Quantitative MRI on MR-Linacs: Towards Biological Image-Guided Adaptive Radiotherapy.

Recognizing the potential of quantitative imaging biomarkers (QIBs) in radiotherapy, many studies have investigated the prognostic value of quantitative MRI (qMRI). With the introduction of MRI-guided radiotherapy systems, the practical challenges of repeated imaging have been substantially reduced. Since patients are treated inside an MRI scanner, acquisition of qMRI can be done during each fraction with limited or no prolongation of the fraction duration. In this review paper, we identify the steps that need been taken to move from MR as an imaging technique to a useful biomarker for MRI-guided radiotherapy (MRgRT).

Recommendations for improved reproducibility of ADC derivation on behalf of the Elekta MRI-linac consortium image analysis working group.

BACKGROUND AND PURPOSE: The apparent diffusion coefficient (ADC), a potential imaging biomarker for radiotherapy response, needs to be reproducible before translation into clinical use. The aim of this study was to evaluate the multi-centre delineation- and calculation-related ADC variation and give recommendations to minimize it. MATERIALS AND METHODS: Nine centres received identical diffusion-weighted and anatomical magnetic resonance images of different cancerous tumours (adrenal gland, pelvic oligo metastasis, pancreas, and prostate). All centres delineated the gross tumour volume (GTV), clinical target volume (CTV), and viable tumour volume (VTV), and calculated ADCs using both their local calculation methods and each of the following calculation conditions: b-values 0-500 vs. 150-500 s/mm2, region-of-interest (ROI)-based vs. voxel-based calculation, and mean vs. median. ADC variation was assessed using the mean coefficient of variation across delineations (CVD) and calculation methods (CVC). Absolute ADC differences between calculation conditions were evaluated using Friedman's test. Recommendations for ADC calculation were formulated based on observations and discussions within the Elekta MRI-linac consortium image analysis working group. RESULTS: The median (range) CVD and CVC were 0.06 (0.02-0.32) and 0.17 (0.08-0.26), respectively. The ADC estimates differed 18% between b-value sets and 4% between ROI/voxel-based calculation (p-values < 0.01). No significant difference was observed between mean and median (p = 0.64). Aligning calculation conditions between centres reduced CVC to 0.04 (0.01-0.16). CVD was comparable between ROI types. CONCLUSION: Overall, calculation methods had a larger impact on ADC reproducibility compared to delineation. Based on the results, significant sources of variation were identified, which should be considered when initiating new studies, in particular multi-centre investigations.

Visualising spatial heterogeneity in glioblastoma using imaging habitats.

Glioblastoma is a high-grade aggressive neoplasm characterised by significant intra-tumoral spatial heterogeneity. Personalising therapy for this tumour requires non-invasive tools to visualise its heterogeneity to monitor treatment response on a regional level. To date, efforts to characterise glioblastoma's imaging features and heterogeneity have focussed on individual imaging biomarkers, or high-throughput radiomic approaches that consider a vast number of imaging variables across the tumour as a whole. Habitat imaging is a novel approach to cancer imaging that identifies tumour regions or 'habitats' based on shared imaging characteristics, usually defined using multiple imaging biomarkers. Habitat imaging reflects the evolution of imaging biomarkers and offers spatially preserved assessment of tumour physiological processes such perfusion and cellularity. This allows for regional assessment of treatment response to facilitate personalised therapy. In this review, we explore different methodologies to derive imaging habitats in glioblastoma, strategies to overcome its technical challenges, contrast experiences to other cancers, and describe potential clinical applications.

The future of MRI in radiation therapy: Challenges and opportunities for the MR community.

Radiation therapy is a major component of cancer treatment pathways worldwide. The main aim of this treatment is to achieve tumor control through the delivery of ionizing radiation while preserving healthy tissues for minimal radiation toxicity. Because radiation therapy relies on accurate localization of the target and surrounding tissues, imaging plays a crucial role throughout the treatment chain. In the treatment planning phase, radiological images are essential for defining target volumes and organs-at-risk, as well as providing elemental composition (e.g., electron density) information for radiation dose calculations. At treatment, onboard imaging informs patient setup and could be used to guide radiation dose placement for sites affected by motion. Imaging is also an important tool for treatment response assessment and treatment plan adaptation. MRI, with its excellent soft tissue contrast and capacity to probe functional tissue properties, holds great untapped potential for transforming treatment paradigms in radiation therapy. The MR in Radiation Therapy ISMRM Study Group was established to provide a forum within the MR community to discuss the unmet needs and fuel opportunities for further advancement of MRI for radiation therapy applications. During the summer of 2021, the study group organized its first virtual workshop, attended by a diverse international group of clinicians, scientists, and clinical physicists, to explore our predictions for the future of MRI in radiation therapy for the next 25 years. This article reviews the main findings from the event and considers the opportunities and challenges of reaching our vision for the future in this expanding field.

Longitudinal Correlations Between Intravoxel Incoherent Motion (IVIM) and Dynamic Contrast-Enhanced (DCE) MRI During Radiotherapy in Prostate Cancer Patients.

Purpose: Intravoxel incoherent motion (IVIM) is a promising technique that can acquire perfusion information without the use of contrast agent, contrary to the more established dynamic contrast-enhanced (DCE) technique. This is of interest for treatment response monitoring, where patients can be imaged on each treatment fraction. In this study, longitudinal correlations between IVIM- and DCE parameters were assessed in prostate cancer patients receiving radiation treatment. Materials and Methods: 20 prostate cancer patients were treated on a 1.5 T MR-linac with 20 x 3 or 3.1 Gy. Weekly IVIM and DCE scans were acquired. Tumors, the peripheral zone (PZ), and the transition zone (TZ) were delineated on a T2-weighted scan acquired on the first fraction. IVIM and DCE scans were registered to this scan and the delineations were propagated. Median values from these delineations were used for further analysis. The IVIM parameters D, f, D* and the product fD* were calculated. The Tofts model was used to calculate the DCE parameters Ktrans, kep and ve. Pearson correlations were calculated for the IVIM and DCE parameters on values from the first fraction for each region of interest (ROI). For longitudinal analysis, the repeated measures correlation coefficient was used to determine correlations between IVIM and DCE parameters in each ROI. Results: When averaging over patients, an increase during treatment in all IVIM and DCE parameters was observed in all ROIs, except for D in the PZ and TZ. No significant Pearson correlations were found between any pair of IVIM and DCE parameters measured on the first fraction. Significant but low longitudinal correlations were found for some combinations of IVIM and DCE parameters in the PZ and TZ, while no significant longitudinal correlations were found in the tumor. Notably in the TZ, for both f and fD*, significant longitudinal correlations with all DCE parameters were found. Conclusions: The increase in IVIM- and DCE parameters when averaging over patients indicates a measurable response to radiation treatment with both techniques. Although low, significant longitudinal correlations were found which suggests that IVIM could potentially be used as an alternative to DCE for treatment response monitoring.

T1ρ for Radiotherapy Treatment Response Monitoring in Rectal Cancer Patients: A Pilot Study.

Quantitative MRI has the potential to produce imaging biomarkers for the prediction of early response to radiotherapy treatment. In this pilot study, a potential imaging biomarker, the T1ρ relaxation time, is assessed for this purpose. A T1ρ sequence was implemented on a 1.5 T MR-linac system, a system that combines an MRI with a linear accelerator for radiation treatment. An agar phantom with concentrations of 1-4% w/w was constructed for technical validation of the sequence. Phantom images were assessed in terms of short-term repeatability and signal-to-noise ratio. Twelve rectal cancer patients, who were treated with 5 × 5 Gy, were imaged on each treatment fraction. Individual changes in the T1ρ values of the gross tumor volume (GTV) showed an increase for most patients, although a paired t-test comparing values in the GTV from the first to the last treatment fraction showed no statistically significant difference. The phantom measurements showed excellent short-term repeatability (0.5-1.5 ms), and phantom T1ρ values corresponded to the literature values. T1ρ imaging was implemented successfully on the MR-linac, with a repeatability comparable to diagnostic systems, although clinical benefit in terms of treatment response monitoring remains to be demonstrated.

Eye-specific quantitative dynamic contrast-enhanced MRI analysis for patients with intraocular masses.

OBJECTIVE: Dynamic contrast enhanced (DCE)-MRI is currently not generally used for intraocular masses as lesions are small, have an inhomogeneous T1 and the eye is prone to motion. The aim of this paper is to address these eye-specific challenges, enabling accurate ocular DCE-MRI. MATERIALS & METHODS: DCE-MRI of 19 uveal melanoma (UM) patients was acquired using a fat-suppressed 3D spoiled gradient echo sequence with TWIST (time-resolved angiography with stochastic trajectories sequence). The analysis consisted of a two-step registration method to correct for both head and eye motion. A T1 map was calculated to convert signal intensities to concentrations. Subsequently, the Tofts model was fitted voxel wise to obtain Ktrans and ve. RESULTS: Registration significantly improved the concentration curve quality (p < 0.001). The T1 of melanotic lesions was significantly lower than amelanotic lesions (888 ms vs 1350 ms, p = 0.03). The average achieved B1+ in the lesions was 91%. The average Ktrans was 0.46 min-1 (range 0.13-1.0) and the average ve was 0.22 (range 0.10-0.51). CONCLUSION: Using this eye-specific analysis, DCE of intraocular masses is possible which might aid in the diagnosis, prognosis and follow-up of UM.

Daily Intravoxel Incoherent Motion (IVIM) In Prostate Cancer Patients During MR-Guided Radiotherapy-A Multicenter Study.

PURPOSE: Daily quantitative MR imaging during radiotherapy of cancer patients has become feasible with MRI systems integrated with linear accelerators (MR-linacs). Quantitative images could be used for treatment response monitoring. With intravoxel incoherent motion (IVIM) MRI, it is possible to acquire perfusion information without the use of contrast agents. In this multicenter study, daily IVIM measurements were performed in prostate cancer patients to identify changes that potentially reflect response to treatment. MATERIALS AND METHODS: Forty-three patients were included, treated with 20 fractions of 3 Gy on a 1.5 T MR-linac. IVIM measurements were performed on each treatment day. The diffusion coefficient (D), perfusion fraction (f), and pseudo-diffusion coefficient (D*) were calculated based on the median signal intensities in the non-cancerous prostate and the tumor. Repeatability coefficients (RCs) were determined based on the first two treatment fractions. Separate linear mixed-effects models were constructed for the three IVIM parameters. RESULTS: In total, 726 fractions were analyzed. Pre-treatment average values, measured on the first fraction before irradiation, were 1.46 × 10-3 mm2/s, 0.086, and 28.7 × 10-3 mm2/s in the non-cancerous prostate and 1.19 × 10-3 mm2/s, 0.088, and 28.9 × 10-3 mm2/s in the tumor, for D, f, and D*, respectively. The repeatability coefficients for D, f, and D* in the non-cancerous prostate were 0.09 × 10-3 mm2/s, 0.05, and 15.3 × 10-3 mm2/s. In the tumor, these values were 0.44 × 10-3 mm2/s, 0.16, and 76.4 × 10-3 mm2/s. The mixed effects analysis showed an increase in D of the tumors over the course of treatment, while remaining stable in the non-cancerous prostate. The f and D* increased in both the non-cancerous prostate and tumor. CONCLUSIONS: It is feasible to perform daily IVIM measurements on an MR-linac system. Although the repeatability coefficients were high, changes in IVIM perfusion parameters were measured on a group level, indicating that IVIM has potential for measuring treatment response.

Integration of quantitative imaging biomarkers in clinical trials for MR-guided radiotherapy: Conceptual guidance for multicentre studies from the MR-Linac Consortium Imaging Biomarker Working Group.

Quantitative imaging biomarkers (QIBs) derived from MRI techniques have the potential to be used for the personalised treatment of cancer patients. However, large-scale data are missing to validate their added value in clinical practice. Integrated MRI-guided radiotherapy (MRIgRT) systems, such as hybrid MRI-linear accelerators, have the unique advantage that MR images can be acquired during every treatment session. This means that high-frequency imaging of QIBs becomes feasible with reduced patient burden, logistical challenges, and costs compared to extra scan sessions. A wealth of valuable data will be collected before and during treatment, creating new opportunities to advance QIB research at large. The aim of this paper is to present a roadmap towards the clinical use of QIBs on MRIgRT systems. The most important need is to gather and understand how the QIBs collected during MRIgRT correlate with clinical outcomes. As the integrated MRI scanner differs from traditional MRI scanners, technical validation is an important aspect of this roadmap. We propose to integrate technical validation with clinical trials by the addition of a quality assurance procedure at the start of a trial, the acquisition of in vivo test-retest data to assess the repeatability, as well as a comparison between QIBs from MRIgRT systems and diagnostic MRI systems to assess the reproducibility. These data can be collected with limited extra time for the patient. With integration of technical validation in clinical trials, the results of these trials derived on MRIgRT systems will also be applicable for measurements on other MRI systems.

ADC measurements on the Unity MR-linac - A recommendation on behalf of the Elekta Unity MR-linac consortium.

BACKGROUND AND PURPOSE: Diffusion-weighted imaging (DWI) for treatment response monitoring is feasible on hybrid magnetic resonance linear accelerator (MR-linac) systems. The MRI scanner of the Elekta Unity system has an adjusted design compared to diagnostic scanners. We investigated its impact on measuring the DWI-derived apparent diffusion coefficient (ADC) regarding three aspects: the choice of b-values, the spatial variation of the ADC, and scanning during radiation treatment. The aim of this study is to give recommendations for accurate ADC measurements on Unity systems. MATERIALS AND METHODS: Signal-to-noise ratio (SNR) measurements with increasing b-values were done to determine the highest bvalue that can be measured reliably. The spatial variation of the ADC was assessed on six Unity systems with a cylindrical phantom of 40 cm diameter. The influence of gantry rotation and irradiation was investigated by acquiring DWI images before and during treatment of 11 prostate cancer patients. RESULTS: On the Unity system, a maximum b-value of 500 s/mm2 should be used for ADC quantification, as a trade-off between SNR and diffusion weighting. Accurate ADC values were obtained within 7 cm from the iso-center, while outside this region ADC values deviated more than 5%. The ADC was not influenced by the rotating linac or irradiation during treatment. CONCLUSION: We provide Unity system specific recommendations for measuring the ADC. This will increase the consistency of ADC values acquired in different centers on the Unity system, enabling large cohort studies for biomarker discovery and treatment response monitoring.

Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer.

BACKGROUND AND PURPOSE: A wide variation of MRI systems is a challenge in multicenter imaging biomarker studies as it adds variation in quantitative MRI values. The aim of this study was to design and test a quality assurance (QA) framework based on phantom measurements, for the quantitative MRI protocols of a multicenter imaging biomarker trial of locally advanced cervical cancer. MATERIALS AND METHODS: Fifteen institutes participated (five 1.5 T and ten 3 T scanners). Each institute optimized protocols for T2, diffusion-weighted imaging, T1, and dynamic contrast-enhanced (DCE-)MRI according to system possibilities, institutional preferences and study-specific constraints. Calibration phantoms with known values were used for validation. Benchmark protocols, similar on all systems, were used to investigate whether differences resulted from variations in institutional protocols or from system variations. Bias, repeatability (%RC), and reproducibility (%RDC) were determined. Ratios were used for T2 and T1 values. RESULTS: The institutional protocols showed a range in bias of 0.88-0.98 for T2 (median %RC = 1%; %RDC = 12%), -0.007 to 0.029 × 10-3 mm2/s for the apparent diffusion coefficient (median %RC = 3%; %RDC = 18%), and 0.39-1.29 for T1 (median %RC = 1%; %RDC = 33%). For DCE a nonlinear vendor-specific relation was observed between measured and true concentrations with magnitude data, whereas the relation was linear when phase data was used. CONCLUSION: We designed a QA framework for quantitative MRI protocols and demonstrated for a multicenter trial for cervical cancer that measurement of consistent T2 and apparent diffusion coefficient values is feasible despite protocol differences. For DCE-MRI and T1 mapping with the variable flip angle method, this was more challenging.

Locally advanced rectal cancer: 3D diffusion-prepared stimulated-echo turbo spin-echo versus 2D diffusion-weighted echo-planar imaging.

BACKGROUND: Diffusion-weighted imaging (DWI) has shown great value in rectal cancer imaging. However, traditional DWI with echo-planar imaging (DW-EPI) often suffers from geometrical distortions. We applied a three-dimensional diffusion-prepared stimulated-echo turbo spin-echo sequence (DPsti-TSE), allowing geometrically undistorted rectal DWI. We compared DPsti-TSE with DW-EPI for locally advanced rectal cancer DWI. METHODS: For 33 prior-to-treatment patients, DWI images of the rectum were acquired with DPsti-TSE and DW-EPI at 3 T using b-values of 200 and 1000 s/mm2. Two radiologists conducted a blinded scoring of the images considering nine aspects of image quality and anatomical quality. Tumour apparent diffusion coefficient (ADC) and distortions were compared quantitatively. RESULTS: DPsti-TSE scored significantly better than DW-EPI in rectum distortion (p = 0.005) and signal pileup (p = 0.001). DPsti-TSE had better tumour Dice similarity coefficient compared to DW-EPI (0.84 versus 0.80, p = 0.010). Tumour ADC values were higher for DPsti-TSE compared to DW-EPI (1.47 versus 0.86 × 10-3 mm2/s, p < 0.001). Radiologists scored DPsti-TSE significantly lower than DW-EPI on aspects of overall image quality (p = 0.001), sharpness (p < 0.001), quality of fat suppression (p < 0.001), tumour visibility (p = 0.009), tumour conspicuity (p = 0.010) and rectum wall visibility (p = 0.005). CONCLUSIONS: DPsti-TSE provided geometrically less distorted rectal cancer diffusion-weighted images. However, the image quality of DW-EPI over DPsti-TSE was referred on the basis of several image quality criteria. A significant bias in tumour ADC values from DPsti-TSE was present. Further improvements of DPsti-TSE are needed until it can replace DW-EPI.

Quantitative Magnetic Resonance Imaging for Biological Image-Guided Adaptive Radiotherapy.

MRI-guided radiotherapy systems have the potential to bring two important concepts in modern radiotherapy together: adaptive radiotherapy and biological targeting. Based on frequent anatomical and functional imaging, monitoring the changes that occur in volume, shape as well as biological characteristics, a treatment plan can be updated regularly to accommodate the observed treatment response. For this purpose, quantitative imaging biomarkers need to be identified that show changes early during treatment and predict treatment outcome. This review provides an overview of the current evidence on quantitative MRI measurements during radiotherapy and their potential as an imaging biomarker on MRI-guided radiotherapy systems.

Histopathological Features of MRI-Invisible Regions of Prostate Cancer Lesions.

BACKGROUND: Previous studies have reported tumor volume underestimation with multiparametric (mp)MRI in prostate cancer diagnosis. PURPOSE: To investigate why some parts of lesions are not visible on mpMRI by comparing their histopathology features to those of visible regions. STUDY TYPE: Retrospective. POPULATION: Thirty-four patients with biopsy-proven prostate cancer scheduled for prostatectomy (median 68.7 years). FIELD STRENGTH/SEQUENCE: T2 -weighted, diffusion-weighted imaging, T2 mapping, and dynamic contrast-enhanced MRI on two 3T systems and one 1.5T system. ASSESSMENT: Two readers delineated suspicious lesions on mpMRI. A pathologist delineated the lesions on histopathology. A patient-customized mold enabled the registration of histopathology and MRI. On histopathology we identified mpMRI visible and invisible lesions. Subsequently, within the visible lesions we identified regions that were visible and regions that were invisible on mpMRI. For each lesion and region the following characteristics were determined: size, location, International Society of Urological Pathology (ISUP) grade, and Gleason subpatterns (density [dense/intermediate], tumor morphology [homogeneous/heterogeneous], cribriform growth [yes/no]). STATISTICAL TESTS: With generalized linear mixed-effect modeling we investigated which features explain why a lesion or a region was invisible on MRI. We compared imaging values (T2 , ADC, and Ktrans ) for these features with one-way analysis of variance (ANOVA). RESULTS: Small, anterior, and ISUP grade 1-2 lesions (n = 34) were missed more frequent than large, posterior, ISUP grade ≥ 3 lesions (n = 35). Invisible regions on mpMRI had lower tumor density, heterogeneous tumor morphology, and were located in the transition zone. Both T2 and ADC values were higher in "intermediate" compared with "dense" regions (P = 0.002 and < 0.001) and in regions with heterogeneous compared with homogeneous morphology (P < 0.001 and 0.03). Ktrans was not significantly different (P = 0.24 and 0.99). DATA CONCLUSION: Regions of prostate cancer lesions that are invisible on mpMRI have different histopathology features than visible regions. This may have implications for monitoring during active surveillance and focal treatment strategies. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:1235-1246.

Quantitative MRI Changes During Weekly Ultra-Hypofractionated Prostate Cancer Radiotherapy With Integrated Boost.

Purpose: Quantitative MRI reflects tissue characteristics. As possible changes during radiotherapy may lead to treatment adaptation based on response, we here assessed if such changes during treatment can be detected. Methods and Materials: In the hypoFLAME trial patients received ultra-hypofractionated prostate radiotherapy with an integrated boost to the tumor in 5 weekly fractions. We analyzed T2 and ADC maps of 47 patients that were acquired in MRI exams prior to and during radiotherapy, and performed rigid registrations based on the prostate contour on anatomical T2-weighted images. We analyzed median T2 and ADC values in three regions of interest (ROIs): the central gland (CG), peripheral zone (PZ), and tumor. We analyzed T2 and ADC changes during treatment and compared patients with and without hormonal therapy. We tested changes during treatment for statistical significance with Wilcoxon signed rank tests. Using confidence intervals as recommended from test-retest measurements, we identified persistent T2 and ADC changes during treatment. Results: In the CG, median T2 and ADC values significantly decreased 12 and 8%, respectively, in patients that received hormonal therapy, while in the PZ these values decreased 17 and 18%. In the tumor no statistically significant change was observed. In patients that did not receive hormonal therapy, median ADC values in the tumor increased with 20%, while in the CG and PZ no changes were observed. Persistent T2 changes in the tumor were found in 2 out of 24 patients, while none of the 47 patients had persistent ADC changes. Conclusions: Weekly quantitative MRI could identify statistically significant ADC changes in the tumor in patients without hormonal therapy. On a patient level few persistent T2 changes in the tumor were observed. Long-term follow-up is required to relate the persistent T2 and ADC changes to outcome and evaluate the applicability of quantitative MRI for response based treatment adaptation.

MRI basics for radiation oncologists.

MRI is increasingly used in radiation oncology to facilitate tumor and organ-at-risk delineation and image guidance. In this review, we address issues of MRI that are relevant for radiation oncologists when interpreting MR images offered for radiotherapy. Whether MRI is used in combination with CT or in an MRI-only workflow, it is generally necessary to ensure that MR images are acquired in treatment position, using the positioning and fixation devices that are commonly applied in radiotherapy. For target delineation, often a series of separate image sets are used with distinct image contrasts, acquired within a single exam. MR images can suffer from image distortions. While this can be avoided with dedicated scan protocols, in a diagnostic setting geometrical fidelity is less relevant and is therefore less accounted for. Since geometrical fidelity is of utmost importance in radiation oncology, it requires dedicated scan protocols. The strong magnetic field of an MRI scanner and the use of radiofrequency radiation can cause safety hazards if not properly addressed. Safety screening is crucial for every patient and every operator prior to entering the MRI room.

Multiparametric MRI Tumor Probability Model for the Detection of Locally Recurrent Prostate Cancer After Radiation Therapy: Pathologic Validation and Comparison With Manual Tumor Delineations.

PURPOSE: Focal salvage treatments of recurrent prostate cancer (PCa) after radiation therapy require accurate delineation of the target volume. Magnetic resonance imaging (MRI) is used for this purpose; however, radiation therapy-induced changes complicate image interpretation, and guidelines are lacking on the assessment and delineation of recurrent PCa. A tumor probability (TP) model was trained and independently tested using multiparametric magnetic resonance imaging (mp-MRI) of patients with radio-recurrent PCa. The resulting probability maps were used to derive target regions for radiation therapy treatment planning. METHODS AND MATERIALS: Two cohorts of patients with radio-recurrent PCa were used in this study. All patients underwent mp-MRI (T2 weighted, diffusion-weighted imaging, and dynamic contrast enhanced). A logistic regression model was trained using imaging features from 21 patients with biopsy-proven recurrence who qualified for salvage treatment. The test cohort consisted of 17 patients treated with salvage prostatectomy. The model was tested against histopathology-derived tumor delineations. The voxel-wise TP maps were clustered using k-means to generate a gross tumor volume (GTV) contour for voxel-level comparisons with manual tumor delineations performed by 2 radiologists and with histopathology-validated contours. Later, k-means was used with 3 clusters to define a clinical target volume (CTV), high-risk CTV, and GTV, with increasing tumor risk. RESULTS: In the test cohort, the model obtained a median (range) area under the curve of 0.77 (0.41-0.99) for the whole prostate. The GTV delineation resulted in a median sensitivity of 0.31 (0-0.87) and specificity of 0.97 (0.84-1.0) with no significant differences between model and manual delineations. The 3-level clustering GTV and high-risk CTV delineations had median sensitivities of 0.17 (0-0.59) and 0.49 (0-0.97) and specificities of 0.98 (0.84-1.00) and 0.94 (0.84-0.99), respectively. CONCLUSIONS: The TP model had a good performance in predicting voxel-wise presence of recurrent tumor. Model-derived tumor risk levels achieved sensitivity and specificity similar to manual delineations in localizing recurrent tumor. Voxel-wise TP derived from mp-MRI can in this way be incorporated for target definition in focal salvage of radio-recurrent PCa.

Feasibility and accuracy of quantitative imaging on a 1.5 T MR-linear accelerator.

PURPOSE: Systems for magnetic resonance (MR-) guided radiotherapy enable daily MR imaging of cancer patients during treatment, which is of interest for treatment response monitoring and biomarker discovery using quantitative MRI (qMRI). Here, the performance of a 1.5 T MR-linac regarding qMRI was assessed on phantoms. Additionally, we show the feasibility of qMRI in a prostate cancer patient on this system for the first time. MATERIALS AND METHODS: Four 1.5 T MR-linac systems from four institutes were included in this study. T1 and T2 relaxation times, and apparent diffusion coefficient (ADC) maps, as well as dynamic contrast enhanced (DCE) images were acquired. Bland-Altman statistics were used, and accuracy, repeatability, and reproducibility were determined. RESULTS: Median accuracy for T1 ranged over the four systems from 2.7 to 14.3%, for T2 from 10.4 to 14.1%, and for ADC from 1.9 to 2.7%. For DCE images, the accuracy ranged from 12.8 to 35.8% for a gadolinium concentration of 0.5 mM and deteriorated for higher concentrations. Median short-term repeatability for T1 ranged from 0.6 to 5.1%, for T2 from 0.4 to 1.2%, and for ADC from 1.3 to 2.2%. DCE acquisitions showed a coefficient of variation of 0.1-0.6% in the signal intensity. Long-term repeatability was 1.8% for T1, 1.4% for T2, 1.7% for ADC, and 17.9% for DCE. Reproducibility was 11.2% for T1, 2.9% for T2, 2.2% for ADC, and 18.4% for DCE. CONCLUSION: These results indicate that qMRI on the Unity MR-linac is feasible, accurate, and repeatable which is promising for treatment response monitoring and treatment plan adaptation based on daily qMRI.

Improved repeatability of dynamic contrast-enhanced MRI using the complex MRI signal to derive arterial input functions: a test-retest study in prostate cancer patients.

PURPOSE: The arterial input function (AIF) is a major source of uncertainty in tracer kinetic (TK) analysis of dynamic contrast-enhanced (DCE)-MRI data. The aim of this study was to investigate the repeatability of AIFs extracted from the complex signal and of the resulting TK parameters in prostate cancer patients. METHODS: Twenty-two patients with biopsy-proven prostate cancer underwent a 3T MRI exam twice. DCE-MRI data were acquired with a 3D spoiled gradient echo sequence. AIFs were extracted from the magnitude of the signal (AIFMAGN ), phase (AIFPHASE ), and complex signal (AIFCOMPLEX ). The Tofts model was applied to extract Ktrans , kep and ve . Repeatability of AIF curve characteristics and TK parameters was assessed with the within-subject coefficient of variation (wCV). RESULTS: The wCV for peak height and full width at half maximum for AIFCOMPLEX (7% and 8%) indicated an improved repeatability compared to AIFMAGN (12% and 12%) and AIFPHASE (12% and 7%). This translated in lower wCV values for Ktrans (11%) with AIFCOMPLEX in comparison to AIFMAGN (24%) and AIFPHASE (15%). For kep , the wCV was 16% with AIFMAGN , 13% with AIFPHASE , and 13% with AIFCOMPLEX . CONCLUSION: Repeatability of AIFPHASE and AIFCOMPLEX is higher than for AIFMAGN , resulting in a better repeatability of TK parameters. Thus, use of either AIFPHASE or AIFCOMPLEX improves the robustness of quantitative analysis of DCE-MRI in prostate cancer.