (AUC = 0.886 and 0.812, respectively) compared to other single parameters. The combined model improved the diagnostic efficiency with an AUC of 0.968 (whole tumor) and 0.893 (single slice), respectively.ConclusionsWhole tumor volume ROI demonstrated better diagnostic ability than single slice ROI analysis, which indicated whole tumor histogram analysis of IVIM and DKI hold greater potential than single slice ROI analysis to be a promising tool of predicting therapeutic response to chemoimmunotherapy in advanced NSCLC at initial state.

Histogram analysis of intravoxel incoherent motion imaging: Correlation with molecular prognostic factors and combined subtypes of breast cancer

PurposeTo look for links between diffusion and IVIM parameters and different molecular subtypes and prognostic factors through histogram analysis. Materials and methodsA total of 139 patients with breast cancer who had pre-operative MRI examinations were enrolled in this retrospective study. Histograms of the diffusion and IVIM parameters were analyzed for the whole tumor, and an association was investigated between the parameters and the different molecular prognostic factors and subtypes using the nonparametric test, Spearman's rank correlation, and receiver operating characteristic (ROC) curve. ResultsThe histogram metrics of the diffusion and IVIM parameters were significantly different for molecular prognostic factors such as human epidermal receptor factor-2 (HER2), progesterone receptor, estrogen receptor, and ki-67. All histogram metrics displayed a poor correlation with all groups (r = −0.28-0.29). There were significant differences in the histogram metrics for the Luminal B-HER2 (−) vs. HER2-positive (non-luminal) subtypes in the mean and 10th percentile D, with the area under the curves (AUCs) of 0.742 and 0.700, respectively, and for the Luminal A and HER2-positive (non-luminal) subtypes in the 90th percentile and entropy of D*, with AUCs of 0.769 and 0.727, respectively. ConclusionThe histogram metrics of IVIM parameters exhibited links with breast cancer prognosis factors and combined subtypes.

Image denoising and model-independent parameterization for IVIM MRI

Objective. To improve intravoxel incoherent motion imaging (IVIM) magnetic resonance Imaging quality using a new image denoising technique and model-independent parameterization of the signal versus b-value curve. Approach. IVIM images were acquired for 13 head-and-neck patients prior to radiotherapy. Post-radiotherapy scans were also acquired for five of these patients. Images were denoised prior to parameter fitting using neural blind deconvolution, a method of solving the ill-posed mathematical problem of blind deconvolution using neural networks. The signal decay curve was then quantified in terms of several area under the curve (AUC) parameters. Improvements in image quality were assessed using blind image quality metrics, total variation (TV), and the correlations between parameter changes in parotid glands with radiotherapy dose levels. The validity of blur kernel predictions was assessed by the testing the method's ability to recover artificial ‘pseudokernels’. AUC parameters were compared with monoexponential, biexponential, and triexponential model parameters in terms of their correlations with dose, contrast-to-noise (CNR) around parotid glands, and relative importance via principal component analysis. Main results. Image denoising improved blind image quality metrics, smoothed the signal versus b-value curve, and strengthened correlations between IVIM parameters and dose levels. Image TV was reduced and parameter CNRs generally increased following denoising. AUC parameters were more correlated with dose and had higher relative importance than exponential model parameters. Significance. IVIM parameters have high variability in the literature and perfusion-related parameters are difficult to interpret. Describing the signal versus b-value curve with model-independent parameters like the AUC and preprocessing images with denoising techniques could potentially benefit IVIM image parameterization in terms of reproducibility and functional utility.

Quantitative characterization of breast lesions and normal fibroglandular tissue using compartmentalized diffusion-weighted model: comparison of intravoxel incoherent motion and restriction spectrum imaging

BackgroundTo compare the compartmentalized diffusion-weighted models, intravoxel incoherent motion (IVIM) and restriction spectrum imaging (RSI), in characterizing breast lesions and normal fibroglandular tissue.MethodsThis prospective study enrolled 152 patients with 157 histopathologically verified breast lesions (41 benign and 116 malignant). All patients underwent a full-protocol preoperative breast MRI, including a multi-b-value DWI sequence. The diffusion parameters derived from the mono-exponential model (ADC), IVIM model (Dt, Dp, f), and RSI model (C1, C2, C3, C1C2, F1, F2, F3, F1F2) were quantitatively measured and then compared among malignant lesions, benign lesions and normal fibroglandular tissues using Kruskal-Wallis test. The Mann-Whitney U-test was used for the pairwise comparisons. Diagnostic models were built by logistic regression analysis. The ROC analysis was performed using five-fold cross-validation and the mean AUC values were calculated and compared to evaluate the discriminative ability of each parameter or model.ResultsAlmost all quantitative diffusion parameters showed significant differences in distinguishing malignant breast lesions from both benign lesions (other than C2) and normal fibroglandular tissue (all parameters) (all P < 0.0167). In terms of the comparisons of benign lesions and normal fibroglandular tissues, the parameters derived from IVIM (Dp, f) and RSI (C1, C2, C1C2, F1, F2, F3) showed significant differences (all P < 0.005). When using individual parameters, RSI-derived parameters-F1, C1C2, and C2 values yielded the highest AUCs for the comparisons of malignant vs. benign, malignant vs. normal tissue and benign vs. normal tissue (AUCs = 0.871, 0.982, and 0.863, respectively). Furthermore, the combined diagnostic model (IVIM + RSI) exhibited the highest diagnostic efficacy for the pairwise discriminations (AUCs = 0.893, 0.991, and 0.928, respectively).ConclusionsQuantitative parameters derived from the three-compartment RSI model have great promise as imaging indicators for the differential diagnosis of breast lesions compared with the bi-exponential IVIM model. Additionally, the combined model of IVIM and RSI achieves superior diagnostic performance in characterizing breast lesions.

Toward Precision Diagnosis: Machine Learning in Identifying Malignant Orbital Tumors With Multiparametric 3 T MRI.

Orbital tumors present a diagnostic challenge due to their varied locations and histopathological differences. Although recent advancements in imaging have improved diagnosis, classification remains a challenge. The integration of artificial intelligence in radiology and ophthalmology has demonstrated promising outcomes. This study aimed to evaluate the performance of machine learning models in accurately distinguishing malignant orbital tumors from benign ones using multiparametric 3 T magnetic resonance imaging (MRI) data. In this single-center prospective study, patients with orbital masses underwent presurgery 3 T MRI scans between December 2015 and May 2021. The MRI protocol comprised multiparametric imaging including dynamic contrast-enhanced (DCE), diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), as well as morphological imaging acquisitions. A repeated nested cross-validation strategy using random forest classifiers was used for model training and evaluation, considering 8 combinations of explanatory features. Shapley additive explanations (SHAP) values were used to assess feature contributions, and the model performance was evaluated using multiple metrics. One hundred thirteen patients were analyzed (57/113 [50.4%] were women; average age was 51.5 ± 17.5 years, range: 19-88 years). Among the 8 combinations of explanatory features assessed, the performance on predicting malignancy when using the most comprehensive model, which is the most exhaustive one incorporating all 46 explanatory features-including morphology, DWI, DCE, and IVIM, achieved an area under the curve of 0.9 [0.73-0.99]. When using the streamlined "10-feature signature" model, performance reached an area under the curve of 0.88 [0.71-0.99]. Random forest feature importance graphs measured by the mean of SHAP values pinpointed the 10 most impactful features, which comprised 3 quantitative IVIM features, 4 quantitative DCE features, 1 quantitative DWI feature, 1 qualitative DWI feature, and age. Our findings demonstrate that a machine learning approach, integrating multiparametric MRI data such as DCE, DWI, IVIM, and morphological imaging, offers high-performing models for differentiating malignant from benign orbital tumors. The streamlined 10-feature signature, with a performance close to the comprehensive model, may be more suitable for clinical application.

IVIM and DCE-MRI in Predicting Phenotypic Subtypes and Nottingham Prognostic Index of Breast Cancer.

To explore the value of intravoxel incoherent motion (IVIM) and dynamic contrast enhanced MRI (DCE-MRI) for predicting phenotypic subtypes and Nottingham prognostic index (NPI) of breast cancer. Descriptive study. Place and Duration of the Study: Department of Radiology, Affiliated Hospital of Jining Medical University, Jining, Shandong, China, from March 2020 to January 2022. One hundred and forty-one breast cancer patients with preoperative IVIM and DCE imaging were collected. IVIM parameters of D, D*, f, and DCE-MRI parameters of Ktrans, Kep, and Ve were measured. Receiver operating characteristic curves were conducted to assess the diagnostic efficacies. Additionally, 40 patients collected from February 2022 to July 2022 were enrolled as validation cohort. The D value in HER2-enriched (HER2-E) was lower than that in non-HER-E, while D*, Ktrans, and Ve values were higher than that in non-HER-E (p < 0.001, 0.046, < 0.001, and < 0.001, respectively). D + Ktrans + Ve showed an optimal diagnostic efficiency (AUC = 0.868). Meanwhile, D* and f values of triple-negative breast cancer (TNBC) were higher than those of non-TNBC, and Ve value of TNBC was lower than that of non-TNBC (p = 0.013, 0.006, and < 0.001, respectively). D* + f + Ve showed the best prediction performance (AUC = 0.849). Additionally, D and Kep were independent predictors of NPI (p < 0.001, and 0.002, respectively). D + Kep showed a good diagnostic efficiency (AUC = 0.818). The combined IVIM and DCE-MRI model showed enhanced diagnostic efficiency in predicting phenotypic subtypes and NPI of breast cancer, and might thus be considered efficient in therapy decision-making for patients. Breast neoplasms, Intravoxel incoherent motion, Dynamic contrast enhanced magnetic resonance imaging, Phenotypic subtypes, Nottingham prognostic index.

Relating pre-treatment non-Gaussian intravoxel incoherent motion diffusion-weighted imaging to human papillomavirus status and response in oropharyngeal carcinoma

Background and purposeDiffusion-weighted imaging (DWI) is a promising technique for response assessment in head-and-neck cancer. Recently, we optimized Non-Gaussian Intravoxel Incoherent Motion Imaging (NG-IVIM), an extension of the conventional apparent diffusion coefficient (ADC) model, for the head and neck. In the current study, we describe the first application in a group of patients with human papillomavirus (HPV)-positive and HPV-negative oropharyngeal squamous cell carcinoma. The aim of this study was to relate ADC and NG-IVIM DWI parameters to HPV status and clinical treatment response. Materials and methodsThirty-six patients (18 HPV-positive, 18 HPV-negative) were prospectively included. Presence of progressive disease was scored within one year. The mean pre-treatment ADC and NG-IVIM parameters in the gross tumor volume were compared between HPV-positive and HPV-negative patients. In HPV-negative patients, ADC and NG-IVIM parameters were compared between patients with and without progressive disease. ResultsADC, the NG-IVIM diffusion coefficient D, and perfusion fraction f were significantly higher, while pseudo-diffusion coefficient D* and kurtosis K were significantly lower in the HPV-negative compared to HPV-positive patients. In the HPV-negative group, a significantly lower D was found for patients with progressive disease compared to complete responders. No relation with ADC was observed. ConclusionThe results of our single-center study suggest that ADC is related to HPV status, but not an independent response predictor. The NG-IVIM parameter D, however, was independently associated to response in the HPV-negative group. Noteworthy in the opposite direction as previously thought based on ADC.

Evaluation of Whole Brain Intravoxel Incoherent Motion (IVIM) Imaging.

Intravoxel Incoherent Motion (IVIM) imaging provides non-invasive perfusion measurements, eliminating the need for contrast agents. This work explores the feasibility of IVIM imaging in whole brain perfusion studies, where an isotropic 1 mm voxel is widely accepted as a standard. This study follows the validity of a time-limited, precise, segmentation-ready whole-brain IVIM protocol suitable for clinical reality. To assess the influence of SNR on the estimation of S0, f, D*, and D IVIM parameters, a series of measurements and simulations were performed in MATLAB for the following three estimation techniques: segmented grid search, segmented curve fitting, and one-step curve fitting, utilizing known "ground truth" and noised data. Scanner-specific SNR was estimated based on a healthy subject IVIM MRI study in a 3T scanner. Measurements were conducted for 25.6 × 25.6 × 14.4 cm FOV with a 256 × 256 in-plane resolution and 72 slices, resulting in 1 × 1 × 2 mm voxel size. Simulations were performed for 36 SNR levels around the measured SNR value. For a single voxel grid, the search algorithm mean relative error Ŝ0, f^, D^*, and D^ of at the expected SNR level were 5.00%, 81.91%, 76.31%, and 18.34%, respectively. Analysis has shown that high-resolution IVIM imaging is possible, although there is significant variation in both accuracy and precision, depending on SNR and the chosen estimation method.

Evaluation of Local Vascular Perfusion in the Lower Extremities on Intravoxel Incoherent Motion Imaging before and after Endovascular Therapy.

To evaluate improvement in local vascular perfusion of the lower limbs on intravoxel incoherent motion (IVIM) imaging after endovascular therapy (EVT). IVIM imaging was performed on 20 lower limbs of 16 patients with lower extremity arterial diseases before and after EVT. To estimate IVIM, diffusion-weighted lower-limb axial images (number of slices = 25 and slice thickness = 3.5mm) were acquired using different b values (0, 300, and 1000s/mm2). IVIM imaging with the simplified IVIM techniques was performed. The perfusion-related coefficient (D* [10-3mm2/s]), perfusion fraction (f [%]), and D*f product (10-3mm2/s %) were calculated before and 2-3days after EVT. The ankle brachial index (ABI), mean D* (10-3mm2/s), mean f (%), and mean D*f product (10-3mm2/s %) before and after EVT were compared. Successful revascularization was achieved in all cases. After EVT, the mean ABI significantly increased from 0.59 ± 0.19 to 0.87 ± 0.15 (p < 0.001, paired t test). The mean D* (10-3mm2/s) (22.08 ± 3.26 versus 24.87 ± 2.65, p = 0.005, paired t test), and D*f product (10-3mm2/s%) (551.03 ± 79.02 versus 634.55 ± 76.96, p = 0.002, paired t-test) of the lower limbs significantly increased after EVT, whereas f (%) (25.00 ± 1.28 versus 25.52 ± 1.61, p = 0.261, paired t-test) did not significantly increased after EVT. D* (10-3mm2/s) and D*f product (10-3mm2/s %) on IVIM imaging could evaluate improvement in local vascular perfusion of the lower limbs after EVT. Level 4, Case Series.