Average Apparent Diffusion Coefficient Research Articles

Diffusion tensor imaging in idiopathic inflammatory myopathies: A case–control study

Objectives: It was aimed to assess the potential of diffusion tensor imaging (DTI) in detecting muscle inflammation in individuals affected by idiopathic inflammatory myopathies (IIM) compared to healthy controls. Specifically, we investigated the impact of myositis-related inflammation on the diffusion of water molecules across the sarcolemma and its detectability through DTI. Material and Methods: This prospective cross-sectional observational study included 36 patients diagnosed with IIM ([based on clinical tests manual muscle testing [MMT8] and serological marker N-acetyl-cystein(NAC)-activated creatine kinase [CPK-NAC]) and 51 healthy controls. All participants underwent bilateral thigh magnetic resonance imaging (MRI) with a DTI protocol. For patients, three region of interests (ROI) (80 mm2 to 130 mm2) were delineated on the most affected muscle containing maximum signal intensity (edema), while in controls, the ROIs were drawn on the healthy vastus lateralis muscle and average of five readings were used for statistical analysis. Average apparent diffusion coefficient (ADC) value, fractional anisotropy (FA), and three eigenvalues: Maximum (λ1), middle (λ2), and minimum (λ3) were measured in all the subjects. Results: The average age for cases and controls was 33.08 ± 12.45 years and 40.70 ± 17.17 years, respectively, with no significant age or gender distribution differences. MMT8 scores averaged 103.33 ± 36.42, and CPK-NAC values averaged 4323.44 ± 6354.45 U/L. DTI analysis revealed significantly higher average ADC values in patients (2.07 ± 0.45) compared to controls (1.76 ± 0.26) with a P < 0.001. FA values showed no significant difference (0.38 ± 0.19 in patients vs. 0.33 ± 0.09 in controls, P = 0.094). The three eigenvalues in the patients were 2.76 ± 0.63, 2.35 ± 0.33, and 1.30 ± 0.29, respectively, and in controls were 2.37 ± 0.36, 1.72 ± 0.25, and 1.21 ± 0.27, respectively, with P = 0.001, <0.001, and 0.818, respectively. Conclusion: In individuals with IIM, disruptions in the sarcolemma lead to altered water molecule diffusion, detectable through DTI. The study demonstrated significant differences in average DTI ADC, maximum (λ1), and middle (λ2) eigenvalues between cases and controls (P < 0.001). Integrating DTI into routine myopathy MRI may enhance the differentiation between inflamed and normal muscles. Limitations included the absence of follow-up to observe treatment effects and the non-characterization of IIM into distinct subtypes.

Diffusion-Weighted Imaging Does Not Differentiate Between Bacterial and Fungal Skull Base Osteomyelitis.

Apparent diffusion coefficient (ADC) value helps in differentiating infections from neoplasms on magnetic resonance imaging (MRI). We investigate the diffusion-weighted images in skull base osteomyelitis (SBO) to evaluate if ADC values can differentiate fungal and bacterial SBO and to analyse the microbiology of all SBO patients. Retrospective observational study. Quaternary care referral centre. A retrospective review of 142 patients diagnosed and treated for SBO patients from January 2010 to May 2023 was done. Chi-square or Fisher's exact test was used to compare ADC values of bacterial and fungal SBO. The most common pathogens isolated were Pseudomonas (42.2%), Aspergillus (30.98%), and S. aureus (23.94%). The average ADC value of affected soft tissues among patients was 1.13 ± 0.26 × 10-3 mm2/s compared to the average ADC value of normal soft tissue, 1.34 ± 0.31 × 10-3 mm2/s. There was no statistical significance when comparing the average ADC values of bacterial and fungal SBO patients (p value = 0.142). This study suggests that though infection due to Pseudomonas was the commonest, it was detected only in 42.2% of patients. More than half of the cases had organisms other than Pseudomonas, demanding the clinician to obtain deeper biopsies early in the course of the disease for microbiological analysis. DWI does not help differentiate bacterial and fungal SBO, again emphasising the need for deeper tissue biopsies in all these patients to assist in the early identification of the pathogen.

Cystitis glandularis: MR imaging characteristics in 27 patients.

To explore the diagnostic characteristics of cystitis glandularis (CG) using magnetic resonance imaging (MRI). A retrospective study was conducted on pathologically confirmed patients who underwent bladder MRI examination between January 2019 and November 2023. Image analysis was jointly conducted, with emphasis on lesion location, morphology, size, signal intensity, and pattern of enhancement, by two genitourinary radiologists with 22 and 15 years of experience, respectively. A total of 27 patients with 27 lesions were included (median age 47 years, 24 males). The lesions were mostly located in the bladder trigone area (18/27). The lesions could be categorized as focal thickening (17/27), nodular (8/27), and diffuse thickening of the entire bladder (2/27) in morphological terms. On T2-weighted imaging (T2WI), 15 of 17 focal thickening lesions appeared as a slightly hyperintense thickened inner layer, with a higher signal in the center of the thickened inner layer, resembling a sandwich sign, and 6 of 8 nodular lesions were slightly hyperintense. On T1-weighted imaging (T1WI), 19 patients showed slight hypointensity. The lesions on DWI showed mainly high (5/27) and slightly high signal (21/27), with an average mean apparent diffusion coefficient (mADC) value of 2.171 ± 0.052 × 10-3mm2/s. Among the 23 patients who underwent dynamic contrast-enhanced (DCE) scanning, 18 lesions showed mild enhancement in the arterial phase (average 1.7 times comparing to unenhanced phase), and the degree of enhancement gradually increased in the venous and delayed phases (average 2.2 and 2.3 times compared to the unenhanced phase, respectively), showing a progressive enhancement pattern. On MRI, the majority of CG manifest as focal thickening or nodules in the bladder trigone area, showing slight hyperintensity on T2WI, slight hypointensity on T1WI, and a progressive enhancement pattern, without significant restriction on DWI. Focal thickening lesions may exhibit a special sandwich sign.

Multiparametric quantitative MRI of healthy adult pancreas: correlations with gender and age

BackgroundThe pancreas plays an important role in the nutrition and metabolism of the whole body. Many disease processes including obesity, diabetes mellitus (DM), acute or chronic pancreatitis, and pancreatic carcinoma result in abnormality of pancreas morphology and function. Magnetic resonance imaging (MRI) provides quantitative parameters including T1, T2, and apparent diffusion coefficient (ADC) values for evaluating normal and abnormal pancreas. Based on the normal range of these quantitative parameters, pancreatic abnormality could be detected early. However, the range and the relationship of T1, T2, and ADC values with gender and age groups using the same dataset have not been explored.PurposeTo establish the ranges of MRI tissue and functional parameters, including T1, T2, and ADC values, in healthy adult pancreas and their correlations with gender, subregion, and age.Materials and methodsThe T1, T2, and ADC values of healthy pancreas in 86 adults were measured using a 3.0-T MRI scanner. The average T1, T2, and ADC values were obtained in the whole pancreas and subregions (head, neck, body, and tail). Their correlations with gender and age were investigated.ResultsThe T1, T2, and ADC values of the whole pancreas from all subjects were 870.07 ± 61.86 ms, 44.07 ± 6.14 ms, and 1.072 ± 0.212 × 10−3 mm2/s, respectively. T2 values were significantly different between genders (P < 0.05). No significant differences were found between subregions. The T1, T2, and ADC values differed significantly among the age groups (P < 0.05). The T1 value revealed a moderately positive correlation, while the T2 and ADC values displayed negative correlations with age (r = 0.31, −0.45, and −0.39, respectively). The combination of T1, T2, and ADC values achieved the highest AUC value and showed a significant difference compared to T1, T2, and ADC values alone in predicting age older than 45 years.ConclusionThis study established the normal ranges of T1, T2, and ADC. We found that T2 is different between men and women, and T1, T2, and ADC are age-dependent. These results could be useful for quantitative MRI of pancreatic disease.

Diffusion tensor imaging of vastus lateralis in patients with inflammatory myopathies.

Inflammation in patients with myositis would increase diffusion of water molecules across sarcolemma that could be detected with the help of diffusion tensor imaging (DTI). We aimed to determine an association between DTI of vastus lateralis (VL) and histopathological findings in cases of myositis and to estimate diagnostic performance of different MRI variables in predicting histopathological outcomes. This prospective cross-sectional observational study included 43 patients with myositis. MRI of bilateral thighs with DWI/DTI protocol was performed in all the patients. Thirty-three patients further underwent biopsy of right VL muscle. Imaging analysis included grading of "Muscle oedema" based on signal intensity (SI) and extent and "fatty infiltration" based on extent on conventional sequences and, acquiring DWI and DTI parameters. Gold standard method to determine inflammation in muscles was histopathological examination. Comparison of DTI/DWI variables with clinical and histopathological variables was done. The average DWI apparent diffusion coefficient (ADC) and DTI ADC values in the patients were 1.77 ± 0.35 and 2.06 ± 0.35 respectively. The average functional anisotropy (FA) was 0.39 ± 0.17 and, the 3 eigenvalues in the patients were 2.96 ± 0.63, 2.05 ± 0.32, and 1.20 ± 0.39 respectively. VL oedema SI weighted score was the best parameter for predicting effaced fascicular architecture and marked lymphocytic inflammation in endomysium on histopathology. VL fatty infiltration weighted score was the best parameter in predicting perifascicular atrophy. Addition of DWI or DTI didn't add significantly in determining active inflammation in cases of myositis.

Utility of diffusion-weighted imaging in differentiating benign and malignant breast lesions.

Breast cancer presents a significant global health burden. An accurate differentiation between benign and malignant lesions is imperative for timely intervention. While dynamic contrast enhanced MRI (DCE-MRI) is highly sensitive, its specificity is limited. This has led to the exploration of diffusion-weighted imaging (DWI) in distinguishing between benign and malignant breast lesions. The study aimed to explore the diagnostic utility of DWI in distinguishing between benign and malignant breast lesions. Assessment of 38 breast lesions using DWI with a b value of 800 s/mm2, performed with 3 Tesla MRI. The diagnostic performance of two different region of Interest (ROI) placement approaches was compared to obtain a feasible cut-off value of apparent diffusion coefficient (ADC) to differentiate between malignant and benign lesions. The histopathological reports were used as the gold standard. ADC values of malignant lesions were significantly lower than those of benign lesions (0.84 × 10-3 mm2/s vs. 1.54 × 10-3 mm2/s). The average ADC measured using a small-sized 2D ROI including the darkest part in the ADC map, performed better than the large 2D ROI covering the entire lesion. Using a cut-off value of 0.98 × 10-3 mm2/s, ADC obtained high sensitivity (90%) and specificity (88.9%) in distinguishing between benign and malignant breast lesions. Utilising quantitative analysis of DWI with ADC value measurement, reliably distinguished between benign and malignant breast lesions in this cohort, especially when employing a higher b value of 800 s/mm2.

Prostate magnetic resonance imaging (MRI) in patients with hip implants-presetting a protocol using a phantom.

Metal structures are a source of artifacts that significantly complicate the interpretation of magnetic resonance imaging (MRI). The use of prostate MRI as a preliminary test in men with a suspicion on prostate cancer leads to an increased use of the test. The aim of this study was to solve a clinically significant problem: to ensure the reduction of artifacts from metal hip implants during prostate MRI. Another goal was to evaluate the impact of artifact reduction methods on quantitative measurements. The prostate gland (PG) phantom model was a cylinder filled with an aqueous solution of polyvinylpyrrolidone at the concentrations of 40%, 30%, and 20% [central zone (CZ), peripheral zone (PZ), and "lesion", respectively]. Phantom MRI study was conducted on Philips Ingenia 1.5T and Philips Ingenia 3T scanners. For 1.5 T, the reduction in the influence of artifacts inside region of interest (ROI) was observed, expressed in a decrease in the average apparent diffusion coefficient (ADC) (CZ, PZ, "lesion") for the manual artifact reduction (MAR) and ZOOM (title of software artifact reduction) techniques compared to the standard method. For 3T this effect was not detected. The same ADC results were obtained for Standard and MAR techniques, and increased ADC values for ZOOM. Despite the fact that the spread of ADC values on 3.0T scanners was minimal, there was a significant deviation of ADC values from the reference ones (up to 30.4%). Therefore, it is necessary to use a correction coefficient in the ADC calculation for the 3.0 T device. In the presented clinical case, high-quality tomograms were obtained without any artifacts, despite the presence of two hip replacement devices in the scanning area. The accurate prostate MRI in the presence of implants is essential for an accurate diagnosis. This approach allows to reduce artifacts from hip implants, to visualize PG and periprostatic tissue in the best way, and to detect malignant and benign changes.

The time threshold to reperfusion for DWI reversal in acute ischemic stroke depends on pre-interventional ADC value.

The aims of this study are to explore the apparent diffusion coefficient (ADC)-dependent thresholds for time to reperfusion in reversible lesions following mechanical thrombectomy for acute ischemic stroke, and to investigate the associated risks of hemorrhagic transformation. We conducted a retrospective case-control study, enrolling patients with large-vessel occlusion who underwent mechanical thrombectomy in Otaru General Hospital from 2016 to 2021. Reversible lesions were identified using volumetric ADC data, and the mean time from image to reperfusion (TIR) in each ADC range was compared between groups with and without reversible lesions, as well as those with and without parenchymal hematoma. The Wilcoxon rank sum test and chi-square test were used for comparison between two groups, and receiver operating characteristic curves were created to determine optimal thresholds. Seventy-five patients were included and 581 volumetric data were obtained. The mean TIR in the group with reversible lesions was shorter than in that without, and time thresholds were 131, 123 and 112min for ADC values > 540 × 10-6, 500-540 × 10-6 and 440-500 × 10-6 mm2/s, respectively. Furthermore, in patients with parenchymal hematoma, the mean TIR was significantly longer, and the average ADC value was significantly lower than those without hematoma. The time thresholds for the irreversible ischemic core may vary depending on the ADC value, and they may be shorter when the ADC value is lower. Moreover, both the low ADC value and the late reperfusion might be associated with an increased risk of parenchymal hematoma.

Classification of Brain Infarction using Deep Learning techniques on Magnetic Resonance Imaging (MRI)

Background: This research concentrated on employing Convolutional Neural Networks (CNNs) to classify brain infarctions. A stroke occurs due to a blockage or hemorrhage in the blood vessels, which disrupts or diminishes the blood supply to the brain, leading to the death of brain cells. With the progress in machine learning techniques in medical imaging, early detection of stroke has become increasingly feasible, playing a crucial role in the diagnosis and treatment of this potentially fatal condition. Objective: Building an intelligent CNN model to classify types of brain infarction using up algorithms Median Filter preserving edges and efficient minimization of noise with durability against flowing noise, Fuzzy C – Mean to Image Segmentation and Recognition of Region of Interest for infarction location and size of the infarction for brain infarction Identification, GLCM, and FOSF. Methodology: The research was carried out in a private hospital; in Baghdad, Iraq, on 60 patients with brain infarction. Included were 9 hyperacute (>6 hours post brain infarction), 19 acute (7-72 hours), 12 subacute (4-7 days), and 20 chronic (< 15 days). We have introduced a Convolutional Neural Network (CNN) model as a solution to predict the likelihood of a patient experiencing a stroke at an early stage, aiming for maximize effectiveness and precision. Results: The ROC curve demonstrates that the average apparent diffusion coefficient (aveg (ADC) mm²/s) and relative apparent diffusion coefficient (rADC%) are two reliable and effective measures for monitoring brain infarction developments. High sensitivity indicates a strong ability to detect true positives (cases of infarction), while the shape and position of the curve suggest that average (ADC) mm²/s also maintains a reasonable specificity, minimizing false positives. This balance results in high overall diagnostic accuracy. Discussion: Both rACD (%) and aveg (ADC) mm²/s are robust indicators for the imaging monitoring of brain infarction developments. The inclusion of AI in the assessment process may result marginal improvements in specificity and accuracy.

Comparative study of ultra-high field diffusion-weighted MRI imaging between hepatocellular carcinoma and paracancerous, distant cancerous, and background liver tissues

Objective: To investigate the differences in multi-b-value apparent diffusion coefficient (ADC) and exponential apparent diffusion coefficient (eADC) between hepatocellular carcinoma (HCC) and paracancerous liver tissue, distant cancerous liver tissue, and background liver tissues by ultra-high field 3.0T diffusion-weighted (DWI) MRI imaging. Methods: Sixty-eight consecutive HCC cases confirmed by surgical pathology from January 2018 to October 2021 were enrolled and divided into a cirrhosis (n=39) and a non-cirrhosis group (n=29) according to the presence or absence of cirrhosis.The average ADC and eADC of liver tissues of paracancerous (including proximal and distal), distant cancerous, and background were measured by DWI images with diffusion sensitivity factors (b) of 50, 100, 400, 600 s/mm2, and 1 000 s/mm2, respectively. The Kruskal-Wallis H test and Bonferroni method were used to test the differences between the measured values of the five tissues. The statistical differences were used to evaluate the diagnostic efficacy of the five tissues by parametric receiver operating characteristic (ROC) curve and area under the curve (AUC). Results: The comparison of average ADC and eADC among five types of tissues in the liver cirrhosis group showed that the average ADC and eADC measured at b values of 50, 100, 400, and 600 s/mm2 had statistically significant differences (adjusted P<0.005) between cancerous and proximal paracancerous, distal paracancerous, distant cancerous, and background liver tissue, as well as the average ADC measured at b=1 000 s/mm2 between cancerous and proximal paracancerous tissue. The average ADC and eADC in the non-cirrhosis group had statistically significant differences (adjusted P<0.005) between cancerous and proximal paracancerous, distant paracancerous, distant cancerous, and background liver tissue measured at b values of 50, 100, and 400 s/mm2, respectively. The average ADC and eADC measured at b=600 s/mm2 showed statistically significant differences (adjusted P<0.005) between cancerous and proximal paracancerous, distal paracancerous, and distant cancerous liver tissue, as well as the average ADC measured at b=1 000 s/mm2 between cancerous and distal paracancerous, and distant cancerous liver tissue. The average ADC and eADC in the cirrhosis group had no statistically significant difference between the proximal paracancerous and the distant cancerous, as well as the background liver tissue measured at b-values of 50, 100, 400, 600, and 1 000 s/mm2, respectively (adjusted P>0.005), while there were statistically significant differences (adjusted P<0.005) in the average ADC values in the non-cirrhosis group between the proximal paracancerous and the distant paracancerous and background liver tissues at b=50 s/mm2, as well as the average ADC and eADC values between the proximal paracancerous and the distant liver tissues at b=100 s/mm2. The average ADC and eADC values measured in the cirrhosis group and non-cirrhosis group had no statistically significant difference between the distant paracancerous, distant cancerous, and background liver tissue (adjusted P>0.005). The efficacy of average ADC and eADC in distinguishing five types of tissues (cancerous and proximal paracancerous, distant paracancerous, distant cancerous, and background liver tissue) showed that in the cirrhosis group, the diagnostic efficacy was best at b=50 s/mm2. The area under the ROC curve (AUC) of average ADC was 0.815, 0.828, 0.855, and 0.855, respectively, and the AUC of average eADC was 0.815, 0.830, 0.856, and 0.855, respectively. The diagnostic efficacy was best in the non cirrhosis group at b=100 s/mm2, with average ADC AUCs of 0.787, 0.823, 0.841, and 0.821, and average eADC AUCs of 0.836, 0.874, 0.893, and 0.873, respectively. The AUC of the average ADC in the non-cirrhosis group for distinguishing between proximal paracancerous and distant cancerous liver tissues, as well as proximal paracancerous and background liver tissues, with b=50 s/mm2, were 0.605 and 0.604, respectively. The average AUC of ADC and eADC for distinguishing between proximal paracancerous and distant liver tissues with b=100 s/mm2 were 0.619 and 0.620, respectively. Conclusion: The average ADC and eADC measured by multiple b-values are helpful in distinguishing HCC from proximal paracancerous, distal paracancerous, distant-cancerous, and background liver tissues in patients with cirrhosis and non-cirrhosis, while the average ADC and eADC at b=50 s/mm2 and 100 s/mm2 exhibit differences between the proximal paracancerous from the distant cancerous liver tissue and background liver tissue in patients with non-cirrhosis.

Multiparametric quantitative magnetic resonance imaging of uterine fibroids for prediction of growth rate-a pilot study.

Uterine fibroid (UF) growth rate and future morbidity cannot be predicted. This can lead to sub-optimal clinical management, with women being lost to follow-up and later presenting with severe disease that may require hospitalization, transfusions, and urgent surgical interventions. Multi-parametric quantitative magnetic resonance imaging (MRI) could provide a biomarker to predict growth rate facilitating better-informed disease management and better clinical outcomes. We assessed the ability of putative quantitative and qualitative MRI predictive factors to predict UF growth rate. Twenty women with UFs were recruited and completed baseline and follow-up MRI exams, 1-2.5 years apart. The subjects filled out symptom severity and health-related quality of life questionnaires at each visit. A standard clinical pelvic MRI non-contrast exam was performed at each visit, followed by a contrast-enhanced multi-parametric quantitative MRI (mp-qMRI) exam with T2, T2*, and apparent diffusion coefficient (ADC) mapping and dynamic contrast-enhanced MRI. Up to 3 largest fibroids were identified and outlined on the T2-weighted sequence. Fibroid morphology and enhancement patterns were qualitatively assessed on dynamic contrast-enhanced MRI. The UFs' volumes and average T2, T2*, and ADC values were calculated. Pearson correlation coefficients were calculated between UF growth rate and T2, T2*, ADC, and baseline volume. Multiple logistic regression and receiver operating characteristic (ROC) analysis were performed to predict fast-growing UFs using combinations of up to 2 significant predictors. A significance level of alpha =0.05 was used. Forty-four fibroids in 20 women had growth rate measurement available, and 36 fibroids in 16 women had follow-up quantitative MRI available. The distribution of fibroid growth rate was skewed, with approximately 20% of the fibroids exhibiting fast growth (>10 cc/year). However, there were no significant changes in median baseline and follow-up values of symptom severity and health-related quality of life scores. There was no change in average T2, T2*, and ADC at follow-up exams and there was a moderate to strong correlation to the fibroid growth rate in baseline volume and average T2 and ADC in slow-growing fibroids (<10 cc/year). A multiple logistic regression to identify fast growing UFs (>10 cc/year) achieved an area under the curve (AUC) of 0.80 with specificity of 69% at 100% sensitivity. The mp-qMRI parameters T2, ADC, and UF volume obtained at the time of initial fibroid diagnosis may be able to predict UF growth rate. Mp-qMRI could be integrated into the management of UFs, for individualized care and improved clinical outcomes.

MRI manifestations of 40 cases with the hepatic epithelioid hemangioendothelioma classification based on the morphology and size

Objective: To explore the MRI characteristics of the hepatic epithelioid hemangioendothelioma (HEHE) classification according to morphology and size. Methods: The clinical, pathological, and MRI imaging data of 40 cases with HEHE confirmed pathologically from December 2009 to September 2021 were retrospectively analyzed. A paired sample t-test was used for comparison between the two groups. Results: There were 40 cases (5 solitary, 24 multifocal, 9 local fusion, and 2 diffuse fusion) and 214 lesions (163 nodules, 31 masses, and 20 fusion foci). The most common features of lesions were subcapsular growth and capsular depression. The signal intensity of lesions ≤1cm was usually uniform with whole or ring enhancement. Nodules and mass-like lesions ≥1cm on a T1-weighted image had slightly reduced signal intensity or manifested as a halo sign. Target signs on a T2-weighted image were characterized by: target or centripetal enhancement; fusion-type lesions; irregular growth and hepatic capsular retraction, with ring or target-like enhancement in the early stage of fusion and patchy irregular enhancement in the late stage; blood vessels traversing or accompanied by malformed blood vessels; focal bleeding; an increasing proportion of extrahepatic metastases and abnormal liver function with the type of classified manifestation; primarily portal vein branches traversing; and reduced overall intralesional bleeding rate (17%). Lollipop signs were presented in 19 cases, with a high expression rate in mass-type lesions (42%). The fusion lesions were expressed, but the morphological manifestation was atypical. The diffusion-weighted imaging mostly showed high signal or target-like high signal. An average apparent diffusion coefficient of lesions was (1.56±0.36) ×10(-3)mm(2)/s, which was statistically significantly different compared with that of adjacent normal liver parenchyma (t=8.28, P<0.001). Conclusion: The MRI manifestations for the HEHE classification are closely related to the morphology and size of the lesions and have certain differences and characteristics that are helpful for the diagnosis of the disease when combined with clinical and laboratory examinations.

Diffusion tensor imaging and fiber tractography of the normal epididymis.

To evaluate the feasibility of diffusion tensor imaging (DTI) and fiber tractography (FT) of the normal epididymis and to determine normative apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values. Twenty-eight healthy volunteers underwent MRI of the scrotum, including DTI on a 3.0T system. For each anatomic part of the epididymis (head, body and tail) free-hand regions of interest were drawn and the mean ADC and FA were measured by two radiologists in consensus. Parametric statistical tests were used to determine intersubject differences in ADC and FA between the anatomic parts of each normal epididymis and between bilateral epididymides. Fiber tracts of the epididymis were reconstructed using the MR Diffusion tool. The mean ADC and FA of the normal epididymis was 1.31 × 10-3 mm2/s and 0.20, respectively. No differences in ADC (p = 0.736) and FA (p = 0.628) between the anatomic parts of each normal epididymis were found. Differences (p = 0.020) were observed in FA of the body between the right and the left epididymis. FT showed the fiber tracts of the normal epididymis. Main study's limitations include the following: small number of participants with narrow age range, absence of histologic confirmation and lack of quantitative assessment of the FT reconstructions. DTI and FT of the normal epididymis is feasible and allow the noninvasive assessment of the structural and geometric organization of the organ.

Correlation of ADC values of adult brain tumors with the diagnosis and pathological grade: A cross-sectional multicenter study.

Brain tumors are common, requiring physicians to have a precise understanding of them for accurate diagnosis and treatment. Considering that various histological tumor types present different cellularity, we conducted this research to examine the role of apparent diffusion coefficient (ADC) values in the differential diagnosis and pathologic grading of brain tumor types. In this cross-sectional study, we gathered pathology reports of histological samples of adult brain tumors. The tissue sample of brain tumors were examined histologically by a pathologist. The magnetic resonance imaging data of these patients were interpreted by a neuroradiologist. The measured ADC values and ADC ratios were calculated. Standard mean ADC values were expressed as 10- 6 mm2/s. The findings were compared according to the histological diagnosis of each tumor. Sixty-eight patients were included in the study: 34 (50%) were male, and 34 (50%) were female. The average age of the patients was 51.69 + 16.40 years. In the examination of tumor type, 16 (23.5%) were astrocytoma, 9 (13.2%) were oligodendroglioma, 20 (29.4%) were glioblastoma, 4 (5.9%) were medulloblastoma, and 19 (27.9%) were metastatic tumors. the average value of ADC was statistically significantly different according to the pathological type of tumor (p < 0.001). The two-by-two comparison of average ADC among tumor types revealed significant differences, except for oligodendroglioma and glioblastoma (p-value = 0.87) and glioblastoma and medulloblastoma (p-value = 0.347). The average value of ADC and ADC ratio was statistically significantly different according to the pathological grade of the tumor (p < 0.001). In the two-by-two comparison of average ADC between all pathological grades of the tumor showed a significance difference except for Grade I and Grade II (p-value = 0.355). The mean value of ADC and ADC ratio for glioblastoma and metastatic tumors showed no significant difference. The assessment of brain tumor grade through ADC examination will help to estimate prognosis and devising suitable therapeutic strategies.

Normal Brain and Brain Tumor ADC: Changes Resulting From Variation of Diffusion Time and/or Echo Time in Pulsed-Gradient Spin Echo Diffusion Imaging.

Increasing gradient performance on modern magnetic resonance imaging scanners has profoundly reduced the attainable diffusion and echo times for clinically available pulsed-gradient spin echo (PGSE) sequences. This study investigated how this may impact the measured apparent diffusion coefficient (ADC), which is considered an important diagnostic marker for differentiation between normal and abnormal brain tissue and for therapeutic follow-up. Diffusion time and echo time dependence of the ADC were evaluated on a high-performance 3 T magnetic resonance imaging scanner. Diffusion PGSE brain scans were performed in 10 healthy volunteers and in 10 brain tumor patients using diffusion times of 16, 40, and 70 ms, echo times of 60, 75, and 104 ms at 3 b-values (0, 100, and 1000 s/mm 2 ), and a maximum gradient amplitude of 68 mT/m. A low gradient performance system was also emulated by reducing the diffusion encoding gradient amplitude to 19 mT/m. In healthy subjects, the ADC was measured in 6 deep gray matter regions and in 6 white matter regions. In patients, the ADC was measured in the solid part of the tumor. With increasing diffusion time, a small but significant ADC increase of up to 2.5% was observed for 6 aggregate deep gray matter structures. With increasing echo time or reduced gradient performance, a small but significant ADC decrease of up to 2.6% was observed for 6 aggregate white matter structures. In tumors, diffusion time-related ADC changes were inconsistent without clear trend. For tumors with diffusivity above 1.0 μm 2 /ms, with prolonged echo time, there was a pronounced ADC increase of up to 12%. Meanwhile, for tumors with diffusivity at or below 1.0 μm 2 /ms, no change or a reduction was observed. Similar results were observed for gradient performance reduction, with an increase of up to 21%. The coefficient of variation determined in repeat experiments was 2.4%. For PGSE and the explored parameter range, normal tissue ADC changes seem negligible. Meanwhile, observed tumor ADC changes can be relevant if ADC is used as a quantitative biomarker and not merely assessed by visual inspection. This highlights the importance of reporting all pertinent timing parameters in ADC studies and of considering these effects when building scan protocols for use in multicenter investigations.

Validation of daily 0.35 T diffusion-weighted MRI for MRI-guided glioblastoma radiotherapy.

MRI-Linac systems enable daily diffusion-weighed imaging (DWI) MRI scans for assessing glioblastoma tumor changes with radiotherapy treatment. Our study assessed the image quality of echoplanar imaging (EPI)-DWI scans compared with turbo spin echo (TSE)-DWI scans at 0.35 Tesla (T) and compared the apparent diffusion coefficient (ADC) values and distortion of EPI-DWI on 0.35 T MRI-Linac compared to high-field diagnostic MRI scanners. The calibrated National Institute of Standards and Technology (NIST)/Quantitative Imaging Biomarkers Alliance (QIBA) Diffusion Phantom was scanned on a 0.35 T MRI-Linac, and 1.5 T and 3 T MRI with EPI-DWI. Five patients were scanned on a 0.35 T MRI-Linac with a TSE-DWI sequence, and five other patients were scanned with EPI-DWI on a 0.35 T MRI-Linac and a 3 T MRI. The quality of images was compared between the TSE-DWI and EPI-DWI on the 0.35 T MRI-Linac assessing signal-to-noise ratios and presence of artifacts. EPI-DWI ADC values and distortion magnitude were measured and compared between 0.35 T MRI-Linac and high-field MRI for both phantom and patient studies. The average ADC differences between EPI-DWI acquired on the 0.35 T MRI-Linac, 1.5 T and 3 T MRI scanners and published references in the phantom study were 1.7%, 0.4% and 1.0%, respectively. Comparing the ADC values based on EPI-DWI in glioblastoma tumors, there was a 3.36% difference between 0.35 and 3 T measurements. Susceptibility-induced distortions in the EPI-DWI phantoms were 0.46±1.51mm for 0.35 MRI-Linac, 0.98±0.51mm for 1.5 T MRI and 1.14±1.88mm for 3 T MRI; for patients -0.47±0.78mm for 0.35 T and 1.73±2.11mm for 3 T MRIs. The mean deformable registration distortion for a phantom was 1.1±0.22mm, 3.5±0.39mm and 4.7±0.37mm for the 0.35 T MRI-Linac, 1.5 T MRI, and 3 T MRI scanners, respectively; for patients this distortion was -0.46±0.57mm for 0.35 T and 4.2±0.41mm for 3 T. EPI-DWI 0.35 T MRI-Linac images showed higher SNR and lack of artifacts compared with TSE-DWI, especially at higher b-values up to 1000 s/mm2. EPI-DWI on a 0.35 T MRI-Linac showed superior image quality compared with TSE-DWI, minor and less distortions than high-field diagnostic scanners, and comparable ADC values in phantoms and glioblastoma tumors. EPI-DWI should be investigated on the 0.35 T MRI-Linac for prediction of early response in patients with glioblastoma.

Value of perfusion parameters from golden-angle radial sparse parallel dynamic contrast-enhanced magnetic resonance imaging in predicting pathological complete response after neoadjuvant chemoradiotherapy for locally advanced rectal cancer

Non-invasive methods for predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) can provide distinct leverage in the management of patients with locally advanced rectal cancer (LARC). This study aimed to investigate whether including the golden-angle radial sparse parallel (GRASP) dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) perfusion parameter (Ktrans), in addition to tumor regression grading (TRG) and apparent diffusion coefficient (ADC) values, can improve the predictive ability for pCR. Patients with LARC who underwent nCRT and subsequent surgery were included. The imaging parameters were compared between patients with and without pCR. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive ability of these parameters for pCR. A total of 111 patients were included in the study. A pCR was obtained in 32 patients (28.8%). MRI-based TRG (mrTRG) showed a negative correlation with pCR (r = -0.61, P < 0.001), and the average ADC value showed a positive correlation with pCR (r = 0.62, P < 0.001). Before nCRT, Ktrans in the pCR group was significantly higher than in the non-pCR group (1.30 ± 0.24 vs. 0.88 ± 0.34, P < 0.001), but no difference was identified after nCRT. Following ROC curve analysis, the area under the curve (AUC) of mrTRG (level 1-2), average ADC value, and Ktrans value for predicting pCR were 0.738 [95% confidence interval (CI): 0.65-0.82], 0.78 (95% CI: 0.69-0.86), and 0.84 (95% CI: 0.77-0.92), respectively. The model combining the three parameters had significantly higher predictive ability for pCR (AUC: 0.94, 95% CI: 0.88-0.98). The use of a combination of the GRASP DCE-MRI Ktrans with mrTRG and ADC can lead to a better pCR predictive performance.

Evaluation of multiplexed sensitivity encoding diffusion-weighted imaging in detecting uterine lesions: Image quality optimization

PurposeTo compare the image quality of multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) and single-shot echo-planar imaging (SS-EPI-DWI) techniques in uterine MRI. MethodsEighty-eight eligible patients underwent MUSE-DWI and SS-EPI-DWI examinations simultaneously using a 3.0 T MRI system. Two radiologists independently performed quantitative and qualitative analysis of the two groups of images using a double-blind method. The weighted Kappa test was used to evaluate the interobserver agreement. Wilcoxon's rank sum test was used for qualitative parameters, and paired t-test was used for quantitative parameters. Spearman rank correlation analysis was used to obtained correlation between pathological results and mean apparent diffusion coefficient (ADC) value. ResultsThe qualitative and quantitative analysis of the images by the two radiologists were in good or excellent agreement, with weighted kappa value ranging from 0.636 to 0.981. The scores of total subjective image quality (15.4 ± 0.99) and signal-to-noise ratio (158.99 ± 60.71) of MUSE-DWI were significantly higher than those of SS-EPI-DWI (12.93 ± 1.62 P < 0.001; 130.23 ± 48.29 P < 0.05). It effectively reduced image distortion and artifact, and had better lesion conspicuity. There was no significant difference in contrast-to-noise ratio score and average ADC values between the two DWI sequences. The average ADC values of the two DWI sequences were highest in the normal uterus group and lowest in the endometrial cancer group, with statistically significant differences among groups (P < 0.01). In addition, the average ADC values of the two DWI sequences were negatively correlated with the type of lesions, decreasing with the malignancy of the lesions (r = −0.805 P < 0.01, r = −0.815 P < 0.01). ConclusionCompared to SS-EPI-DWI, MUSE-DWI can significantly reduce distortion, artifacts, and fuzziness in MRI of uterine lesions, which is more conducive to lesion detection.

Deep learning assisted atlas-based delineation of the skeleton from Whole-Body Diffusion Weighted MRI in patients with malignant bone disease

BackgroundWhole-Body Diffusion Weighted MRI (WBDWI) can provide a basis for quantifying disease volume and apparent diffusion coefficient (ADC) to aid disease staging and response assessment of metastatic bone disease. In this study, we develop atlas-based registration assisted by two AI-based models (i) body-region classification for automatic identification of legs, pelvis, lumbar/thoracic/cervical spine and head, and (ii) automated spinal cord segmentation, to automatically delineate the whole skeleton on WBDWI. MethodsTraining and validation of AI-based models were performed on 40 patients (32:8 split) with confirmed Advanced Prostate Cancer (APC) who underwent baseline and after treatment WBDWI scans. Consequent atlas-based skeleton segmentation accuracy was validated using leave-one-out cross-validation in a cohort of 15 patients with Multiple Myeloma (MM). ResultsAtlas-based registration in combination with AI-based models demonstrated higher performance in segmenting the spinal column when compared with the traditional techniques (average dice score for the lumbar, thoracic and cervical spine of 0.77/0.78/0.68 compared to 0.71/0.69/0.51 from the conventional method, respectively). Similar performances were observed for long bones, pelvic girdle and ribcage (average dice score of 0.65/0.7/0.47, respectively). Our approach demonstrated lower relative median ADC and skeleton volume difference between estimation and ground truth compared to conventional atlas-based methods (average relative median ADC and skeleton volume difference of 10.3/2.1% compared to 13/2.7%, respectively). ConclusionAI-augmented atlas-based registration substantially improves the accuracy of derived skeletal segmentations from WBDWI compared with traditional atlas-based registration.

Deep Learning for Delineation of the Spinal Canal in Whole-Body Diffusion-Weighted Imaging: Normalising Inter- and Intra-Patient Intensity Signal in Multi-Centre Datasets.

Whole-Body Diffusion-Weighted Imaging (WBDWI) is an established technique for staging and evaluating treatment response in patients with multiple myeloma (MM) and advanced prostate cancer (APC). However, WBDWI scans show inter- and intra-patient intensity signal variability. This variability poses challenges in accurately quantifying bone disease, tracking changes over follow-up scans, and developing automated tools for bone lesion delineation. Here, we propose a novel automated pipeline for inter-station, inter-scan image signal standardisation on WBDWI that utilizes robust segmentation of the spinal canal through deep learning. We trained and validated a supervised 2D U-Net model to automatically delineate the spinal canal (both the spinal cord and surrounding cerebrospinal fluid, CSF) in an initial cohort of 40 patients who underwent WBDWI for treatment response evaluation (80 scans in total). Expert-validated contours were used as the target standard. The algorithm was further semi-quantitatively validated on four additional datasets (three internal, one external, 207 scans total) by comparing the distributions of average apparent diffusion coefficient (ADC) and volume of the spinal cord derived from a two-component Gaussian mixture model of segmented regions. Our pipeline subsequently standardises WBDWI signal intensity through two stages: (i) normalisation of signal between imaging stations within each patient through histogram equalisation of slices acquired on either side of the station gap, and (ii) inter-scan normalisation through histogram equalisation of the signal derived within segmented spinal canal regions. This approach was semi-quantitatively validated in all scans available to the study (N = 287). The test dice score, precision, and recall of the spinal canal segmentation model were all above 0.87 when compared to manual delineation. The average ADC for the spinal cord (1.7 × 10-3 mm2/s) showed no significant difference from the manual contours. Furthermore, no significant differences were found between the average ADC values of the spinal cord across the additional four datasets. The signal-normalised, high-b-value images were visualised using a fixed contrast window level and demonstrated qualitatively better signal homogeneity across scans than scans that were not signal-normalised. Our proposed intensity signal WBDWI normalisation pipeline successfully harmonises intensity values across multi-centre cohorts. The computational time required is less than 10 s, preserving contrast-to-noise and signal-to-noise ratios in axial diffusion-weighted images. Importantly, no changes to the clinical MRI protocol are expected, and there is no need for additional reference MRI data or follow-up scans.