Relaxation-Diffusion T2-ADC Correlations in Breast Cancer Patients: A Spatiotemporally Encoded 3T MRI Assessment.

Quantitative correlations between T2 and ADC values were explored on cancerous breast lesions using spatiotemporally encoded (SPEN) MRI. To this end, T2 maps of patients were measured at more than one b-value, and ADC maps at several echo time values were recorded. SPEN delivered quality, artifact-free, TE-weighted DW images, from which T2-ADC correlations could be obtained despite the signal losses brought about by diffusion and relaxation. Data confirmed known aspects of breast cancer lesions, including their reduced ADC values vs. healthy tissue. Data also revealed an anticorrelation between the T2 and ADC values, when comparing regions with healthy and diseased tissues. This is contrary to expectations based on simple water restriction considerations. It is also contrary to what has been observed in a majority of porous materials and tissues. Differences between the healthy tissue of the lesion-affected breast and healthy tissue in the contralateral breast were also noticed. The potential significance of these trends is discussed, as is the potential of combining T2- and ADC-weightings to achieve an enhanced endogenous MRI contrast about the location of breast cancer lesions.

Probing lipids relaxation times in breast cancer using magnetic resonance spectroscopic fingerprinting.

OBJECTIVES: To investigate the clinical relevance of the relaxation times of lipids within breast cancer and normal fibroglandular tissue in vivo, using magnetic resonance spectroscopic fingerprinting (MRSF). METHODS: Twelve patients with biopsy-confirmed breast cancer and 14 healthy controls were prospectively scanned at 3 T using a protocol consisting of diffusion tensor imaging (DTI), MRSF, and dynamic contrast-enhanced (DCE) MRI. Single-voxel MRSF data was recorded from the tumor (patients) - identified using DTI - or normal fibroglandular tissue (controls), in under 20 s. MRSF data was analyzed using in-house software. Linear mixed model analysis was used to compare the relaxation times of lipids in breast cancer VOIs vs. normal fibroglandular tissue. RESULTS: Seven distinguished lipid metabolite peaks were identified and their relaxation times were recorded. Of them, several exhibited statistically significant changes between controls and patients, with strong significance (p < 10-3) recorded for several of the lipid resonances at 1.3 ppm (T1 = 355 ± 17 ms vs. 389 ± 27 ms), 4.1 ppm (T1 = 255 ± 86 ms vs. 127 ± 33 ms), 5.22 ppm (T1 = 724 ± 81 ms vs. 516 ± 62 ms), and 5.31 ppm (T2 = 56 ± 5 ms vs. 44 ± 3.5 ms, respectively). CONCLUSIONS: The application of MRSF to breast cancer imaging is feasible and achievable in clinically relevant scan time. Further studies are required to verify and comprehend the underling biological mechanism behind the differences in lipid relaxation times in cancer and normal fibroglandular tissue. KEY POINTS: •The relaxation times of lipids in breast tissue are potential markers for quantitative characterization of the normal fibroglandular tissue and cancer. •Lipid relaxation times can be acquired rapidly in a clinically relevant manner using a single-voxel technique, termed MRSF. •Relaxation times of T1 at 1.3 ppm, 4.1 ppm, and 5.22 ppm, as well as of T2 at 5.31 ppm, were significantly different between measurements within breast cancer and the normal fibroglandular tissue.

Diffusion Is Directional: Innovative Diffusion Tensor Imaging to Improve Prostate Cancer Detection.

In the prostate, water diffusion is faster when moving parallel to duct and gland walls than when moving perpendicular to them, but these data are not currently utilized in multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) detection. Diffusion tensor imaging (DTI) can quantify the directional diffusion of water in tissue and is applied in brain and breast imaging. Our aim was to determine whether DTI may improve PCa detection. We scanned patients undergoing mpMRI for suspected PCa with a DTI sequence. We calculated diffusion metrics from DTI and diffusion weighted imaging (DWI) for suspected lesions and normal-appearing prostate tissue, using specialized software for DTI analysis, and compared predictive values for PCa in targeted biopsies, performed when clinically indicated. DTI scans were performed on 78 patients, 42 underwent biopsy and 16 were diagnosed with PCa. The median age was 62 (IQR 54.4-68.4), and PSA 4.8 (IQR 1.3-10.7) ng/mL. DTI metrics distinguished PCa lesions from normal tissue. The prime diffusion coefficient (λ1) was lower in both peripheral-zone (p < 0.0001) and central-gland (p < 0.0001) cancers, compared to normal tissue. DTI had higher negative and positive predictive values than mpMRI to predict PCa (positive predictive value (PPV) 77.8% (58.6-97.0%), negative predictive value (NPV) 91.7% (80.6-100%) vs. PPV 46.7% (28.8-64.5%), NPV 83.3% (62.3-100%)). We conclude from this pilot study that DTI combined with T2-weighted imaging may have the potential to improve PCa detection without requiring contrast injection.

Diffusivity in breast malignancies analyzed for b > 1000 s/mm2 at 1 mm in-plane resolutions: Insight from Gaussian and non-Gaussian behaviors.

Diffusion-weighted imaging (DWI) can improve breast cancer characterizations, but often suffers from low image quality -particularly at informative b > 1000 s/mm2 values. The aim of this study was to evaluate multishot approaches characterizing Gaussian and non-Gaussian diffusivities in breast cancer. This was a prospective study, in which 15 subjects, including 13 patients with biopsy-confirmed breast cancers, were enrolled. DWI was acquired at 3 T using echo planar imaging (EPI) with and without zoomed excitations, readout-segmented EPI (RESOLVE), and spatiotemporal encoding (SPEN); dynamic contrast-enhanced (DCE) data were collected using three-dimensional gradient-echo T1 weighting; anatomies were evaluated with T2 -weighted two-dimensional turbo spin-echo. Congruence between malignancies delineated by DCE was assessed against high-resolution DWI scans with b-values in the 0-1800 s/mm2 range, as well as against apparent diffusion coefficient (ADC) and kurtosis maps. Data were evaluated by independent magnetic resonance scientists with 3-20 years of experience, and radiologists with 6 and 20 years of experience in breast MRI. Malignancies were assessed from ADC and kurtosis maps, using paired t tests after confirming that these values had a Gaussian distribution. Agreements between DWI and DCE datasets were also evaluated using Sorensen-Dice similarity coefficients. Cancerous and normal tissues were clearly separable by ADCs: by SPEN their average values were (1.03 ± 0.17) × 10-3 and (1.69 ± 0.19) × 10-3  mm2 /s (p < 0.0001); by RESOLVE these values were (1.16 ± 0.16) × 10-3 and (1.52 ± 0.14) × 10-3 (p = 0.00026). Kurtosis also distinguished lesions (K = 0.64 ± 0.15) from normal tissues (K = 0.45 ± 0.05), but only when measured by SPEN (p = 0.0008). The best statistical agreement with DCE-highlighted regions arose for SPEN-based DWIs recorded with b = 1800 s/mm2 (Sorensen-Dice coefficient = 0.67); DWI data recorded with b = 850 and 1200 s/mm2 , led to lower coefficients. Both ADC and kurtosis maps highlighted the breast malignancies, with ADCs providing a more significant separation. The most promising alternative for contrast-free delineations of the cancerous lesions arose from b = 1800 s/mm2 DWI. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 3.

Effect of IV Administration of a Gadolinium-Based Contrast Agent on Breast Diffusion-Tensor Imaging.

OBJECTIVE. The purpose of this study was to quantify changes in diffusion-tensor imaging (DTI) parameters before and after IV administration of a gadolinium-based contrast agent (GBCA) and explore the influence of those parameters on breast cancer diagnosis. SUBJECTS AND METHODS. A prospective cohort of 26 women with BI-RADS categories 0, 4, 5, or 6 underwent 3-T breast MRI with sequential DTI before GBCA administration and immediately after. Quantitative image analysis using dedicated DTI software yielded parametric DTI maps of each directional diffusion coefficient (DDC), mean diffusivity, and maximal anisotropy of the lesions and normal tissue. The color maps were evaluated and the lesion DTI parameters were compared before and after GBCA administration using appropriate statistical tests. RESULTS. Of the cohort, 58% had cancer (13 infiltrating ductal carcinoma, two ductal carcinoma in situ) and 42% had benign or normal results. All breast cancers were visually detected in the DDC λ1 maps before and after GBCA administration. Mean cancer size derived from λ1 maps before GBCA administration was 15.3 mm (range, 3.3-72.3 mm), and was not statistically significantly different from the size derived after GBCA administration of 17.3 mm (range, 3.9-71.0 mm). After GBCA administration, the cancers exhibited statistically significantly lower DDCs, mean diffusivity, and b0 intensity (p < 0.05), and no change in maximal anisotropy compared with before GBCA administration, whereas these parameters in normal and benign lesions did not change significantly after GBCA administration. The mean AUC values before and after GBCA administration, ranging from 0.735 to 0.985 and from 0.867 to 0.990, respectively, were not statistically significantly different for all parameters aside from λ3. CONCLUSION. Diagnostic accuracy using DTI was equivalent before and after GBCA administration, despite a change in the values of the DTI parameters. However, the limitations in standardization of contrast enhancement implies that unenhanced diffusion measurements should be preferred.

Diffusion-weighted breast MRI of malignancies with submillimeter resolution and immunity to artifacts by spatiotemporal encoding at 3T.

PURPOSE: Diffusion weighted imaging (DWI) is increasingly used in evaluating breast cancer, as complement to DCE measurements of superior spatial resolution. Extracting fine morphological features in DWI is complicated by limitations that sequences such as EPI face, when applied to heterogeneous organs. This study investigates the ability of spatiotemporal encoding (SPEN) MRI to screen breast cancers and define diffusivity features at mm and sub-mm resolutions on a 3T scanner METHODS: Twenty-one patients with biopsy-confirmed breast cancer lesions were examined by T2-weighted and DCE protocols, by EPI-based DWI, and by SPEN-based protocols optimized for SNR, robustness and spatial resolution, respectively. RESULTS: Excellent agreement was found between the diffusivity parameters measured by all SPEN protocols and by EPI, with the lower ADCs characteristic of tumors being readily detected. SPEN provided systematically better SNR and improved qualitative results, particularly when dealing with small lesions surrounded by fatty tissue, or lesions close to tissue/air interfaces. SPEN-derived ADC maps collected at sub-mm in-plane resolutions recapitulated the high-resolution morphology shown by lesions using more sensitive DCE protocols. CONCLUSION: Measurements on a patient cohort validated SPEN's ability to quantify the diffusivity changes associated with the presence of breast cancers, while imaging the lesions with reduced distortions at sub-mm resolutions.

Noncontrast Breast MRI During Pregnancy Using Diffusion Tensor Imaging: A Feasibility Study.

BACKGROUND: Pregnancy-associated breast cancer (PABC) is often a delayed diagnosis and contrast-enhanced MRI is contraindicated because gadolinium agents are known to cross the placenta. PURPOSE: To investigate the feasibility and clinical utility of noncontrast breast MRI using diffusion tensor imaging (DTI) in the diagnostic workup of PABC. STUDY TYPE: Prospective. POPULATION: Between November 2016 and January 2018, 25 pregnant participants (median gestational age: 17 weeks) were recruited from eight referral breast-care centers nationwide. Imaging indications included: newly-diagnosed PABC (n = 10, with 11 lesions), palpable mass/mastitis (n = 4), high-risk screening (n = 10), and monitoring neoadjuvant-chemotherapy response (n = 1). FIELD STRENGTH/SEQUENCE: 1.5T, T2 -weighted, and DTI sequences, prone position, with a scan duration of ∼12 minutes. ASSESSMENT: DTI parametric maps were generated and analyzed at pixel resolution, with reference to ultrasound (US) and pathology. STATISTICAL TESTS: Two-tailed Student's t-test was applied for evaluating differences between DTI parameters of PABC vs. healthy fibroglandular tissue. Pearson's correlation test was applied to measure the agreements between λ1-based longest tumor diameter, US, and pathology. RESULTS: All scans were technically completed and reached diagnostic quality, except one with notable motion artifacts due to positional discomfort, which was excluded. Nine out of 11 known PABC lesions and one newly-diagnosed lesion were visible on λ1, λ2, λ3, mean diffusivity (MD), and λ1-λ3 maps, with substantial parametric contrast compared with the apparently normal contralateral fibroglandular tissue (P < 0.001 for all). Two lesions of 0.7 cm were not depicted by the diffusivity maps. Tumor diameter measured on a thresholded λ1 map correlated well with US (r = 0.97) and pathology (r = 0.95). Malignancy was excluded by DTI parametric maps in scans of symptomatic and high-risk patients, in agreement with US follow-up, except for one false-positive case. DATA CONCLUSION: Noncontrast breast MRI is feasible and well-tolerated during pregnancy. Further studies with a larger and homogeneous cohort are required to validate DTI's additive diagnostic value, albeit this study suggests a potential adjunct role for this noninvasive approach in breast evaluation during pregnancy. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:508-517.

Quantitative evaluation of breast cancer response to neoadjuvant chemotherapy by diffusion tensor imaging: Initial results.

BACKGROUND: Diffusion tensor imaging (DTI) yields several parameters that have not been tested in response evaluation to neoadjuvant chemotherapy (NAC). PURPOSE: To evaluate and compare in reference to histopathology findings the ability of DTI and dynamic contrast-enhanced (DCE) MRI to monitor response to NAC. STUDY TYPE: Retrospective. POPULATION: Twenty patients treated with neoadjuvant chemotherapy. FIELD STRENGTH/SEQUENCE: 1.5T MRI axial, bilateral T2 -weighted, DTI, and DCE-MRI. ASSESSMENT: A standardized blinded image analysis at pixel resolution generated color-coded maps of DTI and DCE parameters STATISTICAL TESTS: Pearson's correlation analysis and Bland-Altman plots of the DTI and DCE size changes and of the pathological final residual tumor diameter and DCE or DTI final diameter, from pre- to post-NAC. Spearman coefficient of rank correlation between the DTI and DCE size changes from pre- to post-NAC and Miller and Payne (M&P) pathological response grading. Receiver operating characteristic curve analyses to differentiate between responders to nonresponders on the basis of the DTI and DCE percent size changes and the changes in DTI parameters. RESULTS: DTI and DCE changes in the cancers' diameter and volume from pre- to post-NAC exhibited high and significant Pearson correlation (r = 0.82 P = 1.2 × 10-5 ). The DTI volume changes exhibited a significant Spearman coefficient rank correlation (0.68, P = 0.001) with the pathological M&P grading and differentiated between responders and nonresponders with area-under-the-curve (AUC) of 0.83 ± 0.10. A similar AUC for differentiating responders from nonresponders was exhibited by the changes in the highest diffusion coefficient (0.84 ± 0.11) and the mean diffusivity (0.83 ± 0.11). The DTI residual-tumor-diameter showed a high and significant Pearson correlation (r = 0.87 P = 1.2 × 10-6 ) to pathology tumor diameter. DATA CONCLUSION: DTI monitors changes in cancer size and diffusion tensor parameters in response to NAC with an accuracy equivalent to that of DCE, enabling differentiation of responders from nonresponders and assessment of residual tumor size in high congruence with pathology. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2018;47:1080-1090.

Next-generation sequencing of patients with congenital anosmia.

We performed whole exome or genome sequencing in eight multiply affected families with ostensibly isolated congenital anosmia. Hypothesis-free analyses based on the assumption of fully penetrant recessive/dominant/X-linked models obtained no strong single candidate variant in any of these families. In total, these eight families showed 548 rare segregating variants that were predicted to be damaging, in 510 genes. Three Kallmann syndrome genes (FGFR1, SEMA3A, and CHD7) were identified. We performed permutation-based analysis to test for overall enrichment of these 510 genes carrying these 548 variants with genes mutated in Kallmann syndrome and with a control set of genes mutated in hypogonadotrophic hypogonadism without anosmia. The variants were found to be enriched for Kallmann syndrome genes (3 observed vs. 0.398 expected, p = 0.007), but not for the second set of genes. Among these three variants, two have been already reported in genes related to syndromic anosmia (FGFR1 (p.(R250W)), CHD7 (p.(L2806V))) and one was novel (SEMA3A (p.(T717I))). To replicate these findings, we performed targeted sequencing of 16 genes involved in Kallmann syndrome and hypogonadotrophic hypogonadism in 29 additional families, mostly singletons. This yielded an additional 6 variants in 5 Kallmann syndrome genes (PROKR2, SEMA3A, CHD7, PROK2, ANOS1), two of them already reported to cause Kallmann syndrome. In all, our study suggests involvement of 6 syndromic Kallmann genes in isolated anosmia. Further, we report a yet unreported appearance of di-genic inheritance in a family with congenital isolated anosmia. These results are consistent with a complex molecular basis of congenital anosmia.

Monitoring In-Vivo the Mammary Gland Microstructure during Morphogenesis from Lactation to Post-Weaning Using Diffusion Tensor MRI.

Lactation and the return to the pre-conception state during post-weaning are regulated by hormonal induced processes that modify the microstructure of the mammary gland, leading to changes in the features of the ductal / glandular tissue, the stroma and the fat tissue. These changes create a challenge in the radiological workup of breast disorder during lactation and early post-weaning. Here we present non-invasive MRI protocols designed to record in vivo high spatial resolution, T2-weighted images and diffusion tensor images of the entire mammary gland. Advanced imaging processing tools enabled tracking the changes in the anatomical and microstructural features of the mammary gland from the time of lactation to post-weaning. Specifically, by using diffusion tensor imaging (DTI) it was possible to quantitatively distinguish between the ductal / glandular tissue distention during lactation and the post-weaning involution. The application of the T2-weighted imaging and DTI is completely safe, non-invasive and uses intrinsic contrast based on differences in transverse relaxation rates and water diffusion rates in various directions, respectively. This study provides a basis for further in-vivo monitoring of changes during the mammary developmental stages, as well as identifying changes due to malignant transformation in patients with pregnancy associated breast cancer (PABC).

Can diffusion tensor anisotropy indices assist in breast cancer detection?

PURPOSE: To evaluate whether the various anisotropy indices derived from breast diffusion tensor imaging (DTI) can characterize the healthy breast structure and differentiate cancer from normal breast tissue. MATERIALS AND METHODS: Six healthy volunteers and retrospectively selected 24 breast cancer patients were imaged at 3T. DTI included two b-values 0 and 700 sec/mm2 with 20-64 gradient directions and TE of 120 or 90 msec. The normalized anisotropy indices: fractional anisotropy (FA), relative anisotropy (RA), and 1-volume ratio (1-VR), as well as the absolute maximal anisotropy index (λ1 -λ3 ) were compared. RESULTS: The spatial distribution of the various anisotropy indices in healthy volunteers exhibited a high congruence (Pearson correlation coefficients range: 0.79-1.0). All indices showed a statistically significant reduction (P < 0.001) following shortening of the diffusion time. Significantly lower λ1 -λ3 values were found in cancers as compared to normal breast tissue (P < 6.0 × 10-7 ), while the values of the normalized indices in cancers were not significantly different from those in normal breast tissue (P < 0.65 for FA, P < 0.6 for RA, and P < 0.2 for 1-VR). The contrast-to-noise ratio of λ1 -λ3 was significantly higher (P < 0.001) than those of the normalized anisotropy indices, and the area under the curve in a receiver operating characteristic analysis exhibited the highest value for λ1 -λ3 (0.89 ± 0.04 vs. 0.51-0.54 for the other anisotropy indices). CONCLUSION: Water diffusion anisotropy in the healthy breast can be similarly mapped by the normalized indices and by λ1 -λ3 . However, the normalized anisotropy indices fail to differentiate cancer from normal breast tissue, whereas λ1 -λ3 can assist in differentiating cancer from normal breast tissue. J. Magn. Reson. Imaging 2016;44:1624-1632.

Removing silicone artifacts in diffusion-weighted breast MRI by means of shift-resolved spatiotemporally encoding.

PURPOSE: Evaluate the usefulness of diffusion-weighted spatiotemporally encoded (SPEN) methods to obtain apparent diffusion coefficient (ADC) maps of fibroglandular human breast tissue, in the presence of silicone implants. METHODS: Seven healthy volunteers with breast augmentation were scanned at 3 Tesla (T) using customized SPEN sequences yielding separate silicone and water (1) H images in one scan, together with their corresponding diffusion-weightings. RESULTS: SPEN's ability to deliver multiple spectrally resolved images in a single scan, coupled to the method's substantial robustness to magnetic field heterogeneities, served to acquire ADC maps that could be freed from contributions that did not belong to fibroglandular tissue. CONCLUSION: SPEN-based sequences incorporating spectral discrimination and diffusion-weighting enable the acquisition of reliable ADC maps despite the presence of dominant signals from silicone implants, thereby opening new screening possibilities for the identification of malignancies in breast augmented patients.

Overcoming limitations in diffusion-weighted MRI of breast by spatio-temporal encoding.

PURPOSE: Evaluating the usefulness of diffusion-weighted spatio-temporal encoding (SPEN) methods to provide quantitative apparent diffusion coefficient (ADC)-based characterizations of healthy and malignant human breast tissues, in comparison with results obtained using techniques based on spin-echo echo planar imaging (SE-EPI). METHODS: Twelve healthy volunteers and six breast cancer patients were scanned at 3T using scanner-supplied diffusion-weighted imaging EPI sequences, as well as two fully refocused SPEN variants programmed in-house. Suitable codes were written to process the data, including calculations of the actual b-values and retrieval of the ADC maps. RESULTS: Systematically better images were afforded by the SPEN scans, with negligible geometrical distortions and markedly weaker ghosting artifacts arising from either fat tissues or from strongly emitting areas such as cysts. SPEN-derived images provided improved characterizations of the fibroglandular tissues and of the lesions' contours. When translated into the calculation of the ADC maps, there were no significant differences between the mean ADCs derived from SPEN and SE-EPI: if reliable images were available, both techniques showed that ADCs decreased by nearly two-fold in the malignant lesion areas. CONCLUSION: SPEN-based sequences yielded diffusion-weighted breast images with minimal artifacts and distortions, enabling the calculation of improved ADC maps and the identification of decreased ADCs in malignant regions.

Tracking the mammary architectural features and detecting breast cancer with magnetic resonance diffusion tensor imaging.

Breast cancer is the most common cause of cancer among women worldwide. Early detection of breast cancer has a critical role in improving the quality of life and survival of breast cancer patients. In this paper a new approach for the detection of breast cancer is described, based on tracking the mammary architectural elements using diffusion tensor imaging (DTI). The paper focuses on the scanning protocols and image processing algorithms and software that were designed to fit the diffusion properties of the mammary fibroglandular tissue and its changes during malignant transformation. The final output yields pixel by pixel vector maps that track the architecture of the entire mammary ductal glandular trees and parametric maps of the diffusion tensor coefficients and anisotropy indices. The efficiency of the method to detect breast cancer was tested by scanning women volunteers including 68 patients with breast cancer confirmed by histopathology findings. Regions with cancer cells exhibited a marked reduction in the diffusion coefficients and in the maximal anisotropy index as compared to the normal breast tissue, providing an intrinsic contrast for delineating the boundaries of malignant growth. Overall, the sensitivity of the DTI parameters to detect breast cancer was found to be high, particularly in dense breasts, and comparable to the current standard breast MRI method that requires injection of a contrast agent. Thus, this method offers a completely non-invasive, safe and sensitive tool for breast cancer detection.

Diffusion tensor magnetic resonance imaging of the pancreas.

PURPOSE: To develop a diffusion-tensor-imaging (DTI) protocol that is sensitive to the complex diffusion and perfusion properties of the healthy and malignant pancreas tissues. MATERIALS AND METHODS: Twenty-eight healthy volunteers and nine patients with pancreatic-ductal-adenocacinoma (PDAC), were scanned at 3T with T2-weighted and DTI sequences. Healthy volunteers were also scanned with multi-b diffusion-weighted-imaging (DWI), whereas a standard clinical protocol complemented the PDAC patients' scans. Image processing at pixel resolution yielded parametric maps of three directional diffusion coefficients λ1, λ2, λ3, apparent diffusion coefficient (ADC), and fractional anisotropy (FA), as well as a λ1-vector map, and a main diffusion-direction map. RESULTS: DTI measurements of healthy pancreatic tissue at b-values 0,500 s/mm² yielded: λ1 = (2.65±0.35)×10⁻³, λ2 = (1.87±0.22)×10⁻³, λ3 = (1.20±0.18)×10⁻³, ADC = (1.91±0.22)×10⁻³ (all in mm²/s units) and FA = 0.38±0.06. Using b-values of 100,500 s/mm² led to a significant reduction in λ1, λ2, λ3 and ADC (p<.0001) and a significant increase (p<0.0001) in FA. The reduction in the diffusion coefficients suggested a contribution of a fast intra-voxel-incoherent-motion (IVIM) component at b≤100 s/mm², which was confirmed by the multi-b DWI results. In PDACs, λ1, λ2, λ3 and ADC in both 0,500 s/mm² and 100,500 s/mm² b-values sets, as well as the reduction in these diffusion coefficients between the two sets, were significantly lower in comparison to the distal normal pancreatic tissue, suggesting higher cellularity and diminution of the fast-IVIM component in the cancer tissue. CONCLUSION: DTI using two reference b-values 0 and 100 s/mm² enabled characterization of the water diffusion and anisotropy of the healthy pancreas, taking into account a contribution of IVIM. The reduction in the diffusion coefficients of PDAC, as compared to normal pancreatic tissue, and the smaller change in these coefficients in PDAC when the reference b-value was modified from 0 to 100 s/mm², helped identifying the presence of malignancy.

Diffusion-tensor MR imaging of the breast: hormonal regulation.

PURPOSE: To investigate the parameters obtained with magnetic resonance (MR) diffusion-tensor imaging (DTI) of the breast throughout the menstrual cycle phases, during lactation, and after menopause, with and without hormone replacement therapy (HRT). MATERIALS AND METHODS: All protocols were approved by the internal review board, and signed informed consent was obtained from all participants. Forty-five healthy volunteers underwent imaging by using T2-weighted and DTI MR sequences at 3 T. Premenopausal volunteers (n = 16) underwent imaging weekly, four times during one menstrual cycle. Postmenopausal volunteers (n = 19) and lactating volunteers (n = 10) underwent imaging once. The principal diffusion coefficients (λ1, λ2, and λ3), apparent diffusion coefficient (ADC), fractional anisotropy (FA), and maximal anisotropy (λ1-λ3) were calculated pixel by pixel for the fibroglandular tissue in the entire breast. RESULTS: In all premenopausal volunteers, the DTI parameters exhibited high repeatability, remaining almost equal along the menstrual cycle, with a low mean within-subject coefficient of variance of λ1, λ2, λ3, and ADC (1%-2% for all) and FA (5%), as well as a high intraclass correlation of 0.92-0.98. The diffusion coefficients were significantly lower (a) in the group without HRT use as compared with the group with HRT use (P < .01) and premenopausal volunteers (P < .01) and (b) in the lactating volunteers as compared with the premenopausal volunteers (P < .005). No significant differences in DTI parameters were found between premenopausal volunteers free of oral contraceptives and those who used oral contraceptives (P = .28-0.82) and between premenopausal volunteers and postmenopausal volunteers who used HRT (P = .31-0.93). CONCLUSION: DTI parameters are not sensitive to menstrual cycle changes, while menopause, long-term HRT, and presence of milk in lactating women affected the DTI parameters. Therefore, the timing for performing breast DTI is not restricted throughout the menstrual cycle, whereas the modulations in diffusion parameters due to HRT and lactation should be taken into account in DTI evaluation.

Parametric diffusion tensor imaging of the breast.

OBJECTIVES: To investigate the ability of parametric diffusion tensor imaging (DTI), applied at 3 Tesla, to dissect breast tissue architecture and evaluate breast lesions. MATERIALS AND METHODS: All protocols were approved and a signed informed consent was obtained from all subjects. The study included 21 healthy women, 26 women with 33 malignant lesions, and 14 women with 20 benign lesions. Images were recorded at 3 Tesla with a protocol optimized for breast DTI at a spatial resolution of 1.9 × 1.9 × (2-2.5) mm3. Image processing algorithms and software, applied at pixel resolution, yielded vector maps of prime diffusion direction and parametric maps of the 3 orthogonal diffusion coefficients and of the fractional anisotropy and maximal anisotropy. RESULTS: The DTI-derived vector maps and parametric maps revealed the architecture of the entire mammary fibroglandular tissue and allowed a reliable detection of malignant lesions. Cancer lesions exhibited significantly lower values of the orthogonal diffusion coefficients, λ1, λ2, λ3, and of the maximal anisotropy index λ1-λ3 as compared with normal breast tissue (P < 0.0001) and to benign breast lesions (P < 0.0009 and 0.004, respectively). Maps of λ1 exhibited the highest contrast-to-noise ratio enabling delineation of the cancer lesions. These maps also provided high sensitivity/specificity of 95.6%/97.7% for differentiating cancers from benign lesions, which were similar to the sensitivity/specificity of dynamic contrast-enhanced magnetic resonance imaging of 94.8%/92.9%. Maps of λ1-λ3 provided a secondary independent diagnostic parameter with high sensitivity of 92.3%, but low specificity of 69.5% for differentiating cancers from benign lesions. CONCLUSION: Mapping the diffusion tensor parameters at high spatial resolution provides a potential novel means for dissecting breast architecture. Parametric maps of λ1 and λ1-λ3 facilitate the detection and diagnosis of breast cancer.

Principal component analysis of dynamic contrast enhanced MRI in human prostate cancer.

OBJECTIVES: To develop and evaluate a fast, objective and standardized method for image processing of dynamic contrast enhanced MRI of the prostate based on principal component analysis (PCA). MATERIALS AND METHODS: The study was approved by the institutional internal review board; signed informed consent was obtained. MRI of the prostate at 3 Tesla was performed in 21 patients with biopsy proven cancers before radical prostatectomy. Seven 3-dimensional gradient echo datesets, 2 pre and 5 post-gadopentetate dimeglumine injection (0.1 mmol/kg), were acquired within 10.5 minutes at high spatial resolution. PCA of dynamic intensity-scaled (IS) and enhancement-scaled (ES) datasets and analysis by the 3-time points (3TP) method were applied using the latter method for adjusting the PCA eigenvectors. RESULTS: PCA of 7 IS datasets and 6 ES datasets yielded their corresponding eigenvectors and eigenvalues. The first IS-eigenvector captured the major part of the signal variance because of a signal change between the precontrast and the first postcontrast arising from the inhomogeneous surface coil reception profile. The next 2 IS-eigenvectors and the 2 dominant ES-eigenvectors captured signal changes because of tissue contrast-enhancement, whereas the remaining eigenvectors captured noise changes. These eigenvectors were adjusted by rotation to reach congruence with the wash-in and wash-out kinetic parameters defined according to the 3TP method. The IS and ES-eigenvectors and rotation angles were highly reproducible across patients enabling the calculation of a general rotated eigenvector base that served to rapidly and objectively calculate diagnostically relevant projection coefficient maps for new cases. We found for the a priori selected prostate cancer patients that the projection coefficients of the IS-2nd eigenvector provided a higher accuracy for detecting biopsy proven cancers (94% sensitivity, 67% specificity, 80% ppv, and 89% npv) than the projection coefficients of the ES-2nd rotated and non rotated eigenvectors. CONCLUSIONS: PCA adjusted to correlate with physiological parameters selects a dominant eigenvector, free of the inhomogeneous radio-frequency field reception-profile and noise-components. Projection coefficient maps of this eigenvector provide a fast, objective, and standardized means for visualizing prostate cancer.