IVIM-DKI for differentiation between prostate cancer and benign prostatic hyperplasia: comparison of 1.5 T vs. 3 T MRI

Virtual MR Elastography and Multi-b-value DWI Models for Predicting Microvascular Invasion in Solitary BCLC Stage A Hepatocellular Carcinoma

RATIONALE AND OBJECTIVES

To evaluate the performance of virtual MR elastography (vMRE) for predicting microvascular invasion (MVI) in Barcelona Clinic Liver Cancer (BCLC) stage A (≤ 5.0 cm) hepatocellular carcinoma (HCC) and to construct a combined nomogram based on vMRE, multi-b-value DWI models, and clinical-radiological (CR) features.

METHODS

Consecutive patients with suspected HCC who underwent multi-b-value DWI examinations were prospectively collected. Quantitative parameters from vMRE, mono-exponential, intravoxel incoherent motion, and diffusion kurtosis imaging models were obtained. Multivariate logistic regression was used to identify independent MVI predictors and build prediction models. A combined MRI_Score was constructed using independent quantitative parameters. A visualized nomogram was built based on significant CR features and MRI_Score. The predictive performance of quantitative parameters and models was evaluated.

RESULTS

The study included 103 patients (median age: 56 years; range: 35-70 years; 87 males and 16 females). Diffusion-based shear modulus (μDiff) exhibited a predictive performance for MVI with area under the curve (AUC) of 0.735. The MRI_Score was developed employing true diffusion coefficient (D), mean kurtosis (MK), and μDiff. CR model and MRI_Score achieved AUCs of 0.787 and 0.840, respectively. The combined nomogram based on AFP, corona enhancement, tumor capsule, TTPVI, and MRI_Score significantly improved the predictive performance to an AUC of 0.931 (Delong test p < 0.05).

CONCLUSION

vMRE exhibited great potential for predicting MVI in BCLC stage A HCC. The combined nomogram integrating CR features, vMRE, and quantitative diffusion parameters significantly improved the predictive accuracy and could potentially assist clinicians in identifying appropriate treatment options.

Synthetic DWI: contrast improvement for diffusion-weighted imaging in prostate using T1 shine-through by synthesizing images with adjusted TR and TE

OBJECTIVE

To investigate whether synthetic DWI (SyDWI) calculated with short TR and zero TE can improve diffusion contrast in prostate compared to conventional DWI acquired with standard TR and TE.

MATERIALS AND METHODS

Thirty-two patients who underwent multiparametric MRI (mp-MRI) on a 3.0 T scanner were enrolled. For SyDWI, DWIs at b0 were acquired with two different TRs and TEs in addition to b1000 and b2000 images acquired with single conventional TR and TE. Contrast ratio (CR) was compared between SyDWI calculated with TR of 1000 ms and TE of 0 ms and conventional DWI acquired with TR of 6000 ms and TE of 70 ms.

RESULTS

The mean CR between prostate cancer (PCa) and normal prostate, and between PCa and benign prostatic hyperplasia (BPH), is significantly higher in SyDWI compared to conventional DWI for both b-values of 1000 and 2000 s/mm2. In addition, contrast within some lesions is now visualized, suggesting that tumour heterogeneity can be observed that is not seen with conventional DWI.

CONCLUSION

SyDWI calculated with TR of 1000 ms and TE of 0 ms significantly improves diffusion contrast between PCa and normal prostate or BPH, and within the lesion, compared to conventional DWI as a result of T1 shine-through.

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

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 < 0.001).

CONCLUSION

IVIM and DKI provide added value in the evaluation of CR to NAT in LARC.

KEY POINTS

Question The current conventional imaging evaluation system lacks adequacy for assessing CR to NAT in LARC. Findings Significantly improved diagnostic performance was observed with the histogram analysis of IVIM and DKI in conjunction with conventional MRI. Clinical relevance IVIM and DKI provide significant value in evaluating CR to NAT in LARC, which bears significant implications for reducing surgical complications and facilitating organ preservation.

Unveiling the diagnostic potential of diffusion kurtosis imaging and intravoxel incoherent motion for detecting and characterizing prostate cancer: a meta-analysis

PURPOSE

This study aims to assess the diagnostic capabilities of Diffusion Kurtosis Imaging (DKI) and Intravoxel Incoherent Motion (IVIM) in prostate cancer (PCa) detection and characterization.

MATERIALS

A comprehensive search was conducted across PubMed, Scopus, Web of Science, and the Cochrane Library for articles published up to September 10, 2023, that evaluated the diagnostic efficacy of MD, MK, Dt, f, and Dp parameters. Data were pooled using a bivariate mixed-effects regression model and analyzed with R software.

RESULTS

In total, 27 studies were included. The analysis revealed distinct diagnostic efficacies for DKI and IVIM. In the overall model, sensitivity and specificity were 0.807 and 0.797, respectively, with prospective studies showing higher specificity (0.858, p = 0.024). The detection model yielded increased sensitivity (0.845) and specificity (0.812), with DKI outperforming IVIM in both metrics (sensitivity: 0.87, p = 0.043; specificity: 0.837, p = 0.26); MD had high sensitivity (0.88) and specificity (0.82), while MK's specificity was significantly higher (0.854, p = 0.04); Dp's sensitivity was significantly lower (0.64, p = 0.016). In characterization, sensitivity and specificity were 0.708 and 0.735, respectively, with no significant differences between DKI and IVIM or Gleason Scores; MK had higher sensitivity (0.78, p = 0.039), and f's sensitivity was significantly lower (0.51, p = 0.019).

CONCLUSION

In summary, the study underscores DKI's enhanced diagnostic accuracy over IVIM in detecting PCa, with MK standing out for its precision. Conversely, Dp and f lag in diagnostic performance. Despite these promising results, the study highlights the imperative for standardized protocols and study designs to achieve reliable and consistent outcomes.

Trends in brain MRI and CP association using deep learning

Cerebral palsy (CP) is a neurological disorder that dissipates body posture and impairs motor functions. It may lead to an intellectual disability and affect the quality of life. Early intervention is critical and challenging due to the uncooperative body movements of children, potential infant recovery, a lack of a single vision modality, and no specific contrast or slice-range selection and association. Early and timely CP identification and vulnerable brain MRI scan associations facilitate medications, supportive care, physical therapy, rehabilitation, and surgical interventions to alleviate symptoms and improve motor functions. The literature studies are limited in selecting appropriate contrast and utilizing contrastive coupling in CP investigation. After numerous experiments, we introduce deep learning models, namely SSeq-DL and SMS-DL, correspondingly trained on single-sequence and multiple brain MRIs. The introduced models are tailored with specialized attention mechanisms to learn susceptible brain trends associated with CP along the MRI slices, specialized parallel computing, and fusions at distinct network layer positions to significantly identify CP. The study successfully experimented with the appropriateness of single and coupled MRI scans, highlighting sensitive slices along the depth, model robustness, fusion of contrastive details at distinct levels, and capturing vulnerabilities. The findings of the SSeq-DL and SMSeq-DL models report lesion-vulnerable regions and covered slices trending in age range to assist radiologists in early rehabilitation.

Quantitative magnetic resonance imaging in prostate cancer: A review of current technology

Prostate cancer (PCa) imaging forms an important part of PCa clinical management. Magnetic resonance imaging is the modality of choice for prostate imaging. Most of the current imaging assessment is qualitative i.e., based on visual inspection and thus subjected to inter-observer disagreement. Quantitative imaging is better than qualitative assessment as it is more objective, and standardized, thus improving interobserver agreement. Apart from detecting PCa, few quantitative parameters may have potential to predict disease aggressiveness, and thus can be used for prognosis and deciding the course of management. There are various magnetic resonance imaging-based quantitative parameters and few of them are already part of PIRADS v.2.1. However, there are many other parameters that are under study and need further validation by rigorous multicenter studies before recommending them for routine clinical practice. This review intends to discuss the existing quantitative methods, recent developments, and novel techniques in detail.

Reproducibility of spatial penalty-based methodologies for intravoxel incoherent motion analysis with diffusion MRI

Objective was to assess the precision and reproducibility of spatial penalty-based intravoxel incoherent motion (IVIM) methods in comparison to the conventional bi-exponential (BE) model-based IVIM methods. IVIM-MRI (11 b-values; 0-800 s/mm2) of forty patients (N = 40; Age = 17.7 ± 5.9 years; Male:Female = 30:10) with biopsy-proven osteosarcoma were acquired on a 1.5 Tesla scanner at 3 time-points: (i) baseline, (ii) after 1-cycle and (iii) after 3-cycles of neoadjuvant chemotherapy. Diffusion coefficient (D), Perfusion coefficient (D*) and Perfusion fraction (f) were estimated at three time-points in whole tumor and healthy muscle tissue using five methodologies (1) BE with three-parameter-fitting (BE), (2) Segmented-BE with two-parameter-fitting (BESeg-2), (3) Segmented-BE with one-parameter-fitting (BESeg-1), (4) BE with adaptive Total-Variation-penalty (BE + TV) and (5) BE with adaptive Huber-penalty (BE + HPF). Within-subject coefficient-of-variation (wCV) and between-subject coefficient-of-variation (bCV) of IVIM parameters were measured in healthy and tumor tissue. For precision and reproducibility, intra-scan comparison of wCV and bCV among five IVIM methods were performed using Friedman test followed by Wilcoxon-signed-ranks (WSR) post-hoc test. Experimental results demonstrated that BE + TV and BE + HPF showed significantly (p < 10-3) lower wCV and bCV for D (wCV: 24-32%; bCV: 22-31%) than BE method (wCV: 38-49%; bCV: 36-46%) across three time-points in healthy muscle and tumor. BE + TV and BE + HPF also demonstrated significantly (p < 10-3) lower wCV and bCV for estimating D* (wCV: 89-108%; bCV: 83-102%) and f (wCV: 55-60%; bCV: 56-60%) than BE, BESeg-2 and BESeg-1 methods (D*-wCV: 102-122%; D*-bCV: 98-114% and f-wCV: 96-130%; f-bCV: 94-125%) in both tumor and healthy tissue across three time-points. Spatial penalty based IVIM analysis methods BE + TV and BE + HPF demonstrated lower variability and improved precision and reproducibility in the current clinical settings.

Probing Renal Microstructure and Function with Advanced Diffusion MRI: Concepts, Applications, Challenges, and Future Directions

Diffusion measurements in the kidney are affected not only by renal microstructure but also by physiological processes (i.e., glomerular filtration, water reabsorption, and urine formation). Because of the superposition of passive tissue diffusion, blood perfusion, and tubular pre-urine flow, the limitations of the monoexponential apparent diffusion coefficient (ADC) model in assessing pathophysiological changes in renal tissue are becoming apparent and motivate the development of more advanced diffusion-weighted imaging (DWI) variants. These approaches take advantage of the fact that the length scale probed in DWI measurements can be adjusted by experimental parameters, including diffusion-weighting, diffusion gradient directions and diffusion time. This forms the basis by which advanced DWI models can be used to capture not only passive diffusion effects, but also microcirculation, compartmentalization, tissue anisotropy. In this review, we provide a comprehensive overview of the recent advancements in the field of renal DWI. Following a short introduction on renal structure and physiology, we present the key methodological approaches for the acquisition and analysis of renal DWI data, including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), non-Gaussian diffusion, and hybrid IVIM-DTI. We then briefly summarize the applications of these methods in chronic kidney disease and renal allograft dysfunction. Finally, we discuss the challenges and potential avenues for further development of renal DWI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Comparison of diagnostic performance between diffusion models parameters and mono-exponential apparent diffusion coefficient in patients with prostate cancer: A systematic review and meta-analysis

Background

The importance of diffusion in prostate cancer (PCa) diagnosis has been widely proven. Several studies investigated diffusion models in PCa diagnosis.

Materials and Methods

This systematic review and meta-analysis study was performed to evaluate the ability of three diffusion models to diagnose PCa from the scientific electronic databases Embase, PubMed, Scopus, and Web of Science (ISI) for the period up to March 2022 to identify all relevant articles.

Results

Eighteen studies were included in the systematic review section (7 diffusion kurtosis imaging [DKI] studies, 4 diffusion tensor imaging [DTI] studies, 4 intravoxel incoherent motion [IVIM] studies, and 3 IVIM-DKI studies). Pooled sensitivity, specificity, accuracy, and summary area under each diffusion model's curve (AUC) and 95% confidence intervals (CIs) were calculated. The pooled accuracy and 95% CI on detection (differentiation of tumor from normal tissue and benign prostatic hyperplasia/prostatitis) were obtained for apparent diffusion coefficient (ADC) at 87.97% (84.56%-91.38%) for DKI parameters (Gaussian diffusion [DK] 87.94% [78.71%-97.16%] and deviation from Gaussian diffusion [K] 86.84% [81.83%-91.85%]) and IVIM parameters (true molecular diffusion [DIVIM] 81.73% [72.54%-90.91%], perfusion-related diffusion [D*] 65% [48.47%-81.53%] and perfusion fraction [f] 80.36% [64.23%-96.48%]). The AUC values and 95% CI in the detection of PCa were obtained for ADC at 0.95 (0.92-0.97), for DKI parameters (DK 0.94 [0.89-0.99] and K 0.93 [0.90-0.96]) and for IVIM parameters (DIVIM 0.85 [0.80-0.91], D* 0.60 [0.43-0.77] and f 0.73 [0.63-0.84]). Two studies showed that the DTI accuracy values were 97.34% and 85%. For IVIM-kurtosis model in PCa detection, two studies stated that the DIVIM-K and KIVIM-K accuracy values were 85% and 84.44% (the pooled accuracy; 84.64% with 95% CI 75.78%-93.50%), and 72.50% and 71.11% (the pooled accuracy, 72.10% with 95% CI 64.73%-79.48%), respectively.

Conclusion

Our findings showed that among the DKI, IVIM, and ADC parameters, it seems that ADC, Dk, DIVIM, and K are the most important, which can be used as an indicator to distinguish PCa from normal tissue. The DKI model probably has a higher ability to detect PCa from normal tissue than the IVIM model. DKI probably has the same diagnostic performance in PCa detection and grading compared to diffusion-weighted imaging and ADC.

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.

Pancreatic Mass Characterization Using IVIM-DKI MRI and Machine Learning-Based Multi-Parametric Texture Analysis

Non-invasive characterization of pancreatic masses aids in the management of pancreatic lesions. Intravoxel incoherent motion-diffusion kurtosis imaging (IVIM-DKI) and machine learning-based texture analysis was used to differentiate pancreatic masses such as pancreatic ductal adenocarcinoma (PDAC), pancreatic neuroendocrine tumor (pNET), solid pseudopapillary epithelial neoplasm (SPEN), and mass-forming chronic pancreatitis (MFCP). A total of forty-eight biopsy-proven patients with pancreatic masses were recruited and classified into pNET (n = 13), MFCP (n = 6), SPEN (n = 4), and PDAC (n = 25) groups. All patients were scanned for IVIM-DKI sequences acquired with 14 b-values (0 to 2500 s/mm2) on a 1.5T MRI. An IVIM-DKI model with a 3D total variation (TV) penalty function was implemented to estimate the precise IVIM-DKI parametric maps. Texture analysis (TA) of the apparent diffusion coefficient (ADC) and IVIM-DKI parametric map was performed and reduced using the chi-square test. These features were fed to an artificial neural network (ANN) for characterization of pancreatic mass subtypes and validated by 5-fold cross-validation. Receiver operator characteristics (ROC) analyses were used to compute the area under curve (AUC). Perfusion fraction (f) was significantly higher (p < 0.05) in pNET than PDAC. The f showed better diagnostic performance for PDAC vs. MFCP with AUC:0.77. Both pseudo-diffusion coefficient (D*) and f for PDAC vs. pNET showed an AUC of 0.73. ADC and diffusion coefficient (D) showed good diagnostic performance for pNET vs. MFCP with AUC: 0.79 and 0.76, respectively. In the TA of PDAC vs. non-PDAC, f and combined IVIM-DKI parameters showed high accuracy ≥ 84.3% and AUC ≥ 0.84. Mean f and combined IVIM-DKI parameters estimated that the IVIM-DKI model with TV texture features has the potential to be helpful in characterizing pancreatic masses.

Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI

Current challenges of using serum prostate-specific antigen (PSA) level-based screening, such as the increased false positive rate, inability to detect clinically significant prostate cancer (PCa) with random biopsy, multifocality in PCa, and the molecular heterogeneity of PCa, can be addressed by integrating advanced multiparametric MR imaging (mpMRI) approaches into the diagnostic workup of PCa. The standard method for diagnosing PCa is a transrectal ultrasonography (TRUS)-guided systematic prostate biopsy, but it suffers from sampling errors and frequently fails to detect clinically significant PCa. mpMRI not only increases the detection of clinically significant PCa, but it also helps to reduce unnecessary biopsies because of its high negative predictive value. Furthermore, non-Cartesian image acquisition and compressed sensing have resulted in faster MR acquisition with improved signal-to-noise ratio, which can be used in quantitative MRI methods such as dynamic contrast-enhanced (DCE)-MRI. With the growing emphasis on the role of pre-biopsy mpMRI in the evaluation of PCa, there is an increased demand for innovative MRI methods that can improve PCa grading, detect clinically significant PCa, and biopsy guidance. To meet these demands, in addition to routine T1-weighted, T2-weighted, DCE-MRI, diffusion MRI, and MR spectroscopy, several new MR methods such as restriction spectrum imaging, vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) method, hybrid multi-dimensional MRI, luminal water imaging, and MR fingerprinting have been developed for a better characterization of the disease. Further, with the increasing interest in combining MR data with clinical and genomic data, there is a growing interest in utilizing radiomics and radiogenomics approaches. These big data can also be utilized in the development of computer-aided diagnostic tools, including automatic segmentation and the detection of clinically significant PCa using machine learning methods.

Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine

Simple Summary

Magnetic Resonance Imaging (MRI) is a consolidated imaging tool for the multiparametric assessment of tissues in various pathologies from degenerative and inflammatory diseases to cancer. In recent years, the continuous technological evolution of the equipment has led to the development of sequences that provide not only anatomical but also functional and metabolic information. In addition, there is a growing and emerging field of research in clinical applications using MRI to exploit the diagnostic and therapeutic capabilities of nanocompounds. This review illustrates the application of the most advanced magnetic resonance techniques in the field of nanomedicine.

Abstract

In the last decades, nanotechnology has been used in a wide range of biomedical applications, both diagnostic and therapeutic. In this scenario, imaging techniques represent a fundamental tool to obtain information about the properties of nanoconstructs and their interactions with the biological environment in preclinical and clinical settings. This paper reviews the state of the art of the application of magnetic resonance imaging in the field of nanomedicine, as well as the use of nanoparticles as diagnostic and therapeutic tools, especially in cancer, including the characteristics that hinder the use of nanoparticles in clinical practice.

Using IVIM Parameters to Differentiate Prostate Cancer and Contralateral Normal Tissue through Fusion of MRI Images with Whole-Mount Pathology Specimen Images by Control Point Registration Method

The intravoxel incoherent motion (IVIM) model may enhance the clinical value of multiparametric magnetic resonance imaging (mpMRI) in the detection of prostate cancer (PCa). However, while past IVIM modeling studies have shown promise, they have also reported inconsistent results and limitations, underscoring the need to further enhance the accuracy of IVIM modeling for PCa detection. Therefore, this study utilized the control point registration toolbox function in MATLAB to fuse T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) MRI images with whole-mount pathology specimen images in order to eliminate potential bias in IVIM calculations. Sixteen PCa patients underwent prostate MRI scans before undergoing radical prostatectomies. The image fusion method was then applied in calculating the patients’ IVIM parameters. Furthermore, MRI scans were also performed on 22 healthy young volunteers in order to evaluate the changes in IVIM parameters with aging. Among the full study cohort, the f parameter was significantly increased with age, while the D* parameter was significantly decreased. Among the PCa patients, the D and ADC parameters could differentiate PCa tissue from contralateral normal tissue, while the f and D* parameters could not. The presented image fusion method also provided improved precision when comparing regions of interest side by side. However, further studies with more standardized methods are needed to further clarify the benefits of the presented approach and the different IVIM parameters in PCa characterization.