Automated tracking of morphologic changes in weekly magnetic resonance imaging during head and neck radiotherapy.

BACKGROUND AND PURPOSE: Anatomic changes during head and neck radiotherapy can impact dose delivery, necessitate adaptive replanning, and indicate patient-specific response to treatment. We have developed an automated system to track these changes through longitudinal MRI scans to aid identification and clinical intervention. The purpose of this article is to describe this tracking system and present results from an initial cohort of patients. MATERIALS AND METHODS: The Automated Watchdog in Adaptive Radiotherapy Environment (AWARE) was developed to process longitudinal MRI data for radiotherapy patients. AWARE automatically identifies and collects weekly scans, propagates radiotherapy planning structures, computes structure changes over time, and reports important trends to the clinical team. AWARE also incorporates manual structure review and revision from clinical experts and dynamically updates tracking statistics when necessary. AWARE was applied to patients receiving weekly T2-weighted MRI scans during head and neck radiotherapy. Changes in nodal gross tumor volume (GTV) and parotid gland delineations were tracked over time to assess changes during treatment and identify early indicators of treatment response. RESULTS: N = 91 patients were tracked and analyzed in this study. Nodal GTVs and parotids both shrunk considerably throughout treatment (-9.7 ± 7.7% and -3.7 ± 3.3% per week, respectively). Ipsilateral parotids shrunk significantly faster than contralateral (-4.3 ± 3.1% vs. -2.9 ± 3.3% per week, p = 0.005) and increased in distance from GTVs over time (+2.7 ± 7.2% per week, p < 1 × 10-5 ). Automatic structure propagations agreed well with manual revisions (Dice = 0.88 ± 0.09 for parotids and 0.80 ± 0.15 for GTVs), but for GTVs the agreement degraded 4-5 weeks after the start of treatment. Changes in GTV volume observed by AWARE as early as one week into treatment were predictive of large changes later in the course (AUC = 0.79). CONCLUSION: AWARE automatically identified longitudinal changes in GTV and parotid volumes during radiotherapy. Results suggest that this system may be useful for identifying rapidly responding patients as early as one week into treatment.

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.

Extracting diffusion tensor fractional anisotropy and mean diffusivity from 3-direction DWI scans using deep learning.

PURPOSE: To develop and evaluate machine-learning methods that reconstruct fractional anisotropy (FA) values and mean diffusivities (MD) from 3-direction diffusion MRI (dMRI) acquisitions. METHODS: Two machine-learning models were implemented to map undersampled dMRI signals with high-quality FA and MD maps that were reconstructed from fully sampled DTI scans. The first model was a previously described multilayer perceptron (MLP), which maps signals and FA/MD values from a single voxel. The second was a convolutional neural network U-Net model, which maps dMRI slices to full FA/MD maps. Each method was trained on dMRI brain scans (N = 46), and reconstruction accuracies were compared with conventional linear-least-squares (LLS) reconstructions. RESULTS: In an independent testing cohort (N = 20), 3-direction U-Net reconstructions had significantly lower absolute FA error than both 3-direction MLP (U-Net3-dir : 0.06 ± 0.01 vs. MLP3-dir : 0.08 ± 0.01, P < 1 × 10-5 ) and 6-direction LLS (LLS6-dir : 0.09 ± 0.03, P = 1 × 10-5 ). The MD errors were not significantly different among 3-direction MLP (0.06 ± 0.01 × 10-3 mm2 /s), 3-direction U-Net (0.06 ± 0.01 × 10-3 mm2 /s), and 6-direction LLS (0.07 ± 0.02 × 10-3 mm2 /s, P > .1). CONCLUSION: The proposed U-Net model reconstructed FA from 3-direction dMRI scans with improved accuracy compared with both a previously described MLP approach and LLS fitting from 6-direction scans. The MD reconstruction accuracies did not differ significantly between reconstructions.

Prostate diffusion MRI with minimal echo time using eddy current nulled convex optimized diffusion encoding.

BACKGROUND: Prostate diffusion-weighted imaging (DWI) using monopolar encoding is sensitive to eddy-current-induced distortion artifacts. Twice-refocused bipolar encoding suppresses eddy current artifacts, but increases echo time (TE), leading to lower signal-to-noise ratio (SNR). Optimization of the diffusion encoding might improve prostate DWI. PURPOSE: To evaluate eddy current nulled convex optimized diffusion encoding (ENCODE) for prostate DWI with minimal TE. STUDY TYPE: Prospective cohort study. POPULATION: A diffusion phantom, an ex vivo prostate specimen, 10 healthy male subjects (27 ± 3 years old), and five prostate cancer patients (62 ± 7 years old). FIELD STRENGTH/SEQUENCE: 3T; single-shot spin-echo echoplanar DWI. ASSESSMENT: Eddy-current artifacts, TE, SNR, apparent diffusion coefficient (ADC), and image quality scores from three independent readers were compared between monopolar, bipolar, and ENCODE prostate DWI for standard-resolution (1.6 × 1.6 mm2 , partial Fourier factor [pF] = 6/8) and higher-resolution protocols (1.6 × 1.6 mm2 , pF = off; 1.0 × 1.0 mm2 , pF = 6/8). STATISTICAL TESTING: SNR and ADC differences between techniques were tested with Kruskal-Wallis and Wilcoxon signed-rank tests (P < 0.05 considered significant). RESULTS: Eddy current suppression with ENCODE was comparable to bipolar encoding (mean coefficient of variation across three diffusion directions of 9.4% and 9%). For a standard-resolution protocol, ENCODE achieved similar TE as monopolar and reduced TE by 14 msec compared to bipolar, resulting in 27% and 29% higher mean SNR in prostate transition zone (TZ) and peripheral zone (PZ) (P < 0.05) compared to bipolar, respectively. For higher-resolution protocols, ENCODE achieved the shortest TE (67 msec), with 17-21% and 58-70% higher mean SNR compared to monopolar (TE = 77 msec) and bipolar (TE = 102 msec) in PZ and TZ (P < 0.05). No significant differences were found in mean TZ (P = 0.91) and PZ ADC (P = 0.94) between the three techniques. ENCODE achieved similar or higher image quality scores than bipolar DWI in patients, with mean intraclass correlation coefficient of 0.77 for overall quality between three independent readers. DATA CONCLUSION: ENCODE minimizes TE (improves SNR) and reduces eddy-current distortion for prostate DWI compared to monopolar and bipolar encoding. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1526-1539.

Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign.

PURPOSE: The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. METHODS: Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × 10-3mm2/s (VADC>1.5), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. RESULTS: VADC>1.5 classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with VADC>1.5 ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign-all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited VADC>1.5 ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. CONCLUSION: Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with VADC>1.5 achieving > 90% classification specificity in both internal and validation cohorts. VADC>1.5 exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG.

Intracranial disease control for EGFR-mutant and ALK-rearranged lung cancer with large volume or symptomatic brain metastases.

PURPOSE/OBJECTIVE(S): Tyrosine kinase inhibitors (TKIs) are commonly employed for patients with brain metastases from lung cancer and specific driver mutations. We sought to identify the correlation between intracranial tumor burden and outcomes in patients with brain metastases treated with TKIs. MATERIALS/METHODS: We identified and retrospectively reviewed cases of EGFR-mutant or ALK-rearranged lung cancer with brain metastases at any time during their cancer course. Clinical characteristics and treatment information were abstracted from the medical records. Brain metastases were contoured to calculate total volume of disease at diagnosis and after initial therapy. High intracranial burden was defined as either > 10 brain metastases, volume of brain metastases > 15 cc, or largest lesion > 3 cm. Intracranial response was determined according to Response Assessment in Neuro-Oncology (RANO) criteria on the patient level. We determined the correlation between clinical and imaging characteristics and intracranial progression free survival (IC-PFS) and overall survival (OS). RESULTS: Fifty-seven patients with EGFR (n = 49) and ALK (n = 8) alterations were identified. Median follow-up from initial brain metastasis diagnosis was 17 months. Neurological symptoms were present in 54% at brain metastasis diagnosis. For those receiving TKIs alone or TKIs with radiation, at least a partial intracranial response (≥ 65% volume reduction) at 3 months from starting therapy was achieved in 94% and 58%. Progressive intracranial disease at 3 months occurred in 6.3% and 8.3%. Patients with high intracranial burden (n = 21) had a median 17 brain metastases, 6.5 cc volume, and 1.9 cm maximal tumor diameter. Median IC-PFS and OS for patients with high intracranial burden was 13.9 and 35.4 months. Patients with high intracranial burden and neurological symptoms at diagnosis had similar IC-PFS and OS compared to those with low burden and absence of neurological symptoms (p > 0.05 for each). CONCLUSION: Most patients receiving TKIs as part of their initial therapy achieve an early and durable volumetric intracranial response, irrespective of presenting disease burden or neurologic symptoms.

Normal tissue dose and risk estimates from whole and partial breast radiation techniques.

PURPOSE: To compare radiation dose to organs at risk in patients with early-stage breast cancer treated with lumpectomy and intraoperative radiation therapy with CT-guided HDR brachytherapy (precision breast IORT; PB-IORT) and those treated with external beam whole breast irradiation (WB-DIBH) or partial breast irradiation (PB-DIBH) with deep inspiratory breath hold. METHODS: We retrospectively identified 52 consecutive patients with left-sided breast cancers treated with either PB-IORT (n = 17, 76% outer breast) on a phase I clinical trial, adjuvant PB-DIBH (n = 18, 56% outer breast, 6% cavity boost), or WB-DIBH (n = 17, 76% outer breast, 53% with lumpectomy cavity boost). Conventional (2 Gy/fraction) or moderate hypofractionation (2.66 Gy/fraction) was prescribed for the external beam cohorts and 12.5 Gy in 1 fraction to 1 cm from the balloon surface was prescribed to the HDR brachytherapy cohort. CT-based planning was used for all patients. Organ at risk doses and excess risk ratios (ERR) for secondary lung cancers, contralateral breast cancers, and cardiac toxicity were compared between treatment techniques. RESULTS: Compared to WB-DIBH and PB-DIBH, PB-IORT resulted in lower ipsilateral lung V5, V10, V20, mean, and max dose (P < .05). Mean ipsilateral lung BED3Gy was as follows: 1.32 Gy for PB-IORT, 4.33 Gy for WB-DIBH, 3.35 Gy for PB-DIBH. The ERR for lung cancer was lowest for PB-IORT (P < .001). There was significantly higher contralateral breast max dose but lower mean BED3Gy for WB-DIBH compared with PB-IORT (P = .012, P = .011, respectively). Mean contralateral breast BED3Gy was as follows: 0.10 Gy for PB-IORT, 0.06 Gy for WB-DIBH, and 0.08 Gy for PB-DIBH. The ERR for contralateral breast cancer was low for all breast techniques, but WB-DIBH showed lower ERR compared to PB-IORT (P = .019). Mean heart BED2Gy was higher with PB-IORT at 1.26 Gy compared to 0.48 Gy and 0.24 Gy for WB-DIBH and PB-DIBH, respectively (P < .001). CONCLUSIONS: Patients with early-stage breast cancer treated with PB-IORT and with tissue-sparing external beam techniques all received low organ at risk doses, but PB-IORT resulted in far lower ipsilateral lung dose compared with external beam techniques. Our data indicate the lowest mean contralateral breast BED in the WB-DIBH group, likely due to the simplicity of the field design in low-risk patients using tangential whole breast radiation. External beam using DIBH results in lowest heart dose, but all techniques were well within recommended heart constraints.

Effect of flow-encoding strength on intravoxel incoherent motion in the liver.

PURPOSE: To study the impact of variable flow-encoding strength on intravoxel incoherent motion (IVIM) liver imaging of diffusion and perfusion. THEORY: Signal attenuation in DWI arises from (1) intravoxel microvascular blood flow, which depends on the flow-encoding strength α (first gradient moment) of the diffusion-encoding waveform, and (2) intravoxel spin diffusion, which depends on the b-value of the diffusion-encoding gradient waveforms α and b-value. Both are linked to the diffusion-encoding gradient waveform and conventionally are not independently controlled. METHODS: In this work a convex optimization framework was used to generate gradient waveforms with independent α and b-value. Thirty-six unique α and b-value sample points from 5 different gradient waveforms were used to reconstruct perfusion fraction (f), coefficient of diffusion (D), and blood velocity standard deviation (Vb ) maps using a recently proposed IVIM model. Faster acquisition strategies were evaluated with 1000 random subsampling strategies of 16, 8, and 4 α and b-value. Among the subsampled reconstructions, the sampling schemes that minimized the difference with the fully sampled reconstruction were reported. RESULTS: Healthy volunteers (N = 9) were imaged on a 3T scanner. Liver perfusion and diffusion estimates using the fully sampled IVIM method were f = 0.19 ± 0.06, D = 1.15 ± 0.15 × 10-3 mm2 /s, and Vb = 5.22 ± 3.86 mm/s. No statistical differences were found between the fully sampled and 2-times undersampled reconstruction (f = 0.2 ± 0.07, D = 1.19 ± 0.15 × 10-3 mm2 /s, Vb = 5.79 ± 3.43 mm/s); 4-times undersampled (f = 0.2 ± 0.06, D = 1.15 ± 0.17 × 10-3 mm2 /s, Vb = 4.66 ± 3.61 mm/s), or 8-times undersampled ( f = 0.2 ± 0.06, D = 1.23 ± 0.22 × 10-3 mm2 /s, Vb = 4.99 ± 3.82 mm/s) approaches. CONCLUSION: We demonstrate the IVIM signal's dependence on the b-value, the diffusion-encoding time and the flow-encoding strength and observe in vivo the ballistic regime signature of microperfusion in the liver. This work also demonstrates that using an IVIM model and sampling scheme matched to the ballistic regime, pixel-wise IVIM parameter maps are possible when sampling as few as 4 IVIM signals.

Time-optimized 4D phase contrast MRI with real-time convex optimization of gradient waveforms and fast excitation methods.

PURPOSE: To shorten 4D flow acquisitions by shortening TRs with fast RF pulses and gradient waveforms. Real-time convex optimization is used to generate these gradients waveforms on the scanner. THEORY AND METHODS: RF and slab-select waveforms were shortened with a minimum phase SLR excitation and the time-optimal variable-rate selective excitation method. Real-time convex optimization was used to shorten bipolar and spoiler gradients by finding the shortest gradient waveforms that satisfied constraints on scan parameters, gradient hardware, M0 , M1 , and peripheral nerve stimulation. Waveforms were calculated and TE and/or TR values were compared for a range of scan parameters and compared to a conventional 4D flow sequence. The method was tested in flow phantoms, and in the aorta and neurovasculature of volunteers (N = 10). Additionally, eddy current error was measured in a large phantom. RESULTS: TEs and TRs were shortened by 21-32% and 20-34%, respectively, compared to the conventional sequence over a range of scan parameters. Bland-Altman analysis of 2 flow phantom configurations showed flow rate bias of 0.3 mL/s and limits of agreement (LOA) of [-6.9, 7.5] mL/s for a cardiac phantom and a bias of -0.1 mL/s with LOA = [-0.4, 0.2] mL/s for a neuro phantom. Similar agreement was also seen for flow measurements in volunteers (bias = -1.0 and -0.1 mL/s, LOA = [-34.9, 33.0] and [-0.7, 0.6] mL/s). Measured eddy currents were 39% larger with the CVX + mpVERSE method. CONCLUSION: The real-time optimized 4D flow gradients and fast slab-selection excitation methods produced up to 34% faster TRs with excellent flow measurement agreement compared to a conventional 4D flow sequence.

Increased intratumoral infiltration in IDH wild-type lower-grade gliomas observed with diffusion tensor imaging.

PURPOSE: Diffuse lower grade gliomas (LGG) with isocitrate dehydrogenase (IDH) gene mutations (IDHMUT) have a distinct survival advantage compared with IDH wild-type (IDHWT) cases but the mechanism underlying this disparity is not well understood. Diffusion Tensor Imaging (DTI) has identified infiltrated non-enhancing tumor regions that are characterized by low isotropic (p) and high anisotropic (q) diffusion tensor components that associate with poor survival in glioblastoma. We hypothesized that similar regions are more prevalent in IDHWT (vs. IDHMUT) LGG. METHODS: p and q maps were reconstructed from preoperative DTI scans in N = 41 LGG patients with known IDH mutation and 1p/19q codeletion status. Enhancing and non-enhancing tumor volumes were autosegmented from standard (non-DTI) MRI scans. Percentage non-enhancing tumor volumes exhibiting low p and high q (Vinf) were then determined using threshold values (p = 2 × 10-3mm2/s, q = 3 × 10-4 mm2/s) and compared between IDHWT and IDHMUT LGG, and between IDHMUT LGG with and without 1p/19q codeletion. RESULTS: Vinf volumes were significantly larger in IDHWT LGG than in IDHMUT LGG (35.4 ± 18.3% vs. 15.9 ± 7.6%, P < 0.001). Vinf volumes did not significantly differ between IDHMUT LGG with and without 1p/19q codeletion (17.1 ± 9.5% vs. 14.8 ± 5.8%, P = 1.0). CONCLUSION: IDHWT LGG exhibited larger volumes with suppressed isotropic diffusion (p) and high anisotropic diffusion (q) which reflects regions with increased cell density but non-disrupted neuronal structures. This may indicate a greater prevalence of infiltrative tumor in IDHWT LGG.

Eddy current-nulled convex optimized diffusion encoding (EN-CODE) for distortion-free diffusion tensor imaging with short echo times.

PURPOSE: To design and evaluate eddy current-nulled convex optimized diffusion encoding (EN-CODE) gradient waveforms for efficient diffusion tensor imaging (DTI) that is free of eddy current-induced image distortions. METHODS: The EN-CODE framework was used to generate diffusion-encoding waveforms that are eddy current-compensated. The EN-CODE DTI waveform was compared with the existing eddy current-nulled twice refocused spin echo (TRSE) sequence as well as monopolar (MONO) and non-eddy current-compensated CODE in terms of echo time (TE) and image distortions. Comparisons were made in simulations, phantom experiments, and neuro imaging in 10 healthy volunteers. RESULTS: The EN-CODE sequence achieved eddy current compensation with a significantly shorter TE than TRSE (78 versus 96 ms) and a slightly shorter TE than MONO (78 versus 80 ms). Intravoxel signal variance was lower in phantoms with EN-CODE than with MONO (13.6 ± 11.6 versus 37.4 ± 25.8) and not different from TRSE (15.1 ± 11.6), indicating good robustness to eddy current-induced image distortions. Mean fractional anisotropy values in brain edges were also significantly lower with EN-CODE than with MONO (0.16 ± 0.01 versus 0.24 ± 0.02, P < 1 x 10-5 ) and not different from TRSE (0.16 ± 0.01 versus 0.16 ± 0.01, P = nonsignificant). CONCLUSIONS: The EN-CODE sequence eliminated eddy current-induced image distortions in DTI with a TE comparable to MONO and substantially shorter than TRSE. Magn Reson Med 79:663-672, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Simultaneous measurement of T2 and apparent diffusion coefficient (T2 +ADC) in the heart with motion-compensated spin echo diffusion-weighted imaging.

PURPOSE: To evaluate a technique for simultaneous quantitative T2 and apparent diffusion coefficient (ADC) mapping in the heart (T2 +ADC) using spin echo (SE) diffusion-weighted imaging (DWI). THEORY AND METHODS: T2 maps from T2 +ADC were compared with single-echo SE in phantoms and with T2 -prepared (T2 -prep) balanced steady-state free precession (bSSFP) in healthy volunteers. ADC maps from T2 +ADC were compared with conventional DWI in phantoms and in vivo. T2 +ADC was also demonstrated in a patient with acute myocardial infarction (MI). RESULTS: Phantom T2 values from T2 +ADC were closer to a single-echo SE reference than T2 -prep bSSFP (-2.3 ± 6.0% vs 22.2 ± 16.3%; P < 0.01), and ADC values were in excellent agreement with DWI (0.28 ± 0.4%). In volunteers, myocardial T2 values from T2 +ADC were significantly shorter than T2 -prep bSSFP (35.8 ± 3.1 vs 46.8 ± 3.8 ms; P < 0.01); myocardial ADC was not significantly (N.S.) different between T2 +ADC and conventional motion-compensated DWI (1.39 ± 0.18 vs 1.38 ± 0.18 mm2 /ms; P = N.S.). In the patient, T2 and ADC were both significantly elevated in the infarct compared with remote myocardium (T2 : 40.4 ± 7.6 vs 56.8 ± 22.0; P < 0.01; ADC: 1.47 ± 0.59 vs 1.65 ± 0.65 mm2 /ms; P < 0.01). CONCLUSION: T2 +ADC generated coregistered, free-breathing T2 and ADC maps in healthy volunteers and a patient with acute MI with no cost in accuracy, precision, or scan time compared with DWI. Magn Reson Med 79:654-662, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Sympathetic modulation of electrical activation in normal and infarcted myocardium: implications for arrhythmogenesis.

The influence of cardiac sympathetic innervation on electrical activation in normal and chronically infarcted ventricular myocardium is not understood. Yorkshire pigs with normal hearts (NL, n = 12) or anterior myocardial infarction (MI, n = 9) underwent high-resolution mapping of the anteroapical left ventricle at baseline and during left and right stellate ganglion stimulation (LSGS and RSGS, respectively). Conduction velocity (CV), activation times (ATs), and directionality of propagation were measured. Myocardial fiber orientation was determined using diffusion tensor imaging and histology. Longitudinal CV (CVL) was increased by RSGS (0.98 ± 0.11 vs. 1.2 ± 0.14m/s, P < 0.001) but not transverse CV (CVT). This increase was abrogated by ß-adrenergic receptor and gap junction (GJ) blockade. Neither CVL nor CVT was increased by LSGS. In the peri-infarct region, both RSGS and LSGS shortened ARIs in sinus rhythm (423 ± 37 vs. 322 ± 30 ms, P < 0.001, and 423 ± 36 vs. 398 ± 36 ms, P = 0.035, respectively) and altered activation patterns in all animals. CV, as estimated by mean ATs, increased in a directionally dependent manner by RSGS (14.6 ± 1.2 vs. 17.3 ± 1.6 ms, P = 0.015), associated with GJ lateralization. RSGS and LSGS inhomogeneously modulated AT and induced relative or absolute functional activation delay in parts of the mapped regions in 75 and 67%, respectively, in MI animals, and in 0 and 15%, respectively, in control animals (P < 0.001 for both). In conclusion, sympathoexcitation increases CV in normal myocardium and modulates activation propagation in peri-infarcted ventricular myocardium. These data demonstrate functional control of arrhythmogenic peri-infarct substrates by sympathetic nerves and in part explain the temporal nature of arrhythmogenesis.NEW & NOTEWORTHY This study demonstrates regional control of conduction velocity in normal hearts by sympathetic nerves. In infarcted hearts, however, not only is modulation of propagation heterogeneous, some regions showed paradoxical conduction slowing. Sympathoexcitation altered propagation in all infarcted hearts studied, and we describe the temporal arrhythmogenic potential of these findings.Listen to this article's corresponding podcast at http://ajpheart.podbean.com/e/sympathetic-nerves-and-cardiac-propagation/.

Convex optimized diffusion encoding (CODE) gradient waveforms for minimum echo time and bulk motion-compensated diffusion-weighted MRI.

PURPOSE: To evaluate convex optimized diffusion encoding (CODE) gradient waveforms for minimum echo time and bulk motion-compensated diffusion-weighted imaging (DWI). METHODS: Diffusion-encoding gradient waveforms were designed for a range of b-values and spatial resolutions with and without motion compensation using the CODE framework. CODE, first moment (M1 ) nulled CODE-M1 , and first and second moment (M2 ) nulled CODE-M1 M2 were used to acquire neuro, liver, and cardiac ADC maps in healthy subjects (n=10) that were compared respectively to monopolar (MONO), BIPOLAR (M1 = 0), and motion-compensated (MOCO, M1 + M2 = 0) diffusion encoding. RESULTS: CODE significantly improved the SNR of neuro ADC maps compared with MONO (19.5 ± 2.5 versus 14.5 ± 1.9). CODE-M1 liver ADCs were significantly lower (1.3 ± 0.1 versus 1.8 ± 0.3 × 10-3 mm2 /s, ie, less motion corrupted) and more spatially uniform (6% versus 55% ROI difference) than MONO and had higher SNR than BIPOLAR (SNR = 14.9 ± 5.3 versus 8.0 ± 3.1). CODE-M1 M2 cardiac ADCs were significantly lower than MONO (1.9 ± 0.6 versus 3.8 ± 0.3 x10-3 mm2 /s) throughout the cardiac cycle and had higher SNR than MOCO at systole (9.1 ± 3.9 versus 7.0 ± 2.6) while reporting similar ADCs (1.5 ± 0.2 versus 1.4 ± 0.6 × 10-3 mm2 /s). CONCLUSIONS: CODE significantly improved SNR for ADC mapping in the brain, liver and heart, and significantly improved DWI bulk motion robustness in the liver and heart. Magn Reson Med 77:717-729, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Predicting radiation-induced immune suppression in lung cancer patients treated with stereotactic body radiation therapy.

BACKGROUND: Stereotactic body radiation therapy (SBRT) is known to modulate the immune system and contribute to the generation of anti-tumor T cells and stimulate T cell infiltration into tumors. Radiation-induced immune suppression (RIIS) is a side effect of radiation therapy that can decrease immunological function by killing naive T cells as well as SBRT-induced newly created effector T cells, suppressing the immune response to tumors and increasing susceptibility to infections. PURPOSE: RIIS varies substantially among patients and it is currently unclear what drives this variability. Models that can accurately predict RIIS in near real time based on treatment plan characteristics would allow treatment planners to maintain current protocol specific dosimetric criteria while minimizing immune suppression. In this paper, we present an algorithm to predict RIIS based on a model of circulating blood using early stage lung cancer patients treated with SBRT. METHODS: This Python-based algorithm uses DICOM data for radiation therapy treatment plans, dose maps, patient CT data sets, and organ delineations to stochastically simulate blood flow and predict the doses absorbed by circulating lymphocytes. These absorbed doses are used to predict the fraction of lymphocytes killed by a given treatment plan. Finally, the time dependence of absolute lymphocyte count (ALC) following SBRT is modeled using longitudinal blood data up to a year after treatment. This model was developed and evaluated on a cohort of 64 patients with 10-fold cross validation. RESULTS: Our algorithm predicted post-treatment ALC with an average error of 0.24 ± 0.21 × 10 9 $0.24 \pm 0.21 \times {10}^9$ cells/L with 89% of the patients having a prediction error below 0.5 × 109 cells/L. The accuracy was consistent across a wide range of clinical and treatment variables. Our model is able to predict post-treatment ALC < 0.8 (grade 2 lymphopenia), with a sensitivity of 81% and a specificity of 98%. This model has a ∼38-s end-to-end prediction time of post treatment ALC. CONCLUSION: Our model performed well in predicting RIIS in patients treated using lung SBRT. With near-real time model prediction time, it has the capability to be interfaced with treatment planning systems to prospectively reduce immune cell toxicity while maintaining national SBRT conformity and plan quality criteria.

Reduced-distortion diffusion weighted imaging for head and neck radiotherapy.

Background and purpose: Quantitative Diffusion Weighted Imaging (DWI) has potential value in guiding head and neck (HN) cancer radiotherapy. However, clinical translation has been hindered by severe distortions in standard single-shot Echo-Planar-Imaging (ssEPI) and prolonged scan time and low SNR in Turbo-Spin-Echo (ssTSE) sequences. In this study, we evaluate "multi-shot" (ms) msEPI and msTSE acquisitions in the context of HN radiotherapy. Materials and methods: ssEPI, ssTSE, msEPI with 2 and 3 shots (2sEPI, 3sEPI), and msTSE DWI were acquired in a phantom, healthy volunteers (N=10), and patients with HN cancer (N=5) on a 3-Tesla wide-bore MRI in radiotherapy simulation RF coil setup, with matched spatial resolution (2x2x5mm) and b = 0, 200, 800 s/mm2.Geometric distortions measured with deformable vector field (DVF) and contour analysis, apparent diffusion coefficient (ADC) values, and signal-to-noise-ratio efficiency (SNReff) were quantified for all scans. Results: All techniques significantly (P<1x10-3) reduced distortions compared with ssEPI (DVFmean = 3.1 ± 1.3 mm). Distortions were marginally lower for msTSE (DVFmean = 1.5 ± 0.6 mm) than ssTSE (1.8 ± 0.9 mm), but were slightly higher with 2sEPI and 3sEPI (2.6 ± 1.0 mm, 2.2 ± 1.0 mm). SNReff reduced with decreasing distortion with ssEPI=21.9 ± 7.9, 2sEPI=15.1 ± 5.0, 3sEPI=12.1 ± 4.5, ssTSE=6.0 ± 1.6, and msTSE=5.7 ± 1.9 for b = 0 images. Phantom ADC values were consistent across all protocols (errors ≤ 0.03x10-3mm2/s), but in vivo ADC values were âˆ¼ 4 % lower with msEPI and âˆ¼ 12 % lower with ssTSE/msTSE compared with ssEPI. Conclusions: msEPI and TSE acquisitions exhibited improved geometric distortion at the cost of SNReff and scan time. While msTSE exhibited the least distortion, 3sEPI may offer an appealing middle-ground with improved geometric fidelity but superior efficiency and in vivo ADC quantification.

Multi-modality imaging parameters that predict rapid tumor regression in head and neck radiotherapy.

Background and purpose: Volume regression during radiotherapy can indicate patient-specific treatment response. We aimed to identify pre-treatment multimodality imaging (MMI) metrics from positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT) that predict rapid tumor regression during radiotherapy in human papilloma virus (HPV) associated oropharyngeal carcinoma. Materials and methods: Pre-treatment FDG PET-CT, diffusion-weighted MRI (DW-MRI), and intra-treatment (at 1, 2, and 3 weeks) MRI were acquired in 72 patients undergoing chemoradiation therapy for HPV+ oropharyngeal carcinoma. Nodal gross tumor volumes were delineated on longitudinal images to measure intra-treatment volume changes. Pre-treatment PET standardized uptake value (SUV), CT Hounsfield Unit (HU), and non-gaussian intravoxel incoherent motion DW-MRI metrics were computed and correlated with volume changes. Intercorrelations between MMI metrics were also assessed using network analysis. Validation was carried out on a separate cohort (N = 64) for FDG PET-CT. Results: Significant correlations with volume loss were observed for baseline FDG SUVmean (Spearman ρ = 0.46, p < 0.001), CT HUmean (ρ = 0.38, p = 0.001), and DW-MRI diffusion coefficient, Dmean (ρ = -0.39, p < 0.001). Network analysis revealed 41 intercorrelations between MMI and volume loss metrics, but SUVmean remained a statistically significant predictor of volume loss in multivariate linear regression (p = 0.01). Significant correlations were also observed for SUVmean in the validation cohort in both primary (ρ = 0.30, p = 0.02) and nodal (ρ = 0.31, p = 0.02) tumors. Conclusions: Multiple pre-treatment imaging metrics were correlated with rapid nodal gross tumor volume loss during radiotherapy. FDG-PET SUV in particular exhibited significant correlations with volume regression across the two cohorts and in multivariate analysis.

Clinical Experience With an Offline Adaptive Radiation Therapy Head and Neck Program: Dosimetric Benefits and Opportunities for Patient Selection.

PURPOSE: The objective of this study was to develop a linear accelerator (LINAC)-based adaptive radiation therapy (ART) workflow for the head and neck that is informed by automated image tracking to identify major anatomic changes warranting adaptation. In this study, we report our initial clinical experience with the program and an investigation into potential trigger signals for ART. METHODS AND MATERIALS: Offline ART was systematically performed on patients receiving radiation therapy for head and neck cancer on C-arm LINACs. Adaptations were performed at a single time point during treatment with resimulation approximately 3 weeks into treatment. Throughout treatment, all patients were tracked using an automated image tracking system called the Automated Watchdog for Adaptive Radiotherapy Environment (AWARE). AWARE measures volumetric changes in gross tumor volumes (GTVs) and selected normal tissues via cone beam computed tomography scans and deformable registration. The benefit of ART was determined by comparing adaptive plan dosimetry and normal tissue complication probabilities against the initial plans recalculated on resimulation computed tomography scans. Dosimetric differences were then correlated with AWARE-measured volume changes to identify patient-specific triggers for ART. Candidate trigger variables were evaluated using receiver operator characteristic analysis. RESULTS: In total, 46 patients received ART in this study. Among these patients, we observed a significant decrease in dose to the submandibular glands (mean ± standard deviation: -219.2 ± 291.2 cGy, P < 10-5), parotids (-68.2 ± 197.7 cGy, P = .001), and oral cavity (-238.7 ± 206.7 cGy, P < 10-5) with the adaptive plan. Normal tissue complication probabilities for xerostomia computed from mean parotid doses also decreased significantly with the adaptive plans (P = .008). We also observed systematic intratreatment volume reductions (ΔV) for GTVs and normal tissues. Candidate triggers were identified that predicted significant improvement with ART, including parotid ΔV = 7%, neck ΔV = 2%, and nodal GTV ΔV = 29%. CONCLUSIONS: Systematic offline head and neck ART was successfully deployed on conventional LINACs and reduced doses to critical salivary structures and the oral cavity. Automated cone beam computed tomography tracking provided information regarding anatomic changes that may aid patient-specific triggering for ART.

Automatic segmentation for magnetic resonance imaging guided individual elective lymph node irradiation in head and neck cancer patients.

Background and purpose: In head and neck squamous cell carcinoma (HNSCC) patients, the radiation dose to nearby organs at risk can be reduced by restricting elective neck irradiation from lymph node levels to individual lymph nodes. However, manual delineation of every individual lymph node is time-consuming and error prone. Therefore, automatic magnetic resonance imaging (MRI) segmentation of individual lymph nodes was developed and tested using a convolutional neural network (CNN). Materials and methods: In 50 HNSCC patients (UMC-Utrecht), individual lymph nodes located in lymph node levels Ib-II-III-IV-V were manually segmented on MRI by consensus of two experts, obtaining ground truth segmentations. A 3D CNN (nnU-Net) was trained on 40 patients and tested on 10. Evaluation metrics were Dice Similarity Coefficient (DSC), recall, precision, and F1-score. The segmentations of the CNN was compared to segmentations of two observers. Transfer learning was used with 20 additional patients to re-train and test the CNN in another medical center. Results: nnU-Net produced automatic segmentations of elective lymph nodes with median DSC: 0.72, recall: 0.76, precision: 0.78, and F1-score: 0.78. The CNN had higher recall compared to both observers (p = 0.002). No difference in evaluation scores of the networks in both medical centers was found after re-training with 5 or 10 patients. Conclusion: nnU-Net was able to automatically segment individual lymph nodes on MRI. The detection rate of lymph nodes using nnU-Net was higher than manual segmentations. Re-training nnU-Net was required to successfully transfer the network to the other medical center.