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Technical development and implementation of 3D-QALAS on a 1.5T MR-Linac for the brain: A prospective R-IDEAL stage 0/1 technology development report.

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The authors' code

Jupyter notebook · 334 lines · 13 KB · GPL-3.0

  1. # %% [markdown]
  2. # # Import necessary packages
  4. # %%
  5. from collections import Counter
  6. import os
  8. import cmcrameri.cm as cm_ref
  9. import matplotlib.pyplot as plt
  10. plt.rcParams['font.family'] = 'sans-serif'
  11. plt.rcParams['font.sans-serif'] = ['Arial']
  12. import nibabel as nib
  13. import numpy as np
  16. # %% [markdown]
  17. # # Define global variables
  19. # %%
  20. BASE_PATH = 'data'
  21. CMAPS = {
  22. 'T1': cm_ref.lipari,
  23. 'T2': cm_ref.navia,
  24. 'PD': cm_ref.glasgow
  25. }
  26. UNIT_TYPES = {
  27. 'T1': 'ms',
  28. 'T2': 'ms',
  29. 'PD': 'pu'
  30. }
  31. MIN_RANGE_CLINICAL = {
  32. 'T1': 200,
  33. 'T2': 35,
  34. 'PD': 20
  35. }
  36. MAX_RANGE_CLINICAL = {
  37. 'T1': 2500,
  38. 'T2': 120,
  39. 'PD': 120
  40. }
  41. NIST_REF_ENVIRONMENT = '3T-20C'
  42. NIST_REF_VALUES = {
  43. 'T1-vials': {
  44. 'T1-values': {
  45. '3T-20C': np.array([
  46. 1990.4, 1434.6, 1018.4, 743.3, 532.2, 382.1, 270.3, 194.2, 137.4, 94.3, 67.3, 48.3, 34, 24.4
  47. ])
  48. },
  49. 'T2-values': {
  50. '3T-20C': np.array([
  51. 1573.7, 1102.5, 761.6, 551.9, 396.3, 278.8, 196.5, 140.5, 98.9, 67.8, 48.2, 34.7, 24.3, 17.5
  52. ])
  53. }
  54. },
  55. 'T2-vials': {
  56. 'T1-values': {
  57. '3T-20C': np.array([
  58. 2406.3, 2152.6, 1879.5, 1605.8, 1291.9, 1062.7, 811.8, 633.9, 496.3, 326, 254.9, 185.7, 130.4, 94.9
  59. ])
  60. },
  61. 'T2-values': {
  62. '3T-20C': np.array([
  63. 621.6, 433.4, 288.3, 199.1, 131.8, 96.4, 65.6, 47.5, 35.2, 21.7, 16.5, 11.8, 8.1, 5.8
  64. ])
  65. }
  66. },
  67. 'PD-vials': {
  68. 'PD-values': {
  69. '3T-20C': np.array([
  70. 5.1, 10.2, 15, 20.7, 25.5, 30, 34.9, 39.8, 48.4, 59.1, 68.5, 78.3, 90.6, 99.5
  71. ])
  72. }
  73. }
  74. }
  75. # 3T -> 1.5T conversion factor?
  76. T1_15T = np.array([1741, 1270, 962, 685, 487, 346, 246, 179, 124, 88, 62, 44, 31, 22])
  77. T2_15T = np.array([1540, 1130, 845, 617, 430, 306, 218, 159, 110, 77, 54, 39, 28, 20])
  78. T1_3T = np.array([1884, 1330, 987, 690, 485, 342, 241, 175, 121, 86, 60, 43, 30, 21])
  79. T2_3T = np.array([1489, 1026, 737, 521, 354, 248, 175, 126, 87, 61, 42, 31, 22, 15])
  80. [m_T1,b_T1] = np.polyfit(T1_3T, T1_15T, 1)
  81. [m_T2,b_T2] = np.polyfit(T2_3T, T2_15T, 1)
  82. CONVERSION_FACTOR = {
  83. 'T1': (m_T1, b_T1),
  84. 'T2': (m_T2, b_T2),
  85. 'PD': (1, 0)
  86. }
  87. print(f"T1_3T = {CONVERSION_FACTOR['T1'][0]:.2f} * T1_1.5T + {CONVERSION_FACTOR['T1'][1]:.2f}")
  88. print(f"T2_3T = {CONVERSION_FACTOR['T2'][0]:.2f} * T2_1.5T + {CONVERSION_FACTOR['T2'][1]:.2f}")
  89. print(f"PD_3T = PD_1.5T")
  92. # %% [markdown]
  93. # # Define the functions used
  95. # %%
  96. def LCCC_score(x, y):
  97. """
  98. Lin's Concordance Correlation Coefficient
  100. """
  101. sxy = np.sum((x - x.mean())*(y - y.mean()))/x.shape[0]
  102. rhoc = 2*sxy / (np.var(x) + np.var(y) + (x.mean() - y.mean())**2)
  103. return rhoc
  106. # %% [markdown]
  107. # # Extract the vial values
  109. # %%
  110. n_sessions = 5
  111. n_reps = 2
  112. all_vial_values = {}
  115. for ref_type in ['T1','T2','PD']:
  116. all_vial_values[ref_type] = {
  117. 'mean': [],
  118. 'std': []
  119. }
  120. ref_vals = NIST_REF_VALUES[f'{ref_type}-vials'][f'{ref_type}-values'][NIST_REF_ENVIRONMENT]
  121. ref_vals = CONVERSION_FACTOR[ref_type][0] * ref_vals + CONVERSION_FACTOR[ref_type][1]
  122. all_vial_values[ref_type]['ref'] = ref_vals[::-1]
  124. for temp_session in range(1,n_sessions+1,1):
  125. vials_mask = nib.load(os.path.join(BASE_PATH, 'phantom', f'session{temp_session}', 'segmentation_vials.nii'))
  126. vials_mask = np.flip(np.transpose(vials_mask.get_fdata(), (2,1,0)), axis=0)
  127. temp_vals = {
  128. 'mean': [],
  129. 'std': []
  130. }
  132. for temp_rep in range(1,n_reps+1,1):
  133. temp_ims = nib.load(os.path.join(BASE_PATH, 'phantom', f'session{temp_session}', f'rep{temp_rep}', f'{ref_type}.nii'))
  134. temp_ims = np.flip(np.transpose(temp_ims.get_fdata(), (2,1,0)), axis=0)
  136. temp_temp_vals = {
  137. 'mean': [],
  138. 'std': []
  139. }
  140. if ref_type == 'T1':
  141. ind_start = 1
  142. ind_stop = 14
  143. if ref_type == 'T2':
  144. ind_start = 15
  145. ind_stop = 28
  146. if ref_type == 'PD':
  147. ind_start = 29
  148. ind_stop = 42
  149. for vial_num in range(ind_start,ind_stop+1,1):
  150. temp_temp_vals['mean'].append(np.mean(temp_ims[vials_mask == vial_num]))
  151. temp_temp_vals['std'].append(np.std(temp_ims[vials_mask == vial_num]))
  152. for k in temp_temp_vals.keys():
  153. temp_vals[k].append(temp_temp_vals[k])
  154. print(f'DONE: {ref_type}, Session: {temp_session}/{n_sessions}, Rep: {temp_rep}/{n_reps}')
  156. for k in temp_temp_vals.keys():
  157. all_vial_values[ref_type][k].append(np.array(temp_vals[k]))
  159. for k in all_vial_values[ref_type].keys():
  160. all_vial_values[ref_type][k] = np.array(all_vial_values[ref_type][k])
  163. # %% [markdown]
  164. # # Plot the concordance
  166. # %%
  167. fig,ax = plt.subplots(nrows=1, ncols=3, figsize=(18,6))
  168. for r,ref_type in enumerate(['T1','T2','PD']):
  170. ref_vals = all_vial_values[ref_type]['ref']
  171. clinical_inds = np.where((ref_vals >= MIN_RANGE_CLINICAL[ref_type]) & (ref_vals <= MAX_RANGE_CLINICAL[ref_type]))[0]
  172. ref_vals = ref_vals[clinical_inds]
  174. temp_values = np.squeeze(np.reshape(all_vial_values[ref_type]['mean'], (1,-1,14)))
  175. temp_values = temp_values[:,clinical_inds]
  176. n_measurements = temp_values.shape[0]
  177. ref_vals = np.array(n_measurements * [ref_vals])
  179. lccc = LCCC_score(ref_vals.flatten(), temp_values.flatten())
  180. # [m,b] = np.polyfit(ref_vals, np.mean(temp_values, axis=0), 1)
  181. # ax[r].plot([0, MAX_RANGE_CLINICAL[ref_type]], [b,m*MAX_RANGE_CLINICAL[ref_type]+b], linestyle='--', color='r')
  182. m, residuals, rank, s = np.linalg.lstsq(ref_vals.flatten()[:,None], temp_values.flatten(), rcond=None)
  183. m = m[0]
  184. ax[r].plot([0, MAX_RANGE_CLINICAL[ref_type]], [0,m*MAX_RANGE_CLINICAL[ref_type]], linestyle='--', color='r')
  186. for measurement in range(n_measurements):
  187. if measurement == 0:
  188. # if b < 0:
  189. # temp_label = f'y = {m:.2f}x - {abs(b):.0f} (LCCC = {lccc:.2f})'
  190. # else:
  191. # temp_label = f'y = {m:.2f}x + {b:.0f} (LCCC = {lccc:.2f})'
  192. temp_label = f'y = {m:.2f}x (LCCC = {lccc:.2f})'
  193. ax[r].scatter(ref_vals[measurement,:], temp_values[measurement,:], s=100, color='k', edgecolors='k', label=temp_label)
  194. else:
  195. ax[r].scatter(ref_vals[measurement,:], temp_values[measurement,:], s=100, color='k', edgecolors='k')
  196. ax[r].plot([0, MAX_RANGE_CLINICAL[ref_type]], [0, MAX_RANGE_CLINICAL[ref_type]], linestyle='--', color='k')
  198. ax[r].set_xlim([0, MAX_RANGE_CLINICAL[ref_type]])
  199. ax[r].set_ylim([0, MAX_RANGE_CLINICAL[ref_type]])
  200. ax[r].set_xlabel(f'Reference {ref_type} ({UNIT_TYPES[ref_type]})', fontsize=24)
  201. ax[r].set_ylabel(f'Measured {ref_type} ({UNIT_TYPES[ref_type]})', fontsize=24)
  202. ax[r].tick_params(axis='both', which='major', labelsize=22)
  203. ax[r].legend(loc='upper left', framealpha=1, fontsize=21)
  204. plt.tight_layout()
  205. plt.show()
  208. # %% [markdown]
  209. # # Band-Altman Plot
  211. # %%
  212. fig,ax = plt.subplots(nrows=1, ncols=3, figsize=(18,6))
  213. for r,ref_type in enumerate(['T1','T2','PD']):
  215. ref_vals = all_vial_values[ref_type]['ref']
  216. clinical_inds = np.where((ref_vals >= MIN_RANGE_CLINICAL[ref_type]) & (ref_vals <= MAX_RANGE_CLINICAL[ref_type]))[0]
  217. ref_vals = ref_vals[clinical_inds]
  218. temp_values = np.squeeze(np.reshape(all_vial_values[ref_type]['mean'], (1,-1,14)))
  219. temp_values = temp_values[:,clinical_inds]
  220. n_measurements = temp_values.shape[0]
  221. ref_vals = np.array(n_measurements * [ref_vals])
  223. all_vals = []
  224. for measurement in range(n_measurements):
  225. temp_x_vals = ref_vals[measurement]
  226. temp_y_vals = temp_values[measurement]
  227. ax[r].scatter(np.mean([temp_x_vals, temp_y_vals], axis=0), temp_y_vals - temp_x_vals, s=100, color='k', edgecolors='k')
  228. all_vals.append(temp_y_vals - temp_x_vals)
  229. all_vals = np.array(all_vals).flatten()
  230. min_y = np.mean(all_vals)-1.96*np.std(all_vals)
  231. max_y = np.mean(all_vals)+1.96*np.std(all_vals)
  232. range_y = max_y - min_y
  233. ax[r].plot([0,MAX_RANGE_CLINICAL[ref_type]], [0,0], linestyle='-', color='k')
  234. ax[r].plot([0,MAX_RANGE_CLINICAL[ref_type]], [np.mean(all_vals),np.mean(all_vals)], linestyle='-', color='g')
  235. ax[r].plot([0,MAX_RANGE_CLINICAL[ref_type]], [min_y,min_y], linestyle='--', color='g')
  236. ax[r].plot([0,MAX_RANGE_CLINICAL[ref_type]], [max_y,max_y], linestyle='--', color='g')
  238. ax[r].set_xlim([0, MAX_RANGE_CLINICAL[ref_type]])
  239. ax[r].set_ylim([min_y-0.75*range_y, max_y+0.75*range_y])
  240. ax[r].set_xlabel(f'Mean {ref_type} ({UNIT_TYPES[ref_type]})', fontsize=24)
  241. ax[r].set_ylabel(f'Meas. - Ref. {ref_type} ({UNIT_TYPES[ref_type]})', fontsize=24)
  242. ax[r].tick_params(axis='both', which='major', labelsize=22)
  243. plt.tight_layout()
  244. plt.show()
  247. # %% [markdown]
  248. # # Plot both repeatability / reproducibility
  250. # %%
  251. fig,ax = plt.subplots(nrows=1, ncols=3, figsize=(18,6))
  252. for r,ref_type in enumerate(['T1','T2','PD']):
  253. ref_vals = all_vial_values[ref_type]['ref']
  254. clinical_inds = np.where((ref_vals >= MIN_RANGE_CLINICAL[ref_type]) & (ref_vals <= MAX_RANGE_CLINICAL[ref_type]))[0]
  255. ref_vals = ref_vals[clinical_inds]
  256. temp_values_repeatability = np.mean(100 * np.std(all_vial_values[ref_type]['mean'], axis=1) / np.mean(all_vial_values[ref_type]['mean'], axis=1), axis=0)
  257. temp_values_repeatability = temp_values_repeatability[clinical_inds]
  258. temp_values_reproducibility = np.mean(100 * np.std(all_vial_values[ref_type]['mean'], axis=0) / np.mean(all_vial_values[ref_type]['mean'], axis=0), axis=0)
  259. temp_values_reproducibility = temp_values_reproducibility[clinical_inds]
  261. ax[r].bar(x=ref_vals, height=temp_values_reproducibility, width=(MAX_RANGE_CLINICAL[ref_type]-MIN_RANGE_CLINICAL[ref_type])/25, color='k', label='Reproducibility')
  262. ax[r].bar(x=ref_vals, height=temp_values_repeatability, width=(MAX_RANGE_CLINICAL[ref_type]-MIN_RANGE_CLINICAL[ref_type])/25, color='grey', label='Repeatability')
  263. ax[r].set_xlim([0, MAX_RANGE_CLINICAL[ref_type]])
  264. ax[r].set_ylim([0, 10])
  265. ax[r].set_xlabel(f'Reference {ref_type} ({UNIT_TYPES[ref_type]})', fontsize=24)
  266. ax[r].set_ylabel(f'CoV (%)', fontsize=24)
  267. ax[r].tick_params(axis='both', which='major', labelsize=22)
  268. ax[r].legend(fontsize=20)
  269. plt.tight_layout()
  270. plt.show()
  273. # %% [markdown]
  274. # # Distortion heatmap / scatter (combined across all dimensions)
  276. # %%
  277. geo_coords = [(36, 87, 128), (36, 128, 88), (36, 167, 128), (36, 127, 168), (76, 47, 128), (76, 88, 88), (76, 87, 128), (76, 87, 168), (76, 127, 48), (76, 127, 88), (76, 128, 128), (76, 127, 168), (76, 127, 208), (76, 167, 88), (76, 167, 128), (76, 167, 168), (76, 207, 128), (116, 47, 87), (116, 47, 128), (116, 47, 168), (116, 88, 48), (116, 87, 88), (116, 87, 127), (116, 88, 168), (116, 88, 208), (116, 127, 48), (116, 127, 88), (116, 126, 127), (116, 127, 168), (116, 127, 208), (116, 168, 48), (116, 167, 88), (116, 167, 128), (116, 167, 167), (116, 167, 208), (116, 207, 88), (116, 207, 128), (116, 207, 168), (155, 47, 128), (155, 87, 88), (155, 88, 128), (155, 87, 168), (155, 127, 48), (155, 127, 88), (155, 128, 128), (155, 127, 168), (155, 127, 208), (155, 167, 88), (155, 167, 128), (155, 167, 168), (155, 207, 128), (194, 88, 128), (194, 127, 88), (194, 127, 128), (194, 128, 168), (194, 167, 128)]
  278. voxel_size = [1,1,1]
  279. center_coords = (116,126,127)
  280. actual_coords = (
  281. np.array([center_coords[0] + 40*v for v in [-2,-1,0,1,2]]),
  282. np.array([center_coords[1] + 40*v for v in [-2,-1,0,1,2]]),
  283. np.array([center_coords[2] + 40*v for v in [-2,-1,0,1,2]])
  284. )
  285. dim_colors = ['r','g','b']
  286. possible_x_vals = np.array([0.0, 40.0, 56.56854249, 69.2820323, 80.0, 89.4427191])
  287. possible_y_vals = np.array([-2, -1, 0, 1, 2])
  289. all_plot_vals = []
  290. for v in range(len(geo_coords)):
  291. temp_coord = [geo_coords[v][0], geo_coords[v][1], geo_coords[v][2]]
  292. nearest_coords = []
  294. for temp_dim in range(len(actual_coords)):
  295. nearest_ind = np.argmin(abs(temp_coord[temp_dim] - actual_coords[temp_dim]))
  296. nearest_coords.append(actual_coords[temp_dim][nearest_ind])
  297. distance_from_center = np.sqrt(
  298. (nearest_coords[0] - center_coords[0])**2 +
  299. (nearest_coords[1] - center_coords[1])**2 +
  300. (nearest_coords[2] - center_coords[2])**2
  301. )
  302. distortion = np.sqrt(
  303. (temp_coord[0] - nearest_coords[0])**2 +
  304. (temp_coord[1] - nearest_coords[1])**2 +
  305. (temp_coord[2] - nearest_coords[2])**2
  306. )
  307. all_plot_vals.append((distance_from_center, distortion))
  309. # Scatter
  310. plt.figure(figsize=(10,6))
  311. final_plot_vals = Counter(all_plot_vals)
  312. for temp_coord in final_plot_vals.keys():
  313. plt.scatter(temp_coord[0], temp_coord[1], s=10*final_plot_vals[temp_coord], color='k')
  315. final_plot_vals = Counter(all_plot_vals)
  316. all_box_vals = []
  317. for temp_x in possible_x_vals:
  318. temp_box_vals = []
  319. for temp_coord in final_plot_vals.keys():
  320. if abs(temp_coord[0] - temp_x) < 1:
  321. temp_box_vals.append(temp_coord[1])
  322. all_box_vals.append(temp_box_vals)
  323. res = plt.boxplot(all_box_vals, positions=possible_x_vals, widths=[5]*len(all_box_vals), medianprops=dict(color='r', linewidth=2.5))
  324. ax = plt.gca()
  325. ax.tick_params(axis='both', which='major', labelsize=20)
  326. ax.set_xticks([0,20,40,60,80,100])
  327. ax.set_xticklabels([0,20,40,60,80,100])
  328. plt.xlabel('Distance From Center (mm)', fontsize=22)
  329. plt.ylabel('Distortion (mm)', fontsize=22)
  330. plt.tight_layout()
  331. plt.show()
  333. [print(np.mean(res['medians'][i].get_xdata()), res['medians'][i].get_ydata()[0], res['boxes'][i].get_ydata()[0], res['boxes'][i].get_ydata()[2]) for i in range(len(res['medians']))]

analysis_paper.ipynb at commit b9644f1, under GPL-3.0 · at the source

Overview

Authors: Lucas McCullum1,2, Ashley Harrington1,2, Natalie West1,2, Grace Nehring1,2, Ziyu Fu1,3, Ergys Subashi4, Brian A. Taylor3, Ken‐Pin Hwang3, Clifton D. Fuller2
ORCID iDs: Lucas McCullum, Ashley Harrington, Natalie West, Grace Nehring, Ziyu Fu, Ergys Subashi, Brian A. Taylor, Ken‐Pin Hwang, Clifton D. Fuller
  1. UT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences Houston Texas USA
  2. Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston Texas USA
  3. Department of Imaging Physics The University of Texas MD Anderson Cancer Center Houston Texas USA
  4. Department of Radiation Physics The University of Texas MD Anderson Cancer Center Houston Texas USA
Institutions: The University of Texas MD Anderson Cancer Center (United States); The University of Texas Health Science Center at Houston (United States)
Journal: Journal of applied clinical medical physics, volume 27, issue 11, article e70886
Dates: received 27 April 2026; accepted 6 October 2026; published online 28 October 2026; in print November 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/acm2.70886 · PMID 42949424 · PMCID PMC13715509 · OpenAlex W7134972904
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.64898/2026.03.09.26347967
Status: code verified (how the code is found and verified)
Categories: structural MRI / diffusion (modality), human (organism), other condition (population), clinical / translational (subfield)
Methods: fMRI & imaging, Statistics
Keywords: 3D‐QALAS, adaptive radiation therapy, brain, cancer, MRI, MR‐Linac, QALAS, quantitative imaging, radiation therapy, radiotherapy, SyMRI, SyntheticMR
MeSH: Brain*, Image Processing, Computer-Assisted*, Imaging, Three-Dimensional*, Magnetic Resonance Imaging*, Particle Accelerators*, Phantoms, Imaging*, Radiotherapy Planning, Computer-Assisted*, Adult, Feasibility Studies, Healthy Volunteers, Humans, Prospective Studies, Radiotherapy Dosage, Radiotherapy, Intensity-Modulated, Reproducibility of Results (* major topic)
Topic: Advanced MRI Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: national institutes of health (R01CA257814‐02S2); National Institutes of Health Career Development Award (K23DA049216); National Science Foundation (NSF)/National Institutes of Health (NIH) National Cancer Institute (NCI); Smart and Connected Health (SCH) (R01CA257814); NIH National Institute of Dental and Craniofacial Research (NIDCR) (R01DE028290); NIH NCI MD Anderson Cancer Center Support Grant (CCSG) (P30CA016672)
Citations: not cited yet (Europe PMC); 68 references in the paper

Abstract

Background: Quantitative relaxometry of the brain on the integrated MRI / linear accelerator (MR‐Linac) at high isotropic reconstructed resolution is currently limited due to prohibitively long scan times and limited field‐of‐views.

Purpose: The purpose of this study was to assess the technical feasibility of the 3D‐QALAS technique on the 1.5T MR‐Linac which can acquire whole‐brain 1 mm isotropic quantitative T1, T2, and PD maps along with multiple synthetic images in a 7‐minute acquisition time.

Methods: A 1 mm isotropic 3D‐QALAS acquisition was scanned in both phantoms and five healthy volunteers on the 1.5T Elekta Unity MR‐Linac device with scan times around seven minutes. A test‐retest protocol across five independent sessions for the phantom was conducted. The Lin's Concordance Correlation Coefficient (LCCC), repeatability, and reproducibility between measured and reference quantitative T1, T2, and PD values were determined in the phantom. Distortion was also studied. Vendor‐provided reconstruction through SyMRI was performed to extract synthetic images and brain volume metric assessments in the healthy volunteers.

Results: The slope and LCCC between the measured and phantom reference values was 1.02 (1.00), 1.09 (0.90), and 0.99 (1.00) for T1, T2, and PD, respectively. Median distortion across the phantom remained below 2 mm. The repeatability and reproducibility coefficient‐of‐variation (CoV) was under 8% for all measured values. The measured brain volumes in the healthy volunteer was within expected age‐adjusted reference values.

Conclusions: The technical feasibility of using 3D‐QALAS on the integrated 1.5T MR‐Linac was confirmed. Applying this technique to the adaptive radiation therapy workflow of the brain will provide new opportunities to integrate quantitative imaging relaxometry biomarkers at 1 mm isotropic reconstructed resolution.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

Lucas-Mc/QALAS-MRL_R-IDEAL_0-1

License: GPL-3.0
State: the link answers, verified on 1 November 2026
Evidence: files inventoried
Commit: b9644f13826dc672f45a4502ca8e252868407066, 11 March 2026
Languages: Jupyter (1)
Size: 6 files, 1 script
Software Heritage: not archived
Found in: “DATA AVAILABILITY STATEMENT”
Holds: README, license file, environment (requirements.txt), 1 notebook
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NiBabel (1 file), NumPy (1 file)
Availability: 1 check, the latest on 1 November 2026: the link answers
  • 1 November 2026: the link answers
3 files
At the source: github.com/Lucas-Mc/QALAS-MRL_R-IDEAL_0-1

The paper's code and data availability statement is in the Data section.

Tracing map

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Data

Datasets cited

Data availability statement

All relevant image files and contours in NIfTI format are to be made publicly available after manuscript acceptance at the following FigShare URL: https://doi.org/10.6084/m9.figshare.31594819. The accompanying code for image visualization and statistical analysis will be made publicly available at the following URL: https://github.com/Lucas‐Mc/QALAS‐MRL_R‐IDEAL_0‐1 (https://github.com/Lucas-Mc/QALAS-MRL_R-IDEAL_0-1).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 1 November 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 12 keywords, 15 MeSH terms, 6 funders, 68 references.

Cite

This paper

McCullum, L., Harrington, A., West, N., Nehring, G., Fu, Z., Subashi, E., Taylor, B. A., Hwang, K., & Fuller, C. D. (2026). Technical development and implementation of 3D-QALAS on a 1.5T MR-Linac for the brain: A prospective R-IDEAL stage 0/1 technology development report. Journal of applied clinical medical physics, 27(11), e70886. https://doi.org/10.1002/acm2.70886

BibTeX

@article{mccullum2026technical,
author = {McCullum, Lucas and Harrington, Ashley and West, Natalie and Nehring, Grace and Fu, Ziyu and Subashi, Ergys and Taylor, Brian A. and Hwang, Ken‐Pin and Fuller, Clifton D.},
title = {{Technical development and implementation of 3D-QALAS on a 1.5T MR-Linac for the brain: A prospective R-IDEAL stage 0/1 technology development report}},
journal = {Journal of applied clinical medical physics},
year = {2026},
month = nov,
volume = {27},
number = {11},
pages = {e70886},
publisher = {Wiley},
issn = {1526-9914},
doi = {10.1002/acm2.70886},
url = {https://doi.org/10.1002/acm2.70886},
pmid = {42949424},
pmcid = {PMC13715509}
}

RIS

TY - JOUR
AU - McCullum, Lucas
AU - Harrington, Ashley
AU - West, Natalie
AU - Nehring, Grace
AU - Fu, Ziyu
AU - Subashi, Ergys
AU - Taylor, Brian A.
AU - Hwang, Ken‐Pin
AU - Fuller, Clifton D.
TI - Technical development and implementation of 3D-QALAS on a 1.5T MR-Linac for the brain: A prospective R-IDEAL stage 0/1 technology development report
T2 - Journal of applied clinical medical physics
J2 - J Appl Clin Med Phys
PY - 2026
DA - 2026/11/01
VL - 27
IS - 11
SP - e70886
SN - 1526-9914
PB - Wiley
DO - 10.1002/acm2.70886
UR - https://doi.org/10.1002/acm2.70886
LA - en
ER -

CSL-JSON

{
"id": "10.1002/acm2.70886",
"type": "article-journal",
"title": "Technical development and implementation of 3D-QALAS on a 1.5T MR-Linac for the brain: A prospective R-IDEAL stage 0/1 technology development report",
"container-title": "Journal of applied clinical medical physics",
"author": [
{
"family": "McCullum",
"given": "Lucas"
},
{
"family": "Harrington",
"given": "Ashley"
},
{
"family": "West",
"given": "Natalie"
},
{
"family": "Nehring",
"given": "Grace"
},
{
"family": "Fu",
"given": "Ziyu"
},
{
"family": "Subashi",
"given": "Ergys"
},
{
"family": "Taylor",
"given": "Brian A."
},
{
"family": "Hwang",
"given": "Ken‐Pin"
},
{
"family": "Fuller",
"given": "Clifton D."
}
],
"container-title-short": "J Appl Clin Med Phys",
"volume": "27",
"issue": "11",
"page": "e70886",
"DOI": "10.1002/acm2.70886",
"PMID": "42949424",
"PMCID": "PMC13715509",
"ISSN": "1526-9914",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/acm2.70886",
"language": "en",
"issued": {
"date-parts": [
[
2026,
11,
1
]
]
}
}

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