Evaluating the relationship between magnetic resonance image quality metrics and deep learning-based segmentation accuracy of brain tumors.

BACKGROUND: Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal-to-noise, contrast-to-noise) and segmentation accuracy. PURPOSE: Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL-based brain tumor segmentation accuracy toward developing more generalizable models for multi-institutional data. METHODS: We trained a 3D DenseNet model on the BraTS 2020 cohorts for segmentation of tumor subregions enhancing tumor (ET), peritumoral edematous, and necrotic and non-ET on MRI; with performance quantified via a 5-fold cross-validated Dice coefficient. MRI scans were evaluated through the open-source quality control tool MRQy, to yield 13 IQMs per scan. The Pearson correlation coefficient was computed between whole tumor (WT) dice values and IQM measures in the training cohorts to identify quality measures most correlated with segmentation performance. Each selected IQM was used to group MRI scans as "better" quality (BQ) or "worse" quality (WQ), via relative thresholding. Segmentation performance was re-evaluated for the DenseNet model when (i) training on BQ MRI images with validation on WQ images, as well as (ii) training on WQ images, and validation on BQ images. Trends were further validated on independent test sets derived from the BraTS 2021 training cohorts. RESULTS: For this study, multimodal MRI scans from the BraTS 2020 training cohorts were used to train the segmentation model and validated on independent test sets derived from the BraTS 2021 cohort. Among the selected IQMs, models trained on BQ images based on inhomogeneity measurements (coefficient of variance, coefficient of joint variation, coefficient of variation of the foreground patch) and the models trained on WQ images based on noise measurement peak signal-to-noise ratio (SNR) yielded significantly improved tumor segmentation accuracy compared to their inverse models. CONCLUSIONS: Our results suggest that a significant correlation may exist between specific MR IQMs and DenseNet-based brain tumor segmentation performance. The selection of MRI scans for model training based on IQMs may yield more accurate and generalizable models in unseen validation.

A model combining pretreatment MRI radiomic features and tumor-infiltrating lymphocytes to predict response to neoadjuvant systemic therapy in triple-negative breast cancer.

PURPOSE: We aimed to develop a predictive model based on pretreatment MRI radiomic features (MRIRF) and tumor-infiltrating lymphocyte (TIL) levels, an established prognostic marker, to improve the accuracy of predicting pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) in triple-negative breast cancer (TNBC) patients. METHODS: This Institutional Review Board (IRB) approved retrospective study included a preliminary set of 80 women with biopsy-proven TNBC who underwent NAST, pretreatment dynamic contrast enhanced MRI, and biopsy-based pathologic assessment of TIL. A threshold of ≥ 20% was used to define high TIL. Patients were classified into pCR and non-pCR based on pathologic evaluation of post-NAST surgical specimens. pCR was defined as the absence of invasive carcinoma in the surgical specimen. Segmentation and MRIRF extraction were done using a Food and Drug Administration (FDA) approved software QuantX. The top five features were combined into a single MRIRF signature value. RESULTS: Of 145 extracted MRIRF, 38 were significantly correlated with pCR. Five nonredundant imaging features were identified: volume, uniformity, peak timepoint variance, homogeneity, and variance. The accuracy of the MRIRF model, P = .001, 72.7% positive predictive value (PPV), 72.0% negative predictive value (NPV), was similar to the TIL model (P = .038, 65.5% PPV, 72.6% NPV). When MRIRF and TIL models were combined, we observed improved prognostic accuracy (P < .001, 90.9% PPV, 81.4% NPV). The models area under the receiver operating characteristic curve (AUC) was 0.632 (TIL), 0.712 (MRIRF) and 0.752 (TIL + MRIRF). CONCLUSION: A predictive model combining pretreatment MRI radiomic features with TIL level on pretreatment core biopsy improved accuracy in predicting pCR to NAST in TNBC patients.

Temperature mapping of exothermic in situ chemistry: imaging of thermoembolization via MR.

Purpose: MR temperature imaging (MRTI) was employed for visualizing the spatiotemporal evolution of the exotherm of thermoembolization, an investigative transarterial treatment for solid tumors. Materials and methods: Five explanted kidneys were injected with thermoembolic solutions, and monitored by MRTI. In three nonselective experiments, 5 ml of 4 mol/l dichloroacetyl chloride (DCA-Cl) solution in a hydrocarbon vehicle was injected via the main renal artery. For two of these three, MRTI temperature data were compared to fiber optic thermal probes. Another two kidneys received selective injections, treating only portions of the kidneys with 1 ml of 2 mol/l DCA-Cl. MRTI data were acquired and compared to changes in pre- and post-injection CT. Specimens were bisected and photographed for gross pathology 24 h post-procedure. Results: MRTI temperature estimates were within ±1 °C of the probes. In experiments without probes, MRTI measured increases of 30 °C. Some regions had not reached peak temperature by the end of the >18 min acquisition. MRTI indicated the initial heating occurred in the renal cortex, gradually spreading more proximally toward the main renal artery. Gross pathology showed the nonselective injection denatured the entire kidney whereas in the selective injections, only the treated territory was coagulated. Conclusion: The spatiotemporal evolution of thermoembolization was visualized for the first time using noninvasive MRTI, providing unique insight into the thermodynamics of thermoembolization. Précis Thermoembolization is being investigated as a novel transarterial treatment. In order to begin to characterize delivery of this novel treatment modality and aid translation from the laboratory to patients, we employ MR temperature imaging to visualize the spatiotemporal distribution of temperature from thermoembolization in ex vivo tissue.

Referenceless magnetic resonance temperature imaging using Gaussian process modeling.

PURPOSE: During magnetic resonance (MR)-guided thermal therapies, water proton resonance frequency shift (PRFS)-based MR temperature imaging can quantitatively monitor tissue temperature changes. It is widely known that the PRFS technique is easily perturbed by tissue motion, tissue susceptibility changes, magnetic field drift, and modality-dependent applicator-induced artifacts. Here, a referenceless Gaussian process modeling (GPM)-based estimation of the PRFS is investigated as a methodology to mitigate unwanted background field changes. The GPM offers a complementary trade-off between data fitting and smoothing and allows prior information to be used. The end result being the GPM provides a full probabilistic prediction and an estimate of the uncertainty. METHODS: GPM was employed to estimate the covariance between the spatial position and MR phase measurements. The mean and variance provided by the statistical model extrapolated background phase values from nonheated neighboring voxels used to train the model. MR phase predictions in the heating ROI are computed using the spatial coordinates as the test input. The method is demonstrated in ex vivo rabbit liver tissue during focused ultrasound heating with manually introduced perturbations (n = 6) and in vivo during laser-induced interstitial thermal therapy to treat the human brain (n = 1) and liver (n = 1). RESULTS: Temperature maps estimated using the GPM referenceless method demonstrated a RMS error of <0.8°C with artifact-induced reference-based MR thermometry during ex vivo heating using focused ultrasound. Nonheated surrounding areas were <0.5°C from the artifact-free MR measurements. The GPM referenceless MR temperature values and thermally damaged regions were within the 95% confidence interval during in vivo laser ablations. CONCLUSIONS: A new approach to estimation for referenceless PRFS temperature imaging is introduced that allows for an accurate probabilistic extrapolation of the background phase. The technique demonstrated reliable temperature estimates in the presence of the background phase changes and was demonstrated useful in the in vivo brain and liver ablation scenarios presented.

Velocity navigator for motion compensated thermometry.

OBJECT: Proton resonance frequency shift thermometry is sensitive to breathing motion that leads to incorrect phase differences. In this work, a novel velocity-sensitive navigator technique for triggering MR thermometry image acquisition is presented. MATERIALS AND METHODS: A segmented echo planar imaging pulse sequence was modified for velocity-triggered temperature mapping. Trigger events were generated when the estimated velocity value was less than 0.2 cm/s during the slowdown phase in parallel to the velocity-encoding direction. To remove remaining high-frequency spikes from pulsation in real time, a Kalman filter was applied to the velocity navigator data. A phantom experiment with heating and an initial volunteer experiment without heating were performed to show the applicability of this technique. Additionally, a breath-hold experiment was conducted for comparison. RESULTS: A temperature rise of ΔT = +37.3°C was seen in the phantom experiment, and a root mean square error (RMSE) outside the heated region of 2.3°C could be obtained for periodic motion. In the volunteer experiment, a RMSE of 2.7°C/2.9°C (triggered vs. breath hold) was measured. CONCLUSION: A novel velocity navigator with Kalman filter postprocessing in real time significantly improves the temperature accuracy over non-triggered acquisitions and suggests being comparable to a breath-held acquisition. The proposed technique might be clinically applied for monitoring of thermal ablations in abdominal organs.

Quantitative comparison of thermal dose models in normal canine brain.

PURPOSE: Minimally invasive thermal ablative therapies as alternatives to conventional surgical management of solid tumors and other pathologies is increasing owing to the potential benefits of performing these procedures in an outpatient setting with reduced complications and comorbidity. Magnetic resonance temperature imaging (MRTI) measurement allows existing thermal dose models to use the spatiotemporal temperature history to estimate the thermal damage to tissue. However, the various thermal dose models presented in the literature employ different parameters and thresholds, affecting the reliability of thermal dosimetry. In this study, the authors quantitatively compared three thermal dose models (Arrhenius rate process, CEM43, and threshold temperature) using the dice similarity coefficient (DSC). METHODS: The DSC was used to compare the spatial overlap between the region of thermal damage as predicted by the models for in vivo normal canine brain during thermal therapy to the region of thermal damage as revealed by contrast-enhanced T1-weighted images acquired immediately after therapy (< 20 min). The outer edge of the hyperintense rim of the ablation region was used as the surrogate marker for the limits of thermal coagulation. The DSC was also used to investigate the impact of varying the thresholds on each models' ability to predict the zone of thermal necrosis. RESULTS: At previously reported thresholds, the authors found that all three models showed good agreement (defined as DSC > 0.7) with post-treatment imaging. All three models examined across the range of commonly applied thresholds consistently showed highly accurate spatial overlap, low variability, and little dependence on temperature uncertainty. DSC values corresponding to cited thresholds were not significantly different from peak DSC values. CONCLUSIONS: Thus, the authors conclude that the all three thermal dose models can be used as a reliable surrogate for postcontrast tissue damage verification imaging in rapid ablation procedures and can also be used to enhance the capability of MRTI to control thermal therapy in real time.